Data & AI Insights
Perspectives on data governance, semantic modelling, data engineering and agentic AI — refreshed from our research desk. Sharp, current and built for people who ship.
Scaling Agentic AI: From Pilot Projects to Enterprise-Wide Deployment
Most agentic pilots stall before production. The ones that scale share bounded scope, governed data and evaluations built in from the first sprint.
Establishing Modern Federated Data Governance to Enhance AI
Federated governance moves ownership to domain teams without losing central standards, the model that keeps AI initiatives both fast and auditable.
Establishing a Governed Semantic Layer to Unify BI Metrics and AI
A governed semantic layer gives BI metrics and AI one shared definition, so dashboards agree with each other and language models stop inventing numbers.
Establishing a Robust AI-Agent Engineering Capability for Success
AI-agent engineering is becoming its own discipline, part data engineering, part evaluation, part operations. Here is the capability worth building for.
Implementing a Data Products and Data Mesh Operating Model
A data mesh gives domain teams ownership of data as a product, with central standards for quality, discovery and access instead of a central bottleneck.
Operationalising EU AI Act and India DPDP Compliance for Data Teams
The EU AI Act and India's DPDP turn data protection into engineering work: classification, lineage and retention rules your pipelines have to enforce.
Harnessing the Model Context Protocol to Integrate AI with Enterprise Data
Connecting AI agents to enterprise data through the Model Context Protocol is essential for maximising operational efficiency and decision-making.
Harness Knowledge Graphs and GraphRAG to Reduce AI Hallucination
Vector search retrieves similar text; GraphRAG retrieves relationships. Grounding a model on a knowledge graph is what makes its answers traceable.
Mastering Dimensional and Data Vault Models in the Medallion Lakehouse
Data Vault integrates and historises; dimensional models serve consumption. In a medallion lakehouse you need both, at different layers of the stack.
Implementing ELT on an Open Lakehouse with Apache Iceberg or Delta Lake
ELT on Iceberg or Delta lets you land data first and model it later, keeping storage open and query engines interchangeable as requirements change.
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