Data Science Capability Maturity Model

A Data Science Capability Maturity Model is a analytics capability maturity model for a data science practice.

• AKA: ML Capability Model.
• Context:
  • It can (typically) involve Data Science Maturity Levels, such as:
    • Initial (chaotic, ad hoc, individual heroics) - the starting point for use of a new or undocumented repeat process.
    • Repeatable - the process is at least documented sufficiently such that repeating the same steps may be attempted.
    • Defined - the process is defined/confirmed as a standard business process
    • Capable - the process is quantitatively managed in accordance with agreed-upon metrics.
    • Efficient - process management includes deliberate process optimization/improvement.
  • …
• Counter-Example(s):
  • a Mobile Development Maturity Model.
  • a Software Development Maturity Model.
  • a Data Scientist Career Ladder.
  • an Experimentation Capability Model.
• See: Information Technology Maturity Model, ML Model Deployment Platform.

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References

2016

• https://blog.dominodatalab.com/introducing-the-data-science-maturity-model/
  • QUOTE:

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