In the world of “data everywhere”, Data Governance is becoming even more important. Organizations that develop a data warehouse ‘single source of truth’ need data governance to ensure that a Standard Business Language (SBL) is developed and agreed to, and the various sources of data are integrated with consistent and reliable definitions and business rules. Decisions around who can use what data, and validating that the data being used and how it’s used meets regulatory and compliance requirements are important.
As the enterprise data management solutions grow and broaden to incorporate Enterprise Application Integration (EAI), Master Data Management (MDM), increasing use of external data, real time data solutions, data lakes, cloud, etc., Data Governance is even more important. While there may be value in having data, if it’s not accurate, no-one can use it, and it isn’t managed, the value of the data (wherever it resides) diminishes greatly.
The foundational and implementation activities needed to initiate and successfully scale a Data Governance capability remain the same:
- a discovery phase to assess sentiment, define the current and future data landscape, identify stakeholders, prioritize opportunities (and business value) and focus areas, and start to develop goals and a Data Governance roadmap
- a foundational implementation phase to put the organization around data governance in place, communicate and educate stakeholders, secure executive support, define metrics for success, and begin with an initial project, process or data set
- a scalable implementation that includes tools, workflows, and a focus on continuous improvement
Upcoming articles will describe approaches to each of these phases. Working through these phases with the desired future state in mind, and with a high level roadmap to get there, will provide you with a greater probability of establishing a data governance capability that will scale in the long run.
This article originally appeared in Nancy's blog on CIO.com.