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Capability Maturity Model (CMM) for Analytics and Data Science

1. Initial (Chaotic)

  • Characteristics:
  • The organization is unpredictable, reactive, and lacks stable processes.

  • Data science projects are often one-offs, with minimal documentation or repeatability.

  • Metrics:

  • Percentage of undocumented data science projects.
  • Lack of standardized tools and platforms.

  • Number of ad hoc requests versus planned projects.

  • Steps to Improve:

  • Recognize the importance of data science maturity.
  • Invest in basic data science training.
  • Document ongoing projects and results.

2. Repeatable

  • Characteristics:

  • The organization can repeat previous successes because there are some basic processes in place, though they may not be strictly followed.

  • Metrics:

  • Number of standardized processes implemented.
  • Percentage of projects with documented methodologies.

  • Number of recurring data issues or errors.

  • Steps to Improve:

  • Develop and document standard data science processes.
  • Train teams on these processes.
  • Initiate basic version control and collaboration tools.

3. Defined

  • Characteristics:
  • Processes are documented, standardized, and integrated into the organization.

  • There's a consistent approach to data science projects.

  • Metrics:

  • Percentage of data science staff trained on standard processes.
  • Consistency in tools and platforms used.

  • Time taken from project initiation to delivery.

  • Steps to Improve:

  • Create a centralized repository for processes, tools, and methodologies.
  • Implement regular training and onboarding programs.
  • Adopt a consistent set of tools and platforms.

4. Managed

  • Characteristics:
  • The organization monitors and controls its processes using data.

  • There's an understanding of the quality and outcomes of data science projects.

  • Metrics:

  • Number of projects meeting predefined success criteria.
  • Regularity of process audits and reviews.

  • Number of ongoing improvement initiatives.

  • Steps to Improve:

  • Implement advanced data quality and validation checks.
  • Monitor projects in real-time to identify bottlenecks and areas of improvement.
  • Use metrics and feedback to refine processes continuously.

5. Optimizing

  • Characteristics:
  • Continuous process improvement is ingrained in the culture.

  • The organization is proactive and uses insights to drive further refinement and innovation in data science projects.

  • Metrics:

  • Number of innovative data science projects launched.
  • Frequency of process enhancements based on feedback and learnings.

  • Level of automation in data processing and model deployment.

  • Steps to Improve:

  • This is the highest level of maturity, so the focus is on maintaining this status and innovating.
  • Foster a culture of continuous learning and exploration.
  • Regularly revisit and refine processes, always looking for efficiency gains and areas of innovation.