Analytics & AI

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.

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.