Why the "Three Vs" May not Help your Big Data Initiative
Tony Cosentino, vice president and research director at research and advisory services firm Ventana Research, sees unanticipated outcomes and challenges with big data implementations. He warns that defining big data by the “three Vs” - volume, velocity and variety - turns into a very technical discussion and encourages organizations to focus instead on the decision-making process to find the most value from the data.
Cosentino discussed his perspective and recommendation of an alternate way to define big data with Information Management in a recent interview.
Ventana Research has conducted extensive research about big data and big data analytics. Would you talk about that research and some of the implications for the future of big data?
One key finding in our big data analytics research shows how early we are in this market. That is, the most important capability of big data analytics by far is predictive analytics, but among companies that have deployed big data analytics, descriptive analytic approaches of query and reporting and data discovery are more readily available than predictive capabilities. Such statistics are likely a [result] of big data technologies such as Hadoop, and their associated distributions, having prioritized the ability to run descriptive statistics through standard SQL, which is the most common method for implementing analysis on Hadoop. It is likely a walk-before-you-run situation, and what we will see going forward is more predictive capabilities put on top of big data. We are already seeing this with a number of vendors, but the ecosystem is still quite fragmented.
What new or surprising trends around big data stand out from your research and work with clients?
Perhaps the most surprising finding is that communication and knowledge sharing is a primary benefit of big data analytics initiatives, but it is a latent one. Among organizations planning to deploy big data analytics, the benefits most often anticipated are faster response to opportunities and threats, improving efficiency and improving the customer experience. However, after a big data analytics system has been deployed, the benefits most often mentioned as achieved are better communication and knowledge sharing. It’s noteworthy that the benefit of communication and knowledge sharing, while not a priority before deployment (not even in the top five), becomes top-ranked after deployment. In my opinion, this reflects how [difficult] defining these initiatives can be for a company culture and that the biggest unanticipated challenge as well as a key benefit of these initiatives is organizational collaboration.
How much confusion do you think still exists in the big data marketplace?
Our big data analytics research shows that there is still much confusion in this market. This is reflected by the lack of agreement upon what we mean by big data analytics. If we think about big data analytics in terms of the Vs (volume, velocity and variety), the research shows that organizations prioritize these by variety, volume and then velocity. The problem is that bringing the three Vs together focuses us on a broad ranging, but technological discussion. Big data ends up being more of an ethos than something tangible that delivers value to an organization. As we go forward, I anticipate the discussion to continue in the direction of what I have defined as the business Ws: the “What” (i.e., the data), the “So What” (i.e., the implications of the data), the “Now What” (i.e., the decisions made from the data), and “Then What” (closing the loop after the decision either from a human or a machine perspective). This gets us thinking more in terms of decision-making processes and prevents us from being mired in ambiguous big data discussions.