As a data governance leader, how can you ensure that data quality is maintained throughout the organization?

Defining metrics and dimensions is one thing, but keeping them relevant requires periodic assessment to ensure that the data is of high standard, and the current measures in place are detecting quality issues efficiently and correctly, with minimal false positives. The report should be flagged to the relevant team for action. The business criticality and the scope of impact basically determine how robust the quality measures need to be and how much automation to bring in to apply the fixes.

Within an organization, use automated tools for continuous monitoring, validation, and data standardization to ensure ongoing accuracy. Regularly track quality metrics and generate progress reports. Along with treat data quality as an ongoing process, continually refining practices.

If maintaining data quality is only Chief Data Officer’s (CDO’s) responsibility, it’s almost guaranteed that it will fail. How would you ensure that data quality is maintained throughout the organization? The answer is shared responsibility. 1. The first step is to ensure that metrics related to data quality are tied to goals for all levels of leadership in lines of business. 2. Place the data talent in key data positions throughout the lines of business. Data steward and data owners should be the ones who understand the data and know how to manage it. 3. Establish baseline requirements based on the criticality of the data in policies and procedures. 4. Monitor the effectiveness of the data quality control.

Picture a scenario where executives from a leading bank engage in discussions with experts, focusing on the criticality of data accuracy. Together, they define data quality dimensions and metrics tailored to the bank's objectives. For instance, they emphasize accuracy in customer account balances and completeness in loan application data. By aligning these metrics with their strategic goals, they ensure that every piece of data contributes meaningfully to their operations.

Within the dynamic realm of banking, leadership recognizes the pivotal role of dedicated data stewards in upholding integrity across our organization, aligning with the stringent requirements of privacy laws such as General Data Protection Regulation (GDPR). These stewards serve as passionate advocates for data quality, ensuring adherence to GDPR regulations alongside industry best practices. By clearly defining roles and responsibilities, we foster a culture of accountability and collaboration, reaffirming our unwavering commitment to excellence in data management while prioritizing compliance.

Building and maintaining data quality is about nurturing a culture that values it at every level. Culture eats strategy for breakfast - every time. Data literacy plays a pivotal role here. It's all about fostering a sustainable data culture. John Ladley's definition of Data Literacy resonates deeply: "Everyone instinctively thinks about data as part of their jobs." This mindset shift is what we aim for—a workplace where understanding and utilizing data become second nature to every individual. It's through this kind of collective effort that an organization can ensure that data quality becomes a cornerstone of organizational success.

Improving data quality is key to ensuring that the data remains fit for purpose. To maintain quality, it’s important to have a continuous improvement cycle to assess metrics, identify issues, and apply fixes at the ‘MASTER’ source so that all downstream applications consuming the data benefit from the fixes. Having good lineage is important to trace back to the master source. Prioritization of fixes is based on the criticality of the attribute and the scope of impact. It’s good to involve business and stakeholders to decide when to fix based on monetary or brand value impact.

In addition to the aforementioned steps, establishing data standards and policies plays a pivotal role in ensuring data quality. By defining precise guidelines for data collection, storage, usage, and management, organizations create a solid foundation for crafting accurate data quality (DQ) rules that are aligned with their specific needs and objectives. This not only fosters consistency and reliability in data processes but also facilitates effective decision-making and compliance with regulatory requirements.