Data Governance & Data Quality
Obtaining and maintaining credibility is one of the key focus areas of any Data Governance Team. The type of credibility a data governance team looks for is that they help ensure that the data being entered into a system is complete, accurate, and understood from an enterprise viewpoint. To manage the lower-level data quality, the birth of a Data Quality Team is usually necessary. This team would consist of representatives from various departments within the enterprise. Likewise, a Data Quality Team can be composed of totally different sets of people who will check for quality from the outside looking in.
A Data Quality Team is tasked to use software, tools, analysis, and historical knowledge to check for errors within the data structure, ensure that the data being entered is foolproof and ultimately, ensures that data updates are correct and can be pushed live after new sets of information come in.
The following are the main focus areas of a data quality program:
- Raw Data
- Classified Data
- Collected Data
- Data needed by data stakeholders and data stewards
Because of the increasing demand for accurate data, another role of the Data Quality Team is to stay in constant communication with the various departments within the organization and come up with quality-related procedures or initiatives that will help the enterprise in a global way.
The Data Quality Team is accountable for the following:
- After gathering all data quality processes/initiatives from the various departments, it is the main responsibility of the Data Quality Team to consolidate these and make it into one large, fluid process that can be applicable to all
- This team needs to ensure that quality-related updates are being disseminated to concerned and affected departments
- This team must ensure that there are no quality gaps, inconsistencies, or variances where data quality is concerned within the system
- Quality-focused goals should be streamlined to match that of the enterprise’s mission/vision