Managing your Data Assets: The interview
summary
Company Things Solver describes two ongoing data governance projects: one cleaning product transaction tables, one ingesting semi‑structured HR and ATS feeds. Teams document schemas in Confluence, run SQL transformations with dvt, and record column metadata with dbt. Pipelines enforce breaking tests (stop runs) and warning checks, send change alerts to Slack via S3+AWS Lambda triggers, and prevent blind corrections. A client reports saving three man hours per day after these validations and transparency measures.
AI-written summary · about
chapters (6)
- 0:00Start
- 0:09Milos Milovanovich
- 7:40Data Quality
- 13:23Data Quality Validation
- 22:16What Are the Crucial Steps Needed To Be Done so the Organization Can Ensure the High Quality Data Governance
- 27:26What Was the Hardest Challenge You Had on Data Governance Project
related talks
16mTurning Data into Action: The Path to Successful Data Governance
37mData Swamp to Data Swimming Pool: Cleaning up the Mess
23mData Team as an Optimisation Problem
42mHow Data & Software Eng. Teams Collaborate to Ensure Smooth Data Integrations
27mData team strategies: unleashing your team
27mLive in the Data Wild West: The Data Contracts Sheriff
38mKeeping pace with data infrastructure so it doesn't outrun you
18mData quality: the key to long term happiness