data architecture

The Four Fundamentals of a Successful Data Architecture

There are lots of people who call themselves Data Architects, and they have produced even more Data Architectures. Every data system, in fact, has a data architecture – whether by design, or by chance. Not all of these data systems, unfortunately, have good Data Architectures. The question is, how do you tell the difference between a good architecture, and a not so good one?

Does meta data matter any more? (aka Data vs Meta data)

I was asked the other day to explain how I would implement an architecture for meta data management as opposed to data management. The question actually stopped me for a moment because it is not a straight forward as it first sounds. In fact, it may be simpler than it sounds.

Why You Need a Data Architecture

A couple weeks ago, I was in a meeting and someone said something that really resonated with me, “Our data has grown somewhat organically”.

I love that statement, because it’s true of every organization regardless of size. Data really is organic. Not in the chemical sense, nor the the marketing sense (as in the "organic" section of your local grocery store), but in how it behaves. Data usually starts small, but it grows. It doesn't just grow in volume, but in scope, and in different directions. Data is like a tree in your yard. If it is taken care of, it will be healthy and strong. But it can also grow in ways that are not useful, like a hedge taking over a sidewalk, or a large branch of your favourite tree that threatens your house whenever the wind blows.