We use cookies. You have options. Cookies help us keep the site running smoothly and inform some of our advertising, but if you’d like to make adjustments, you can visit our Cookie Notice page for more information.
We’d like to use cookies on your device. Cookies help us keep the site running smoothly and inform some of our advertising, but how we use them is entirely up to you. Accept our recommended settings or customise them to your wishes.

What is your SDQ?

When we talk about the discipline of analytics, there’s an endless number of meanings and problems we can try to address. Fundamentally, analytics is about taking data and converting that into information and knowledge. Knowledge can be described in a variety of ways including a better understanding, predicting something, explaining why things occur, and revealing something previously unknown. However, at the end of the day, I would argue that analytics really only has one higher order purpose – to inform a better decision. 

Think about any organization or enterprise and the number of decisions faced every day. Some of these decisions do not really impact an outcome, but others can decide the fate of an organization. Now, of course, I don’t presume to think that analytics can drive an optimal decision every single time, but if we were to introduce the smart decision quotient (SDQ) – meaning the number of decisions that were informed by facts through the use of analytics – where would your company rank?  I’ll take myself for example, running an analytical services business. The most important asset in my business is our analytics talent and people. It also represents the biggest cost in a services firm. As I challenge myself, how much are we really using all the data available to ensure that we attract and retain the best talent in the industry? I would have to admit that our SDQ would be painfully low!!  (BTW, I’ve started on an internal initiative to improve this!!!)

My argument is that having the highest SDQ will absolutely create separation and competitive advantage. Therefore, an analytic project or initiative needs to include one simple thought – how will it eventually inform a smarter decision?  As we design our analytic solutions and methodologies, having a clear plan for how to apply that to decisioning is a key connection point that needs to be made.  This is even more critical when doing unsupervised data mining tasks – when we look to uncover something about data. As we don’t know what we’re looking for (until we find it), we need to quickly recognize that when we find something, it’s only meaningful if it’s actionable to inform a decision. That is a much better definition of a statistically significant number – something we can act on.

Join the Discussion