Data-Driven Culture
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.