Data & Analytics: Trusted Metrics in the LLM Age
Cloud warehouses, semantic models, and governed datasets remained essential as organisations balanced dashboards with early analytics copilots.
Data and analytics in 2023 had to serve two masters: reliable operational reporting and exploratory questions posed in natural language. Leaders still expected dashboards they could defend in board meetings — even as teams piloted assistants over curated datasets.
Modern data stacks
Cloud warehouses — Snowflake, BigQuery, Synapse — and lakehouse patterns simplified large-scale storage. dbt kept tested SQL transformations in version control. Power BI semantic models gave business users self-service with guardrails.
Quality before copilots
Generative tools amplified existing data problems. Master data management, column lineage, and role-based access reduced conflicting KPIs. Organisations that certified datasets first got safer answers from early LLM analytics experiments.
Governance and GDPR
Knowing who could see personal or financial fields — and why — remained non-negotiable. Analytics engineering and privacy teams worked together before opening new AI interfaces to wide audiences.
PrequaliQ helps organisations connect source systems, model data responsibly, and build reporting that teams actually use — not shelf-ware.