A Strategic Mistake With Big Data

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.

To see a video version of this blog, visit my YouTube channel.

 

Originally published by the International Institute for Analytics

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  • http://www.facebook.com/people/Azana-Baksh/100003026037089 Azana Baksh

    Great article Bill. With the explosion of big data, companies are faced with data challenges in three different areas. First, you know the type of results you want from your data but it’s computationally difficult to obtain. Second, you know the questions to ask but struggle with the answers and need to do data mining to help find those answers. And third is in the area of data exploration where you need to reveal the unknowns and look through the data for patterns and hidden relationships. The open source HPCC Systems big data processing platform can help companies with these challenges by deriving insights from massive data sets quick and simple. Designed by data scientists, it is a complete integrated solution from data ingestion and data processing to data delivery. More info at http://hpccsystems.com

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