Some of your data is generated under your own roof. The rest arrives from brokers, customers, third parties and platforms you don't run. We help you tell the difference, work out where the real risk sits, and govern each kind on its own terms before it drives a decision.
Cleansing, lineage, stewardship, remediation. Useful work, but it asks what's broken rather than where the break entered the chain. By the time a defect shows up in a report, it has usually already shaped a decision.
You control some of it at the moment it's created. The rest turns up carrying someone else's assumptions, gaps and edge cases. Most governance frameworks happily treat both the same, which is where the trouble starts.
When inherited data is trusted as though it were homegrown, the risk goes quiet. It tends to stay quiet until it surfaces in a pricing call, a model output, or a regulator's letter.
Our starting point is simple. Data quality risk depends on where the data came from and how much control you actually had when it was created. Everything else, the lineage, the cleansing, the stewardship, sits on top of that.
So governance, assurance and investment priorities ought to be shaped by that source-control reality, not by where the data happens to live in your estate or what label someone put on it five years ago.
We help organisations build that picture properly, working from a first diagnostic through to governance readiness and, where it matters, AI input assurance.
Telling the data you own apart from the data you've inherited, and being honest about what that difference does to your real risk profile.
Looking at how data origin and processing state combine to create hidden risk categories that most frameworks never surface.
A structured way of checking whether your governance capabilities actually fit the source-control risks you're carrying right now.
For AI systems that depend on data the organisation didn't produce, where input quality and assurance need to be answered together.
Every engagement is built around the source-control reality of your data, not a generic framework dropped in from somewhere else.
A short, sharp engagement that maps your critical data domains by source, control, processing state and business reliance.
A broader look at how you're currently spotting, governing and controlling data quality risk across both internal and external sources.
A structured readiness review that checks whether your governance capability actually fits the data risks you're carrying, sorted by exposure rather than org chart.
Sector-specific work for insurers wrestling with broker, customer, third-party and telematics data flows and what they mean for governance.
For AI systems built on data you didn't produce. Covers both the business view (will it work?) and the regulator view (can you show your working?).
Short, focused sessions for leadership teams. Source-control thinking applied to your real questions, not generic AI or governance theory.
If you're accountable for data, risk, governance or AI in a regulated business, you've probably had a version of this conversation already. The people we work with usually sit in one of these seats.
Chief Data Officers, Chief Risk Officers, Heads of Data Governance and Data Quality leaders who need a defensible read on data risk before the next investment or audit cycle.
AI governance leaders, model risk teams and AI deployment owners dealing with systems built on broker, customer, vendor or platform data they don't fully control.
Insurance executives, underwriting, claims, pricing, fraud and compliance leaders, plus internal audit and regulatory affairs in regulated, data-intensive businesses.
Every engagement is built to leave you with answers you can act on, framed for the people in the room who actually have to decide.
A clear picture of where your critical data risk is being produced, where it's being inherited, and which sources are doing the most damage.
A straight answer on which risks are yours to manage at source, which ones are imported, and which ones quietly fall between the two.
Where accountability sits in the wrong place, where controls are calibrated to the wrong risk, and where assurance is being asked to carry weight it can't.
A sequenced view of what to tackle now, what to plan for, and what to leave alone, with the rationale you'll need to defend it.
A short, no-cost call to understand where you sit, what's prompting the work, and whether we're a fit.
A focused review of your critical data, scoped to the question you're trying to answer. Moves quickly.
A clear, defensible findings pack with prioritised recommendations and a target-state roadmap.
Ongoing advisory through implementation, board reporting cycles or regulator engagement, only if it helps.
Start with a short, no-cost diagnostic fit meeting. We'll work out together whether we're the right people to help, and what a useful first piece of work would look like.