All successful data quality management programs include data governance practices, policies, and processes. Every successful data quality program is aligned with a strong data governance practice.
Data quality is like the weather: many people and organizations talk about it but few are willing to address its challenges. Data governance can play an active role in the improvement of an organization’s data quality. A strong data governance program can support an organization’s efforts to develop a permanent approach in the improvement of the organization’s data quality.
Challenges to Organizational Data Quality
Poor data quality abounds; every organization can recite many instances where a data quality problem caused extra work, lost revenue, higher costs, production problems, etc.… However, many companies struggle to address and resolve their data quality issues from an enterprise data management perspective. “If you talk about the data, you’re talking about the integrity of the business,” says Philip Say, an SAP director of solution marketing for ERP and finance applications. “Companies are grappling with understanding what they have really created — what’s really running their business.”
Awareness of bad data is on the rise, and the practices needed to address data integrity issues across the enterprise are now available through the development of strong enterprise data governance programs. Using the best practices of data governance can enable an organization to face their data challenges and improve the quality and usability of their data permanently, since many data governance processes are focused on improving data and metadata quality in source systems and in analytical solutions (data warehouses, data marts, etc.)
Historically, companies have allowed IT to shoulder the burden of correcting data quality problems, despite the fact that many data quality issues arise in the business and not from information technology issues. Many companies “solve” the data quality problem by having large numbers of staff extract data from various sources, parse it and re-key it into spreadsheets so management could have some analytical data. This process does not resolve any data quality problems, and may create additional errors made in the parsing and re-keying of the source data.
Regulations, Data Quality, and Data Governance
As compliance regulations, such as the Sarbanes-Oxley Act, HIPPA, GDPR, etc., have emerged, the ultimate responsibility for accurate data has shifted from IT to business leaders. The development of an enterprise data governance program provides the organization with the opportunity to establish and implement processes for fixing the data quality problems of the past and avoiding new data quality errors.
When organizations examine data management and data governance issues, they discover two major problem areas: responsibility and management. Data governance professionals and the business data stewards form the foundation of the data governance program. Together, they are responsible for the data instances (values) and the metadata (definitions and context) for that data – the two parts of data quality management. Therefore, it is important that any data governance program include the design and implementation of a data quality improvement practice, one that involves business data stewards from each subject area.
Data Quality Program and Data Governance Requires Organizational Change
The change in responsibility for data quality is also a change to the organizational culture, driven from the executive layer. Executives and business managers must understand the scope of the data quality problem and support the development processes that combine data governance with data quality activities. Once executives are committed to this approach, the entire organization must be made aware of the effort, the focus on data quality and its alignment with data governance, and each staff member’s responsibility for data quality improvement. Bad or missing data elements create chaos and poor results, and possibly economic disaster. Having a strong data stewardship program with business data stewards who have been trained in the concepts and practices of data governance and data quality improvement will help to instill a “data quality first” culture in all business units.
Conclusion
If an organization wishes to improve its data quality and maintain that improvement, it must start with the foundation of a robust data governance and data stewardship program. Aligning all data quality improvement and management efforts with data governance will ensure continued success and accurate, actionable data and information for any organization.
