The Current Situation
During the two to three years in which the Big Data buzzword has been, well, buzzing around, it has often been considered little more than a science experiment. For some, Big Data and high-performance computing were two sides of the same coin. Like HPC, many considered Big Data to be beneficial only to fringe organizations in research, academia and government. Today, the attitdue towards Big Data is very different. Unlike the previous view of Big Data, many enterprises are seeing the benefits and considering it a way to create business value, stimulate innovation and competitively differentiate itself from others in its market.
Many consider the hardening of the Hadoop framework as the impetus. However, I consider this as one element. For me, one of the biggest stimulant to the acceptance of Big Data in the enterprise is the smartphone. iPhones, Android and Windows Phones, with their ability to aggreate location, loyalty and payment data gives enterprises unique opportunities to individualize experiences.
Examples of this include banks. In the US, where regulations required banks to operate mortgage, credit cards and retail banking separately, and where the recent consolidation of the banking market meant that one bank can own multiple brands and products, there is often significant overlap in customer data. This overlap is not often particularly evident and is loosely related between brands. The problem for banks is that this loose association can lead to frustration of customers and increase risk for fraud and credit exposure for the bank itself. By using Big Data to discover the banking patterns, preferences and use cases for credit versus debit, online versus in person, consistent versus intermittent late payments, the bank has a richer understanding of each individual customer. It also helps the customer to improve its experience and interaction with the bank.
Smartphone banking apps are helping banks to reduce the cost of transactions by reducing in-person interactions that are, generally, the most costly to process. Smartphone apps can also help remind customers of upcoming credit card or mortgage payments by providing the ability to transfer money immediately from one account to another to satisfy the monthly payments.
This example is also applicable in the retail industry, where loyalty data helps the retailer to send out more relevant promotions and increase in-store impulse sales. Traditional passive loyalty cards are challenged by the fact that they cannot interact with the customer, and requires the customer to actually be in the store, making a purchase before data on the customer can be collected. Using a smartphone app, the customer can be alerted automatically when they are close to a store, and promotionals can be proactively pushed to attract the customer into the store and, once inside, the customer can also be presented with additional promotions that make them feel like they are being valued on an individual level by the retailer.
For telecommunicatinos giants, particularly wireless providers, data on the position of wireless phones can be used to aggregate these positions to determine location, clustering and speed of wireless users, and can then be used to help determine traffic patterns. This data originates from smartphones.
One of the biggest challenges for Big Data is how creative users can be in leveraging data that is managed by Big Data solutions. The industry still needs to have more pragmatic examples of the value of Big Data for each market. However, early adopters are likely to generate new revenue and profit opportunties that could dramatically change the dynamics for their specific markets.
Well, if your enterprise can benefit from smartphone apps, then it can benefit from Big Data. The real question about Big Data in the enterprise is, really, how can your enterprise benefit if it has an intimate understanding of each individual customer? Take those hypotheses and start using Big Data solutions!