Applying AI to Data

Everyone’s definition of data is in constant motion from day to day and hour to hour. What’s essential is relative and subjective, subject to market change.

And perishable: like a living creature - like each of us - data is transforming constantly, inevitably. It rapidly decays without help (like some of us!).??

If you look in the mirror, you may see yourself aging, but you’re only seeing the surface. Most likely, your health is most affected by what’s happening beneath. The same is valid with data. You may have ten thousand data points, but you can’t know what’s important from a distance: what you can trust and act on and what you can’t.

AI is a tool that, when used correctly, can transform data into a bespoke knowledge base that your competitors won’t have.?

But it’s not a simple process. AI is, at its heart, just a set of instructions. When you ask it to digest data, you request the algorithm's questions and expect relevant answers. Getting answers is not automatic, and the quality of the results is not assured.

There are two primary considerations to address beforehand:

1. How good is your data? Even the best algorithm can only produce results already part of the underlying data set. Or put another way, garbage in, garbage out. If your data set doesn’t contain the information you need, you won’t get good results, no matter how many ways you refine it.

1. What questions do you ask? The questions can determine the value of your results, and it’s not always obvious what the right questions are. You need a clear vision of your goals to lay the groundwork for actionable intelligence.

In my last post, I defined a hierarchy beginning with raw data and ending with intelligence.? Ask yourself: do I have information, or do I just have silos of data?