Maintaining high-quality data is becoming more and more important for any organisation. According to Gartner, poor data quality can cost organisations around $12.9 million a year! However, many organisations also find themselves stuck in a cycle of reactive data quality measures, which often lead to short-term fixes rather than long-term solutions.
In today's blog, I will explore how to shift from reactive to proactive data quality management by leveraging a Data Governance framework.
Most organisations nowadays recognise the importance of data quality. They most likely have data cleansing routines as data is loaded into data warehouses. However, these efforts are typically tactical fixes addressing issues only when they are detected. For example, missing fields might be defaulted to a placeholder value, which may be better than an empty field, but does not ensure that the data is correct.
Proactive data quality involves preventing data issues from occurring in the first place. This shift requires more than just addressing problems as they arise. It means having a strong approach to managing data quality, which can be achieved through Data Governance.
Implementing a Data Governance framework is crucial for proactive data quality. Data Governance establishes the roles, responsibilities and processes needed to manage data quality consistently across the organisation. It ensures that data quality is maintained at the source, reducing the need for repeated data cleansing and enabling more reliable data usage.
Data Governance is a massive support towards achieving proactive data quality rather than reactive. See below for some key steps in using Data Governance to make this happen.
Steps to Proactive Data Quality Through Data Governance
1. Get Buy-In from Stakeholders - You will need to encourage senior stakeholders to understand and support the need for Data Governance. To do this, align your Data Governance goals with the organisation's strategic objectives to demonstrate its value.
2. Identify Data Owners and Stewards - These individuals are accountable and responsible for the data quality for their data.
3. Define Data Quality Standards - Next, work with the Data Owners and Data Stewards to establish clear data quality criteria.. This involves defining what constitutes acceptable data quality and setting rules for data entry and processing.
4. Implement Data Quality Processes - Use the data quality rules to develop and implement processes for data quality reporting and issue resolution. Regularly monitor data quality and report any issues to the Data Owners and Data Stewards for resolution.
5. Create a Data Glossary/Catalogue - Develop a Data Glossary that includes definitions and business rules for all critical data elements. This helps ensure consistency and clarity across the organisation.
6. Establish a Data Governance Committee - Form a committee that oversees the implementation of Data Governance policies and procedures. This committee should regularly review data quality reports and address any escalated issues. Read my previous blog on Data Governance Committee’s here.
It's true, that transitioning to proactive data quality is not an overnight task, but it is essential for long-term success. By implementing a Data Governance framework, organisations can ensure that data quality is managed proactively, leading to more reliable data and better business outcomes. Remember, Data Governance is not just an add-on; it is the foundation that supports all your data quality initiatives.