Analytics & AI

A $600,000 Salesforce contract, cancelled in two months. Enterprise transformations that run for years, weighed down by governance nobody could shortcut. Having worked inside both, I know why these aren't really the same debate — and that gap is the part worth talking about.

Databricks Genie moved to pay-as-you-go billing on 6 July 2026. This article sets out what the change requires of data team leaders, and what it requires of data engineers, analysts, and data scientists who use Genie directly, within a governed Data Integration, Analytics, and AI capability

Data and analytics job postings are down twice as much as the broader tech market. At the same time, engineering hiring overall is resilient, and AI agents now build the majority of new databases on major platforms. Here's what the actual data says is happening

Tokenmaxxing — the practice of treating AI token consumption as a measure of productivity — emerged from Silicon Valley in early 2026. It is now showing up in data engineering teams across industries, and the costs are less visible but no less real

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.