A ski slope of data


There have been many times throughout my career in tech when the problem described is not the problem that needs to be solved. This has often been the case when people have complained about the ineffectiveness of a software application only to find out that it is not set up correctly or that they have not been trained to use it properly. The software is rarely the problem, its application often is.

On investigation it turns out that many of the issues are to do with the quality of the data that the application uses. Poor data is misinterpreted as poor software . If your data is wrong or missing, the software will not function, reports will be irrelevant and the systems may end up making the business less efficient rather than realising the nirvana that it was bought for. So much time will be spent complaining about the systems, correcting information and developing ineffective workarounds.

So it was that I ended up once again trying to help sort out another set of data. Our small group needed to understand the relationship between the different component parts of the data. Which were most important to the company and which were less relevant? Which do we have, which do we need to find? What needs to be corrected?

I think in metaphors and imagined a pristine mountain covered in snow with a ski lift running up to a cafe at the top. The cafe represented the highest level of the data, in this case a ‘property’. From this vantage point I could see different ski runs making their way down the slopes. Each represented a different attribute of the data. Some fizzled out after a short while and others stopped at refreshment points, such as a ‘unit’ only to give rise to more ski runs. The whole of the data map was laid out on the mountain.

We started at the top and skied down our data runs. Each was checked for clarity, correctness and usefulness. Everything was documented on our way to the bottom. Surprise surprise, we discovered things along the way. Data we needed already existed but in the wrong place. The application had functionality we didn’t know about. Different teams are using different fields to record the same thing.

It was a hugely useful exercise and we now have a list of things to get on with.

The software is rarely the problem, the data often is.

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