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Representative POMs are described for Data Preparation at each level of maturity.

2Basic

• Practice

  Audit the data to identify errors, gaps, anomalies, and inaccuracies that need to be corrected.

  Outcome

  There is clarity on the quality of data available and what needs to be done to improve its collection and to correct it.

  Metrics

  • Number of data collection problems identified per data set.
  • Number of data quality issues identified per data set.

3Intermediate

• Practices

  • Put in place a workflow (sequence of data preparation steps) to address and correct data errors, anomalies, and inaccuracies.
  • Validate the effectiveness and efficiency of the data preparation process and adjust it as required.

  Outcome

  Processes are in place to resolve any issues that have been identified in the collection of data and its suitability for analytics.

  Metric

  Number of workflows and processes in place to ensure the accuracy and quality of data.

4Advanced

• Practice

  Build on the effectiveness of the data preparation process by using advanced techniques and tools to transform the data for analysis.

  Outcome

  Advanced and automated data preparation techniques including warehousing are used — to ensure a very high level of data quality and the appropriate transformation prior to analysis.

  Metrics

  • Percentage automation of data preparation.
  • Percentage of raw data that is transformed for analysis.

5Optimized

• Practice

  Use research and feedback from data analytics to continually improve how data is prepared.

  Outcome

  Research and feedback identify new approaches for data preparation.

  Metrics

  • Number of improvements implemented per data set per year.
  • Percentage cost savings delivered by data preparation improvements.