Much has happened since the introduction of Business Analytics (BA) as a framework for data-driven decisions. The amount of data has exploded and technologies like Internet of Things and Machine Learning are only creating more data.
Even though the potential for making better decisions based on data improves, it hasn’t necessarily become easier. But the amount of data not only grows, it also accelerates. It arrives faster and is faster to become irrelevant again. So in order to compete on data-driven decisions, one must be able to make better decisions, faster.
You may ask yourself, what are you really going to use all that extra data for?
To put it a little bluntly: If you do not manage to turn your data investments into better decisions, the investment is wasted, and you risk falling behind in the race with the competitors who have already figured it out.
So, what's the next step after Machine Learning? And how do you translate the extra insight that data and Machine Learning provide into better decisions? To give an answer to this, let’s take a look at data and Machine Learning in the context of Business Analytics, and see how optimization can be used to compliment the BA portfolio you might already have.