By: Dana Gardner, Principal Analyst, Interarbor Solutions Posted: 7th May 2013 Copyright Interarbor Solutions © 2013

Dell Software has delivered Foglight for Virtualization, Enterprise Edition to extend the depth and breadth of managing and optimizing server virtualization as well as virtual desktop infrastructure (VDI) and their joint impact on such IT resources as storage.

Building on the formerly named Quest vFoglight Pro virtualization management solution, Dell re-branded vFoglight to Foglight for Virtualization to make it the core platform to the Foglight family. Foglight is not sitting still either. Improvements this year move beyond monitoring support for VMware View VDI, to later support for VMware vCloud Director, OpenStack, and Citrix Xen VDI. [Disclosure: Dell Software and WMware are sponsors of BriefingsDirect podcasts.]

The higher value from such ecosystem and heterogeneous management support is the ability for virtualization server and system administrators to comprehensively optimize various flavors of data-center server virtualization, as well as the major VDI types, with added capabilities to track and analyze performance from the application level all the way to the server and storage hardware level. This week's announcements have also shown a spotlight on the recently updated Foglight for Storage Management 2.5.

“With Foglight for Virtualization, Enterprise Edition, Dell is showing its commitment to offering a  solution that encompasses all aspects of virtual infrastructure performance monitoring and management, built on a platform that can scale as the infrastructure grows,” said Steve Rosenberg, general manager for Performance Monitoring, Dell. “This new release expands Foglight’s ability not only to monitor the additional infrastructure area of VDI, but also to correlate metrics from VDI with performance for applications, the virtual layer, the network, and underlying servers and storage.”

Dell Software also last week released a series of BYOD-targeted products and services, which are related to the better VDI management capabilities. That's because many enterprises and mid-market firms that are tasked with moving quickly to BYOD are using VDI to do it.

With the increasing adoption of VMware View in virtualized data centers (including for MSPs), VDI support is fast becoming a mainstay for today’s IT departments and managed service providers. VDI and server virtual machines (VMs) often utilize the same hardware components. Yet, both of these virtualized infrastructures serve different users and have separate requirements and resource demands, explained John Maxwell, vice president of product management for performance monitoring for virtualization, networking,storage and hardware at Dell Software.

Single-source solution
As a result, VDI and server VMs require dedicated performance monitoring systems. However, these systems must also be connected, because so many underlying resources are often shared. Agent-based Foglight for Virtualization, Enterprise Edition offers virtualization administrators a more single-source solution that not only identifies and fixes performance issues within VMware View, but continues to run all features available in vOPS Server Enterprise with no effect on overall vCenter performance.

Foglight for Storage Management 2.5 has been released as an optional "cartridge" to Foglight for Virtualization. Foglight for Storage Management now offers physical storage performance reporting in addition to virtual reporting, providing customers with complete "VM to physical LUN" visibility. 

Additional enhancements in this release include LUN latency reporting, NPIV support, and the ability for customers to purchase the product either as a stand-alone cartridge, or as an optional cartridge to Foglight for Virtualization.

Additionally, Foglight is a unified performance monitoring platform that allows individual product solutions, delivered as sets of pluggable “cartridges,” to run stand-alone or to interoperate. Each individual product delivers best-of-breed functionality to the admin for that area, while simultaneously integrating with other cartridges to deliver true end-to-end monitoring from end-user experience to the underlying storage and server hardware layers, and everything in between, said Maxwell.

Foglight for Virtualization Enterprise Edition 6.8 is available now for a 45-day trial from www.quest.com. Pricing starts at $799 per socket. Foglight for Storage Management 2.5 is also available now for a 45-day trial from www.quest.com.  Pricing starts at $499 per socket.

Because Foglight is built on a common architecture to support the cartridges, it seems likely that it will move from an on-premises only offering to a SaaS-based version too, especially to support cloud- and MSP-based VDI offerings, and also to manage hybrid VDI implementations.

By: David Norfolk, Practice Leader - Development, Bloor Research Posted: 3rd May 2013 Copyright Bloor Research © 2013

Salesforce.com is about a lot more than customer relationship management these days. Perhaps one of the most interesting facets of the product is salesforce.com as a trusted platform for enterprise cloud computing; together with Salesforce AppExchange, which it describes as "the world's leading business apps marketplace". Although, as I think that most businesses still run on in-premises hardware behind the firewall even now, I'm not sure how much real competition there is for that title (unless Apple and Android see themselves as supporting business apps, as they almost certainly do).

Anyway, whatever the status quo now, I really do think that a switch has clicked over and most people are prepared to consider, at least, trusting cloud apps these days - which makes developing them an interesting career choice for developers.

To stimulate this opportunity further Salesforce announced a 5 million Euro challenge to start-up developers at its Customer Company Tour in London this week. Start-ups will get the chance to approach investors with their ideas for apps at a series of Innovation Challenge events throughout Europe (September to November 2013), negotiate investment funding - and then the winners will be able to build, package and sell their apps on the Salesforce AppExchange.

This idea apparently even attracts the endorsement of Boris Johnson, hardly my first idea of a tech guru: "London is brimming with tech talent which is breeding a wave of innovative start-ups with the potential to grow fast", he says. "Salesforce.com's Innovation Challenge presents a fantastic opportunity for London's silicon entrepreneurs to take their business to the next level of success". That is probably true - although, in my opinion, London's developers may face stiff competition from elsewhere in Europe (or even just from elsewhere in the UK)...

Of course, to share in this investment pot, the apps will have to be developed on or ported to the Saleforce.com cloud platform. I guess Saleforce.com is a success story, so that won't worry developers too much, much as being a Microsoft developer never worried anyone much, but in these days of cross-platform development for Android and Apple, I guess I wouldn't want my apps to be only on Salesforce AppExchange.

By: Helena Schwenk, Principal Analyst, MWD Advisors Posted: 1st May 2013 This work is licensed under a Creative Commons License

In an effort to cement its position in the social marketing space, Adobe recently announced some key enhancements to its Adobe Social product suite during its Digital Marketing Summit in London. In particular the company has incorporated predictive analytics into its social publishing product to help marketers get visibility into what content, keywords and post timings will lead to a better level of engagement, for example in terms of Facebook likes and comments.

Behind the scenes the new enhancements use sentiment analysis and predictive text mining algorithms to analyse the types of words, images, links and ad formats that work best with an audience and uses them to build a model that predicts the estimated range for the amount of likes, comments and shares a post will receive. This rather helpful feature will make it easier for marketers to optimise their content since many fly blind when trying to assess how impactful a post may be. Having visibility into the predicted ‘performance’ of a post adds some much needed visibility, as it enables social marketers to tweak, adjust and hopefully optimise their social marketing content before it goes live.

In addition, the tool can track and predict other marketing metrics if instructed to, as well as give recommendations about the time of a post; for example by shifting the scheduled slot if, for instance, a similar post has just been published. Similarly the tool includes a degree of self-learning since it can learn from previous actions and refine its predicted recommendations based on the results of previous posts.

In our view social publishing is becoming a necessary discipline for many marketers as they grapple with creating and automating the delivery of content across multiple social networks sites such as Facebook and Twitter at a time when usage continues to rise. As we all know, social remains a key channel for marketers in support of activities such as creating brand awareness, generating demand, increasing engagement, improving targeting and building advocacy. And while Adobe Social isn’t a one-stop-shop for social marketing—it hasn’t, for instance, got an all-round social campaign management component—its ability to steer marketers towards publishing the right social content at the right time nonetheless helps streamline part of this social marketing process.

The new Facebook predictive publishing enhancements are in beta and are due to be released later this summer as part of Adobe Social; other social platforms will follow in due course.

By: David Norfolk, Practice Leader - Development, Bloor Research Posted: 1st May 2013 Copyright Bloor Research © 2013

I've just had a hands-on demo of Huddle (which describes itself as an "enterprise content collaboration platform") with Jonathan Howell (its CTO) and James Pipe (one of its product managers, focused on mobile and desktop). As I've said before, there are limitations to this, as I'm not working on a real collaboration issue at my workplace, but I have used Huddle before (at the BCS) and I do think its redesigned interface is "cool" and supportive. Huddle's promise to provide its users with "just enough" information to let them get their work done seems a reasonable, and achievable, objective.

Huddle provides cross-platform support, which is good. Somebody can make an update on their desktop and the updated content appears on peoples' iPads and iPhones in real time. The permissions and so on needed to make this work seem reasonably flexible and sufficiently powerful - this is an important aspect of collaboration software. People must be able to collaborate on sensitive information and control who sees what - without obtrusive controls that disincentivise collaboration. Huddle seems to do a reasonable job but this is an area in which any purchaser of collaboration software needs to do its own due diligence; with his own staff, collaborating on tasks they are familiar with.

This is where Huddle's "start small and grow success" approach is good (it isn't unique to Huddle, but that doesn't make it any less worthwhile). The conventional approach to implementing collaboration software, often adopted by vendors of licensed software and driven by the IT group, is to install as many licenses as you can afford (often promoted by bulk discounts) and then look for problems to solve with them. Often a lot of these licenses remain as shelfware. In contrast, Huddle's subscription model means that there's an incentive to only buy as much Huddle as you need and get rid of any subscriptions no-one is using. That's a good start, although an organisation can still choose to mess up a subscription model. However, Huddle (according to Chris Boorman, CMO) is adopting an incremental marketing approach - it encourages a customer to install Huddle for a small group with a real need for collaboration and then supplies experienced mentors to help the initial group make this a success. It aims at 'skills transfer' to its customer and to educate 'champions' amongst its initial customers. It then hopes that its initial deployment will grow, with more subscriptions, as the early adopters demonstrate success. If that is what really happens in practice, it should overcome any prejudices about collaboration shelfware.

Another risk-reduction feature of Huddle is its security certifications - if you understand what these mean and don't see them just as a check-box delivering mindless comfort. Huddle has achieved ISO 27001 certification (part of a range of ISO 27000 standards), which does not guarantee security but does provide a framework for a company to implement security around Huddle and gives all stakeholders a common, defined, vocabulary for discussing security issues. Of course, if you understand certification, you'll now be asking about the scope of assessment and when Huddle was last assessed - there's a starting point for this here.

Moreover, Huddle is Pan Government Accredited (PGA) at Impact Level 2 (IL2), which means "based on good commercial standards, centred around a suitably scoped ISO27001 certification", and claims that it is used by 80 per cent of central UK government departments, including the Cabinet Office, Ministry of Justice, Defra and Department for Business Innovation and Skills. This does not mean that "the government says Huddle is totally secure" or anything like that; but it does give users confidence that it is secure enough to accommodate serious work - although you'd want to do more due diligence (especially around physical access on your premises) if you were using it for, say, personal data or anything else where security is critical.

I'm also impressed that Huddle has what it says is a usable and well-documented RESTful API ("this time around, we got our developers to write the documentation first and then produce the API, so we have confidence in the documentation", says Howell). This should allow customers to integrate Huddle collaboration with other software-supported processes - a useful success factor and will allow a Huddle community to develop, sharing third party Huddle utilities and customisations. Huddle is more likely to succeed as part of a larger community including third parties - better a small slice of a large pie than all of a small pie, perhaps. There isn't a formal AppStore for Huddle yet - but who knows?

I think that Huddle sees its main opportunity as failing or less-than-popular SharePoint installations - and it seems to address many of the issues that SharePoint customers report, including the shelfware issue. Nevertheless, SharePoint is a moving target and Microsoft has a history of reinventing its products without necessarily changing their name. I wonder what Microsoft collaboration around Office365 will look like in 2014? I do think the conventional licensing model, especially for collaboration software, is dying (although I'm not stupid enough to predict the actual death-date - in anything to do with IT, a better model co-exists with the old ways for ages).

Unfortunately, I think that some people who failed with SharePoint will also fail with Huddle (and other collaboration solutions) - and for similar reasons, around blame cultures, egos and politics - and will blame the software instead of their organisational/management failings. To end on an optimistic note, however, this is the sort of customer maturity issue Huddle's "start small and grow with success" model might help with - as long as top management buys into and understands collaboration.

By: Philip Howard, Research Director - Data Management, Bloor Research Posted: 26th April 2013 Copyright Bloor Research © 2013

BLU Acceleration has been introduced with the latest version of IBM DB2 (10.5), for Linux, UNIX and Windows and some elements of the technology have also been included in the Informix Data Warehouse Accelerator.

It is based on a combination of parallel vector processing, dynamic in-memory caching, columnar storage, a query technique known as data skipping and extended compression. It eliminates the need for indexes and aggregation (therefore removing the tuning necessary for these artifacts) and without requiring any change to existing SQL or the schema. It operates on the data while it's still compressed thus saving the CPU time that would otherwise be needed for decompression. Not only does BLU acceleration perform predicates on compressed data, but joins, grouping, in-lists and LIKE predicates as well. IBM claims the technology is "better than in-memory" since whatever data resides in the cache is in-memory optimised, but like a traditional database the data can be larger than cache and pre-fetched on-demand while queries run. That helps for large marts and warehouses where the most active part of the data is small enough to fit in memory (such as the most recent year), but a larger data volume (perhaps 10 years) needs to be available for occasional access.

IBM is reporting some big speedups with significantly reduced DBA tuning. Database memory, workload management, and other configuration details adapt to your server automatically. At their launch event on April 3 IBM reported typical performance gains of 8x–25x, with multiple reference customers and partners standing up with even larger numbers (25x–74x) though they admit that this will vary.

Basically, what this all means is that the relational storage engine in DB2 (as opposed to the XML and graph storage engines) will now be able to store data in either of two types of tables: a conventional row-based relational table or in a compressed, encoded columnar table. Of course, row-based tables are also compressed but you would expect better compression on columns because all data in a single column has the same datatype and you can therefore optimise your compression algorithms more efficiently. As noted, you do not have to change your schema to implement column-based storage.

The advantages of columns have been well rehearsed: they reduce the need for indexes, and often mean reading far less data. However, they are not a panacea: there are some types of queries for which it may still make sense to have indexes even when using columnar storage. Just look at Sybase IQ, which supports a variety of index types despite being exclusively columnar. In the case of DB2 you will have a choice: row-based data with indexes or column-based data without indexes - what this implies is that you will need to think carefully about what data to store in columns and what data in rows. Queries, incidentally, can span both row and column-based storage and IBM claims that it is easy to migrate tables from rows to columns. However, you only get BLU Acceleration for the columnar data.

Data skipping is similar, in theory, to Netezza's zonemaps. That is, it allows queries to skip over data that is not required to answer the query in hand. However, remember that this data is in cache and on disk where it would be in the case of Netezza-based appliances (PureData System for Analytics). The BLU Acceleration technology provides data skipping regardless of whether the data is coming from disk or memory.

The parallelism I mentioned is actually what I would call cross-core parallelism (because I think that's easier to understand - you can parallelise across the cores within a single CPU as well as across sockets). Columnar processing and operating on compressed data in memory are useful tricks, but with workload speedups in the 8x–74x range there is probably more to the technology as well. IBM claims one of their strengths is the efficiency of the parallelism made possible by some deep engineering to reduce "memory access latency", in other words the time it takes to get data from RAM into the CPU where it can be processed. Most systems have a fair bit of memory access latency, but it gets disruptive across sockets. BLU Acceleration is doing something special to keep those latencies low.

The first big question is how this will stack up in performance terms with things such as SAP HANA and other purely in-memory approaches. Certainly, IBM's approach should mean that you need less memory for the same tasks and, other things being equal, this would mean a lower cost offering because there's no need to buy enough memory for all the data to fit there. The question will be about how performance compares. IBM's quoted figures are that things like cube loads and query performance should improve by an order of magnitude and I understand that, for some queries, performance may improve considerably more than this. At their product launch on April 3, IBM quoted some individual query speedups of over 1000x.

The second big question surrounds what used to be Netezza. With these features DB2 is starting to look like something that can compete directly with the PureData System for Analytics for analytic workloads. Of course this has hardware acceleration (FPGAs) that DB2 doesn't have but what is memory if not a hardware accelerator? On the other hand I daresay that the developers working on the Netezza platform will also be looking to see how they can leverage memory (and SSDs) so that they can stay ahead of the DB2 game.

By: Philip Howard, Research Director - Data Management, Bloor Research Posted: 25th April 2013 Copyright Bloor Research © 2013

Actian, previously Ingres, is beginning to act like its erstwhile parent CA. Or, at least, how CA was acting back in the 90s, when it was acquiring everything in sight. Back in December, Actian acquired Versant, this month it finalised its acquisition of Pervasive Software and today (April 25th) it has announced the immediate acquisition of ParAccel.

In other words the company has gone from owning two database management systems (Ingres and Vectorwise) four months ago to now owning five (adding Versant, PSQL - more popularly known, at least to old fogeys like me - as Btrieve, and ParAccel). The question must be why on earth would it want so many?

Of course, PSQL and Ingres are cash cows. Moreover, having Ingres allows Actian to claim that it has tens of thousands of users (even before the Pervasive acquisition) - which it does - even when promoting its Vectorwise product - which doesn't. And, of course, there's plenty of synergy between Actian's database products and Pervasive's data integration and data governance portfolio.

You can also sort of see why Actian might want Versant: because it wants to exploit its technology in the big data space for unstructured data. And what used to TurboRush for Hive also fits into this picture. In fact, Actian's web site makes lots of play about solving big data problems, though as far I can see it has a bunch of parts rather than anything integrated yet: there's is some work to do in pulling everything together but you can see that the potential is there.

But why ParAccel? After all, Actian's marketing is all about the "fact" that Vectorwise is the "the world's fastest analytical database". In which case, why would it need to acquire a (slower) competitor? Of course, the truth is that it is "the world's fastest non-clustered analytical database, according to TPC-H, for capacities up to 1TB, of those companies that have bothered to do a test" and, as it happens, ParAccel (which is clustered) is faster than Vectorwise according to TPC-H.

So, what has ParAccel going for it? As far as I can see: first-class optimisation technology, a (small) cash cow in Amazon which has embedded ParAccel technology into Amazon Redshift (its cloud-based data warehouse offering) and a not very substantial user base. The marketing blurb on the takeover states that ParAccel is a market leader in the big data space. Well, I suppose it depends on what you call a "leader" but, however good ParAccel is from a technical point of view, my understanding of the word leader is clearly not the same as that of Actian's marketing department.

When I first heard about this acquisition I figured that there was one of two conclusions to draw: either Actian was having trouble scaling Vectorwise and needed a different platform to move up-market with or Actian sees the acquisition as a way of acquiring some pieces of useful technology plus increasing its market share. In turns out that it is the former that is the case: the company is talking about Vectorwise up to around 20TB and ParAccel thereafter.

Given that this is the case what is the likely future for both Vectorwise and ParAccel? It cannot conceivably be right to have two such products. Either one will die or they will be merged. The latter would make sense: both products are columnar and while ParAccel has done some clever stuff with the optimiser Actian has focused on vector processing: I see no reason why you shouldn't combine the two.

Of course that poses the question of whether Actian will hoover up any more of the smaller companies in the warehousing marketplace that have clever technology? Infobright would be the obvious target: decent customer base, not directly competitive (tends to target ISVs: and Actian knows very well how to market in that space), columnar, and it has some very clever technology in its Knowledge Grid. We shall watch and see.

By: David Norfolk, Practice Leader - Development, Bloor Research Posted: 25th April 2013 Copyright Bloor Research © 2013

The trouble with collaboration software is that it is really hard to assess without using it in a real community - with real pain points that collaboration can help with. However, a good demo can give you a feel for a product and the attitude of those developing it - which is why this is about 'impressions of Huddle', based on talking with Chris Boorman (Huddle's CMO), James Pipe (one of its product managers, focused on mobile and desktop) and Jonathan Howell (its CTO) and not a review.

My meeting confirmed my original impression that Huddle was very much targeting the Enterprise (i.e., collaboration at scale) - although its approach is to start small, with experienced mentors, and grow on the basis of demonstrable success. I think this is an excellent approach, especially as there seem to be a lot of anecdotal reports around the failure of collaboration initiatives, so you need to achieve confidence and buy-in at the grass-roots level, as well as convincing top management. Personally, although there are real differences in collaboration software, I don't believe that failure is usually due to problems with the software tools (at least, as a primary effect). Building a collaborative culture implies changing company culture and, possibly, treading on the toes of vested interests who may have engineered personal power out of exploiting silos and distrust. I suspect that many failures are due to lack of investment in managing cultural change; mismanaged politics; and the belief that buying and installing the right collaboration tool is all you need to worry about in order to achieve a collaboration culture. In other words, I think that a company that fails to install and get benefit from, say, SharePoint may well also fail with Huddle - for similar reasons - unless it learns from the previous failure and changes its approach.

Which leads me to possible pain points that Huddle might encounter. After a lack of maturity in its customers, and the consequent political and people issues (which its 'start small and grow with success' approach may well be addressing), I think that the difficulty in measuring success could be a real issue. Companies like to look at ROI, but Roger Whitehead (with a great deal of experience in this area) has commented that concentration on ROI too early can stifle a collaboration culture. So, given that good management includes tracking and managing investment in collaboration, what is 'success'? You can measure collaboration using surveys, but collaboration is not really an end in itself: it should deliver business outcomes. But one can fail to achieve these for other reasons besides poor collaboration; and if one does succeed (in business and monetary terms) after a collaboration initiative, how much of this can be allocated to the collaboration initiative and how much to the workers who were able to exploit what collaboration made possible? At the beginning, morale; use of collaboration software; and so on is probably a good-enough metric; but, longer time, I'd expect (on average) that firms with an effective collaboration culture would succeed in business more than other firms - but that might be hard to measure.

Personally, I've suggested that Huddle sets up an externally-facing Analyst community in Huddle, to communicate with us all. The analyst community has sufficient egos and politics to make this a useful proof of concept for us, I'd think.

By: Laurie McCabe, Partner, SMB Group Posted: 20th April 2013 Copyright SMB Group © 2013

After marking my calendar to attend Pervasive’s 2013 Integration World, I had to wonder whether or not Actian’s pending acquisition of Pervasive would be a done deal—or not—by April 14, when the conference was due to kick off.

After all, I figured that if things were still up in the air, I’d probably leave with more questions than answers. Although it seemed pretty clear to me that the combined entity would be able to bring a lot more to the table in the large enterprise big data space, it wasn’t clear to me what it would mean for the merged company’s future in the SMB market.

Evidently, the events team wasn’t sure about whether or not the acquisition would be a done deal in time for the event either, as they had two sets of signage and materials printed up and ready to go for either eventuality.

Fortunately, the acquisition became final on April 11, three days in advance of the event, and the events team got to use the Actian version. And although it’s too soon to expect a roadmap from the freshly combined entity, the event did give me a chance to think about what may be on tap.

Actian connects with Pervasive

First, the background. Privately held Actian Corp closed the deal to acquire Pervasive, which had, prior to this, been publicly traded on NASDAQ, on April 11 for $161.7 million. Under the agreement, Pervasive becomes a wholly owned subsidiary of Actian. In total, the merged company employs about 510 people.

Each company has been around a long time and has deep roots in the database world. Actian got its start from a predecessor company named Ingres in the late 1980s, which went through two acquisitions and a divestiture to then emerge as the new Ingres Corporation, best known for Ingres Database, an SQL relational database management system, available in community (open-source) and enterprise versions. On a quest to evolve into a big data company, the company acquired VectorWise, an analytical database in 2010. In 2011, the company rebranded itself as Actian and, in 2012, it bought object-store database vendor Versant.

Likewise, Pervasive began as a database company in 1982, with its Btrieve offering. After a few acquisitions, spinouts and name changes, the company became Pervasive Software in 1997, when Btrieve evolved into Pervasive PSQL. In 2003, Pervasive entered the integration business when it purchased Data Junction (now Pervasive Data Integrator). Today, many SMB-oriented ISVs use Pervasive data integration solutions in their offerings. Data Integrator technologies are also at the core of Galaxy Marketplace, which Pervasive launched in 2011 (see Pervasive Puts Its Galaxy Integration Community Into Orbit). In addition, Pervasive jumped into the big data arena, most notably with DataRush, a predictive data analytics engine, in 2006.

In both companies, legacy database products still account for a big chunk of revenues, and have funded expansion to develop and/or acquire the big data solutions that they are targeting to fuel future growth. As noted by Steve Shine, Actian CEO in the press release announcing the deal, that target is to deliver big data solutions for enterprises of all sizes:  “Every moment, people, businesses and machines generate explosive volumes and varieties of data leveraging their existing networks and, more increasingly, the cloud. Companies that embrace this data as their most strategic asset will thrive, while those that don’t lose their competitive advantage.”

Giving companies the ability to “Connect, Analyze and Act” is Actian’s corporate mantra. Pervasive gives Actian the strong integration capabilities that it needed to fill out the connect piece of its big data story. Meanwhile, DataRush’s high-powered BI and analytics solutions should significantly beef up analytics and processing capabilities.

Where SMBs have fit into the story to date

Small and medium businesses (SMBs) have been vital to Pervasive. The company has relied primarily on indirect channel partners to reach SMBs. ISVs in particular have been integral to its success. It has partnered with vendors such as Intuit, Salesforce, UserVoice and others who sell through embedded integrations and connectors built with Pervasive Data Integrator and with ISVs, such as GlobalShop, EBP, and Abacus, that build their solutions on Pervasive SQL database. A good strategy, as SMBs don’t often have the bandwidth, expertise or resources to tend to the integration plumbing necessary to connect financials, marketing, CRM and other solutions.

In 2011, Pervasive also introduced Galaxy Integration Marketplace, a portal where users can find integration guidance and buy pre-built integration solutions in Amazon-like fashion. On the flip side, the portal gives integration developers a window into what integrations people are looking for, as well as a lot of very handy tools—including a storefront—to provision and manage products, subscriptions, payment processing, etc.

Currently, Galaxy has about 100 integration apps, from Freshbooks to Salesforce, which is priced at $25 per month, to integrations that are priced at $5000 or $6000 per year.

Pervasive is also working on a new capability, code-named Maestro, that will have a simple mapping interface so providers can map custom fields on top of pre-built connectors. Again, the Galaxy approach makes it easy for under-resourced SMBs to tackle the complicated integration problem.

On the other hand, Pervasive’s big data offerings, such as DataRush, have pretty much been a large enterprise play, and Actian’s primary focus and customer base has resided with large enterprises to date.

Where will SMBs fit in the future?

Will Actian continue to maintain a strong focus on SMBs? In conversations at Integration World, as well as in the press release, Shine indicated that Actian intends to cover the spectrum from large to small: “Actian’s innovations make it easy for organisations large and small to connect, analyse and act on their fast-changing and fast-growing diverse data assets throughout the entire data lifecycle.”

Furthermore, Pervasive has an established and successful model of working with ISVs to embed and sell through its solutions—a solid approach to reach and serve SMBs, who need  integration solutions that they can quickly deploy and from that they can get value. The Galaxy Marketplace complements this approach by adding the value of community insight and new ways to source and purchase integration solutions.

However, Actian will face many challenges as it tries to span from large enterprises to small—especially in increasingly crowded and hyped integration, analytics and big data markets. And, as more SMBs become aware of and educated about what big data is and why they need to have a strategy for it, how will Actian push through the noise and surface to get into consideration in that arena?

Easier said than done—both on engineering and marketing fronts, especially as large customers tend to have a lot more pull than small ones, and the fragmented nature of the market makes SMBs much harder to reach and serve.

Actian will need to make a bold statement. It must double down on engaging SMB-focused developers, SIs and other sell-through partners both within, as well as beyond, its current integration ecosystem. If Actian could, for instance, apply low-friction approaches such as Galaxy into other areas, such as analytics, it could prove a powerful play for helping SMBs not only connect, but to also analyze and act on their data once its integrated.

I’ll be watching to see if Actian chooses to make some significant moves in SMB directions as well as in the large enterprise space. Will SMBs be treated as a strategic market focus, or as business as usual? Actian’s decisions will signal whether it intends to pursue a broader play in the SMB market—or not.

By: Dana Gardner, Principal Analyst, Interarbor Solutions Posted: 11th April 2013 Copyright Interarbor Solutions © 2013

A data dichotomy has changed the face of information management, bringing with it huge new data challenges for businesses to solve.

The dichotomy means that organizations, both large and small, not only need to manage all of their internal data to provide intelligence about their businesses, they need to manage the growing reams of increasingly external big data that enables them to discover new customers and drive new revenue.

The latest BriefingsDirect software how-to discussion then focuses on bringing far higher levels of automation and precision to the task of solving such varied data complexity. By embracing an agnostic, end-to-end tool chain approach to overall data and information management, businesses are both solving complexity and managing data better as a lifecycle.

To gain more insights on where the information management market has been and where it's going, we are joined by Matt Wolken, Executive Director and General Manager for Information Management at Dell Software. The discussion is moderated by Dana Gardner, Principal Analyst at Interarbor Solutions. [Disclosure: Dell Software is a sponsor of BriefingsDirect podcasts.]

Here are some excerpts:

Gardner: What are the biggest challenges that businesses need to solve now when it comes to data and information management?

Wolken: About 10 or 15 years ago, the problem was that data was sitting in individual databases around the company, either in a database on the backside of an application, the customer relationship management (CRM) application, the enterprise resource planning (ERP) application, or in data marts around the company. The challenge was how to bring all this together to create a single cohesive view of the company?

That was yesterday's problem, and the answer was technology. The technology was a single, large data warehouse. All of the data was moved to it, and you then queried that larger data warehouse where all of the data was for a complete answer about your company.

What we're seeing now is that there are many complexities that have been added to that situation over time. We have different vendor silos with different technologies in them. We have different data types, as the technology industry overall has learned to capture new and different types of data—textual data, semi-structured data, and unstructured data—all in addition to the already existing relational data. Now, you have this proliferation of other data types and therefore other databases.

The other thing that we notice is that a lot of data isn't on premise any more. It's not even owned by the company. It's at your software-as-a-service (SaaS) provider for CRM, your SaaS provider for ERP, or your travel or human resources (HR) provider. So data again becomes siloed, not only by vendor and data type, but also by location. This is the complexity of today, as we notice it.

All of this data is spread about, and the challenge becomes how do you understand and otherwise consume that data or create a cohesive view of your company? Then there is still the additional social data in the form of Twitter or Facebook information that you wouldn't have had in prior years. And it's that environment, and the complexity that comes with it, that we really would like to help customers solve.

Gardner: When it comes to this so-called data dichotomy, is it oversimplified to say it's internal and external, or is there perhaps a better way to categorize these larger sets that organizations need to deal with?

Wolken: There's been a critical change in the way companies go about using data. There are some people who want to use data for an outcome-based result. This is generally what I would call the line-of-business concern, where the challenge with data is how do I derive more revenue out of the data source that I am looking at.

What's the business benefit for me examining this data? Is there a new segment I can codify and therefore market to? Is there a campaign that's currently running that is not getting a good response rate, and if so, do I want to switch to another campaign or otherwise improve it midstream to drive more real value in terms of revenue to the company?

That’s the more modern aspect of it. All of the prior activities inside business intelligence (BI)—let’s flip those words around and say intelligence about the business—was really internally focused. How do I get sanctioned data off of approved systems to understand the official company point of view in terms of operations?

That second goal is not a bad goal. That's still a goal that's needed, and IT is still required to create that sanctioned data, that master data, and the approved, official sources of data. But there is this other piece of data, this other outcome that's being warranted by the line of business, which is, how do I go out and use data to derive a better outcome for my business? That's more operationally revenue-oriented, whereas the internal operations are around cost orientation and operations.

So where you get executive dashboards for internal consumption off of BI or intelligence for the business, the business units themselves are about visualization, exploration, and understanding and driving new insights.

It's a change in both focus and direction. It sometimes ends up in a conflict between the groups, but it doesn't really have to be that way. At least, we don't think it does. That's something that we try to help people through: How do you get the sanctioned data you need, but also bring in this third-party data and unstructured data and add nuance to what you are seeing about your company.

Gardner: Do traditional technology offerings allow this dichotomy to be joined, or do we need a different way to create these insights across both internal and external information?

Wolken: There are certainly ways to get to anything. But if you're still amending program after program or technology after technology, you end up with something less than the best path, and there might be new and better ways of doing things.

There are lots of ways to take a data warehouse forward in today's environment, manipulate other forms of data so it can enter a data warehouse or relational data warehouse, and/or go the other way and put everything into an unstructured environment, but there's also another way to approach things, and that’s with an agnostic tool chain.

Tools have existed in the traditional sense for a long time. Generally, a tool is utilized to hide complexity and all of the issues underneath the tool itself. The tool has intelligence to comprehend all of the challenges below it, but it really abstracts that from the user.

We think that instead of buying three or four database types, a structured database, something that can handle text, a solution that handles semi-structured or structured, or even a high performance analytical engine for that matter, what if the tool chain abstracts much of that complexity? This means the tools that you use every day can comprehend any database type, data structure type, or any vendor changes or nuances between platforms.

That's the strategy we’re pursuing at Dell. We’re defining a set of tools—not the underlying technologies or proliferation of technologies—but the tools themselves, so that the day-to-day operations are hidden from the complexity of those underlying sources of vendor, data type, and location.

That's how we really came at it—from a tool-chain perspective, as opposed to deploying additional technologies. We’re looking to enable customers to leverage those technologies for a smoother, more efficient, and more effective operation.

Let's just take data integration as a point. I can sometimes go after certain siloed data integration products. I can go after a data product that goes after cloud resources. I can get a data product that only goes after relational. I can get another data product to extract or load into Hive or Hadoop. But what if I had one that could do all of that? Rather than buying separate ones for the separate use cases, what if you just had one?

Gardner: What are the stakes here? What do you get if you do this right?

Wolken: There are a couple of ways we think about it, one of which is institutional knowledge. Previously, if you brought in a new tool into your environment to examine a new database type, you would probably hire a person from the outside, because you needed to find that skill set already in the market in order to make you productive on day one.

Instead of applying somebody who knows the organization, the data, the functions of the business, you would probably hire the new person from the outside. That's generally retooling your organization.

Or, if you switch vendors, that causes a shift as well. One primary vendor stack is probably a knowledge and domain of one of your employees, and if you switch to another vendor stack or require another vendor stack in your environment, you're probably going to have to retool yet again and find new resources. So that's one aspect of human knowledge and intelligence about the business.

There is a value to sharing. It's a lot harder to share across vendor environments and data environments if the tools can't bridge them. In that case, you have to have third-party ways to bridge those gaps between the tools. If you have sharing that occurs natively in the tool, then you don't have to cross that bridge, you don't have the delay, and you don't have the complexity to get there.

So there is a methodology within the way you run the environment and the way employees collaborate that is also accelerated. We also think that training is something that can benefit from this agnostic approach.

But also, generically, if you're using the same tools, then things like master data management (MDM) challenges become more comprehensive, if the tool chain understands where that MDM is coming from, and so on.

You also codify how and where resources are shared. So if you have a person who has to provision data for an analyst, and they are using one tool to reach to relational data, another to reach into another type of data, or a third-party tool to reach into properties and SaaS environments, then you have an ineffective process.

You're reaching across domains and you're not as effective as you would be if you could do that all with one tool chain.

So those are some of the high-level ideas. That's why we think there's value there. If you go back to what would have existed maybe 10 or 15 years ago, you had one set of staff who used one set of tools to go back against all relational data. It was a construct that worked well then. We just think it needs to be updated to account for the variance within the nuances that have come to the fore as the technology has progressed and brought about new types of technology and databases.

Gardner: What are typically some of the business paybacks, and do they outweigh the cost?

Wolken: It all depends on how you go about it. There are lots of stories about people who go on these long investment cycles into some massive information management strategy change without feeling like they got anything out of it, or at least were productive or paid back the fee.

There's a different strategy that we think can be more effective for organizations, which is to pursue smaller, bite-size chunks of objective action that you know will deliver some concrete benefit to the company. So rather than doing large schemes, start with smaller projects and pursue them one at a time incrementally—projects that last a week and then you have 52 projects that you know derive a certain value in a given time period.

Other things we encourage organizations to do deal directly with how you can use data to increase competitiveness. For starters, can you see nuances in the data? Is there a tool that gives you the capability to see something you couldn't see before? So that's more of an analytical or discovery capability.

There's also a capability to just manage a given data type. If I can see the data, I can take advantage of it. If I can operate that way, I can take advantage of it.

Another thing to think about is what I would call a feedback mechanism, or the time or duration of observation to action. In this case, I'll talk about social sentiment for a moment. If you can create systems that can listen to how your brand is being talked about, how your product is being talked about in the environment of social commentary, then the feedback that you're getting can occur in real time, as the comments are being posted.

Now, you might think you'll get that anyway. I would have gotten a letter from a customer two weeks from now in the postal system that provided me that same feedback. That’s true, but sometimes that two weeks can be a real benefit.

Imagine a marketing campaign that's currently running in the East, with a companion program in the West that's slightly different. Let's say it's a two-week program. It would be nice if, during the first week, you could be listening to social media and find out that the campaign in the West is not performing as well as the one in the East, and then change your investment thesis around the program—cancel the one that's not performing well and double down on the one that's performing well.

There's a feedback mechanism increase that also can then benefit from handling data in a modern way or using more modern resources to get that feedback. When I say modern resources, generally that's pointing towards unstructured data types or textual data types. Again, if you can comprehend and understand those within your overall information management status, you now also have a feedback mechanism that should increase your responsiveness and therefore make your business more competitive as well.

Gardner: Given that these payoffs could be so substantial, what's between companies and the feedback benefits?

Wolken: I think it's complexity of the environment. If you only had relational systems inside your company previously, now you have to go out and understand all of the various systems you can buy, qualify those systems, get pure feedback, have some proofs of concept (POCs) in development, come in and set all these systems up, and that just takes a little bit of time. So the more complexity you invite into your environment, the more challenges you have to deal with.

After that, you have to operate and run it every day. That's the part where we think the tool chain can help. But as far as understanding the environment, having someone who can help you walk through the choices and solutions and come up with one that is best suited to your needs, that’s where we think we can come in as a vendor and add lots of value.

When we go in as a vendor, we look at the customer environment as it was, compare that to what it is today, and work to figure out where the best areas of collaboration can be, where tools can add the most value, and then figure out how and where can we add the most benefit to the user.

What systems are effective? What systems collaborate well? That's something that we have tried to emulate, at least in the tool space. How do you get to an answer? How do you drive there? Those are the questions we’re focused on helping customers answers.

For example, if you've never had a data warehouse before, and you are in that stage, then creating your first one is kind of daunting, both from a price perspective, as well as complexity perspective or know-how. The same thing can occur on really any aspect—textual data, unstructured data, or social sentiment.

Each one of those can appear daunting if you don't have a skill set, or don't have somebody walking you through that process who has done it before. Otherwise, it's trying to put your hands on every bit of data and consume what you can and learning through that process.

Those are some of the things that are really challenging, especially if you're a smaller firm that has a limited number of staff and there's this new demand from the line of business, because they want to go off in a different direction and have more understanding that they couldn't get out of existing systems.

How do you go out and attain that knowledge without duplicating the team, finding new vendor tools, and adding complexity to your environment, maybe even adding additional data sources, and therefore more data-storage requirements. Those are some of the major challenges—complexity, cost, knowledge, and know-how.

Gardner: Why are mid-market organizations now more able to avail themselves of some of these values and benefits than in the past?

Wolken: As the products are well-known, there is more trained staff that understands the more common technologies. There are more codified ways of doing things that a business can take advantage of, because there's a large skill set, and most of the employees may already have that skill set as you bring them into the company.

There are also some advantages just in the way technologies have advanced over the years. Storage used to be very expensive, and then it got a little cheaper. Then solid-state drives (SSD) came along and then that got cheaper as well. There are some price point advantages in the coming years, as well.

Dell overall has maintained the status that we started with when Michael Dell started recreating PCs in his dorm room from standard product components to bring the price down. That model of making technology attainable to larger numbers of people has continued throughout Dell’s history, and we’re continuing it now with our information management software business.

We’re constantly thinking about how we can reduce cost and complexity for our customers. One example would be what we call Quickstart Data Warehouse. It was designed to democratize data to a lower price point, to bring the price and complexity down to a much lower space, so that more people can afford and have their first data warehouse.

We worked with our partner Microsoft, as well as Dell’s own engineering team, and then we qualified the box, the hardware, and the systems to work to the highest peak performance. Then, we scripted an upfront install mechanism that allows the process to be up and running in 45 minutes with little more than directing a couple of IP addresses. You plug the box in, and it comes up in 45 minutes, without you having to have knowledge about how to stand up, integrate, and qualify hardware and software together for an outcome we call a data warehouse.

Another thing we did was include Boomi, which is a connector to automatically go out and connect to the data sources that you have. It's the mechanism by which you bring data into it. And lastly, we included services, in case there were any other questions or problems you had to set it up.

If you have a limited staff, and if you have to go out and qualify new resources and things you don't understand, and then set them up and then actually run them, that’s a major challenge. We're trying to hit all of the steps, and the associated costs—time and/or personnel costs—and remove them as much as we can.

It's one way vendors like Dell are moving to democratize business intelligence a little further, bring it to a lower price point than customers are accustomed to and making it more available to firms that either didn’t have that luxury of that expertise link sitting around the office, or who found that the price point was a little too high.

Gardner: You mentioned this concept of the tool chain several times—being agnostic to the data type, holistic management, complete view, and then of course integrate it. What is it about the tool chain that accomplishes both a comprehensive value, but also allows it to be adopted on a fairly manageable path, rather than all at once?

Wolken: One of the things we find advantageous about entering the market at this point in time is that we're able to look at history, observe how other people have done things over time, and then invest in the market with the realization that maybe something has changed here and maybe a new approach is needed.

Whereas the industry has typically gone down the path of each new technology or advancement of technology requires a new tool, a new product, or a new technology solution, we’ve been able to stand back and see the need for a different approach. We just have a different point of view, which is that an agnostic tool chain can enable organizations to do more.

So when we look at database tools, as an example, we would want a tool that works against all database types, as opposed to one that works against only a single vendor or type of data.

The other thing that we look at is if you walk into an average company today, there are already a lot of things laying around the business. A lot of investment has already been made.

We wanted to be able to snap in and work with all of the existing tools. So, each of the tools that we’ve acquired, or have created inside the company, were made to step into an existing environment, recognize that there were other products already in the environment, and recognize that they probably came from a different vendor or work on a different data type.

That’s core to our strategy. We recognize that people were already facing complexity before we even came into the picture, so we’re focused on figuring out how we snap into what they already have in place, as opposed to a rip-and-replace strategy or a platform strategy that requires all of the components to be replaced or removed in order for the new platform to take its place.

What that means is tools should be agnostic, and they should be able to snap into an environment and work with other tools. Each one of the products in the tool chain we’ve assembled was designed from that point of view.

But beyond that, we’ve also assembled a tool chain in which the entirety of the chain delivers value as a whole. We think that every point where you have agnosticism or every point where you have a tool that can abstract that lower amount of complexity, you have savings.

You have a benefit, whether it’s cost savings, employee productivity, or efficiency, or the ability to keep sanctioned data and a set of tools and systems that comprehend it. The idea being that the entirety of the tool chain provides you with advantages above and beyond what the individual components bring.

Now, we're perfectly happy to help a customer at any point where they have difficultly and any point where our tools can help them, whether it's at the hardware layer, from the traditional Dell way, at the application layer, considering a data warehouse or otherwise, or at the tool layer. But we feel that as more and more of the portfolio—the tool chain—is consumed, more and more efficiency is enabled.

Gardner: It also sounds as if this sets you up for a data and information lifecycle benefits, not just on the business and BI benefits, but also on the IT benefits.

Wolken: One of the problems that you uncover is that there's a lot of data being replicated in a lot of places. One of the advantages that we've put together in the tool chain was to use virtualization as a capability, because you know where data came from and you know that it was sanctioned data. There's no reason to replicate that to disk in another location in the company, if you can just reach into that data source and pull that forward for a data analyst to utilize.

You can virtually represent that data to the user, without creating a new repository for that person. So you're saving on storage and replication costs. So if you’re looking for where is there efficiency in the lifecycle of data and how can you can cut some of those costs, that’s something that jumps right out.

Doing that, you also solve the problem of how to make sure that the data that was provisioned was sanctioned. By doing all of these things, by creating a virtual view, then providing that view back to the analyst, you're really solving multiple pieces of the puzzle at the same time. It really enables you to look at it from an information-management point of view.

Gardner: How should enterprises and mid-market firms get started?

Wolken: Most companies aren’t just out there asking how they can get a new tool chain. That's not really the strategy most people are thinking about. What they are asking is how do I get to the next stage of being an intelligent company? How do I improve my maturity in business intelligence? How would I get from Excel spreadsheets without a data warehouse to a data warehouse and centralized intelligence or sanctioned data?

Each one of these challenges come from a point of view of, how do I improve my environment based upon the goals and needs that I am facing? How do I grow up as a company and get to be more of a data-based company?

Somebody else might be faced with more specific challenges, such a line of business is now asking me for Twitter data, and we have no systems or comprehension to understand that. That's really the point where you ask, what's going to be my strategy as I grow and otherwise improve my business intelligence environment, which is morphing every year for most customers.

That's the way that most people would start, with an existing problem and an objective or a goal inside the company. Generically, over time, the approach to answering it has been you buy a new technology from a new vendor who has a new silo, and you create a new data mart or data warehouse. But this is perpetuating the idea that technology will solve the problem. You end up with more technologies, more vendor tools, more staff, and more replicated data. We think this approach has become dated and inefficient.

But if, as an organization, you can comprehend that maybe there is some complexity that can be removed, while you're making an investment, then you free yourself to start thinking about how you can build a new architecture along the way. It's about incremental improvement as well as tangible improvement for each and every step of the information management process.

So rather than asking somebody to re-architect and rip and replace their tool chain or the way they manage the information lifecycle, I would say you sort of lean into it in a way.

If you're really after a performance metric and you feel like there is a performance issue in an environment, at Dell we have a number of resources that actually benchmark and understand the performance and where bottlenecks are in systems.

Sometimes there’s an issue occurring inside the database environment. Sometimes it's at the integration layer, because integration isn’t happening as well as you think. Sometimes it's at the data warehouse layer, because of the way the data model was set up. Whatever the case, we think there is value in understanding the earlier parts of the chain, because if they’re not performing well, the latter parts of the chain can’t perform either.

And so at each step, we've looked at how you ensure the performance of the data. How do you ensure the performance of the integration environment? How do you ensure the performance of the data warehouse as well? We think if each component of the tool chain in working as well as it should be, then that’s when you enable the entirety of your solution implementation to truly deliver value.

Listen to the podcast. Find it on iTunes. Read a full transcript or download a copy.

By: Dana Gardner, Principal Analyst, Interarbor Solutions Posted: 9th April 2013 Copyright Interarbor Solutions © 2013

Boston-based startup nCrypted Cloud recently launched software of the same name designed to address the security and privacy concerns that have emerged with the use of popular cloud-based storage services.

Available in consumer basic, consumer pro, and enterprise editions, nCrypted Cloud encrypts information stored on popular cloud services such as Dropbox, Google Drive and Microsoft’s SkyDrive. The software is as simple to use as the services it works with, says Nick Stamos, the CEO and Co-Founder of nCrypted Cloud, while offering the robustness and controls that enterprise IT departments need.

Stamos says nCrypted Cloud’s security privacy protections fill a glaring gap in cloud storage services today.

“The promise of the cloud is 'put everything in the cloud and it will be available'—but that’s the problem as well as the promise,” says Stamos, who is also principal and founder of The Stamos Group.

Popular cloud-based storage services lack the security and privacy that enterprises require, yet employees are using them anyway—with the rise of BYOD and mobility, users want access to files anytime, from anywhere. This leaves enterprise IT departments searching for a way to protect corporate information stored in the cloud.

In a related develoment, last month we reported on OwnCloud, Inc. and its release of the latest version of the ownCloud Community Edition with a number of usability, performance, and integration enhancements. The ownCloud file sync and share software, deployed on-premise, not only offers users greater control, but allows organizations to integrate existing security, storage, monitoring and reporting tools.

Mobile data management solutions have proven too restrictive and inflexible, said Stamos, while trying to implement corporate policies that prohibit employees from storing and accessing personal and corporate data from a mobile device is unreasonable. Enterprise IT needs a solution that users won’t attempt to work around, but will embrace, he says.

“We allow users to apply privacy controls to personal data, as well as corporate data, so that if an employee parts ways with a company he can revoke access to that personal data from a corporate device, and vice versa,” explains Stamos. “That makes it a value proposition that users feel comfortable with.” Meanwhile, enterprise security policies can be used to govern work files and allow for revocation of access if needed.

Enhance, not replace
One key distinction about nCrypted Cloud is that it works with existing cloud-storage services, instead of replacing them.

“We provide the same sort of native user experience … so it’s not disruptive end users. The last thing the world needed was a new storage provider,” says Stamos. “What people need is to be able to use the Dropbox they love…in the context of it being more secure by just being able to make folders private or share them securely. They can continue to have their data where it is and how it’s organized without being disruptive in any way, shape or form.”

nCrypted Cloud’s persistent client-side encryption ensures that data isn’t exposed and the software offers comprehensive key-management features to facilitate administration. When a user accesses corporate files from any device, her predefined access policies and sharing status is verified and keys for her user ID are sent to the device.

That client caches keys for offline access to files, and keys can be removed if the access policies change. Users can easily access and share files in different cloud-based storage services and have a single-pane view of cloud and corporate file repositories.

The consumer basic version of nCrypted Cloud is available for free. The consumer pro version costs $5 per month and includes managed secure sharing, some file auditing, and the capability to manage files stored in different cloud services. The enterprise edition – which enters beta testing next week – well be priced at $10 per month. It includes all of the capabilities of the consumer pro version as well as  enhancements such as multiple identities, centralized provisioning and policy control and a full audit trail of 30-day archives. 

Downloads and more information are available at www.ncryptedcloud.com.

(BriefingsDirect contributor Cara Garretson provided editorial assistance and research on this post. She can be reached on LinkedIn.)

By: Helena Schwenk, Principal Analyst, MWD Advisors Posted: 4th April 2013 This work is licensed under a Creative Commons License

This week Accenture quietly announced its acquisition of Australian predictive analytics service vendor ChangeTrack Research Pty Ltd, for an undisclosed amount.

The company offers a methodology and tool—called ChangeTracking—which helps organisations monitor, measure and optimise large scale change management programs, for example where an organisation is undergoing M&A activity, a company-wide ERP or CRM program, or a restructuring exercise. Initiatives of this nature tend to involve managing a complex array of factors and dimensions, including people, technology and processes that need to be continuously aligned and managed; so it’s no surprise that projects often go off track and don’t deliver the business outcome expected. ChangeTrack’s technology aims to address this challenge by providing more rigour and discipline to the measurement process surrounding a program, so managers can pick up on early warn signs and identify where action is needed while change is actually taking place, rather than doing this after the fact (as is often the case).

The notion underlying the offering is that change is not a random set of events, but is in fact a series of very predictable paths that an organisation can take depending on what decisions are made. At the heart of the ChangeTracking tool is a database comprising responses collected from over 600,000 people across more than 300 change programs—both those that have been successful and others less so. Using the insight and knowledge accumulated over the years, and by capturing survey-based performance information from the client, the tool is able to statistically analyse data—using its patented mathematical algorithms—and identify patterns and critical success factors to highlight what is normal and not normal in different types of change programs at different stages. This then forms the basis of an action plan used to help put a program back on track.

ChangeTrack’s methodology and offering has attracted a pretty impressive client list too, including BP, Nike, 3M, LloydsTSB, and Shell. Similarly it has worked with a number of large global consulting firms, including Accenture, with whom it worked as part of product development to help the company understand how consultants plan and implement change in their client organisations. Having a productive working relationship has obviously been an influential factor in Accenture’s decision to acquire the company. The consulting firm is expected to use its new purchase to complement its own set of methods, tools and capabilities for measuring and managing change programs. Longer term, however, we can also envisage the tool and its predictive analytics capabilities being used across a wider range of industries and geographies. The other benefit of being own by one of the largest global consulting firms is that the more clients that use the tool, the more data can be collected and analysed, and the better the tool can get at identifying issues and recommending corrective action—so all in all we see this as a good thing for ChangeTracker and its customers and prospects.

By: David Norfolk, Practice Leader - Development, Bloor Research Posted: 4th April 2013 Copyright Bloor Research © 2013

Now that I'm taking a new interest in social collaboration, I'm getting a little concerned about people implementing social collaboration technology and then looking for a collaboration issue to use it on (as Lucy Wimmer, Head of Global Corporate Communications at Huddle points out: "It's this idea of the technology becoming bigger than the project that is often the issue with legacy systems such as SharePoint"). Or, worse, I'm concerned about managers possibly thinking that installing collaboration software will magically generate a collaborative organisation without any further effort. Surely, however, no IT group would ever install software as an end in itself and then go looking for a problem to fix with it? Well, in my opinion, there's optimism and then there's experience of life...

So, I've had a bit of an informal email discussion about what we see as the issues around social collaboration with Raheel Retiwalla of Fuzed, one of the more interesting social collaboration solutions. Some of the things that he says have "kept us [at Fuzed] up at night wondering about the use of social within the enterprise, its impact, and about how a solution can help harness the power of social within the enterprise" make interesting reading.

He seems to feel that the use of Fuzed has to be largely transparent to existing desktop workers, so that barriers to its adoption are minimised: "our vision of a desktop app is about using actionable notifications that let the user contribute knowledge instantly without having to go to Fuzed...[this includes] integration with productivity tools like Outlook, Word, Excel and even Windows Explorer" so that desktop workers needn't leave their familiar 'productivity tools' environment. That sounds good, although I do have a nagging feeling that some of these productivity tools aren't really as productive as they are cracked up to be (I'm sure they are productive of documents and/or presentations; but I rather wonder about how productive they are of 'waste' too). Still, they are widely used, de-facto desktop standards; I just hope Open Office, say, is also supported. I also note that Raheel is thinking about the gamification of collaboration software - a successful computer game provides a more supportive user experience than most enterprise software can manage currently - but that's an idea worth an article of its own, I think.

Raheel claims that using the power of social to add context is Fuzed's USP "this is a very unique and innovative thinking we have in identifying other ways in which the value of social collaboration can be tapped. It's the idea that people in a community can contribute and connect related pieces of information together which is a powerful capability", he says. This supports use cases in market research, the development of central idea hubs (to support innovation) and knowledge transfer.

He also envisages a rather richer implementation of the App Store concept, "where customers can connect their existing applications so that key information is accessible and therefore easily shareable from within Fuzed". Information from these applications can be connected to other information - as the 'context' talked about in the previous paragraph. This sounds good but I feel that it needs further investigation - the devil may well be in the detail.

Raheel certainly does recognise the scalability issue. With people belonging to multiple communities and sharing information with any and every appropriate stakeholder, might not social collaboration software simply be a source of yet more information overload? "Our thoughts around this," he says, "include providing all controls to the user so that they control people, communities, content and topics that are relevant to them at that moment. This also includes a facility for pausing notifications and providing subscription-only views". Once again, this sounds good, but I'm not writing an InDetail research paper here and I'll have to look at what this actually means in practice, at some future time.

Fuzed does offer role-based security: "who can do what within a community, who can see what information is in a community and what they can do with that information". I must ask about audit trails and versioning too.

I am particularly interested in Raheel's thinking around feedback. In Bloor's context, social collaboration is ultimately about improving the business outcomes for the organisation, which implies monitoring of outcomes; feedback mechanisms; and process improvement. Fused has tools for assessments, surveys, forms and form reviews so that management can tap into a collective feedback capability which lets them monitor - and promote - the success of these communities. However, the effectiveness of this will come from innovative management vision as much as from the use of tools.

Raheel also reminded me of the licensing issue, which I must add to my evolving social collaboration checklist: "as far as I know," he says, "we are the only ones with what we call an active concurrent licensing model where customers pay us only for the maximum numbers of people that are actively using Fuzed at any given moment in time. We further define the term "using Fuzed" as people who are actively contributing knowledge or interacting with the application. For example, if you are only receiving notifications on your desktop or mobile and not actually interacting with those notifications you are not classified as an active concurrent user". Now, that is an interesting approach and merits further analysis. In particular, it gives Fuzed a real incentive for promoting the active use of its collaboration software; rather than for selling collaborative 'shelfware' (this latter is an established collaboration antipattern, it seems to me).

Finally, Raheel comments on Manageability and Marketing. Fuzed provides tools to enable community owners and technicians to control the collaboration system as appropriate. At first, this may seem rather foreign to the idea of collaboration; but light-touch controls will be needed. To take a couple of obvious examples, if social collaboration takes off as promised in a business, it (or the information it supports) will become critical to business success, which implies that business continuity assurance will be needed; and there are significant legal restrictions on the sharing of, say, personal data that would need to be enforced. Controls, versioning and audit trails will be particularly important for the deployment of social collaboration in government organisations, as well. Fuzed also provides analytics capabilities (which, I think, could be used to help to enable 'light touch' controls). According to Raheel: "we have taken analytics one step forward in the sense that we allow network owners to act on the insights gained from the analytics. We allow that by providing a full featured marketing function that lets network owners engage and target specific messages to their audience based on the analytics".

As I've implied already, this piece is no formal review of Fuzed. However, I think I have identified some useful issues that are important to the designers of Fuzed; and these will contribute to our analyses of social collaboration software - and its use cases - generally.

By: David Norfolk, Practice Leader - Development, Bloor Research Posted: 3rd April 2013 Copyright Bloor Research © 2013

Why do I feel the need to talk about enterprise architecture (EA) on an Enterprise Development page? Surely, as a practitioner friend told me recently, EA is for managers who want to interfere, not for practitioners who need to get the job done and actually build software? And there is some truth in that, I sometimes think that an 'architect' is often a 'systems analyst' with hubris issues (and salary to match) - and that EA is an (expensive) way of giving managers the comfortable illusion that they are in control of IT.

However that's just me being cynical (no, I'm not cynical; I just have experience of life). Those are two real EA anti-patterns, but there's a lot more to EA than that. Done right, EA is about building the right software (to support business outcomes and business management's vision for the business), not just about building software right.

Done properly, I think that EA models help to focus all stakeholders in automated business processes on productive 'business outcomes' and that these EA models should transform (with the addition of business detail around governance and risk management; and the technical detail needed to build software) into the automated technology systems that, these days, run the business (in conjunction with manual business decision processes etc., also derived from EA visions). An EA model is an aid to avoiding the waste associated with building the wrong systems; or building the right systems in the wrong business environment. It is also an aid to breaking down silos and fostering true collaboration between the business, IT and other stakeholders

I am particularly interested in open systems EA processes such as TOGAF, maintained by the Open Group; partly because I've seen what happens when top management gets locked into vendor-specific architectural approaches - that's another EA anti-pattern - and the consultancy that comes with them. To be fair, the big EA vendors are considerably more open these days, but I still think that TOGAF and its associated architectural modelling tool, ArchiMate, is a really good starting point for learning about EA (although it isn't quite the only open standards EA game in town these days - check out the OMG's UPDM unified architecture framework, for instance).

So, this article is to alert readers to a useful, free, educational resource for people that aren't yet familiar with EA approaches: the Case Experiences and Best Practices Using ArchiMate® and TOGAF® web seminar that you can find at The Open Group Bookstore here. This webinar was given by an EA practitioner from BiZZdesign and not only gives you an idea of how ArchiMate works (but there are other, more detailed, resources for this at the Bookstore), it also gives you an idea of the value that some organisations are getting out of EA.

By: David Norfolk, Practice Leader - Development, Bloor Research Posted: 3rd April 2013 Copyright Bloor Research © 2013

Social Collaboration always strikes me as more about people and organisational behaviour than technology - although once an organisation has a vision for social collaboration at all levels, technology is a real enabler.

So, now I'm going to get an in-depth briefing on Huddle - and I'm probably less interested in the technology in itself (as long as it works) than in what it actually does to help people work together effectively.

From an initial briefing I'm going to be reviewing against a checklist that will include (in no particular order):

1. What is the Huddle USP? Perhaps this is its content recommendation engine - getting you all that you need and only what you need - right now. A neat trick if you can pull it off reliably!
2. Audit trails. Collaboration is good but if it affects the business (and, if it doesn't, why are you bothering) then there'll be times when you need to revisit who said what and why, especially in regulated businesses (and all businesses are regulated to a greater or lesser extent).
3. Mobility - support for smartphone and tablet mobility is vital to social networking cultures.
4. Collaboration inside and outside the firewall - business outcomes might arise from collaboration with customers and business partners. This implies a good (workable) security model that doesn't impede collaboration.
5. And then there's the conflict resolution model and versioning - if lots of people are collaborating on something (especially something that ends up as a legal document or whatever), there are likely to be subgroups at any one time with different versions of the thing, archived (previous) versions - and some rules about who has the final say on what gets published.
6. Finally, for now, there are best practices and value demonstrations. For instance, Huddle claims success with the UK government G-Cloud and sees that as a 'proof of concept' for access to US government computing. It also points out that audit trails and versioning are particularly important in Government computing

I'm sure there's more, but that's a start. And it isn't just about Huddle, of course. For instance, Fuzed is an up-and-coming "enterprise social knowledge network" vendor with a strong story about managing context - perhaps this is its USP (point 1, above).

Social collaboration technology is often built around activity streams but Fuzed's "Context Stream" augments an activity stream with contextual information about events, content and activities created or shared in Fuzed or external context from tapping into existing applications, internet search or the social web.

There's a Scalability issue - perhaps that's an addition to my evolving checklist - why shouldn't activity streams rapidly become as unmanageable as email, if collaboration takes off? Fuzed would probably say that managing context is key to making collaboration in an organisation manageable and scalable. And perhaps I'd better also add ease of deployment and tool management to my checklist....

Now, I wonder what SharePoint's USP is.....

By: Clive Longbottom, Head of Research, Quocirca Posted: 28th March 2013 Copyright Quocirca © 2013

Dropbox has been a pretty good success, and it is difficult to do it down when it comes to an easy way for an individual to put information in one place for their own use across multiple devices. Dropbox sparked off a raft of 'me-toos' trying to do things just differently enough to create a market for themselves—companies such as SugarSync or Ubuntu's One, or bigger players trying to retain control of their customers such as Apple with iCloud and Microsoft with SkyDrive.

Consumer services are one thing, but there are problems when it comes to the business use of such services; the individual cannot be king here. To the organisation, information is the basis of its intellectual property, and if the information is spread around the cloud, this can be a major issue.

Dropbox was originally aimed purely at individuals and, as they started to use it for work-related documents, enterprises had a couple of major worries. Firstly, they had no visibility of what information was being stored in Dropbox (or any other cloud-based consumer service) and, secondly, it was not being shared across a team in an effective manner.

Dropbox is addressing this through its "business" plans and Microsoft is working through its plans for SkyDrive Pro—but are they doing enough? A look at what other providers such as Box are beginning to put in place, including additional team and organisational functionality, points towards the availability of well-rounded business information sharing system.

One interesting company that is taking things to the next level is Perforce Software. Perforce is best known for its on-premise software configuration management (SCM) tools. This provides the levels of control and ownership that many organisations are looking for that cloud-based systems may be perceived to lack.

Within SCM, teams work together, creating and working on digital assets that need to be managed and controlled at a granular level with high levels of security. 

Hang on—isn't this what's needed for team working on business information as well?

This is exactly what Perforce thought. However, the existing Perforce SCM system was not something that could just be re-badged and thrown over the wall in the hope that users would flock to it and change the world. Perforce is a tool aimed at technical developers and its front end would appear very complex to business users. Even so, Perforce has seen it being used by non-technical users to manage other digital assets.

Perforce could have gone for an approach of taking what they had and cutting out all the functionality that wasn't needed. This may well have worked, but would have presented them with two sets of underlying code to manage, two products with support needs and so on.

What Perforce decide to do was to take the existing Perforce SCM system and keep the engine as it is, but create a new skin over the top, creating Perforce Commons. Starting from the "keep it simple, stupid" school of thought, it started with the very basics—what would users want to do? Well, dragging documents from their device into the system seemed like a good place to start. Once the documents, what next? Well, preview them would seem like a good idea. Put them in folders would keep things clean. Share them between people inside and outside the organisation. Comment on them to create a stream of activity—you get the picture. Start simply and allow the interface to make this happen in the simplest way possible.

However, Commons also allows some advanced features—for example, individuals can work on documents at the same time and three-way comparisons can be carried out to aggregate and resolve comments and changes in an easy manner through an intelligent merge. Ideal when working as a team against the same information assets—parallel work can be carried out, helping to compress timescales.

What Perforce is ending up with is the proven strengths of its SCM product, completely re-skinned so that a business person can use it in a business environment to put documents in a controlled environment so that they can access them from any device wherever they are, share them within their teams and with those outside their teams and enable social collaboration via comments and tagging. Full versioning is there too—and users can send links to people that will always link to the latest version, or to a specific version if the user wants.

This approach takes things beyond where some of the other shared file providers are looking. And for Perforce, it has the luxury of being able to rapidly introduce new capabilities through just surfacing the underlying functionality of the Perforce SCM engine.

There are problems for Perforce, though. Where it is known, it is for SCM—and trying to persuade its SCM users to allow Commons to be used across an organisation may not be easy, although Perforce itself says that its customers are quite open to the proposition. Where it is not known, it has the problem of messaging—does it want to sell SCM or Commons—or both? Each needs different messaging to different groups, but any one sale could cloud the sale of the other. Perforce also has to decide how it works with its channel—the SCM channel will not be well positioned to sell Commons.

It also has to decide what it really is—is it a Dropbox for the enterprise? Is it an evolution of where others such as Box are going? Is it an alternative to SkyDrive Pro? There will be those who want to stay with an on-premise deployment, and Perforce fits the bill well against all these cloud-based services. Indeed, it would be relatively easy for Perforce to create a cloud-based offering and take on these other vendors head-to-head. 

However, to start with, it will be an on-premise only. But there are other on-premise products available—should Perforce be aiming to be SharePoint with bells on, or maybe even Documentum for the masses?

Its future is probably somewhere towards the SharePoint with bells on—and it has an interesting business model where small groups can use it indefinitely with no constraints for free: an interesting offer to the SMB market, but one which, if it becomes Perforce's main market, will produce little in the way of revenues but with considerable cost overheads.

Overall, Commons looks promising. Quocirca expects Perforce to struggle to start with, but it has the capabilities to react to users' wishes and wants rapidly and as long as it sorts out the channel and creates a sustainable business model, Commons could well be a success. 

By: Robin Bloor, Co-Founder, The Bloor Group Posted: 26th March 2013 Copyright The Bloor Group © 2013

The question; "What is Data?" is not so easy to answer. In fact data has many dimensions. Consequently, what I am doing here is assembling a whole series of articles that touch on these various dimensions.

• We can examine the "What is data?" question in a general top-down manner from the perspective of the distinction between knowledge, information and data. This is explored in the following article; What is Data (from the top down)?
• We can look at it from a bottom up manner, starting from a dictionary definition and moving forward to bring up the purpose of XML. This is explored in the following article: What is Data (from the bottom up)?
• Another relevant aspect of data is value; the value of data involves its urgency. Indeed the urgency of data often determines its value in a calculable way. Read When is data urgent? for more on this.
• There's also the Big Data question; Do We Need All This Data? This link takes you to an article which discusses the topic in terms of signal and noise.
• Another way of thinking about the topic of data is in respect of the Data-centric and Process-centric approaches to building computer systems. It is explored in Two Sides To The IT Coin
• There's an ariticle here about unstructured data: Why is the Data So Lumpy?
• Then there's the fact that, surprisingly, Data Doesn't Know What It Is? (It really doesn't)
• You can also examine the dstinct varieties of data. The Species of Data looks into this.
• Finally we can examine data from teh perspective of time. Data: In Its Own Time explores that.

If you have read all of these, then congratulations, that's a lot of words, or perhaps, a lot of data. Nevertheless, I don't suppose for one moment that I've exhausted the topic.

By: David Norris, Practice Leader - Analytics, Bloor Research Posted: 26th March 2013 Copyright Bloor Research © 2013

I have worked in data warehousing and analytics since the idea that Business Intelligence solutions had to offer more than reports added onto operational systems became accepted. But of late, I am facing a realisation: By extracting data from the operational environment, and loading it into a business intelligence environment, we introduce limitations that defeat the basis of why we set about doing it in the first place.

In a traditional data warehouse environment you have complex software to extract data, transform it, and reload it into a new location. Increasingly, that procedure takes a very long time, even when you throw very expensive hardware and software at it, because of the sheer volume involved.

When we have the data, the next step is storing it in a third normal form layer, to isolate the data from changes, and make it independent of future change - all very worthy, but again complex and time consuming. No end user can actually use the data in third normal form so we move the data again into a simpler format in a presentation layer, which also takes time.

The net result is that we have tied up immense amounts of intellect and capital to deliver data to the business that is heavily compromised by latency, cost, and difficulty of use.

As data has gotten really big, we have introduced big data solutions such as Hadoop, which remove some of the complexity by avoiding the structured stores, and exploit the capability to deliver affordable scalable solutions using commodity hardware. There is still a lot of complexity in the solution, because the means of extracting value require MapReduce programming, which is still an arcane skill and not for the average user.

So, after twenty years, I am starting to think the orthodox solutions have run their course and we need to think differently. So when I see something like the Pervasive combination of RushAnalytics and DataRush I am starting to see a solution that offers light at the end of the tunnel. What is required is something to enable Business Intelligence to provide business with insight quickly and affordably. That means commodity hardware, not expensive technical solutions, and software that supports rapid iterative development using visual interfaces, and not complex, arcane programming skills. So we need something that is fast, easy to use, and affordable. If we can tick those boxes, we are starting to get to a position where we can keep up with the demand that the business has for analytics, at a price that makes it economically feasible.

What Pervasive is offering is a platform that offers data access, transformation, analysis, visualisation and delivery using KNIME, an open source visual data analysis workflow environment, on top of Pervasive DataRush parallel dataflow architecture. This means that with Pervasive RushAnalytics, you do not have to move the data into a specific data store before you can start to analyse it. Now domain experts can start to gain insight from data in time spans that are in a different league to the days that traditional analysis takes, and is being achieved on commodity technology. This offers what business really craves - speed of return on investment that is measured in hours, or even minutes, not weeks!

KNIME offers the tools to address the data mining tasks that are required for risk management, fraud detection, and superior decision support that includes association rules, classifiers, clustering, principal component analysis and regression - all of the things that are key to effective data mining, and all via a graphical interface, so it's point and click, not code and sweat. That workflow is then executed on the highly parallel Pervasive DataRush processing engine.

When I first came across DataRush a couple of years ago, I thought it was the best-kept secret in the IT industry. It is designed to enable code to work in parallel across multiple cores without having to redesign things to exploit the additional cores as we move from a single threaded environment up through the various permutations of twin core, quadruple, eight core, sixteen core etc. DataRush detects the number of cores and nodes available at runtime, and adjusts the processing workflow to exploit them, so its model is "Build once and run on whatever," - total future proofing.

I am hoping that this is a sign that we are ending the world, as we know it, where analytics is held back by the technology we make it run on. We enter a new era in which analytics can run unfettered and deliver the returns that we all crave, which is an exciting prospect.

By: Helena Schwenk, Principal Analyst, MWD Advisors Posted: 25th March 2013 This work is licensed under a Creative Commons License

As a UK-based analyst it’s always interesting to observe how US software companies attempt to break into the European market. Revolution Analytics—the commercial open source R software and service provider and one of the vendors I track (read my recent On the Radar profile here)—is currently trying to do just that. The company has recently announced it’s opening up a UK office in London that will become a hub for sales, business and technical services for both UK and European clients. It is one of two offices the company has outside of North America (the other one being in Singapore).

Building an international presence is typically a good sign for a vendor since its points to positive growth and global expansion. Whilst the majority of Revolution’s commercial customers—around 250—are based in North America, the UK and Europe provides a rich picking ground since it brings with it the opportunity to piggy back on the growth of R open source adoption across both academic as well as enterprise institutions. As outlined in this blog, open source adoption in Europe is generally perceived to be ahead of the US, especially in cases where a vendor can take advantage of the communities built around it and where it can utilise government subsidies. While we’re not saying it’s impossible to build a European presence from the US, having a local and regional presence is the most obvious and optimal way of accomplishing this.

Of course Revolution Analytics isn’t the first US software company to look to Europe as part of its growth plans; many before it have tried—some more successfully than others. Most target the UK as an obvious launch pad for seeding the market particularly in Germany, France and Scandinavia. However, in doing so Revolution must also be careful not to fall into the trap of others who tend to think of and treat Europe as one homogeneous region. Being successful involves adapting to not only language and cultural differences, but also needs to take into consideration differences in sales models and channels—Germany has a higher predilection for channel partners compared with other countries, for example. Equally, having the right people in place to direct and oversee this expansion is another key element, and it’s no doubt that the company’s recent strategic hires are part of this overall plan. How successful Revolution will be in taking its Enterprise R platform to a commercial European audience is unknown at this stage, although these seem like logical and sensible first steps.

By: Dana Gardner, Principal Analyst, Interarbor Solutions Posted: 25th March 2013 Copyright Interarbor Solutions © 2013

OwnCloud, Inc. recently released the latest version of the ownCloud Community Edition with a number of usability, performance, and integration enhancements.

Based on an open-source project of the same name, the ownCloud file sync and share software, deployed on-premise, not only offers users greater control, but allows organizations to integrate existing security, storage, monitoring and reporting tools, while still taking advantage of the software’s simplicity and flexibility.

File sync and share services like Dropbox, Google Docs, and Box Inc. have revolutionized the way users share information. These cloud-based services make it easy to share files with clean interfaces and seemingly endless amounts of storage. However, not everyone wants to turn over their information to a service provider—for those who prefer to control how and where their data is stored there’s ownCloud. 

OwnCloud comes in a free, community edition, and the company will launch a commercially supported enterprise edition of the software in the second quarter. That version will target enterprise IT departments in need of on-premise file sync and share for sensitive corporate data. The company estimates it has more than 750,000 users worldwide today.

In the latest offering, the user interface has been streamlined, so that the main web navigation panel is now clearly differentiated from in-app navigations, says Markus Rex, CEO of ownCloud. And the way in which the software’s settings are laid out have been revamped, making it easier to distinguish personal settings from app-specific settings, he says.

“We’ve completely revamped the design with a much simplified interface so you can differentiate the navigation elements and focus on what you want to work with, instead of distracting from that,” says Rex.

New features
This version of ownCloud also features a Deleted Files app that lets users restore accidentally deleted files and folders, and improved app management, so that third-party apps can be easily installed from the central apps repository and automatically removed from the server, if disabled. Also included is a new search engine that lets users find files stored by both name and by content, thanks to the Lucene-based full text search engine app, and a new antivirus feature, courtesy of Clam AV, scans uploaded files for malware. This release also includes improved contacts, calendar and bookmarks, says Rex.

Performance benefits in this release come from improved file cache and faster syncing of the desktop client, according to company officials. Externally mounted file systems such as Google Drive, Dropbox, FTP and others can be scanned on-demand and in the background to increase performance. And hybrid clouds can be created by mixing and matching storage, thanks to file system abstraction that offers more flexibility and greater performance.

“You can get to the data in all of your data silos from one spot on a mobile client or desktop client, so you can get to files you might not be able to access otherwise from those devices,” says Rex.

This release features improved integration with LDAP and Active Directory and an enhanced external storage app to boost performance of integrated secondary storage including Dropbox, Swift, FTP, Google Docs, Amazon S3, WebDAV and external ownCloud servers.

(BriefingsDirect contributor Cara Garretson provided editorial assistance and research on this post. She can be reached on LinkedIn.)

This page has been translated into Spanish language by Maria Ramos from Webhostinghub.com

By: Philip Howard, Research Director - Data Management, Bloor Research Posted: 22nd March 2013 Copyright Bloor Research © 2013

An interesting discussion arose during the TDWI conference in London this week. The question was posed: could you use a graph database to do matching and de-duplication?

The answer must be yes. If Bill Clinton and William Clinton (this was the example posed during the session) have the same relationships they must surely be the same person, though given the nature of some of the ex-president's relationships it would perhaps be better to refer to following the edges of the graph rather than the relationships they represent. In fact, if you are using a graph database to look at terrorist or criminal networks this is precisely one of the things you would be doing as you want to understand which aliases equate to which real individuals.

First of all I should say that I am not aware of any graph vendor packaging up any special facilities to support matching and de-duplication but I imagine that there are things they could do to make this process easier. However, the concept is quite cool. It would mean that you don't need to license such capabilities from the likes of Trillium or Informatica. Of course there are other data cleansing requirements beyond matching but this does tend to be the bedrock for all such environments so could a graph database be a real competitor?

Of course the big advantage is that there is no additional license fee.

What I don't really know is how performance would compare. Vendors in the data quality field are apt to extol that their matching engine can outperform anybody else's: something that is inherently impossible to prove one way or the other, thanks to the fact that you can't compare match accuracy across platforms.

Nevertheless, my guess is that a graph database could seriously outperform a conventional matching engine. That's because graph databases have been explicitly designed to explore relationships and that's precisely what you do when matching: you have two similar but non-identical names and they each have relationships with an address, a mother's name, a phone number, an email address and so on. Instead of searching through table after table: just follow the edges of the graph.

Will we see this become a practical reality? To be honest I don't know. The person who raised this issue in the first place said that his company couldn't use conventional data quality tools for matching (he didn't explain why) so maybe there is a problem out there that can be solved by using graphs. Certainly if you are going to be using a graph database anyway then it may make sense to look at it for this purpose also.

By: Philip Howard, Research Director - Data Management, Bloor Research Posted: 21st March 2013 Copyright Bloor Research © 2013

It is apparent that there is significant demand for SQL in the NoSQL world. Whether it is HQL (the Hive version of SQL) or CQL (the Cassandra version) there are clearly people who like a declarative as opposed to a procedural approach to accessing big data.

However, there is a big problem here: the reason why SQL works as well as it does in traditional, usually relational, environments is that the databases it is accessing have optimisers. Those optimisers fundamentally do two things: first, they rewrite crappy SQL code into something that is as efficient as it can be. That's a big advantage: anybody can write poor code, whether you're using MapReduce or SQL, but with an optimiser that gets handled for you. Of course, there's a downside: it encourages sloppy programming but that's another story.

The second thing that a database optimiser does for you is to determine how best to perform joins and other SQL functions. Of course, you may not have the possibility of a join within a NoSQL database but that in itself is a restriction. Anyway, the optimiser is also optimising the performance of all the other SQL capabilities that are available and some of these, at least, will be available in NoSQL databases.

So the bottom line is that you aren't going to get much in the way of performance out of simply bolting SQL onto a NoSQL source unless you also design in an optimiser.

Interestingly, this is essentially what EMC has done with Hawq SQL in its Pivotal HD product, which is essentially a re-working of the EMC Greenplum parallel engine to support Hadoop. By coming from a conventional database background that already has an optimiser, extending or re-working it to support Hadoop environments is a great deal easier than simply creating a Hadoop optimiser from scratch, and this is precisely what Hawq offers.

Going a step further, it is worth remembering that Teradata's polymorphic storage for Aster Data provides you with the ability to use either a native Aster storage engine or an HDFS storage engine (or both) under Aster Data. And, of course, to do this effectively you need to extend the optimiser so that it supports the whole environment. See also my article "DB2: a relational epithet is no longer enough" in which I described the fact that DB2 already has three storage engines, two of which are NoSQL engines (XML storage and a graph store - though you would typically use XQuery and SPARQL to access these), and that it is likely to add more such stores in the future. Again, one of the reasons why adding new storage engines is no trivial matter is precisely because of the need to extend the optimiser to understand the different storage. However, once you do so you are likely to get much better performance than you would do from a NoSQL database with SQL but no optimiser.

By: Marcia Kaufman, Partner, Hurwitz & Associates Posted: 21st March 2013 Copyright Hurwitz & Associates © 2013

There has been a lot of discussion lately about 'service virtualization', however the term alone can make your head spin. Are we talking about server virtualization? What types of services are involved? What does virtualization have to do with testing? I’d like to quickly clear up any confusion you may have. Service virtualization is used to simulate the behavior of components in an application so you can perform an accurate and timely test in a world of complex interrelated applications. Production services that may not be available for integration testing can be virtualized so the testing can take place at an appropriate time in the software development process.

While quality professionals have always needed to test combinations of code, current methods for writing and combining code have changed so much that traditional approaches to testing can’t get the job done at the right price and the right time. There is a fast growing commercial market for production services that are incorporated as self-contained modules into software applications. Third party services such as PayPal or a credit checking service are increasingly used in customer facing applications.

Use of these third-party services increases the efficiency of software development, but at the same time makes your application dependent on services that you do not control. Consider, for example, the scenario of an online retailer with multiple suppliers. The retailer has created a new mobile application for customers. This application uses a credit check service provided by a third-party vendor. The team can’t test without this dependent component, but it is not available for testing. Without service virtualization, the software development team has some difficult choices to make and none of the options are good. If the development team proceeds without doing the necessary testing, they may introduce errors that are much harder and more costly to fix later on. If the team waits until the third-party service is available, developer productivity will decline and the team may miss production deadlines. In addition, if the third-party service becomes available it can get pretty expensive to test application performance at high usage levels since the service costs money each time it is executed.

So what does the development team do in this situation? Service virtualization is an approach to testing that helps organizations eliminate some of the testing bottlenecks that make it hard to bring new high quality applications to market quickly. Here are five key things you should know about service virtualization.

1. To get started with service virtualization you need to understand your testing methodology and think about where service virtualization can increase team velocity while also helping your team to deliver higher quality software.
2. Use a cost/benefit analysis to select which services should be virtualized. Consider the cost to your company when testing is delayed because dependent services or software are not available for testing. How much is spent on staff needed to set up and maintain test environments? How much do you spend to maintain test environments that are not fully utilized? What is the cost for software licenses in the physical test lab environment? What is the cost of third-party service access fees?
3. Service virtualization can help you find errors in all testing phases—including unit testing, performance testing, integration testing, system testing, system integration testing, user acceptance testing, and operability testing.
4. Recording a service that already exists is a great way to define the behavior of your virtual component. You can use the recording process to identify the behaviors that will need to be simulated so you can create test cases quickly.
5. You can’t expect to virtualize all your components. Therefore, you need to be able to easily move back and forth between real components and virtual components while you are testing. You want to maintain consistency across real and virtual components.

One of the biggest impacts of service virtualization for developers is the ability to validate integrations much earlier in the application life cycle. The software development team can move beyond unit testing and overcome many of the roadblocks that inhibit timely, efficient, and cost effective testing.

By: David Norfolk, Practice Leader - Development, Bloor Research Posted: 21st March 2013 Copyright Bloor Research © 2013

[{page:Service virtualisation:Service virtualisation}] is usually thought of as simply a testing tool. It lets you simulate services (either internal such as SAP, or external such as Salesforce, or even services not yet developed) you need in order to test developing systems without the logistics problem of finding an expert to run the system for you - and without the cost of the licences and infrastructure needed for the real thing.

However, at a breakfast meeting run by CA Technologies and SQS, Burt Klein (a CA Technologies Evangelist for service virtualisation) reminded me that service virtualisation can be rather more than this. It can actually change the whole development process (and culture), around automating the business, for the better:

• It doesn't just let you run tests that were hard to set up before and expensive; it encourages early testing because you can now overlap testing across teams (each team gets its own virtual service and data scenarios to test against, which can't be impacted by what other teams are doing).
• Virtualised services can be set up and operated by code - scripts - which takes a manual component out of the testing and delivery process. This facilitates agile development and deployment and helps to remove barriers between developers and operations staff - it facilitates [{page:DevOps}].
• It helps take software delivery to the maturity of an engineering process. Cars these days don't arrive with several km on the clock after a pre-delivery test drive; the components are built and tested and if the components deliver the expected outcomes, they just fit together and work, With service virtualisation, software component interfaces have been verified as they were built, minimising the need for, and possible impact of, 'end of development' integration and performance testing.
• With service virtualisation, the development environment looks more like the real production environment - thus helping developers to take ownership of business outcomes instead of just supplying coded components. This could be a significant, and desirable, change to development culture.

Of course, the devil is in the detail and I suspect that an organisation needs a certain level of maturity (and to invest in training and 'change mentors') in order to get the most out of service virtualisation. However, the value that people like Klein claim it delivers, from delivery process improvement overall, goes far beyond what you'd get just from making testing a bit easier. Service virtualisation helps you build quality into your automated business systems earlier, as they are being built, when addressing any quality issues is cheaper.

If you want to assess the credibility of this statement, there's a book from CA Press by John Michelsen and Jason English called "Service Virtualisation: reality is overrated" (ISBN 978-1-4302-4671-8) and a community-oriented website. I'm a little cynical about vendor evangelists myself - in the past, I've met plenty of "technology evangelists" in white suits, straight out of college, with their employer's technology hard-wired into their brain - but Klein isn't so young and has paid some dues; and the service virtualisation book is readable (not as turgid as most textbooks) - and I couldn't find any mention of the CA LISA service virtualisation product in its index.

There have to be some issues around service virtualisation: for a start, you had better make sure you can continuously validate the accuracy of your service simulations cheaply and easily (which probably implies both a disciplined development/IT environment and the choice of the right tools); and its adoption implies the management of significant cultural change (which needs to be resourced and managed). Nevertheless, I think that any organisation should be looking at it today, now that the technology has matured a bit; and not simply from a testing point of view.

For instance, service virtualisation can be used to simulate the business environment for training, reducing the cost of training (no licences needed and less hardware) and allowing trainers more control over what trainees have to deal with. As I said, service virtualisation can be more than just a testing tool.

By: Angela Ashenden, Principal Analyst, MWD Advisors Posted: 18th March 2013 This work is licensed under a Creative Commons License

As a UK-based analyst, it’s interesting to watch how US-based vendors manage the move into the European market. For some, it is, for a long time, little more than a line in their marketing materials, perhaps with one or two satellite sales guys based in London. Others however go for a more all-in approach, throwing investment at the exercise to make sure it goes the distance. Fast-growing and venture-backed Palo Alto-based content management and collaboration vendor Box definitely falls into the latter group.

Last week the company visited London as part of its “Box World Tour” (which, amusingly, seems to have just the one stop outside the US), and while here hosted its first European Analyst Day. These types of events are always worth attending, to meet execs in person and gain a more in-depth perspective on the vendor’s overall strategy, as well as its plans for the European market. The benefit of coinciding it with the customer event was that we had the boss in attendance – co-founder and CEO Aaron Levie presented alongside Box’s GM of Enterprise Whitney Bouck, GM for Europe David Quantrell, and Head of Enterprise Product Marketing Robin Daniels. We also had the chance to hear from and speak directly to several Box customers including Schneider Electric, XL Video and Datix.

Unfortunately much of the strategy and roadmap that was shared was under NDA, but what I can comment on is the incredible rate of growth for Box in Europe. The company’s European office (located in London) only opened in August 2012, and yet already employs an astonishing 40 employees, with plans to add a further 60 by the end of 2013. It already has an Enterprise team to support the European market, and has doubled its customer growth in Europe. This rapid expansion is not limited to direct sales—new European partners were also announced, including COMPAREX, Bytes Software Services, and Softcat. What’s more, the company is already looking towards its next moves—including building a presence in both France and Germany. When the company announced its latest round of funding in July last year it pointed to global expansion, and I think it’s clear to see that Box is determined to take advantage of its current market lead to secure the European market.

While it doesn’t strictly fit into my main focus area of social collaboration, I find Box a very interesting vendor to follow for several reasons. First—clearly—its success in this area is fascinating; while the competition (at least at the consumer end of the market) is fierce, Box has done a fantastic job at positioning itself as the business-focused provider of sync and share solutions, gaining a run in the market long before the competition in the wider ECM market and other content and collaboration areas even saw what was happening. Now, of course, new competitors are popping up all the time, and so Box will need to work hard to maintain that position. Secondly, while it is not a social collaboration vendor, social collaboration features are becoming an increasingly important part of the Box user experience, with new capabilities added during 2012 including @mentions, better profile and company directory features, and the ability to assign tasks from directly within a comment thread. While Box might not be planning a direct assault on the social collaboration market (and it’s not—I checked), I do believe that the social collaboration vendors will ultimately encroach more and more on Box’s territory, and it will be interesting to see how the company handles this. At the moment, Box is taking the position of supporting customers in enabling a best-of-breed approach, integrating with vendors such as Jive through the Box Embed capability to surface Box features in the third-party app. Over time though, market consolidation and the cyclic nature of point-solutions-versus-single-stack will start to challenge this position. There’s much jostling to be done before we see the result of that.

As for Box’s impressive growth, my only concern remains one of manageability: while all the company’s staff worked out of the one office in Palo Alto, ensuring new recruits reflected and fitted into the company culture was less of an issue. Building up a new office with all new staff on the other side of the world inevitably means that that culture will change, and the shift from a small company into a mid-sized company can be challenging for all concerned, particularly when you also have to keep your head in a highly competitive and fast-moving market. I think Box has great leadership and is fully aware of the challenge here, but as the pace of growth continues it will be increasingly difficult to manage in a controllable way. One thing’s for sure – if it’s going to be filing for an IPO soon (and the rumours suggest we might be looking at early 2014), there will be plenty happening to provide distraction.

By: David Norris, Practice Leader - Analytics, Bloor Research Posted: 18th March 2013 Copyright Bloor Research © 2013

ActuateOne has a lot going for it: it is easy to use, supports extensive customisation, and can create libraries of reusable components to enhance the productivity of producing BIRT-based reports, dashboards and OLAP cubes.

They can claim, with some justification, they are delivering more insight to more people than the rest of the Business Intelligence community combined. They are expanding those capabilities to embrace lots more data, including the web and big data, with access to data sources such as print streams and document archives and even more people via the Cloud, the web, and mobile devices. But what is really exciting as we enter an era in which business faces unprecedented risk and volatility, is Actuate's recent acquisition of Quiterian, which provides visual data mining, social media and predictive analytics and, being visual, this capability is accessible to non-technical as well as highly skilled data scientists.

BIRT Analytics is based on a hybrid in-memory columnar database, able to consume large volumes of data, at speed. So whilst the existing technology within ActuateOne offers data discovery capability, this advance enables Actuate to offer deep predictive analysis, to really understand why things happen, what other things may happen, and enable the business to adopt the strategies required to survive and prosper.

Visual data mining is key. With the rise of the big data bubble there is unprecedented demand for people who can manipulate and understand data, to enable data to be turned into valuable insights but we cannot wait for another generation of data scientists to pass through the universities and emerge in the work place. We need that capability now. This capability has to be placed in the hands of the savvy business user, the person with the domain understanding, who, with a tool that operates on a point and click, drag and drop basis, can allow them to explore the data, enrich it, and perform powerful analytics.

It is not enough to just understand what has happened in the past. Increasingly, we need to have the capability to build scenarios and forecast probable outcomes. This capability is now within the remit of Actuate, bringing what was once the sort of sophistication that was found in the operations of the Wall Street and City trading desks into the hands of all users. With Actuate, the data in Salesforce, Twitter, Facebook, or Google Analytics can now be accessed, integrated and exploited with ease, without the need for intervention from technical staff. So it's a self-service model, cutting out the need for the technical middle man with the problems of delays and misinterpretation.

This is a mass-market tool that is now offering market-leading capability in the vital areas of fast implementation and minimal overhead, so that return on investment makes it a compelling proposition. It certainly makes ActuateOne an even more compelling proposition worthy of serious consideration.

By: David Norfolk, Practice Leader - Development, Bloor Research Posted: 2nd March 2013 Copyright Bloor Research © 2013

My name is David Norfolk, I'm responsible for [{page:Enterprise Development:Development}] (including [{page:New Development:New Development}]) and [{page:Governance:Governance}] at Bloor; and I'm also now taking over responsibility for [{page:Social Collaboration:Social Collaboration}] technology from Roger Whitehead.

I've known Roger for years, since the introduction of Lotus Notes and Groupware. I think I may even have invented the term Workgroup Computing when I wrote "The Book of Workgroup Computing" Apricot Pc Magazine, ISBN 1858700159 / 1-85870-015-9 EAN 9781858700151) back then; and Roger was one of my sources. I have immense respect for Roger's knowledge and insights in this field (many of them are still available on this site).

I take a fairly broad view of the Social Collaboration topic; which, for me, includes (at one end of the spectrum) "enterprise collaboration" - the technology-enabled ability to exchange and receive information seamlessly across departmental and organisational boundaries. This enables organisations to transact business in today's mobile and agile marketplace and is related to the development of "collaborative teams" in technology-based businesses - I still think that Tom DeMarco's and Timothy Lister's "Peopleware" has relevance here, as recommended reading.

Nevertheless, I think that the emphasis needs to be on Social Collaboration; not only because achieving this, either internally or with individual customers etc., markedly enhances enterprise collaboration.

One characteristic of implementations of "collaboration software" is that they often don't deliver the benefits originally expected - and this is largely due to dysfunctional people issues and management failures (including the choice of the wrong KPIs), rather than to any technology failures with the software or its design. Social collaboration needs to be worked at and involves cultural change; just buying and installing Microsoft SharePoint, say, won't deliver much general collaboration by itself, let alone a social collaboration culture or collaborative teams. However, a top-down vision of a collaborative culture might well be facilitated with the appropriate choice of collaboration software - which might turn out to be Fuzed, or Huddle, or even SharePoint.

Two things need to be stressed: first, that too early concentration on ROI may kill a collaboration culture before it can get going (which isn't to say that you shouldn't measure KPIs and anticipated outcomes); and, second, that resources must be available for education and achieving buy-in to the collaborative culture at all levels. Top management buy-in to the collaborative vision is particularly important: I still remember one of the very early Lotus Notes success stories which was then cancelled by the CEO saying, in effect, "I don't care if this Notes thing is cost-effective and makes the business work better; I simply don't want my minions knowing stuff and collaborating amongst themselves without me and my managers being in control and doling out information to them as we think necessary".

Social collaboration, in our context, is only worthwhile if it contributes to better business outcomes (which can include increased staff or customer loyalty and morale). These outcomes must be measured and monitored; and collaboration software should be seen as an enabling technology for achieving them.

By: Clive Longbottom, Head of Research, Quocirca Posted: 28th February 2013 Copyright Quocirca © 2013

Since the original Basel Accord was agreed and signed in 1988, central governments, driven by the EU, have been trying to ensure that financial institutions were managed in such a way as to provide a solid platform to the global economy. Starting with Basel I, increasing levels of central oversight have been put in place to try and maintain a good view on what could be happening within the markets. Through the Capital Requirements Directive (CRD), first instituted in 2007, certain levels of capital are required to be held by the banks and insurance companies so that they are able to weather any economic storms that come the way of the markets.

CRD IV is the latest version, and it nominally came into effect on January 1st, 2013. "Nominally" will be covered later...

At the highest level, the basis for CRD IV is covered under the Basel II and Basel III Accords for the banks and under Solvency II for insurance companies, which increase the amounts of common equity and Tier 1 Capital that the institutions are required to hold. Basel II also covers how the banks will need to provide centralised prudential reporting—and this mandates the use of the extended business reporting language, XBRL.

In October 2012, Quocirca carried out research across the UK, Germany, France, Italy and Spain for EMC to gauge the preparedness of financial institutions for the use of XBRL as well as their understanding of the whole CRD IV process.

The research provided some interesting findings—just under half of respondents felt that adopting XBRL would be a major impact on the business, with 65% saying that integrating existing systems into an XBRL system would be of major concern. Unfortunately, only 25% of respondents had even chosen an XBRL solution for something that was to be mandated as of January 1st (at the time, only 3 months away), leaving the notion of the financial markets being ready to meet the implementation date as being a bit far-fetched.

But, back to the "nominally". As the financial markets collapsed, the EU went into prevarication mode. There was always a transition period built in to CRD IV and Basel III, but this was meant to be for a move along a maturity model with everyone essentially staying in step along a defined set of processes. Although the nominal dates for CRD IV and Basel III remained as 1st January, the EU started to change the goalposts, saying that banks must hold more liquid assets and so lower their risk if facing another meltdown.

Country financial bodies, such as the Financial Services Authority (FSA) in the UK, had to move to more of an advisory mode—without agreement from the centre, little in the way of solid process guidance could be provided by them.

So, although few banks and insurance companies were ready for the requirements of CRD IV and Basel III on 1st January, it makes little difference, as the central bodies concerned were still fiddling while the economy burned.

However, this is not an adequate excuse for the financial institutions concerned to be so far away from being able to meet the technical requirements of CRD IV. The need for centralised prudential reporting is still there—and the failure to plan to implement XBRL systems means that these institutions are incapable of meeting this need.

At some stage, the Powers That Be will get their act together and CRD IV will become law with the necessary Directives in place. Financial institutions would do well to ensure that they are implementing the right systems now to meet their reporting needs—without them, they will fall foul of legal requirements, which could cost dear in fines.

Quocirca's report on the subject can be downloaded for free here.

By: Grant Davis, Data Modeler/Freelance Writer, bmc Posted: 26th February 2013 Copyright bmc © 2013

IT leadership is tasked with maintaining the processes that help a business operate. These processes can range from telephone connections to computer networks, and the specific role of an IT manager or CIO varies from industry to industry. In 2013, the role is taking on even more responsibility as the technological advancements around the world have increased possibilities in ways never imagined. For instance, an IT department can have much more of an influence on overall business operations now than ever before, and it’s time for leaders to understand this and make a move.

Focus in 2013 needs to revolve around the promotion of collaboration within an IT department. The global market is too competitive to stand back and simply maintain processes. A business needs everyone on board and pushing forward in the same direction, and that includes the IT department. The days of IT simply maintaining network processes need to be a thing of the past, as IT professionals can offer more. Below are specific ways that a CIO or IT manager can increase collaboration within their department, with the goal of enhancing overall business operations.

1. Increase or introduce instant messaging
The key to collaboration is that people can throw ideas off of each other, molding differing opinions to assist a greater cause. If the systems admin can quickly chat to the CIO about a unique network troubleshooting strategy, transparency of separate knowledge will increase. This is a great feature for a department to foster in 2013, because operations are becoming more all-encompassing each day and it’s important to keep up.

An additional example is if the two data modelers for two separate sectors of the company can instantaneously chat throughout the day about their different duties over instant messenger. This continuous communication amongst data management will slowly increase consistency and awareness across the department. This maintains uniformity and department-wide expectations, with results and ideas becoming clearer to everyone.

Instant messaging is a quick way to instill collaboration within an IT department. This holds precisely true when a group chat is involved, because everyone can see what everyone else is talking about. Aside from the practicality of the service helping the processes of the workplace, communication within the office between employees can foster a more fun loving work environment. IT offices tend to have a feel of being overly serious and task oriented, and this is a way to combat that while also increasing the efficiency of production and management. Instant messaging is a great start to increase collaboration in any setting, particularly an IT workspace in 2013.

2. Start a department newsletter.
Many organizations utilise a newsletter to keep the internal workforce updated, but usually not IT departments. It’s not a bad idea, and can add a little bit of spice to a routine office environment. One specific way to carry out the plan is to have a different person write the newsletter each week, starting with the lowest level employee. What this does is teach everyone about their different job roles, and also explain how the department is impacting the company as a whole. When people start realizing how their specific tasks are affecting the overall business model, they can often feel more passionate about what they do on a daily basis.

As we have entered 2013, it’s important that each IT employee understands the impact of his or her job and likewise that of their colleagues. A newsletter can help kick start this process and inspire people to be more interested in their surroundings that fall outside of the specific daily routine. With a process-oriented career, sometimes it takes practical management decisions from a leader to instill this type of collaborative change. As the newsletter evolves, creativity grows exponentially. Innovation stems from unique insight and an IT professional has more to offer than process task and maintenance. Many of them are quality writers, speakers and communicators. It’s time to understand and promote that.

The IT industry is a powerful department that is incorporated within many businesses across America and the world. These departments are home to employees that possess multifaceted skills, and the only way to unleash them for the betterment of a business model is to increase collaboration. Without collaboration, IT professionals stay within the confines of their traditional role. Until 2013 this was enough to help keep a business afloat, but it has come time to push toward new heights and make the industry even better than it already is.

Grant Davis is a Data Modeler by day who writes by night. His passion for computers started when he discovered instant messaging in junior high school. When Grant isn't trying to climb through the computer screen he writes for BMC about ways to optimize job scheduling software .

By: Dana Gardner, Principal Analyst, Interarbor Solutions Posted: 18th February 2013 Copyright Interarbor Solutions © 2013

This BriefingsDirect thought leadership panel discussion comes to you in conjunction with The Open Group Conference held recently in Newport Beach, California. The conference focused on "big data—the transformation we need to embrace today."

The panel of experts explores new trends and solutions in the area of risk management and analysis. Learn now how large enterprises are delivering better risk assessments and risk analysis, and discover how big data can be both an area to protect, but also used as a tool for better understanding and mitigating risks.

The panelists are Jack Freund, PhD, the Information Security Risk Assessment Manager at TIAA-CREF; Jack Jones, Principal of CXOWARE, and Jim Hietala, Vice President, Security for The Open Group. The discussion is moderated by Dana Gardner, Principal Analyst at Interarbor Solutions. [Disclosure: The Open Group is a sponsor of this and other BriefingsDirect podcasts.]

Jack Jones has more than nine years experience as a Chief Information Security Officer (CISO), and is the inventor of the Factor Analysis Information Risk (FAIR) framework. Jack Freund has more than 14 years in enterprise IT experience, is a visiting professor at DeVry University, and chairs a risk-management subcommittee for ISACA.

Here are some excerpts:

Gardner: Why is the issue of risk analysis so prominent now? What's different from, say, five years ago?

Jones: The information security industry has struggled with getting the attention of and support from management and businesses for a long time, and it has finally come around to the fact that the executives care about loss exposure—the likelihood of bad things happening and how bad those things are likely to be.

It's only when we speak in those terms of risk that we make sense to those executives. And once we do that, we begin to gain some credibility and traction in terms of getting things done.

Gardner: So we really need to talk about this in the terms that a business executive would appreciate, not necessarily an IT executive.

Jones: Absolutely. They're tired of hearing about vulnerabilities, hackers, and that sort of thing. It’s only when we can talk in terms of the effect on the business that it makes sense to them.

Freund: The problem that we have as a profession, and I think it’s a big problem, is that we have allowed ourselves to escape the natural trend that the other IT professionals have already taken.

There was a time, years ago, when you could code in the basement, and nobody cared much about what you were doing. But now, largely speaking, developers and systems administrators are very focused on meeting the goals of the organization.

Security has been allowed to miss that boat a little. We have been allowed to hide behind this aura of a protector and of an alerter of terrible things that could happen, without really tying ourselves to the problem that the organizations are facing and how can we help them succeed in what they're doing.

Hietala: There are certainly changes on the threat side of the landscape. Five years ago, you didn’t really have hacktivism or this notion of an advanced persistent threat (APT). That highly skilled attacker taking aim at governments and large organizations didn’t really exist—or didn’t exist to the degree it does today. So that has changed.

You also have big changes to the IT platform landscape, all of which bring new risks that organizations need to really think about. The mobility trend, the cloud trend, the big-data trend that we are talking about today, all of those things bring new risk to the organization.

As Jack Jones mentioned, business executives don't want to hear about, "I've got 15 vulnerabilities in the mobility part of my organization." They want to understand what’s the risk of bad things happening because of mobility, what we're doing about it, and what’s happening to risk over time.

So it’s a combination of changes in the threats and attackers, as well as just changes to the IT landscape, that we have to take a different look at how we measure and present risk to the business.

Gardner: Because we're at a big-data conference, do you share my perception, Jack Jones, that big data can be a source of risk and vulnerability, but also the analytics and the business intelligence (BI) tools that we're employing with big data can be used to alert you to risks or provide a strong tool for better understanding your true risk setting or environment?

Jones: You are absolutely right. You think of big data and, by definition, it’s where your crown jewels, and everything that leads to crown jewels from an information perspective, are going to be found. It's like one-stop shopping for the bad guy, if you want to look at it in that context. It definitely needs to be protected. The architecture surrounding it and its integration across a lot of different platforms and such, can be leveraged and probably result in a complex landscape to try and secure.

There are a lot of ways into that data and such, but at least if you can leverage that same big data architecture, it's an approach to information security. With log data and other threat and vulnerability data and such, you should be able to make some significant gains in terms of how well-informed your analyses and your decisions are, based on that data.

Freund: If we fast-forward it five years, and this is even true today, a lot of people on the cutting edge of big data will tell you the problem isn’t so much building everything together and figuring out what it can do. They are going to tell you that the problem is what we do once we figure out everything that we have. This is the problem that we have traditionally had on a much smaller scale in information security. When everything is important, nothing is important.

Gardner: What parts of organizations aren’t being assessed for risk and should be?

Freund: The big problem that exists largely today in the way that risk assessments are done, is the focus on labels. We want to quickly address the low, medium, and high things and know where they are. But the problem is that there are inherent problems in the way that we think about those labels, without doing any of the analysis legwork.

I think that what’s really missing is that true analysis. If the system goes offline, do we lose money? If the system becomes compromised, what are the cost-accounting things that will happen that allow us to figure out how much money we're going to lose.

That analysis work is largely missing. That’s the gap. The gap is if the control is not in place, then there’s a risk that must be addressed in some fashion. So we end up with these very long lists of horrible, terrible things that can be done to us in all sorts of different ways, without any relevance to the overall business of the organization.

Every day, our organizations are out there selling products, offering services, which is and of itself, its own risky venture. So tying what we do from an information security perspective to that is critical for not just the success of the organization, but the success of our profession.

Jones: Businesses have been making these decisions, chasing the opportunity, but generally, without any clear understanding of the risk implications, at least from the information security perspective. They will have us in the corner screaming and throwing red flags in there, and talking about vulnerabilities and threats from one thing or another.

But, we come to the table with red, yellow, and green indicators, and on the other side of the table, they’ve got numbers. Well, here is what we expect to earn in revenue from this initiative, and the information security people are saying it’s crazy. How do you normalize the quantitative revenue gain versus red, yellow, and green?

Gardner: Jim Hietala, do you see it in the same red, yellow, green or are there some other frameworks or standard methodologies that The Open Group is looking at to make this a bit more of a science?

Hietala: Probably four years ago, we published what we call the Risk Taxonomy Standard which is based upon Factor Analysis Information Risk (FAIR), the management framework that Jack Jones invented. So, we’re big believers in bringing that level of precision to doing risk analysis. Having just gone through training for FAIR myself, as part of the standards effort that we’re doing around certification, I can say that it really brings a level of precision and a depth of analysis to risk analysis that's been lacking frequently in IT security and risk management.

Gardner: Whose job should this fall under? Who is wearing the white hat in the company and can rally the forces of good and make all the bad things managed?

Freund: The profession of IT risk management is changing. That profession will have to sit between the business and information security inclusive of all the other IT functions that make that happen.

In order to be successful sitting between these two groups, you have to be able to speak the language of both of those groups. You have to be able to understand profit and loss and capital expenditure on the business side. On the IT risk side, you have to be technical enough to do all those sorts of things.

But I think the sum total of those two things is probably only about 50 percent of the job of IT risk management today. The other 50 percent is communication. Finding ways to translate that language and to understand the needs and concerns of each side of that relationship is really the job of IT risk management.

To answer your question, I think it’s absolutely the job of IT risk management to do that. From my own experiences with the FAIR framework, I can say that using FAIR is the Rosetta Stone for speaking between those two groups.

It gives you the tools necessary to speak in the insurance and risk terms that business appreciate. And it gives you the ability to be as technical and just nerdy, if you will, as you need to be in order to talk to IT security and the other IT functions in order to make sure everybody is on the same page and everyone feels like their concerns are represented in the risk-assessment functions that are happening.

Gardner: How do you know if you’re doing it right? How do you know if you're moving from yellow to green, instead of to red?

Freund: There are a couple of things in that question. The first is there's this inherent assumption in a lot of organizations that we need to move from yellow to green, and that may not be the case. So, becoming very knowledgeable about the risk posture and the risk tolerance of the organization is a key.

That's part of the official mindset of IT security. When you graduate an information security person today, they are minted knowing that there are a lot of bad things out there, and their goal in life is to reduce them. But, that may not be the case. The case may very well be that things are okay now, but we have bigger things to fry over here that we’re going to focus on. So, that's one thing.

The second thing, and it's a very good question, is how we know that we’re getting better? How do we trend that over time? Overall, measuring that value for the organization has to be able to show a reduction of a risk or at least reduction of risk to the risk-tolerance levels of the organization.

Calculating and understanding that requires something that I always phrase as we have to become comfortable with uncertainty. When you are talking about risk in general, you're talking about forward-looking statements about things that may or may not happen. So, becoming comfortable with the fact that they may or may not happen means that when you measure them today, you have to be willing to be a little bit squishy in how you’re representing that.

In FAIR and in other academic works, they talk about using ranges to do that. So, things like high, medium, and low could be represented in terms of a minimum, maximum, and most likely. And that tends to be very, very effective. People can respond to that fairly well.

Jones: With regard to the data sources, there are a lot of people out there doing these sorts of studies, gathering data. The problem that's hamstringing that effort is the lack of a common set of definitions, nomenclature, and even taxonomy around the problem itself.

You will have one study that will have defined threat, vulnerability, or whatever differently from some other study, and so the data can't be normalized. It really harms the utility of it. I see data out there and I think, "That looks like that can be really useful." But, I hesitate to use it because I don't understand. They don't publish their definitions, approach, and how they went after it.

There's just so much superficial thinking in the profession on this that we now have dug under the covers. Too often, I run into stuff that just can't be defended. It doesn’t make sense, and therefore the data can't be used. It's an unfortunate situation.

I do think we’re heading in a positive direction. FAIR can provide a normalizing structure for that sort of thing. The VERIS framework, which, by the way, is also derived in part from FAIR, also has gained real attraction in terms of the quality of the research they have done and the data they’re generating. We’re headed in the right direction, but we’ve got a long way to go.

Gardner: I'm curious how prevalent cyber insurance is, and is that going to be a leveling effect in the industry where people speak a common language—the equivalent of actuarial tables, but for security in enterprise and cyber security?

Jones: One would dream and hope, but at this point, what I've seen out there in terms of the basis on which insurance companies are setting their premiums and such is essentially the same old “risk assessment” stuff that the industry has been doing poorly for years. It's not based on data or any real analysis per se, at least what I’ve run into. What they do is set their premiums high to buffer themselves and typically cover as few things as possible. The question of how much value it's providing the customers becomes a problem.

Gardner: What's the future of risk management, and what does the cloud trend bring to the table?

Hietala: I’d start with a maxim that comes out of the financial services industry, which is that you can outsource the function, but you still own the risk. That's an unfortunate reality. You can throw things out in the cloud, but it doesn’t absolve you from understanding your risk and then doing things to manage it to transfer it if there's insurance or whatever the case may be.

That's just a reality. Organizations in the risky world we live in are going to have to get more serious about doing effective risk analysis. From The Open Group standpoint, we see this as an opportunity area.

As I mentioned, we’ve standardized the taxonomy piece of the Factor Analysis Information Risk (FAIR) framework. And we really see an opportunity around the profession going forward to help the risk-analysis community by further standardizing FAIR and launching a certification program for a FAIR-certified risk analyst. That's in demand from large organizations that are looking for evidence that people understand how to apply FAIR and use it in doing risk analyses.

Freund: I always try to consider things as they exist within other systems. Risk is a system of systems. There are a series of pressures that are applied, and a series of levers that are thrown in order to release that sort of pressure.

Risk will always be owned by the organization that is offering that service. If we decide at some point that we can move to the cloud and all these other things, we need to look to the legal system. There is a series of pressures that they are going to apply, and who is going to own that, and how that plays itself out.

If we look to the Europeans and the way that they’re managing risk and compliance, they’re still as strict as we in United States think that they may be about things, but there's still a lot of leeway in a lot of the ways that laws are written. You’re still being asked to do things that are reasonable. You’re still being asked to do things that are standard for your industry. But, we'd still like the ability to know what that is, and I don't think that's going to go away anytime soon.

We’re still going to have to make judgment calls. We’re still going to have to do 100 things with a budget for 10 things. Whenever that happens, you have to make a judgment call. What's the most important thing that I care about? And that's why risk management exists, because there’s a certain series of things that we have to deal with. We don't have the resources to do them all, and I don't think that's going to change over time. Regardless of whether the landscape changes, that's the one that remains true.

Jones: If we were to take a snapshot at any given point in time of an organization’s loss exposure, how much risk they have right then, that's a lagging indicator of the decisions they’ve made in the past, and their ability to execute against those decisions.

We can do some great root-cause analysis around that and ask how we got there. But, we can also turn that coin around and ask how good we are at making well-informed decisions, and then executing against them, the asking what that implies from a risk perspective downstream.

If we understand the relationship between our current state, and past and future states, we have those linkages defined, especially, if we have an analytic framework underneath it. We can do some marvelous what-if analysis.

What if this variable changed in our landscape? Let's run a few thousand Monte Carlo simulations against that and see what comes up. What does that look like? Well, then let's change this other variable and then see which combination of dials, when we turn them, make us most robust to change in our landscape.

But again, we can't begin to get there, until we have this foundational set of definitions, frameworks, and such to do that sort of analysis. That's what we’re doing with the Factor Analysis Information Risk (FAIR) framework, but without some sort of framework like that, there's no way you can get there.

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By: Dana Gardner, Principal Analyst, Interarbor Solutions Posted: 29th January 2013 Copyright Interarbor Solutions © 2013

Ford has exploited the strengths of big data analytics by directing them internally to improve business results. In doing so, they scour the metrics from the company’s best processes across myriad manufacturing efforts and through detailed outputs from in-use automobiles—all to improve and help transform their business.

So explains Michael Cavaretta, PhD, Technical Leader of Predictive Analytics for Ford Research and Advanced Engineering in Dearborn, Michigan. Cavaretta is one of a group of experts assembled this week for The Open Group Conference in Newport Beach, California.

Cavaretta has led multiple data-analysis projects at Ford to break down silos inside the company to best define Ford’s most fruitful data sets. Ford has successfully aggregated customer feedback and extracted all the internal data to predict how best new features in technologies will improve their cars.

As a contributor to the The Open Group conference and its focus on "Big Data—The Transformation We Need to Embrace Today," Cavaretta explains how big data is fostering business transformation by allowing deeper insights into more types of data efficiently, and thereby improving processes, quality control, and customer satisfaction.

The interview was moderated by Dana Gardner, Principal Analyst at Interarbor Solutions. [Disclosure: The Open Group is a sponsor of BriefingsDirect podcasts.]

Here are some excerpts:

Gardner: What's different now in being able to get at this data and do this type of analysis from five years ago?

Cavaretta: The biggest difference has to do with the cheap availability of storage and processing power, where a few years ago people were very much concentrated on filtering down the datasets that were being stored for long-term analysis. There has been a big sea change with the idea that we should just store as much as we can and take advantage of that storage to improve business processes.

Gardner: How did we get here? What's the process behind the benefits?

Cavaretta: The process behind the benefits has to do with a sea change in the attitude of organizations, particularly IT within large enterprises. There's this idea that you don't need to spend so much time figuring out what data you want to store and worry about the cost associated with it, and more about data as an asset. There is value in being able to store it, and being able to go back and extract different insights from it. This really comes from this really cheap storage, access to parallel processing machines, and great software.

I like to talk to people about the possibility that big data provides and I always tell them that I have yet to have a circumstance where somebody is giving me too much data. You can pull in all this information and then answer a variety of questions, because you don't have to worry that something has been thrown out. You have everything.

You may have 100 questions, and each one of the questions uses a very small portion of the data. Those questions may use different portions of the data, a very small piece, but they're all different. If you go in thinking, "We’re going to answer the top 20 questions and we’re just going to hold data for that," that leaves so much on the table, and you don't get any value out of it.

We're a big believer in mash-ups and we really believe that there is a lot of value in being able to take even datasets that are not specifically big-data sizes yet, and then not go deep, not get more detailed information, but expand the breadth. So it's being able to augment it with other internal datasets, bridging across different business areas as well as augmenting it with external datasets.

A lot of times you can take something that is maybe a few hundred thousand records or a few million records, and then by the time you’re joining it, and appending different pieces of information onto it, you can get the big dataset sizes.

Gardner: You’re really looking primarily at internal data, while also availing yourself of what external data might be appropriate. Maybe you could describe a little bit about your organization, what you do, and why this internal focus is so important for you.

Cavaretta: I'm part of a larger department that is housed over in the research and advanced-engineering area at Ford Motor Company, and we’re about 30 people. We work as internal consultants, kind of like Capgemini or Ernst & Young, but only within Ford Motor Company. We’re responsible for going out and looking for different opportunities from the business perspective to bring advanced technologies. So, we’ve been focused on the area of statistical modeling and machine learning for I’d say about 15 years or so.

And in this time, we’ve had a number of engagements where we’ve talked with different business customers, and people have said, "We'd really like to do this." Then, we'd look at the datasets that they have, and say, "Wouldn’t it be great if we would have had this. So now we have to wait six months or a year."

These new technologies are really changing the game from that perspective. We can turn on the complete fire-hose, and then say that we don't have to worry about that anymore. Everything is coming in. We can record it all. We don't have to worry about if the data doesn’t support this analysis, because it's all there. That's really a big benefit of big-data technologies.

The real value proposition definitely is changing as things are being pushed down in the company to lower-level analysts who are really interested in looking at things from a data-driven perspective. From when I first came in to now, the biggest change has been when Alan Mulally came into the company, and really pushed the idea of data-driven decisions.

Before, we were getting a lot of interest from people who are really very focused on the data that they had internally. After that, they had a lot of questions from their management and from upper level directors and vice-president saying, "We’ve got all these data assets. We should be getting more out of them." This strategic perspective has really changed a lot of what we’ve done in the last few years.

Gardner: Are we getting to the point where this sort of Holy Grail notion of a total feedback loop across the lifecycle of a major product like an automobile is really within our grasp? Are we getting there, or is this still kind of theoretical. Can we pull it altogether and make it a science?

Cavaretta: The theory is there. The question has more to do with the actual implementation and the practicality of it. We still are talking a lot of data where even with new advanced technologies and techniques that’s a lot of data to store, it’s a lot of data to analyze, there’s a lot of data to make sure that we can mash-up appropriately.

And, while I think the potential is there and I think the theory is there. There is also work in being able to get the data from multiple sources. So everything which you can get back from the vehicle, fantastic. Now if you marry that up with internal data, is it survey data, is it manufacturing data, is it quality data? What are the things do you want to go after first? We can’t do everything all at the same time.

Our perspective has been let’s make sure that we identify the highest value, the greatest ROI areas, and then begin to take some of the major datasets that we have and then push them and get more detail. Mash them up appropriately and really prove up the value for the technologists.

Gardner: Clearly, there's a lot more to come in terms of where we can take this, but I suppose it's useful to have a historic perspective and context as well. I was thinking about some of the early quality gurus like Deming and some of the movement towards quality like Six Sigma. Does this fall within that same lineage? Are we talking about a continuum here over that last 50 or 60 years, or is this something different?

Cavaretta: That’s a really interesting question. From the perspective of analyzing data, using data appropriately, I think there is a really good long history, and Ford has been a big follower of Deming and Six Sigma for a number of years now.

The difference though, is this idea that you don't have to worry so much upfront about getting the data. If you're doing this right, you have the data right there, and this has some great advantages. You’ll have to wait until you get enough history to look for somebody’s patterns. Then again, it also has some disadvantage, which is you’ve got so much data that it’s easy to find things that could be spurious correlations or models that don’t make any sense.

The piece that is required is good domain knowledge, in particular when you are talking about making changes in the manufacturing plant. It's very appropriate to look at things and be able to talk with people who have 20 years of experience to say, "This is what we found in the data. Does this match what your intuition is?" Then, take that extra step.

Gardner: How has the notion of the Internet of things being brought to bear on your gathering of big data and applying it to the analytics in your organization?

Cavaretta: It is a huge area, and not only from the internal process perspective—RFID tags within the manufacturing plans, as well as out on the plant floor, and then all of the information that’s being generated by the vehicle itself.

The Ford Energi generates about 25 gigabytes of data per hour. So you can imagine selling couple of million vehicles in the near future with that amount of data being generated. There are huge opportunities within that, and there are also some interesting opportunities having to do with opening up some of these systems for third-party developers. OpenXC is an initiative that we have going on to add at Research and Advanced Engineering.

We have a lot of data coming from the vehicle. There’s huge number of sensors and processors that are being added to the vehicles. There's data being generated there, as well as communication between the vehicle and your cell phone and communication between vehicles.

There's a group over at Ann Arbor Michigan, the University of Michigan Transportation Research Institute (UMTRI), that’s investigating that, as well as communication between the vehicle and let’s say a home system. It lets the home know that you're on your way and it’s time to increase the temperature, if it’s winter outside, or cool it at the summer time.

The amount of data that’s been generated there is invaluable information and could be used for a lot of benefits, both from the corporate perspective, as well as just the very nature of the environment.

Gardner: Just to put a stake in the ground on this, how much data do cars typically generate? Do you have a sense of what now is the case, an average?

Cavaretta: The Energi, according to the latest information that I have, generates about 25 gigabytes per hour. Different vehicles are going to generate different amounts, depending on the number of sensors and processors on the vehicle. But the biggest key has to do with not necessarily where we are right now but where we will be in the near future.

With the amount of information that's being generated from the vehicles, a lot of it is just internal stuff. The question is how much information should be sent back for analysis and to find different patterns? That becomes really interesting as you look at external sensors, temperature, humidity. You can know when the windshield wipers go on, and then to be able to take that information, and mash that up with other external data sources too. It's a very interesting domain.

Gardner: What skills do you target for your group, and what ways do you think that you can improve on that?

Cavaretta: The skills that we have in our department, in particular on our team, are in the area of computer science, statistics, and some good old-fashioned engineering domain knowledge. We’ve really gone about this from a training perspective. Aside from a few key hires, it's really been an internally developed group.

The biggest advantage that we have is that we can go out and be very targeted with the amount of training that we have. There are such big tools out there, especially in the open-source realm, that we can spin things up with relatively low cost and low risk, and do a number of experiments in the area. That's really the way that we push the technologies forward.

Talking with The Open Group really gives me an opportunity to be able to bring people on board with the idea that you should be looking at a difference in mindset. It's not "Here’s a way that data is being generated, look, try and conceive of some questions that we can use, and we’ll store that too." Let's just take everything, we’ll worry about it later, and then we’ll find the value.

It's important to be thinking about data as an asset, rather than as a cost. You even have to spend some money, and it may be a little bit unsafe without really solid ROI at the beginning. Then, move towards pulling that information in, and being able to store it in a way that allows not just the high-level data scientist to get access to and provide value, but people who are interested in the data overall. Those are very important pieces.

The last one is how do you take a big-data project, how do you take something where you’re not storing in the traditional business intelligence (BI) framework that an enterprise can develop, and then connect that to the BI systems and look at providing value to those mash-ups. Those are really important areas that still need some work.

There are many companies, especially large enterprises, that are looking at their data assets and wondering what can they do to monetize this, not only to just pay for the efficiency improvement but as a new revenue stream.

Gardner: For those organizations that want to get started on this, how do you get started?

Cavaretta: We're definitely a huge believer in pilot projects and proof of concept, and we like to develop roadmaps by doing. So get out there. Understand that it's going to be messy. Understand that it maybe going to be a little bit more costly and the ROI isn't going to be there at the beginning.

But get your feet wet. Start doing some experiments, and then, as those experiments turn from just experimentation into really providing real business value, that’s the time to start looking at a more formal aspect and more formal IT processes. But you've just got to get going at this point.

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