The Data Hierarchy

I’ve written about the value of data in today’s marketplace and some of its challenges.

Similar to everything else, data succumbs to the forces of entropy. When left unattended, it descends into a state of disorder and irrelevance. It loses value. Worse, if you’re trusting it and it has flaws, it can undermine the value of your operation.

On the flip side of that coin, when you enrich data, its value can increase. It’s not enough to aggregate data; that’s the first step. Anyone can aggregate data. Plenty of corporations are happy to sell you aggregated data that may be of little to no value.

Think of data as the following hierarchy:?

DATA: This is unstructured bits of information with little to no inherent value. You can’t trust it. You shouldn’t use it to guide you or base decisions on. But with some work, the transformation into information is achieved.

INFORMATION: You get information by transforming raw data, weeding out the irrelevant parts, and distilling it into something useful. Information is where you can begin to trust what you’re seeing. You can start to bucket out the info for trust. And from Information, you can distill knowledge.

KNOWLEDGE: Knowledge contains the conclusions you derive from information. It’s actionable intelligence you can trust. Tailor it to your specific needs. It’s very likely proprietary and worth defending.

When you transform data into knowledge, you begin to create business intelligence. You can make informed decisions about your value and your marketplace. But we’ve seen repeatedly that this can be a complex, labor-intensive, and costly process. Some companies shortcut through it or avoid it entirely, at their peril.

But there’s hope: AI will open a new avenue for turning raw data into valuable knowledge. In my next post, I’ll talk about applying AI to data.

Sources: https://hbr.org/2016/09/bad-data-costs-the-u-s-3-trillion-per-year