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Data-Driven Culture

D3

Analyse, review and improve, and promote the use of data and data analytics to inform decision-making.

Improvement Planning

Practices-Outcomes-Metrics (POM)

Representative POMs are described for Data-Driven Culture at each level of maturity.

2Basic
  • Practice
    Use awareness training and events (e.g. physical or virtual workshops) to show the benefits of data analytics and to encourage its use in decision-making.
    Outcome
    Awareness on the effectiveness of analytical data in decision-making is being raised.
    Metric
    Number of data analytics events/training.
3Intermediate
  • Practice
    Identify and document the information that decision makers need.
    Outcome
    The identification and use of appropriate information to support informed decision-making is mandated in most cases.
    Metrics
    • Number of decisions identified that need to be supported by data analytics.
    • Number of decisions that are based on analytical data.
4Advanced
  • Practice
    Review both the quality of the data, and the quality of the decisions that are based on that data.
    Outcomes
    • Decision-making uses data that is reviewed for quality.
    • Scrutiny and a 'show me the data' attitude prevail.
    Metrics
    • Number of quality reviews of analytical data.
    • Percentage of data that meets quality standards.
5Optimized
  • Practice
    Create and manage data analytics training, education, certification, and reinforcement programmes to ensure the sustainability and vibrancy of data analytics capabilities across the business ecosystem.
    Outcome
    A data-driven culture is evident in prioritizations and decision-making at all business ecosystem levels.
    Metrics
    • Percentage of revenue that is supported by data analytics.
    • Percentage cost savings that can be directly attributed to data analytics.