Our view on the sectors we work in most, and a running record of named engagements as they clear for reference.
Short, specific points of view from the sectors where we spend the most time. Not case studies — our own read on where the data problem actually sits.
Regulators are starting to ask examiners' questions about AI-driven decisions: where did this number come from, who approved the data behind it. Treating lineage as a compliance afterthought catches up with you eventually.
→ Read our viewThey fail at the data layer underneath it. Multi-agency data sharing, service digitisation and AI-driven citizen services all depend on a data maturity level most departments have never actually measured.
→ Read our viewTraffic, utilities and public safety systems increasingly make real-time decisions on live data feeds. Few operators can say with confidence which of those feeds they would trust under scrutiny.
→ Read our viewClinical and operational AI use cases live or die on explainability and governance, not model accuracy alone. The organisations moving fastest treated this as a data governance problem first.
→ Read our viewAs disclosure requirements tighten, the gap between a sustainability commitment and a defensible one comes down to whether the underlying data would survive an external audit.
→ Read our viewPredictive maintenance, crew scheduling and route optimisation all depend on datasets built for operations, not analytics. Readiness has to be assessed before the AI use case is chosen, not after.
→ Read our view