# Attribution without a data team > You do not need a warehouse to connect effort to results. You need four numbers, a date, and someone willing to write down what they expected before they started. - Source: https://athr21.com/writing/attribution-without-a-data-team/ - Published: 2026-07-21 - Track: The Record - Reading time: 4 minutes - Language: English - Author: Aly Zeineldin, Athr21 --- Every company [I work with](/about/) can tell me their revenue. Almost none can tell me which of last quarter's twenty initiatives moved it. The usual answer is that attribution needs a data team, a warehouse and six months. Sometimes it does. More often the company has the numbers already and is missing something much cheaper: a written statement, made before the work started, about what was supposed to change. ## Why the tooling answer is usually wrong A warehouse tells you what happened. It does not tell you what anyone expected, and without the expectation you cannot say whether the result was the work or the weather. I have watched a team spend five months on a data platform and arrive at exactly the same argument they were having before, only now with better charts. The number went up. Marketing says it was the campaign. Product says it was the onboarding change that shipped the same fortnight. The platform is silent on this, because the disagreement is not about the data. ## The four numbers For any piece of work big enough to argue about later, write these down before it starts. It takes about ten minutes.
1 · The number you think will move
One number, named specifically. Not "engagement". Not "growth". Something like "the share of new accounts that invite a second user within seven days".
2 · Where it is now
The reading on the day you start, with the date. This is the field that gets skipped, and skipping it is what makes the whole exercise arguable afterwards.
3 · Where you think it will be, and by when
A guess is fine. A wrong guess is fine. What matters is that the guess is on the record and dated, because an unwritten expectation is infinitely flexible after the fact.
4 · What else could explain a move
Seasonality, a pricing change, a competitor's outage, the other three things shipping that month. Naming them in advance stops them being discovered conveniently later.
## What this buys you Not certainty. You still will not have a controlled experiment, and for most decisions at this size you do not need one. What you get is [a record](/services/#record) that looks like this:
The number
Second-user invites within seven days
Now · 3 March
11%
Expected · by June
18%
What else could move it
Pricing is also changing in April.
What actually happened
Filled in come June, and now it has something to be compared against.
That is enough to have a real conversation. It is also enough, after eight or nine of them, to see which kinds of bets your company is systematically good and bad at, which is the more valuable output and the one nobody expects. ## The failure mode The obvious risk is that people write soft targets so they always look right. This happens, and it is visible immediately, because soft targets are boring to read. The fix is not enforcement. It is who reads them. If the person reviewing these is looking for accuracy, everyone learns to guess low. If they are looking for how well the team understood their own system, a confident wrong prediction with a good explanation is worth more than a safe correct one, and people write honestly. That distinction is cultural, not technical, which is why buying a tool does not fix it. ## Start with four Do not roll this out. Pick the next four pieces of work that are large enough that somebody will ask about them in six months, and write the four numbers for each. Review them at the end of the quarter, in one sitting, out loud. The first review is usually uncomfortable and usually the most useful hour of the quarter. After three of them the format stops needing a champion, because people start reaching for the old entries themselves.