A Strategic Mistake With Big Data
By Bill Franks, Aug 09, 2012
Companies are scrambling today to understand what big data is and what they should do with it. Many have also come to believe that they’ll need to develop a strategy for big data, which is absolutely true. However, there is one major mistake that I am seeing a number of organizations make. That mistake is the development of a siloed, distinct big data strategy.
Organizations need to ensure that their strategy for big data is a new facet of their overall enterprise data and analytic strategy. After all, organizations already capture a lot of data. They also perform a lot of analytics against that data. Big data certainly expands the possibilities, as well as the challenges. However, at its core, big data is still just more data feeding more analysis. For that reason, it should be folded into a cohesive data and analytics strategy.
What can go wrong if organizations pursue big data as a distinct initiative? Look no further than the mess that many multi-channel retailers got themselves into through their entry into e-commerce. Many, if not most, brick and mortar retailers launched distinct e-commerce divisions. Some were even separate legal entities. As opposed to viewing e-commerce as a new facet of an overall retail strategy, many retailers viewed it as a new paradigm requiring a totally different strategy. Thus, a distinct division with distinct processes and distinct infrastructure was created.
Fast forward to today. Retailers now consider it critical to provide a consistent experience for customers across channels. They want all of their e-commerce data alongside their other data. They want to deliver offers and content seamlessly to customers in multiple channels. Should be pretty easy, right? Wrong.
Recall that many e-commerce divisions were distinct. This led to different supply chains, different promotional strategies, and even different product hierarchies. This last point is one that causes many analytic professionals I know a lot of pain. In many well-known retailers today, I can go into a store and grab a product and then find that same product on the retailer’s website. Guess what? They have no way to match those products in their systems. We can see it is the same product, but the systems can’t. As a result, analytic professionals have to manually match up products for any given cross channel analysis. While efforts are being made to correct this illogical setup, it is very difficult and expensive since the ecommerce processes were planned without regard for later integration requirements.
Let’s bring this back to the topic of developing a big data strategy. Organizations that charge ahead with separate, non-integrated strategies for big data will likely end up with systems and processes that are very difficult to integrate together later. Instead, organizations should think through not just how to tackle big data in a bubble, but also how to integrate big data into the overall infrastructure and current and future analytic processes.
It may take a bit longer to think through the bigger picture up front, but it will really save a lot of time, effort, and money later. There is nothing stopping an organization from aggressively experimenting with big data while it figures out the larger plan. In fact, such experimentation can even be a great way to learn about what the plan should be. But it is critical that the bigger plan is the goal from the start.
Make sure that when you hear the need for a big data strategy in your organization that you speak up and reinforce that the strategy must be an extension of existing data and analytic strategies rather than a strategy all to itself. It will provide a much greater chance of long term success.
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: .