Typically, a hybrid approach works best – top-down for sponsorship, consistency, and resources, but bottom-up to discover what is actually broken and to achieve incremental successes. Improving data quality requires changes in how people think about and behave toward data. Cultural change is challenging. It requires planning, training, and reinforcement. While the specifics of cultural change will differ from organization to organization, most Data Quality Program implementations need to plan for:
- Metrics on the value of data and the cost of poor quality data: One way to raise organizational awareness of the need for Data Quality Management is through metrics that describe the value of data and the return on investment from improvements. These metrics (which differ from data quality scores) provide the basis for funding improvements and changing the behavior of both staff and management.
- Operating model for IT/Business interactions: Business people know what the important data is, and what it means. Data Custodians from IT understand where and how the data is stored, and so they are well placed to translate definitions of data quality into queries or code that identify specific records that do not comply.
- Changes in how projects are executed: Project oversight must ensure project funding includes steps related to data quality (e.g., profiling and assessment, definition of quality expectations, data issue remediation, prevention and correction, building controls and measurements). It is prudent to make sure issues are identified early and to build data quality expectations upfront in projects.
- Changes to business processes: Improving data quality depends on improving the processes by which data is produced. The Data Quality team needs to be able to assess and recommend changes to non-technical (as well as technical) processes that impact the quality of data.
- Funding for remediation and improvement projects: Some organizations do not plan for remediating data, even when they are aware of data quality issues. Data will not fix itself. The costs and benefits of remediation and improvement projects should be documented so that work on improving data can be prioritized.
- Funding for Data Quality Operations: Sustaining data quality requires ongoing operations to monitor data quality, report on findings, and continue to manage issues as they are discovered.
Employees need to think and act differently if they are to produce better quality data and manage data in ways that ensures quality. This requires training and reinforcement. Training should focus on:
- Common causes of data problems
- Relationships within the organization’s data ecosystem and why improving data quality requires an enterprise approach
- Consequences of poor quality data
- Necessity for ongoing improvement (why improvement is not a one-time thing)
- Becoming ‘data-lingual’, about to articulate the impact of data on organizational strategy and success, regulatory reporting, customer satisfaction
Training should also include an introduction to any process changes, with assertions about how the changes improve data quality.
