With big data, CEOs find garbage in is still garbage out

BI has always topped of the list of enterprise priorities -- and execs are always the least satisfied with BI initiatives. Why should big data be any different?

CEOs learn garbage in, garbage out all over again
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Another day, another CEO survey. This one, from KPMG, finds that CEOs don't trust their analytics, the way their team is using or implementing them, or even the data used to make decisions in the first place. In fact, only 31 percent of respondents see their organizations as leaders in the use of data and analytics.

You have to ask: What's the CEO's culpability in all that?

Despite the evidence that math makes better decisions than gut calls, many companies haven't gotten there yet. The approach many are taking is still the wrong one. Deploying big data infrastructure with no plan and no use cases will go the way any IT project with no plan or destination in mind goes.

What's funny about KPMG's survey is that despite the lack of trust in both the analytics and the data, a set of very modern concerns emerge: Customer loyalty, understanding millennials, projecting relevance of current products/services, and understanding customer needs/expectations. You know what you need to do to achieve those things? Fix how you collect data and perform analytics. You know who should really push for that and view themselves as leading that charge? The CEO of any decent company (aided and abetted by a CIO, CDO, CTO, and so on).

The millennial issue in particular connects with other CEO fears about keeping up with the pace of technology and integrating innovation. As late Generation X, I hate dealing with many companies. I can only imagine how young folks feel when a mobile website doesn't work or they can't get it to do everything they need it to do without going to the website -- or worse, actually calling. (BTW, can I pay with Venmo?)

Having worked on numerous big data and BI projects, I've found that the start of the path to success is usually crystal clear: a list of business problems we're trying to solve. The path to failure is nearly as clear: fascination with a technology without any clear understanding of how it fits into any overarching goal or objective. Here's a hint: Just like you can't use a word in its own definition, you can't use a technology in your rationale to deploy it, as in, "we're deploying Hadoop in order to develop a big data competency center." Why the hell are you doing that?

The survey says you're doing it to retain customers or to develop products or to understand how your customers are using your products. Forget about the storage or analytics technology or even the latest machine learning algorithm that shows promise. How would you normally do that? What data would you need? Is it good? What needs to be fixed? Start there before adding new technologies.

Oddly, IT loves doing rudderless deployments. I had a potential deal fall through the cracks when the customer basically said it knew there were people who could help them go the right direction but they were adding technology to keep their IT people engaged. It was fun. Talk about the tail wagging the dog -- randomly adding new technologies to address employee retention concerns? Sorry, it doesn't work quite that way.

Ultimately, the survey points to an overall failure of companies to even transition to 1990s and 2000s technology. I mean, you had data systems before. You had math. You had statistics. Why didn't you capture the data, make sure it was valid, then create analytics around it?

You don't need a data scientist to do the basics, but if you don't even trust the data you have, how are new data systems going to help? If you suck at basic IT, you don't need big data -- you need to clean house.

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