Operationalizing data quality

Once you have formulated your data quality framework, the next step is to operationalize it. Here’s the process we recommend.

Set goals, dimensions and indicators - this is the stepping stone for all data quality work. Begin by defining the scope, outlining business use-case, identifying stakeholders, clarifying business rules and designing business processes. Make sure you clearly communicate the objectives of the data quality initiative to all stakeholders.

Evaluate baselines and pilot - once the business rules are clearly defined and communicated, set up baselines around the rules. Use these baselines to categorize the data into different quality buckets. Then, run a pilot on the existing data to measure the current data quality. This typically results in two things:

  • Identifying the specific actions needed to improve the data quality 

  • Re-thinking business processes to improve data quality over the long term

Collect and clean data - ingest and collect data. Run the quality rules on this data to separate clean data from bad data.

Assess data quality - typically, we use both quantitative and qualitative analytical techniques to do the gap analysis of where the data quality should be based on what’s defined in the first phase and where the data quality actually is.

Report data quality - build comprehensive, yet succinct dashboards that inspire proactive and reactive actions to improve data quality. Enable each group of stakeholders to customize these reports based on their needs and ownership.

Maintain - build and implement processes to maintain the data quality efforts on an ongoing basis. Leverage your organization’s data governance initiatives to make sure data quality is maintained through a sustainable program.

Have a bird’s eye view -