While the business world continues to grapple with the ramifications of Big Data, many retail IT leaders are happily leaving spreadsheets and custom reporting systems behind as they embrace the new pace of information on Big Data-enabled applications.

An Open Road for Big Data

Big Data’s rapid rise to prominence has made it vulnerable to accusations of hype. Wherever a new vehicle outpaces the ability of early adopters to learn and manage the controls, people rightly predict calamity. Riding on buzzwords and industry jargon is bound to make for a bumpy ride!

But make no mistake — beyond the trend and the buzzword, Big Data really does represent a genuine paradigm shift in how we use and interpret information. The early appearance of the Internet inspired a notorious “dotcom” bubble, but even after the bubble burst, the Internet continued to grow and evolve, unabated.

Likewise, whether or not the explosion of Big Data applications makes it hard for business and IT leaders to distinguish which are the most useful, the broad utility of Big Data-enabled analytics is not in doubt. We’re still only beginning to understand the implications, and though the future is shrouded, one thing is clear: the growth of Big Data is NOT slowing.

The Three Dimensions of Big Data

By now, most retailers understand the basic shape of Big Data, even if they don’t know how to convert it to practical value. Big Data is traditionally expressed along three planes:

1) Volume – Though storage is no longer an issue, the concept of Big Data arose as companies struggled to store all the different sources of data streaming in.

2) Velocity – More companies are learning to activate the information in physical objects, from pipes to car seats, using RFID tags, sensors, and smart metering; sometimes called “the Internet of things,” this trend incorporates massive quantities of data at the heady speed of “real time.”

3) Variety – A common complaint with Big Data is its messiness, since data streams come in all types of file formats — the best software services are able to integrate and streamline this unavoidable digital multiplicity.

These three facets don’t encompass all the features of Big Data, but all together, they form a three-dimensional image of what the phenomenon really represents: more.

More Than More

It’s hard to appreciate how a mere quantitative increase — whether in size, speed, or types of data — can effect a qualitative transformation. Why isn’t more just more?

In their book “Big Data: A Revolution That Will Transform How We Live, Work, and Think,” Viktor Mayer-Schonberger and Kenneth Cukier lightly summarize the core concept of their treatise: “The change in scale is a change in state.”

To understand how a change in scale can become a change in state, think about the evolution of images from photography to film. Though fundamentally similar to painting, the invention of photography automated the process of creating images, thereby dramatically speeding up the procedure.

The change in speed from painting to photography makes a distinct qualitative difference, but not enough to constitute a change in state. If photographs changed how images are used, it is only in ways that paintings WOULD be used, if they could be made as efficiently and accurately.

The leap from photography to film is also a change in scale, but it added another dimension: speed AND quantity. Instead of just one rapidly generated picture, film produced twenty-four pictures (or frames) in a single second.

Photography was a change in speed, and film was a change in speed and quantity. The leap from photography to film isn’t just a process change — it’s a material change. Film is a distinct medium than photography, carrying unique possibilities, and acting on viewers in ways that are different than still images.

Data Flow, Not Stop-and-Go

Formerly in the business world, and much like the more archaic photographic practices, data was collected using time-consuming, labor-intensive processes, and despite their rigorous effort, the people who constructed databases could only hope to gather a small fraction of the information. Once their pain-staking work was complete and compiled in spreadsheets, these statisticians and analysts would extrapolate results based on theories, and present their conclusions in ad hoc reports, often well past the period of their relevance.

“Over the course of a year, those using spreadsheets spend an extra 130 hours — more than three whole weeks — on manual tasks, compared with those using treasury-specific applications.” Tim Wheatcroft, The Treasurer magazine, June 2013.

Spreadsheets and other forms of ad hoc reporting represent the old-school “snapshot” approach to data. Snapshot photography; not film – and what was needed to transform data reporting was a dramatic change in state.

These limitations notwithstanding, retailers still embrace these clunkier and inflexible legacy data reporting tools. However, increasingly, companies are moving over to newer, more accessible and cloud-based analytics tools. And this change in state means that business and IT leaders don’t need to extrapolate from samples to drive insights; instead, they can operate by comparing easily visualized trends, made available in real time on readily accessible platforms.

This revolutionary shift translates into:

  • Enhanced data sharing
  • Strategic decision making
  • Cost savings
  • Improved efficiency

Big Data does not have to be delivered as reams of incomprehensible information and rigid calculations. With today’s cloud based solutions, Big Data can be digested in an accessible and meaningful format that is easily leveraged for improved bottom line business results.

For more on how Retailers can use Big Data analytics for enhanced sharing and collaboration, download our white paper “The Collaboration Quadrant.”