DevOps and AI means that Lean Data has another Principle

In an earlier post I expounded on the principles of Lean Data:

• Organisations know what data they hold and manage;
• Data is classified according to subject area and criticality;
• Only the minimum data necessary to Add Value to the business is held;
• Data replication is kept to the minimum level necessary to optimise business performance;
• Data Value is determined by its utility in Serving the Customer, Supporting Essential Capability, Protecting the Organisation, Providing Insight for Business Decision Making.

I also covered Data Portfolio Management. However, what I missed was the emerging issues around database management systems and Machine Learning. It appears that as people start to scale their Lean efforts (Strategic Alignment, Design Thinking, Agile & DevOps) they are beginning to realise that this includes data too.

What becomes increasingly important is having a handle on configuration management over data base design (i.e. schemas) and data sets themselves, ensuring that it is co-ordinated with code configuration management and integration across multiple deployments. This should have been implicit, but apparently it was not.

Therefore I suggest adding the principle that:

• Data configuration management of schemas, test data conditions and machine learning training data is co-ordinated with applications and code configuration management.