Friday, March 30, 2012

4 Big Data Myths - Part I

It was cloud then and it's Big Data now. Every time there's a new disruptive category it creates a lot of confusion. These categories are not well-defined. They just catch on. What hurts the most is the myths. This is the first part of my two-part series to debunk Big Data myths.

Myth # 4: Big Data is about big data

It's a clear misnomer. "Big Data" is a name that sticks but it's not just about big data. Defining a category just based on size of data appears to be quite primitive and rather silly. And, you could argue all day about what size of data qualifies as "big." But, the name sticks, and that counts. The insights could come from a very small dataset or a very large data set. Big Data is finally a promise not to discriminate any data, small or large.

It has been prohibitively expensive and almost technologically impossible to analyze large volumes of data. Not any more. Today, technology — commodity hardware and sophisticated software to leverage this hardware — changes the way people think about small and large data. It's a data continuum. Big Data is not just about technology, either. Technology is just an enabler. It has always been. If you think Big Data is about adopting new shiny technology, that's very limiting. Big Data is an amalgamation of a few trends - data growth of a magnitude or two, external data more valuable than internal data, and shift in computing business models. The companies mainly looked at their operational data, invested into expensive BI solutions, and treated those systems as gold. Very few in a company got very little value out of those systems.

Big Data is about redefining what data actually means to you. Examine the sources that you never cared to look at before, instrument your systems to generate the kind of data that are valuable to you and not to your software vendor. This is not about technology. This is about completely new way of doing business where data finally gets the driver's seat. The conversations about organizations' brands and their competitors' brands are happening in social media that they neither control nor have a good grasp of. At Uber, Bradly Voytek, a neuroscientist is looking at interesting ways to analyze real-time data to improve the way Uber does business. Recently, Target came under fire for using data to predict future needs of a shopper. Opportunities are in abundance.

Myth # 3: Big Data is for expert users    

The last mile of Big Data is the tools. As technology evolves the tools that allow people to interact with data have significantly improved, as well. Without these tools the data is worth nothing. The tools have evolved in all categories ranging from simple presentation charting frameworks to complex tools used for deep analysis. With rising popularity and adoption of HTML 5 and people's desire to consume data on tablets, the investment in presentation side of the tools have gone up. Popular javascript frameworks such as D3 have allowed people to do interesting things such as creating a personal annual report. Availability of a various datasets published by several public sector agencies in the US have also spurred some creative analysis by data geeks such as this interactive report that tracks money as people move to different parts of the country.

The other exciting trend has been the self-service reporting in the cloud and better abstraction tools on top of complex frameworks such as Hadoop. Without self-service tools most people will likely be cut off from the data chain even if they have access to data they want to analyze. I cannot overemphasize how important the tools are in the Big Data value chain. They make it an inclusive system where more people can participate in data discovery, exploration, and analysis. Unusual insights rarely come from experts; they invariably come from people who were always fascinated by data but analyzing data was never part of their day-to-day job. Big Data is about enabling these people to participate - all information accessible to all people.

Coming soon in the Part II: Myth # 2 and Myth # 1.

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