Notes from inside the work.
Written for the people who have to live with the result: heads of data, CFOs, product leads and the analysts holding it together. No listicles, no vendor comparison tables copied from a datasheet.
18 pieces · RSS
First payment default, and the agent that watches it
Of all the numbers a lender reports, first payment default is the one that arrives soonest and hurts most. It is also the one most often looked at monthly, in a…
Read the pieceThe recovery funnel, and the number nobody wants to measure
Collections is the part of a lending business where effort and outcome are least correlated, and where almost everybody measures the wrong thing. The headline number…
ReadHow every analytics role changed in eighteen months
The headline everybody expected was replacement. What happened instead was a shift in which part of each job is scarce. In almost every analytics role, the…
ReadSales pacing, and why the monthly forecast review is three weeks too late
Every lender has a monthly sales number and a monthly forecast. Most compare them at the end of the month, in a meeting, where the only available action is an…
ReadAcquisition source quality, or why your cheapest channel is your most expensive
Every marketing review ranks channels by cost per acquisition. It is the wrong ranking, and almost everybody knows it, and almost nobody changes it — because the…
ReadYour semantic layer is your context layer
Every company that has tried to put an assistant in front of its data has run the same experiment, whether or not they meant to. Point a capable model at the…
ReadOnboarding drop-off, and the identity check that costs more than a pricing decision
Growth leaks quietly through onboarding. Nobody complains, because the people affected simply leave. There is no ticket, no angry email and no spike on any dashboard…
ReadCash, yield and the reconciliation nobody wants to build
Cash is the one part of a lending business where being approximately right is not a position anybody will accept. It is also, in most lenders we meet, the number…
ReadThe KPI framework we build for a consumer lender in the first 30 days
Every consumer lender we meet has a dashboard. Most of them report the same three things: applications, approvals and arrears. Those are the numbers that are easy to…
ReadText-to-SQL fails on your warehouse, and it is not the model's fault
Text-to-SQL benchmarks look encouraging. Then somebody runs the same model against a real warehouse and accuracy falls off a cliff, and the conclusion drawn is that…
ReadPutting AI in a credit decision without losing the argument later
There is a conversation that happens about eighteen months after an AI system goes into a lending decision. Somebody — a supervisor, an ombudsman, a claimant's…
ReadWhy nobody opens your Power BI reports
A company spends nine months building a reporting layer. Usage reports show forty opens in the first week and six a month later. The conclusion that gets drawn is…
ReadGetting a Power BI semantic model ready for Copilot
The pattern is consistent enough that we can usually predict it before opening the file. Copilot gets switched on. There is a burst of enthusiasm. Within about six…
ReadEvals are the new unit tests
Ask a team how they know their AI feature works and you get one of three answers. "It seems good." "We tried a few questions." Or, occasionally, a number — and that…
ReadCredit risk reporting that survives the questions after it
There is a moment in every funder diligence process where someone asks how a number was produced. Not whether it is right — how it was produced. Which system it came…
ReadDatabricks or Snowflake for a lender's data platform
We get asked this most weeks, and the honest first answer is that either will work. A mid-sized consumer lender with a few million rows a day is nowhere near the…
ReadAnalytics pods versus staff augmentation — the difference is who does the thinking
A company needs analytics capacity. Two proposals land. One is three contractors at a day rate. The other is a three-person pod at a monthly fee. The totals are…
ReadThe event taxonomy conversation nobody wants to have
Product analytics projects rarely fail at the analysis. They fail eight months earlier, in the twenty minutes where somebody decided what to call the events. The…
ReadNothing under that tag yet.
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