Data governance
summary
Panelists report concrete practices: banks keep sensitive sets on-prem while translating policies for cloud migration, and teams avoid big‑bang moves by doing one-slice-at-a-time migrations; organizations build a CDO-led operationalization with data stewards moving into product roles; teams run automated scan to map data tools first, then add lineage and metadata; a gamified crowdsourcing app is used to collect and clean records and shows clear uptake; success metrics focus on adoption rate of policies, % of lines-of-business compliant, and value delivered by top datasets.
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chapters (12)
- 0:00Start
- 3:41Value of Data Governance in Analytics
- 12:45What Lessons Were Learned and What Works in Terms of Security and Cleanliness of Data and the Best Practices
- 20:40Gamification
- 26:18Does Data Governance Restrict Agile Practices
- 28:24What Are the Data Governance Changes Needed by a Company When Moving a Data Lake from on-Prem To Cloud
- 32:28Data Governance Where Does It Start
- 35:14What Are some of the Critical Kpis for Data Governance
- 38:34Critical Data Elements
- 40:55Is There a Sequence of Steps and Processes in a Particular Order To Be Executed before Implemented Data Governance like Building Data Lineage Data Metadata Data Management Layer Identifying Existing Data Quality and Finally Building Data Governance Rules
- 42:23Building Data Lineage
- 44:59Why Why Do You Need a Data Governance
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