Unleashing Your Data’s Full Potential Through Data Quality Sprints

To implement an effective Enterprise Data and Analytics program, you need the right tools, processes, and personnel. But none of that matters if the data itself isn’t up to snuff. How do you ensure that your data is of the highest quality? Resolvit’s data quality sprint process may hold the key.

For data quality sprint coordination, we use the Six Sigma Define, Measure, Analyze, Improve, and Control (DMAIC) methodology. DMAIC breaks down the data quality sprint into the following steps:

Define

  • Assembly of a data governance team
  • Establishment of project plans and milestones
  • Definition of data quality rules and measures

Measure

  • Rule-based quality assessments
  • Creation of data profiles
  • Production of sample reports

Analyze

  • Identification of sources of variation
  • Determination of root causes

Improve

  • Solution development
  • Data cleansing (when necessary)
  • Recommendation of short- and long-term solutions

Control

  • Outlining of a monitoring and control system, reports, and dashboards
  • Establishment of standards and procedures
  • Creation of a control plan for hand-off to the process owner

To formally measure your data quality, Resolvit also provides a data quality measurement score based on your data’s accuracy, completeness, conformity, consistency, currency, precision, timeliness, and uniqueness.

Interested in training for a data quality sprint? Click here to begin the conversation.

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