Archives For Capability Maturity Model

IoT is your job. Data Science has always been greedy for complex data and has a pretty good handle on how to process it for insights and predictions.

For most of us and most projects, the practically of getting it to model and having it available to execute run-time algorithms has been the barrier. IoT data is meaningless without algorithms to process it and provide information and predictions/optimizations from it.

IoT is exciting and will change the fundamentals of businesses and industries.  The technology is interesting and very dynamic.  All of this has implications for your analytic operation and practice.

The more interesting and challenging future of IoT (and also part of your job): what are the new processes, user roles, use cases, management scenarios and business cases for IoT. Who will manage the IoT function ‘X’ of the future and what does that role look like.

The other important reason for you to pursue IoT is to keep your data scientists engaged and retained.  Many are still working around the same types of projects, methods, tools etc. that have been around for 10 plus years.  All projects are interesting and challenging but some of them are getting bored.

This means research and discussions with your colleagues, sponsors and stakeholders (while you are still working in the pre-IoT world). Enjoy!

Edward H Vandenberg

This is a challenging subject.  Here is a start:

1) Getting to failure mode quickly – analytics effort is too valuable and the need to stop doing pointless work is great.  Every project has a failure mode and the faster you can find it in an analytics project the more mature the effort is.  Every part of the project has a failure mode. (Side note: how different this is from many ‘technical’ projects, especially IT projects).

2) Not making the same mistake twice – look to Dr. John Elder for common mistakes made by talented people.  The least a mature team can achieve is not repeating mistakes in their work. Home – ERI.

3) Catching your own mistakes – closely related- but creating a mechanism to catch common errors is a level of maturity

4) Repeatability – mature efforts have a level of repeatability across challenging and varied projects.  This is a high mark.  The data and problem statement present unique problems that work against any repeatable effort or thought process.  TBD on whether this capability makes it into the CMM model for Advanced Analytics.

I invite the reader to extend and revise.