Conventional data governance practices come from a simpler time when data management was free from many of today’s challenges, such as big data, data lakes, and self-service reporting and analytics
Traditional data governance focuses 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 works together with prevention. Guides and guardrails reduce the need for gating. The need to exercise controls is minimized when curating, coaching, crowdsourcing, and collaboration are integral parts of governance processes.
In the modern analytics 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 for governing self-service; supplementing controls with collaboration and crowdsourcing
- Engagement models for governing self-service
- Organizational challenges for governing self-service; moving from data stewards to stewardship, curation, and coaching
- Operational challenges for governing self-service; implementing a combination of gates, guardrails, and guides