Your pipeline numbers are probably right. Can you prove it?
Being right and being able to show your work are two different problems. The second one arrives when somebody outside your team asks where the number came from, and the answer is that a person typed it and no one can say which person, or when, or from what. A number nobody can trace is a number nobody has to believe.
Pick the room. Trace one number.
Four links behind any figure on any deal, and the chain is only as good as the weakest. Flip the record and watch which links hold.
Last quarter this said 2.1. Is that the same 2.1?
Show us how these twelve deals were categorised, and when.
That was closed in March. It is showing April.
What controls are there on changes to this data?
The as-of is whenever the query was run.
Sometimes a last-modified-by that survives until the next import.
Nobody builds a history for a database they already trust.
A kind of paper trail, and it resigns when they do.
From the system, not from the person presenting.
Recorded whether or not anybody asked.
A figure from March is retrievable in September.
Attached, because the source is what created the row.
One link of four holds. The other three are somebody's memory, and memory is fine until that somebody hands in their notice.
Four of four hold, and none of them needed a person to decide to keep it.
A quiet year, and a year with one real request in it
A model with every input printed, in hours. The totals are derived from the rows.
A quiet year
- 6 hours on traceability questions off the monthly pack
- 10 hours on forecast and commission disagreements
- 16 hours on one-off asks from finance, a customer or a partner
A year with diligence in it
- 32 hours, because everything above still happens
- 38 hours on the request itself, in a six week window
- No figure at all on reconstructing history that was never kept, because it cannot be bought afterwards at any price
Two things you can check yourself, and one you cannot
The first two are the vendor's own claim, read September 2026. The third is structural and needs no citation.
- 01Field level history is kept by default, and exports.
HubSpot keeps a history on every property and will export it, so a figure carries the person or rule that set it and the date it was set. That single capability answers two of the four links above, and it is on by default; nobody had to decide to build it. A homegrown record almost never has it, because you do not build a history for a database you already trust. HubSpot property history
- 02The record can be built from the work rather than typed after it.
Day.ai creates companies, contacts and opportunities out of connected email and calendar, including history from before signup. A value produced by the work carries the artefact it came from, where a value somebody typed carries only the typing. It is the fourth link in the chain, and the one no amount of data hygiene can retrofit. Day.ai product
- 03A typed number and a derived number are not the same evidence.
No vendor claims this, so there is nothing to link. Two figures can be identical and carry completely different weight: one is somebody's opinion recorded at an unknown moment, the other fell out of something that demonstrably happened. People outside your company discount the first automatically, and they are right to.
Vendors move and their own pages sometimes disagree with each other, so treat both cited lines as dated, not permanent. The four CRMs compared carries the fuller version, each claim sourced the same way.
Three things that are cheap, real, and worth doing this month
- 01An append only change log on the six fields anybody ever asks about.
Amount, stage, close date, owner, and whichever two are peculiar to your business. An afternoon of work.
- 02A written rule for which system wins.
When the CRM and billing disagree, which one is right and when. One paragraph per disagreement, written before the argument.
- 03An as-of date on every number that leaves the building.
Costs nothing, and it kills a whole category of argument. Two people with different numbers usually have the same number from different days.
What none of that can do is recover the history from before you started keeping it. A record built from your email, calendar and calls keeps the source with the number, because the source is what created the row. Kesey is a revenue operations practice, a Day.ai Solutions Partner and an Attio Expert Partner; it is not Day AI, Inc. or Attio Ltd., and it is not affiliated with Clarify or HubSpot.
Frequently asked
How do you prove where a number came from?
You answer four questions about it: what the number is, who set it, when and what it was before, and what it came from. Platforms that keep a history on every field answer the middle two without anybody deciding to build that. The fourth is answered by records built out of your email, calendar and calls, where the number falls out of something that actually happened rather than something somebody typed. Some people call this data provenance or an audit trail. It is simpler than either name makes it sound.
Our numbers are accurate. Is that not enough?
It is enough for you and not enough for anybody else, because nobody outside your company can check whether a number is right. All they can check is whether you can show your work, so that is what they ask for. A quarter of data cleanup makes your numbers more likely to be right and does nothing about this, because a tidied up field with no history is still a number a person typed.
What does it cost us to not be able to show our work?
On the model set out on this page, about 32 hours a year in a quiet year, and around 70 in a year where somebody is buying you or auditing you. At a loaded $125 an hour that is roughly $4,000 and $8,750. There is a third line with no figure on it on purpose: a history you never kept cannot be built afterwards at any price. All you can do is work out today's number again and ask them to take your word for it.
What do buyers actually ask for when they are buying you?
They sample. A handful of deals, traced from the figure in the pack back to whatever created it, with dates. They are not checking your arithmetic; they are checking whether your account of your own business holds up when somebody pulls on it. Two or three figures you cannot trace reads as a problem with the whole record rather than with those deals, which is why it moves the price rather than a slide.
Can we fix this without replacing our CRM?
Mostly, and it is worth doing. A log that only ever adds rows, covering the six fields anybody ever asks about. A written rule for which system to believe when two of them disagree. And a date on every number that leaves the building. That is an afternoon, a paragraph and a habit, and it closes most of the gap from here on. What it cannot do is recover the history from before you started keeping it.
Which CRMs keep a history of every change?
HubSpot keeps a history on every field and will export it, which is the most directly checkable example. Day.ai, Clarify and Attio each build the record out of connected email, calendar and calls to different degrees, which answers a different question: what the number came from in the first place. The four CRMs compared carries the detail with each claim sourced.
Is this a reason to move off a homegrown CRM?
It is one thing to weigh rather than the decision. The underlying argument is to build the tools and never the record: tools are cheap to build and safe to throw away, and the record has to hold up for people who did not build it. Should you build your own CRM sets it out in full.
Take a dozen figures off your last board pack and try to trace each one.
A week of that tells you more than a quarter of data cleanup, and it is the same exercise somebody else will eventually run on you. The findings come to you first.
If the person who could trace them is leaving, what leaves with them.