Defining analytics

If you ask a hundred people to define analytics, you are likely to get a hundred different answers. Each person tends to have his or her own definition in mind that can range from static reports to advanced deep learning expert systems. All tend to call efforts in the wide ranging territory analytics without much further explanation.

We will take a fairly broad definition in this book as we are covering quite a bit of territory. In their best selling book Competing on Analytics, Tom Davenport and Jeanne Harris created a scale, which they called Analytics Maturity. Companies progress to higher levels in the scale as their use of analytics matures, and they begin to compete with other companies by leveraging it.

When we use the word analytics, we will mean using techniques that fall in the range from query/drill down to optimization as shown in the following chart from Competing on Analytics:

We will also take a slightly different philosophy. Unlike the notion of a company progressing through each level to get to the peak of maturity at the upper right with optimization, we will strive to reach success at all levels in parallel.

The idea of a company not being analytically mature unless it is actively employing optimization models at every turn can be dangerous. This puts pressure on a company to focus time and resources where there may not be a return on investment (ROI) for them. Since resources are always limited, this could also cause them to under-invest in projects in other areas that have a higher ROI.

The reason for the lack of ROI is often that a company simply does not have the right data to take full advantage of the more advanced techniques. This could be no fault of their own as the signal in the noise may be just too weak to tease out. This could stem from the state of technology, not yet at the point where the key predictive data can even be monitored. Or even if this is possible, it may be far too expensive to justify capturing it. We will talk about the limitations of available data quite a bit in this book. The goal will always be to maximize ROI at all levels of the maturity model.

We will also take the view that analytics maturity is about having the capability and knowing how to enable the full scale. It is not about what you are doing. It is about what you are capable of doing in order to maximize your sum total ROI across the full scale. Each level can be exploited if an opportunity is spotted. And we want there to be fertile ground for opportunities across the full scale. More about this will be covered throughout the book.