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From Big Data to Botched Data: 5 Steps to Total Big Data Failure

Sri Narayanan | Oct. 7, 2014
How to avoid blunders in modernising your business intelligence efforts.

Just because something is shiny and new, or is now the 'in' thing, doesn't mean it will work for everyone.

Sounds familiar?

If you're a CIO, the temptation of shiny new technology is almost too hard to resist. Nowhere is this more evident than in the dozens of failed Big Data projects deployed across enterprises. The problem is that there is a right way and a wrong way to do Big Data, and judging from recent corporate history, most IT departments and CIOs don't know the difference.

Take JC Penny. Once a darling of the retail consumer experience, the company is a cautionary tale on how not to deploy Big Data. From a leading light in the retail consumer space in 2012, JC Penney witnessed a mass exodus of customers in under a year, prompted by ill-advised and drastic store changes. The culprit? Poor deployment of a large scale, merchandising retail analytics solution.

A few months into a complete store revamp, CEO Ron Johnson implemented what he called a "complete, open and integrated suite" of retail analytics solutions. The hope was that JC Penney would have new customer shopping insights to respond faster to customer preferences.

Johnson began a process of modernising the existing business intelligence (BI) software by deploying a retail merchandising analytics program that provided real-time, mobile insight into item and category performance, including key metrics such as inventory position, sales, stock ledger, cost, forecast, and promotions. On paper, this would simplify processes and capture structured and unstructured data (customers sentiment, etc) so it could deliver the best possible customer experience.

But neither Johnson nor his team gained any meaningful insight from all that analytics solutions. Instead, sales levelled off sharply from US$17 billion in 2011 to US$12.9 billion in 2013. After 17 months on the job, Johnson was fired.

So what went wrong?

Focus on the Data, Not Its Application
The surest way to scupper your Big Data project before achieving any meaningful outcome is to fail to ask one basic question: What do you want to do with the information? Too often, companies don't know what they are looking to achieve with Big Data but they think it will solve their problems. They process large volumes of data without any idea what problem they are actually trying to solve.  Or expect more than it can deliver.

Assume You Have the Right Skills

JC Penney deployed a sophisticated predictive analytics solution and failed to draw meaningful insight from the data. Why? Because they had IT personnel and data scientists asking questions to problems their marketing, sales and merchandising experts should have been asking in the first place.


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