Field notes
Everyone wants a dashboard. Nobody wants the same one.
Marketing reads lead source. Sales reads cycle time, and finance is looking at cost per opportunity. One CRM, four conclusions, and a meeting that settles nothing because nobody agreed what clean data was even for.
When marketing, sales and finance read the same CRM and reach different conclusions, dirty data is rarely the cause. Usually nobody agreed what clean data was supposed to deliver in the first place. Sort out what the numbers mean, then you can argue about what they’re saying.
On this page
Ask five people for “the dashboard” and you get five different requests behind the same word. Marketing wants lead source. Sales wants cycle time, finance wants cost per opportunity, and success is really asking about retention. SameCRM underneath, and by the end of the meeting nothing has been decided.
The data is usually fine. What’s missing is the translation layer on top of it, and almost nobody bothers to build that.
The same row means different things to different people
Take one deal. To marketing it’s the webinar that sourced it, a lead that cost €40. Sales has ninety days into it and a cycle running two weeks longer than the quarter allows. Finance sees a cost per opportunity that only makes sense if the thing closes, and success is already watching an account that will churn in eight months if onboarding slips.
None of them is wrong. They’re reading different columns of the same row and each calling it “the truth”.Lead source, conversion touchpoint, time to convert, cost per lead, cost per opportunity, average customer lifecycle: every one of those is a separate question, and each team only has the budget to act on one of them.
Clean data isn’t the deliverable. A decision is.
Most data projects stop one step early. The CRM gets cleaned and governed, the dedup runs - and then everyone is handed the same report and told to be data-driven.
Clean data is only raw material. What you’re after is a view one specific person can open on a Monday and act on; a dashboard that doesn’t end in a decision is just something to look at.
Everyone sees the same thing. Each person sees their part.
Democratising data doesn’t mean giving everyone access to everything. Do that and you get four people building four spreadsheets off the same export and disagreeing by Thursday. It works when each team gets a view built for the decision they own, every one reading from a single governed source.
Marketing’s view answers which sources produce customers rather than leads. Sales wants to know where deals stall and how long “normal” really takes. Finance is asking what an opportunity costs and when it’s worth it, and success needs to see which accounts are drifting and how early anyone can tell. Same row, different numbers, and that only holds if thestages and definitions mean one thing underneath. Get the definitions wrong and you’ve just spread the disagreement around.
The company only has three numbers
Under all of it sit three questions, and they’re the ones the board keeps coming back to.
What does it cost to win a customer? The real figure is cost per customer, counting every deal you lost getting there, not just the cost per lead. What does it cost to keep one? Support, success, the discount you gave at renewal. And what does it cost to lose one? That’s the number nobody runs: the lost revenue, the cost of replacing them, and the referrals that never happened.
Every team metric should ladder into one of those three. If cost per lead is falling while cost per customer climbs, marketing is winning its own scoreboard and losing the company’s. That gap is the whole reason the views have to sit on the same data.
How you actually build it
Start from the decision the team needs to make, then work back to the chart. Ask each team what they’d do differently if a number moved; if the answer is “nothing”, don’t build it. Then agree the definitions out loud, once: what counts as a source, when the clock starts on time-to-convert, what makes an opportunity an opportunity. Write that down, because the document ends up worth more than the dashboard it feeds.
Then give each team the smallest view that supports their choice, and one shared view showing win, keep, lose. All of it reads from the sameCRM people actually use, so when two teams disagree, they’re arguing about the decision itself and not about whose export is right.
Democratised data comes down to this: everyone reads from the same source, and each person acts on the slice that’s theirs. Handing out access to everything was never what it meant.