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While the buzz in the big data market is around massive amounts of data and what you can do with that data, in truth companies are more interested in the small stuff. Let me explain. One of the greatest benefits of getting hold of lots of data is that a company can see patterns and anomalies that would have gone undetected with a smaller set of data.
Companies are interested in big data not so much because of what that data represents in terms of volume, variety and velocity, but what the analysis of that data can do for the business. As business decision makers begin to recognize the potential of big data, they realize that the biggest insights and hidden gems of knowledge are found when big data actually becomes quite small. However, the analysis must start with very large volumes of data. Exploring the world of big data takes business leaders beyond the data found in their organization’s traditional databases. Researchers, data scientists, and marketers, are analyzing very large volumes of unstructured data from previously untapped sources such as e-mails, customer service records, sensor data, and security logs.
Now that companies have the ability to deal with so much more data, they are free to incorporate a greater variety of data into the mix. For example, they are incorporating social media, mobile phone location, traffic, and weather data into more traditional analysis. But what is the goal? Add more data and more elements so that those patterns emerge. Armed with the insight from analysis, business leaders can take a more precise set of data and compare it to data from a data warehouse or a system of record. As a result, the research becomes more targeted and directed to fit in with the context of the business.
Why do companies need to take this targeted approach to leveraging big data? In essence, companies want to use big data analysis to make a personalized offer that is just right for the customer when that customer is ready to buy. In some cases, the answer may be related not to selling but to diagnosing problems with a manufacturing system or a patient with an unexplained illness.
How do you move from big data analysis to small data insights and personalized action? You need to consider three elements: defining your business problem, defining and analyzing your data sources, and integrating and incorporating your big data analysis with your operational data.
Defining your business problem. Companies are beginning to ask the traditional questions about customers, products, and partners in new ways. For example, are you looking to manage your customer interactions armed with in-depth and customized knowledge about each individual customer? Companies with a focus on driving continuous improvement in customer service are asking, “How can I delight this customer and anticipate their specific needs?” These are important goals for businesses competing in today’s fast-paced, mobile-driven market.
Defining and analyzing your data sources. What information do you need to make the right offer to your buyer when he deciding on a purchase? What information can you glean from outside sources such as social media data? What big data sources do you have available internally that were previously underutilized? For example, can you use text analytics to gain new insight about customers from call center notes, emails, and voice recordings? One important goal with big data analytics is to look for patterns and relationships that apply to your business and narrow down the data set based on business context. Your big data analysis will help you find the small treasures of information in your big data.
Integrating and incorporating the analysis of you big data with your operational data.
After your big data analysis is complete, you need an approach that will allow you to integrate or incorporate the results of your big data analysis into your business process and real-time business actions. This will require some adjustment to the conventional notion of data integration. In order to bring your big data environments and enterprise data environments together, you will need to incorporate new methods of integration that support Hadoop and other nontraditional big data environments. In addition, if you want to incorporate the results of very fast streaming data into your business process, you will need advanced technology that enables you to make decisions in real-time.
Ultimately, if you want to make good decisions based on the results of your big data analysis you need to deliver information at the right time and with the right context. In order to make the results of your analysis actionable, you need to focus more on the small – targeted and personalized results of big data – than on the large data volumes.