Talking about Big Data and Analytics let’s take a closer look at what kind of people you need to be successful in this area. If you want to extract value out of big data, you need to invest in doing so. Like with anything else you want to become good at it can’t be something you do when everything else is done and you have some time to spare. Hence, we’re now looking into what it takes to become a winner in the game of Big Data.
The first question a finance professional should ask is should analytics and Big Data be a part of the finance function, a part of the business or a separate function altogether? Adding another CXO to the executive suite like a CAO require significant funding though and it might be difficult to justify. Also, it should be clear that there’s one set of numbers in a company and while you can discuss who should own them it shouldn’t be spread out on business functions, an analytics function, and Finance. Finance, of course, needs to serve its (internal) customers and deliver the necessary insights for the business to add value. An enabler of that is embedding finance staff into the business functions and letting them specialize in Sales, Operations, Marketing etc. Only when specialized they will understand the business well enough to challenge line managers and deliver value-adding insights. That leads to the next question. Can you be a business expert AND an analytics expert at the same time?
At first glance, we’d argue a clear NO. The simple reason for that is that just like the finance people embedded in the business functions need to specialize in their specific function the so-called data scientists need to do the same. They need to continuously hone their skills within data extraction, data analysis and data synthesis coming up with answers to complex business issues. Then you say how can they do that if they’re not supposed to spend time on understanding the business? Clearly, there need to be meetings taking place between line managers and data scientists for requirements to be agreed upon but more likely than not translators will be needed. Essentially, data is all the same and the task for the data scientist is to find patterns in the data. This requires sifting through terabytes and more terabytes of data using sophisticated algorithms and, generally, analyzing a whole lot of numbers or words. This is no easy task indeed and calls for a specialist. What it then means and how it can be applied in a business sense (s)he will leave the business and the finance people embedded in the business to figure out.
Now that you’re about to invest heavily in Big Data and Big Data Scientists it’s naturally also time to clearly define what exactly is meant with Big Data. We’re no experts so we can’t do it for you but can only try to point you in the right direction. As highlighted in this paper there’s a significant divergence as to what Big Data means and we can probably all agree on that if you’re getting involved with something you don’t really understand the risk of failing is a lot higher. While just creating an Advanced Analytics or Big Data team might sound appealing you need to have a strategy for this area. One of the main points will be to simply agree on what Big Data means to you and then the next step is how you can extract value from it. Not until you’ve got the strategy laid down should you start to invest and indeed you need to invest. Simply thinking you can convert some of your finance or IT people into Big Data Scientists means you still haven’t understood what Big Data is.
What’s your strategy for Big Data? Have you hired a lot of Analytics Analysts and really smart people with PhD’s in Mathematics or Statistics? Perhaps you even have a success story to share?