Conventional data governance practices come from a simpler time when data management was free of many of today’s challenges, such as self-service reporting and analytics. Such practices focus on enforcement of controls and gates, which will continue to be necessary.
However, these methods must be complemented with support for the autonomy and agility of the self-service world. Enforcement must work together with prevention. Guides and guardrails must reduce the need for gating. The need to exercise control is minimized when curating, coaching, crowdsourcing, and collaboration are integral parts of governance processes.
In a self-service world, every data stakeholder plays a part in data governance.
You Will Learn
- Where governance fits within modern data ecosystems, from point of ingestion to reporting and analysis
- How various technologies support governance through the ecosystem
- Process challenges, including how to supplement controls with collaboration and crowd sourcing
- Engagement models for governing self-service
- Organizational challenges, such as moving from data stewards to stewardship, curation, and coaching
- Operational challenges for governing self-service, including implementing a combination of gates, guardrails, and guides
- Data governance officers
- Data stewards
- Compliance and risk officers
- BI and analytics program and project managers
- Everyone with data management responsibilities in a self-service organization