You Don’t Have To ‘Go Big’ To Get Started With Big Data
By Bill Franks, Oct 11, 2012
I wrote a blog for Harvard Business Review recently titled To Succeed With Big Data, Start Small. I argued for the need to take small steps with big data rather than going big (pun intended) from the start. I want to expand upon those thoughts here.
At first glance, the idea of starting small with big data sounds like an oxymoron. It just doesn’t sound right, does it? I believe that if you take the time to think about it, you’ll realize that not only is it the way to go, but it is simply an extension of a method that has been successful in working with new data sources for many years. I will illustrate with a few examples.
When I first started doing customer analytics, all we had was a household name and address file. We thought we were pretty cool when we first appended demographic data to that customer list. The size of the data file seemed huge in those days and was quite difficult to deal with. Did we overlay all of the tens of millions of customers as a starting point, however? No, we did not. First, we sent off a sample to be overlaid. We then explored that sample and assessed exactly which data elements were worth the cost and effort based on how they enhanced our analytics. After that assessment, we proceeded with a full overlay. In other words, we started small.
Fast forward a few more years to the late 1990’s and early 2000’s. During this period most organizations first pondered making all of their transactional data available broadly for analysis. Did they get started by buying systems, developing processes, capturing all their transactional data, and then analyzing it? No, they did not. Or, at least the smart ones didn’t. What most did was to capture and make available a subset of their transactional data. Prototype reports and analytics were created against those subsets. Perhaps the subset of data contained one geographic region, or one division, or just one month of data. The prototypes helped the organization prove the value of the data and how it could flow into analytic processes. Equally important, it also enabled them to better understand what it would take to make it available in full on a regular basis. In other words, they started small.
So now we arrive at today. Big data has suddenly risen in profile and popularity. Everyone feels pressure to hop on the big data bandwagon. There certainly is a lot of value to be captured, as I’ve discussed in past blogs. The one flaw I see, however, is that for some reason many organizations are forgetting to apply the simple approach they applied to new data sources in the past. Instead of starting small with big data, they are jumping in all the way from day one. When the historical path to success was not to jump in with both feet from the start, why would you now do so with big data? People need step back, push the hype from their minds, and think things through.
I suspect that part of the problem is the fact that the name big data gets us in a mindset of “big” and we just can’t get out of it. Add to that the fact that many examples that we hear of in the press may not say much about how the organization got started. Therefore, we assume they started out with all the data from day one. I have seen multiple organizations realize terrific success by starting small with big data. As they have moved to capture more of it and put it to further use, they continue to increase the value they are driving. I have also seen multiple organizations struggle as they focus on capturing all the data right away. This takes a lot of time and investment in advance of any value being demonstrated. As roadblocks come along, it only puts the hypothesized benefits further into the future and today’s costs more into focus. It isn’t a recipe for success.
When your organization decides to tackle big data, consider being the champion of the contrarian and somewhat odd sounding view that you should start small.
About the author
Bill Franks is Chief Analytics Officer for Teradata, where he provides insight on trends in the analytics and big data space and helps clients understand how Teradata and its analytic partners can support their efforts. His focus is to translate complex analytics into terms that business users can understand and work with organizations to implement their analytics effectively. His work has spanned many industries for companies ranging from Fortune 100 companies to small non-profits. Franks also helps determine Teradata’s strategies in the areas of analytics and big data.Franks is the author of the book Taming The Big Data Tidal Wave (John Wiley & Sons, Inc., April, 2012). In the book, he applies his two decades of experience working with clients on large-scale analytics initiatives to outline what it takes to succeed in today’s world of big data and analytics. The book made Tom Peter’s list of 2014 “Must Read” books and also the Top 10 Most Influential Translated Technology Books list from CSDN in China. Franks’ second book The Analytics Revolution (John Wiley & Sons, Inc., September, 2014) lays out how to move beyond using analytics to find important insights in data (both big and small) and into operationalizing those insights at scale to truly impact a business.He is a faculty member of the International Institute for Analytics, founded by leading analytics expert Tom Davenport, and an active speaker who has presented at dozens of events in recent years. His blog, Analytics Matters, addresses the transformation required to make analytics a core component of business decisions. Franks earned a Bachelor’s degree in Applied Statistics from Virginia Tech and a Master’s degree in Applied Statistics from North Carolina State University. More information is available here: .