Data & Analytics: Trusted Metrics in the AI-Assisted Era
Lakehouses, semantic layers, and governed self-service helped leaders trust dashboards when AI-generated summaries proliferated.
Data and analytics in 2024 had to do two jobs at once: give leaders trusted operational metrics, and feed structured, governed datasets into AI and RAG pipelines without duplicating definitions or leaking sensitive fields.
Modern data stacks
Cloud warehouses and lakehouse patterns (Snowflake, BigQuery, Synapse) simplified large-scale storage. dbt kept transformations tested and version-controlled. Power BI semantic models and certified datasets gave business teams self-service with guardrails rather than endless spreadsheet exports.
Data quality and governance
Garbage-in still meant garbage-out — especially when vector embeddings amplified bad source data. Column-level lineage, role-based access, and clear ownership of KPI definitions reduced conflicting numbers across departments. GDPR and internal policy required knowing who could see personal or financial data, and why.
Analytics meets AI
Curated embeddings and metadata made RAG answers more reliable. Observability for data pipelines — freshness alerts, anomaly detection — prevented silent drift from undermining decisions.
PrequaliQ helps organisations connect source systems, model data responsibly, and build reporting that teams actually use — not shelf-ware.