Data is one of the most important organizational resources. Like oil, water, and electricity, data can also be considered a utility. Organizations that learn to harness the potential of this data resource have a distinct business advantage. Sourcing, storing, sharing, securing, analyzing, and presenting data insights are integral to being a learning organization. Big Data is essentially data, further characterized by large volumes, extreme velocity, and myriad variety laced with veracity. These are the popular Vs of Big Data.
The hidden value of big data
A learning organization goes beyond using the inherent characteristics of Big Data by discovering the hidden value. This is the fifth V—value—and it can be summarized as “Agile business decision making.” McKinsey defined Big Data as “large pools of data that can be captured, communicated, aggregated, stored, and analyzed.” 1 This description appears to be more appropriate for large, historical “static” data sets. Data is anything but static. The strategic approach to Big Data is aimed at extracting business value from the dynamically changing data. This discussion is all about an approach, a framework that can convert the “potential” hidden in Big Data into “kinetic” value. This discussion is also about reducing the risks to businesses associated with adopting any new and disruptive technology—like Big Data.
Data is inherently static and factual. It is neither big nor small. Being atomic in nature, it has neither bias nor value. It is the record of an observation as is. When a number of data come together, they create opportunities for connections or relationships. These relationships between numerous data points (data) hide interesting patterns and trends. Analysis is the process of unraveling the patterns and trends. Analysis is also the process of finding the hidden value within these patterns and trends.
With world Internet usage quintupling per decade, there is no upper limit on the number and value of new business opportunities for those who can bend the swelling flood of data to their purposes. Decision makers in all walks of life are keenly interested in “bending” data in many different and unique ways in order to generate insights. In turn, these insights result in accurate and timely business decisions.
This process to explore the hidden value in data is not new. Depending on the mechanisms of observations, the capabilities of sensors and devices, and the speed of storage, substantially large amounts of data can be explored in a short period of time. With increasing data accompanied by enhanced technical capabilities to analyze it, the potential for accuracy and very fine granularity to identify patterns and trends improves, but so also the number of patterns and the complexity of their interconnectivity. The increasing volume and speed of data leads not only to improved analytics but also to highly complex and multidimensional analytics.
The value of big data
Business is not interested in the data per se, nor in its processing complexity. Business is keenly interested in extracting value out of data. The evolution of data processing is based on the need to provide value (see the “Brief History of Data” sidebar). This value is realized through enhanced and Agile decision making. The discipline of information technology (IT) and information science strives to provide this value by understanding the needs and desired outcomes of the business and then working toward providing corresponding solutions.