Data management, data governance, data observability, data fabric, data mesh, DataOps, MLOps, AIOps. It’s a data terminology mess out there. Let’s try and untangle it, because there’s more to words than lingo.

We need XYZ. Definitely. It’s in all analyst reports, it’s trending off the charts, and our competitors have it, too. So let’s find a vendor who’s got it, and get ourselves invested. That should do it.

Sound familiar? Hopefully, technology investment decisions in your company are not made this way. But as technology is evolving faster than ever, it’s hard to keep up with all the terminology. Unfortunately, some people see terminology as an obfuscation layer meant to glorify the ones who come up with it, hype products, and make people who throw terms around appear smart.

There may be some truth in this, but that does not mean terminology is useless. On the contrary, terminology is there to address a real need, which is to describe emerging concepts in a fast-moving domain. Ideally, a shared vocabulary should facilitate understanding of different concepts, market segments, and products.

Case in point: data and metadata management. Have you heard the terms data management, data observability, data fabric, data mesh, DataOps, MLOps and AIOps before? But, do you know what each of them means, exactly, and how they are all related? Here’s your chance to find out.

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