Business Intelligence was a first generation tech idea - popular in the early 2000s, but totally outdated now.

It worked on the premise that all the data should be used to put pretty graphs on C Suite dashboards and these managers would then make good, data-driven decisions.

But what actually happened was that they used these graphs to support their pet project or to scupper those of their rivals. Meanwhile their day was driven by traffic lights - chasing the red ones which were already on their boss's dashboard so they had a confident answer - one which usually involved short-termism. The business went backwards. Fast.

Life meanwhile, except at Oracle, has moved on.

Data is much easier than it was. No longer does it need a data warehouse to pull it all together for processing overnight. Now it is unstructured, on the fly, instantly processed in the cloud and delivered to a mobile.

But the best data doesn't even reach that mobile. It is used by machine learning systems to produce self optimising and evolving systems. Systems which make decisions for themselves, without involving ego-driven C-Suite managers.

There is a singularity here. Systems have near limitless memory - they know what happened last time, the time before and the time before that. They have the processing power to run multiple threads, do continual AB testing and aggregate lots of data sources. They make more informed, more complex decisions than men with dashboards ever can.

Erik Brynolfsson put it well...
"In the first machine age we moved beyond the limitations of human muscle. In the second we will move beyond the limitations of the human mind".

Dilbert had his take too...
"The secret to my success is that I hire people smarter than me... then I tell these people what to do".

We now have machines which are smarter than us. So why are we trying to tell them what to do?

So begin your career by getting the hell out of backward companies like Oracle. Then start thinking ten years ahead, not ten years behind.

The future will not be ruled by machines, or by people, but by people/machine partnerships. Learn how to bring value to those partnerships. Then you can have a career in data and intelligence.