Anti-Pattern for Data
And that's just the tip of the iceberg
When it comes to data, being longer in a data project in an enterprise, I often get a very good big picture about what works well and what not. There are repeating patterns - or better call them Anti-Pattern - you identify as the reason for the problems in creating value from data.
How it started…
I’ve just seen a question Joe Reis raises on LinkedIn “What are the biggest data anti-patterns you see these days?“ - what a wonderful question! And even better answers in the comments. I wrap them together into the six I recognized very often on my own:
Maybe something to print out and repeat every day. Thanks to the people commented!
These patterns are very typical from my understanding. For sure they are much more like having a hype and buzz word mentality, as I have seen e. g. around GenAI several times now.
How it is going…
If you work on a strategic level with the customer you have the chance to recognize such pattern fast and initiate the right initiatives. There is not always the one way that works for everyone, as every company culture respond different to change and have just a certain capacity to do things.
From my experience, the following approaches work well and effect many of the patterns and should therefore be considered:
If you starte making the Anti-Pattern visible this is for sure a good first step. Sometimes things can be solved through technology. But always see the whole picture.
Conclusion
Sometimes what goes wrong is not the cause, just the symptom. Things repeat themselves for data, resulting in patterns and solutions. Be aware of these pattern and invest at the right time in your data maturity is recommended to not needing to reinvent the whole data organization at a later point.
What are your experiences with “Anti-Pattern for data”? What do you experience as helpful to prevent them?
This blog entry is a transfer from my former parallel blog/newsletter focussed on learnings from the field.