Two numbers for one measure. Three records for one customer. No name on either dataset.

The arguments that bring people to this page

None of these arrive described as a governance problem. They arrive as an argument nobody can win, usually in front of somebody senior.

1. 01Two teams quote different figures for the same measure

   The meeting settles it by seniority rather than by evidence, because neither figure can be traced back to what produced it. The next meeting settles it again, differently.

2. 02A nightly job has been quietly dropping rows

   Nothing failed. The load reported success every night. It surfaced when somebody happened to compare a total against a source system by hand, for an unrelated reason.

3. 03A field changed meaning upstream and nobody was told

   A status code was reused, a currency column started arriving in a different unit, an optional field became mandatory. The first people to find out were in a board meeting.

4. 04Nobody can name the owner of a dataset everyone depends on

   It has been in production for years. Three teams read it, two write to it, and the person who built it has left. Changing anything about it is now nobody's decision to make.

5. 05The same customer exists three times, with three spellings

   So does the same supplier, and the same SKU under two part numbers. Every count is slightly wrong and every join loses rows that nobody can account for afterwards.

6. 06Nobody can answer where a number came from

   Not because the answer is complicated, but because getting it means opening a chain of jobs and spreadsheets, then asking a person who is on leave.

If three or more of these are true, what is missing is agreement between your systems, not another report built on top of them.

Where agreement is won, and where it is lost

Two systems do not start disagreeing in the report. They start disagreeing at a boundary, months earlier, where one of them handed over a field whose meaning nobody had written down.

1. 01The definition

   What counts as a customer, when an order becomes revenue, which day a month ends on. An organisation can hold several answers at once, with no record of who chose any of them.

2. 02The contract at the boundary

   The shape, the types, the nullability and the meaning of what one system hands to another, written down and checked on arrival. Without it, an upstream change is discovered downstream, by a person, in a meeting.

3. 03The check that can stop a run

   Quality rules that fail the load rather than warn in a log nobody reads. A warning is a decision to carry on with data you have just been told not to trust.

4. 04The named owner

   One person per dataset who decides what its fields mean and approves changes to them. A team name is not an owner. A distribution list is not an owner.

5. 05The reconciliation

   Systems that are each certain they are right, compared on a schedule, with the differences listed individually rather than netted off into a figure that looks close enough.

6. 06The change process

   What happens when a definition genuinely has to move: who is told, what gets versioned, and what last year's reported number becomes now that it was produced a different way.

Each argument traces back to a control nobody put in

The complaint is always about a number. The fix is always a control at a boundary, one layer away from where the argument is happening.

Left, the argument as it is described to us. Right, the control that was never put in. These controls tend to be missing together rather than one at a time, which is why putting one of them in on its own rarely settles the argument.

These are not six products. They are the six ways systems stop agreeing, and they turn up tangled together in the same engagement.

1. ContractsWritten where one system hands over to anotherAt the boundary

   Shape, types, nullability and meaning for every field crossing a boundary, checked on arrival rather than assumed. When an upstream team changes something, the contract breaks in their pipeline, which is the correct place for it to break.

2. QualityChecks that stop a run instead of writing a line in a logFail, not warn

   Counts against expectation, referential integrity, ranges, freshness, and the business rules that actually matter. A failed check halts the load and reaches a named person. Warnings accumulate somewhere nobody has opened since the week they were built.

3. OwnershipA person per dataset, not a team mailboxOne name

   Every dataset more than one team depends on gets a named owner who decides what its fields mean, approves changes and answers questions about it. We write the register and get the names agreed while everybody is still in the room.

4. Master dataThe same customer, supplier or SKU, onceOne record

   Matching and merging rules for the entities that exist several times over, a survivorship rule deciding which value wins when two records disagree, and a review queue for the pairs the rules are not confident about. Nothing is merged on a hunch.

5. ReconciliationBetween systems that are each certain they are rightRecord by record

   Scheduled comparisons that list differences individually rather than summarising them into a percentage that reads as close enough. The point is to find the differences while there are still a handful of them, not after most have been invoiced.

6. LineageAn answer to the only question Finance ever asksWhere it came from

   A trace from the figure on the screen back through every job, join and definition that produced it, plus the change process for when a definition moves. This is where this work meets our pipeline and storage practice.

   See the data engineering page ↗

Three grades of check, and only one of them stops anything

Your platform probably has quality checks already. If they are grade one, the problem walked straight past them.

1. Grade 1A warning in a log

   It runs, it writes a line, the pipeline carries on. Useful for a short while after somebody builds it, then invisible. A check nobody is subscribed to is not a control, it is material for the post mortem.

2. Grade 2An alert to a named person

   Somebody is told and can act. The right grade wherever stopping the load would cost more than the bad data does. It only works if the person is named in advance and knows they are the one on the hook.

3. Grade 3A gate that fails the run

   The load stops, and downstream consumers keep yesterday's data rather than being handed today's wrong data. Reserved for the checks where wrong is worse than late, which is how anything that gets reported outside the building tends to be weighed.

Deciding the grade for each check is the argument worth having, and it belongs to the business rather than to us. Grade three on everything stops the platform so often that somebody switches the gates off, which leaves you with nothing.

How a disputed measure actually gets settled

Both figures can be correct at once. They answer slightly different questions, and nobody has written the two questions down.

1. 01Have both numbers produced in front of us

   Not described, produced. Whoever owns each figure runs it while we watch, from wherever they really run it, including the spreadsheet step they were not going to mention.

2. 02Trace each one back to raw records

   Job by job, join by join, filter by filter. A dispute can end here, at a date boundary or at a filter somebody added for a sound reason and never removed.

3. 03Write down where the two definitions diverge

   In plain sentences. Cancellations included or not. Returns netted or not. The month closed on the calendar or on the ledger. The currency converted at which rate, on which day.

4. 04Decide which one is the reported figure

   By the people whose decisions depend on it, not by us and not by whoever is most senior in the room. The other definition can survive under a different name, because it was answering a real question.

5. 05Put the decision somewhere a query can reach

   The agreed definition goes into the layer that produces the number. If it lives only in a document, the next round of the same argument is already scheduled, it just has not been booked yet.

6. 06Make the superseded version hard to produce by accident

   The old logic is removed or renamed, the extract that used it is retired, and a check goes in that would have caught the divergence. Otherwise it returns the first time somebody is in a hurry.

One customer, several records, and the rule that decides

Deduplication is the part everyone underestimates, because the hard bit is not the matching. It is agreeing in advance what happens to the fields that disagree.

An illustration of the shape, not a real client's customer list. The names change every time, the three outcomes do not: some records merge on a hard key, some go to a person because the rules are not confident enough, and one turns out to be a different company that happens to share a name. The greyed line is the merge we refuse to make.

The same three phases as every Woodfrog engagement, with the definitions argument started early because it has the longest lead time in the whole programme.

1. Week 1The audit, fixed fee

   Two calls. We take one disputed measure and one shared dataset and follow both back to their sources. You get a one-pager naming what is broken, what it is costing you and the order to fix it in, and you keep it whether or not we go further.

2. Weeks 2 to 6Contracts, gates and owners go in

   A pod of three. Contracts on the boundaries that break most often, gates on the checks where wrong is worse than late, and an ownership register with real names against it. Across our engagements the median time to a first production-grade artefact is six weeks.

3. Weeks 2 to 6The disputed measures get settled in parallel

   Not after the engineering, alongside it. Getting a room to agree what a month end is takes longer than writing the check that enforces the answer, so it starts first.

4. Week 6Reconciliation runs without anyone starting it

   The comparison between systems is on a schedule, and the differences arrive as a list with owners against them rather than as a percentage on a dashboard that nobody can act on.

5. Week 7 onwardOperate

   Quarterly reviews and on-call governance. Definitions drift because businesses change, and the review exists to catch that drift while it is still a conversation rather than a discrepancy in front of a board.

Why this is faster than the last time you tried it

Two things shorten the work: harnesses that have already run in production, and Claude doing the reading that used to consume the opening weeks.

1. Already builtHarnesses we bring with us

   Contract checkers, quality gate runners, duplicate-matching rules and reconciliation comparators, proven in production elsewhere and fitted to your systems rather than written again from an empty repository.

2. AI first passReading what nobody documented

   Claude reads schemas, job code, stored procedures and the report definitions that quietly contradict each other, then drafts candidate definitions and flags the contradictions it cannot resolve. Those flags are where the useful conversations start.

3. AI first passProposing the checks from the data itself

   Candidate quality rules are drafted from what the data actually does rather than from what a template suggests, then argued down to the few worth failing a run for. Proposing is cheap. Deciding the grade is the work.

4. Human callEvery decision that carries weight stays with a person

   Which definition is the reported one, which duplicates merge and which are separate companies, what fails a run and what only warns. AI does not make those calls here, and we will not pretend otherwise on a call.

5. What you keepYours to run without us

   Contracts, checks, the ownership register, the reconciliation jobs and the harnesses, documented and versioned in your repositories. If your team cannot change it without us in the room, we have failed at the part that matters.

What we have actually delivered

There is no published case study for this pillar yet, so these are firm-wide delivery figures. On the call we will say which of them came from work shaped like yours.

We have not published a case study for this pillar

There is no named client story on this page. That is worth explaining rather than papering over with somebody else's number.

1. What we can sayFirm-level figures, not a governance result01

   Woodfrog has delivered for 20+ companies across 50+ projects since 2023, from Pune, working across India, APAC, the UK, Europe and the US. The median time to a first production-grade artefact is six weeks.

2. What we will not doBorrow a result from another pillar02

   The recovery and margin figures published elsewhere on this site belong to one APAC retail grocery engagement and stay on the pages for that work. Reprinting them here would tell you nothing about whether we can get your two teams to agree on one figure.

3. What is claimed hereMethod, not a measured outcome03

   Everything above describes how the work is done: what gets contracted, what fails a run, who owns what. None of it is presented as a result we measured on a governance engagement, because we have not published one.

4. When there is oneThe bar a case study has to clear04

   A named case will appear here once a customer agrees to be named and the numbers have been through their own Finance team. That is the bar every other figure on this site cleared, and we are not lowering it for a page that needs one.

The ones that come up on almost every first call.

• A data test nobody acts on is worse than no test at allMost test suites fail quietly, by being read less each week. The useful cut is not which tests are correct but which ones have a named owner and a first move.
• A pipeline is only as good as its worst backfillDaily runs prove almost nothing. The real test is the morning you find six weeks of wrong data and have to rebuild history while everyone is still reading it.
• Reading change data out of Postgres without slowing the application downLogical replication looks free until a slot stops being consumed and the write-ahead log quietly fills the disk on your primary.