Data And AI Maturity KPIs That Connect To Value Not Hype
Last week, I finished a 12-month data and AI strategy engagement with an SME in the retail space. My client brought me in to take them from early maturity to phase 3 maturity. A business operationalizes data, analytics, and machine learning models at that phase. What does it take to get there?
The transformation, data, and AI strategies are built and implemented.
The data and AI product roadmaps are in place, and the business is scaling data science to handle more use cases.
The Data and Analytics Organization and supporting infrastructure are being built to support those new initiatives.
I will give a talk tomorrow, October 20th, at 8 am PT about data and AI strategy. You can sign up here. I'm also talking with Andreas Welsch about AI strategy on October 26th at 9 am PT. Both are free, and if you decide to join me live, I'll be taking questions during both sessions. While most of my posts about technical strategy are for paid subscribers only, I also have free resources for the community.
Choosing The Right Metrics
There is a lot of ground to cover in 12 months and aligning the business requires consistent progress metrics. The danger in any metric is that people optimize for the metric as soon as it's deployed. That often comes at the expense of the intended outcome.
The KPIs and metrics implemented must strongly connect to the intended outcome's business value. I constantly ask, "What causes data and AI maturity to create value?"
Several vanity metrics are used to measure data maturity that are disconnected from business value. That's a huge pitfall. Data and AI maturity are a means to achieve business goals, making them part of the business's value stream.