Data governance is often perceived at being a thankless but necessary responsibility. All the more reason to systematize its practices within the organization. By relying on data governance software, much of the mundane data tasks can be corralled and possibly automated based on the application. Then by following organizational best practices, companies can reduce the amount of time paid to data governance while maximizing its overall impact.
Establish and meet company goals — Beginning by understanding the data goals of companies begins with senior leaderships view of what they want from their data. Typically this means better data but also cheaper. Senior leadership needs to increase data value by developing trusted data sources with relevant data, becoming transparent, streamlining process and reporting, monitoring overall data quality, complying with regulatory and audit needs, and investing in automation technology to remove potential errors and speed data analysis.
Establish and reach team goals — Data teams must focus on value creation using data analysis. This means something deeper than overcoming problems, it means finding root-causes and fixing those, and forecasting likely futures. To ensure this value, companies need to adopt data cultures supported through communicating topics about data governance and its value.
Data governance tactics — Two tactics stand out in data governance best practices, the identification of a data governance champion, a senior leader who can provide the energy to move data governance forward and energize teams, and senior level steering committees that establish data policies, standards, and procedures.
Data governance tools — Five tools are identified in generic form that dramatically aid data governance:
- Glossary of terms that describe standard data definitions and documentation, exceptions explanations, synonyms, or variants, etc.
- Repository of metadata all departments understand what data they are looking at.
- Data lineage ties together where data is from, where it has been, and how it has been used. Lineage is essential in making meaning of data as an asset.
- Data quality rule writers are applications that reconcile data by setting rules for managing data.
- Analytics and reporting tools such as trackers, monitors, reports, and metrics provide different analytical data views for different groups helping them in their day-to-day operations.
Communication specific and constant — Communication is essential for effective data governance. To be effective, however, different role levels require different level metrics. Senior management needs to see high level metrics without being bogged down in details. Mid-level managers need both the high-level that senior managers see, but also more depth of detail. Data scientists may see exceptional detail, months even years of data details. Likewise, data governance analysts will need a similar detailed view within the domain of dataflows and operational processes in order to investigate root-causes.
Data governance team structure — Five key roles will emerge based on data governance within organizations, though, a staff member may be responsible for multiple roles. Data leaders are responsible for vision and data strategy, while project managers, communication specialists, business-facing positions, and technical positions all work together to push data initiatives.
Make governance a trusted partner — Data governance can be seen as a costly, time consuming burden, but it should be designed as a value generator from the beginning. Avoid reinventing the wheel when it comes to processes and procedures. Promote data focused organizations and teams.