Anti Pattern for Data
...and how to handle them
Being longer in a data strategy project of an enterprise, I often get a very good big picture about what works well and what works wrong, if it comes to data. 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 lately most often on my own:
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:
Conclusion
Somehow things repeat themselves even for data, as patterns and solutions - at least on a high level - occur again and again. To 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.
What are your experiences with “anti pattern for data”? What do you experience as helpful to prevent them?