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Anthropic has published an official integration between Claude and Databricks — giving teams the ability to ask questions of their data in plain English. Here is what your environment needs to look like before that capability actually works.

Most AI initiatives don't fail because of the technology. They fail because the data underneath it was never ready. Here are the six signs your organisation has a data problem — and what to do about it.

67% of businesses use spreadsheet software daily. 94% of those spreadsheets contain errors. The consequences — financial restatements, compliance exposure, decisions made on incorrect data — are well documented. This article examines the shift to self-service analytics and modern data platforms: what the transition involves, what dependencies it creates, and why the effectiveness of any analytics tool ultimately depends on the reliability of the data foundation it sits on.

Boards are asking about AI. Vendors are promising AI-powered everything. But between the enthusiasm and the operational reality sits a question most organisations have not yet answered honestly: is our data good enough to trust with AI? This article examines what AI readiness actually requires across five practical dimensions — and what it looks like when those dimensions are missing.

100 essential terms, explained in plain language for business leaders — not developers. Top 100 Data & AI Terms 2026 The language of data and AI is evolving faster than most organisations can keep up with. New terms appear weekly. Familiar ones get redefined. And the gap between what technology vendors say and what things actually mean keeps widening. This glossary cuts through the noise. A sample of what's inside: Hallucination — When an AI confidently generates information

APIs are the connective tissue of the modern business — the invisible connections that keep your systems talking to each other. Most business leaders have never had the concept explained without jargon. This guide does exactly that, and explains what breaks when APIs are not managed properly.

Most organisations are trying to do analytics before their reporting is reliable, or deploy AI before their analytics is mature. This guide explains what each capability actually is, how they differ, and the sequence that determines whether any of them will work.

Every organisation wants to use AI. Most are not ready for it — not because of the model they choose, but because of the data underneath it. This guide explains why a governed data foundation is the prerequisite for any meaningful AI initiative, and what organisations consistently get wrong by skipping it.

If your business runs on more than five software platforms, your analysts spend more time collecting data than analysing it, or your reports tell different stories depending on which system you look at — this guide was written for you.

Aging ETL tools and integration scripts create six compounding costs that most organisations absorb without realising it — until a key person leaves, a system update breaks everything, or an auditor asks a question nobody can answer.

Most organisations discover integration problems after something breaks — a failed payroll sync, conflicting reports, or a compliance gap nobody noticed. This guide explains what integration risk actually is, the five warning signs your business is exposed, and what good looks like