Importance of Data Maturity before embracing AI
summary
Claims that AI succeeds only after investing in data accessibility, data integration and governance, backed by concrete infrastructure steps: ingestion, block storage (HDFS/Ceph/MinIO), processing (Spark/Flink), a warehouse, then ML ops on Kubernetes with GitLab CI. Notes most companies juggle 15+ sources and that cloud bills often run ~$20K/month so on‑prem or managed one‑command deploys (DataSleeve) and dedicated nodes with ~$100/node/month keep costs down and prevent schema failures via data contracts.
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chapters (11)
- 0:00Data maturity and AI
- 1:08Poor data quality in AI
- 3:55Skipping data maturity for AI
- 6:03Defining data maturity
- 8:28Reliable data infrastructure
- 11:23Cloud vs on-premise
- 14:32DataSleeve and cost efficiency
- 16:19Q&A
- 18:28Customer data maturity awareness
- 20:13Q&A
- 22:45Data maturity journey
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