Best Practices for Engaging Non-Data Experts in Data Quality Efforts
Appoint senior-level business data owners who will be accountable for data quality within their respective areas, even if not directly responsible for managing data quality measures.
Establish a data council consisting of heads from different business lines, including Data Protection Officers, Data Architects, Information and Security Officers, and Project Management Officers. This helps cascade data governance and quality efforts throughout the organisation
3. Tie Data Quality to Business Goals
Demonstrate the impact of data quality on business objectives, emphasising tangible outcomes like potential financial losses resulting from poor data quality.
4. Have Stakeholder Champions
Identify executive leaders and subject matter experts who can highlight the problems caused by bad data quality and the benefits of improving it. Use them as drivers for data quality initiatives and involve them in translating improvements into departmental terminology.
Address stakeholders’ pain points by explaining how improving data quality can resolve their challenges, such as enhancing operational efficiencies and decision-making.
6. Make it Easy, Fun, and Rewarding
Use technology for gamification and competition to engage stakeholders in data quality processes. Tailor the approach to the organisation’s culture and measure what teams and individuals can directly influence.
Successful Methods to Measure and Present Data Quality in Business Terms
1. BKPIs for Data Quality
‘Incident Severity Level’: Measure data quality consistently and granularly by associating data quality rules with severity scores or scales based on their importance to the business.
‘Age of the Issue’: Measure data quality by assessing how long data issues take to be resolved, aiming for shorter resolution times for critical data elements.
Tailored KPIs: Work with data owners to select metrics that align with the organisation’s priorities.