Client work sits behind NDAs, so here are eight things you can inspect end to end: six Power BI builds, each mapped to a sector I have worked in, with their models, measures and data-quality work in public (one of them measured against a deliberately bad version of itself), an interactive MI framework built on synthetic financial services data, and one product you can run against your own tenant export.
Three years of the COREP and FINREP returns 129 EU banks file with their supervisors. The sector’s CET1 ratio is 16.2%; average the banks’ own ratios and you get 22.7%. Every ratio is recomputed from its components, every item code mapped across three renumbered exercises, and the year-to-date P&L taken apart bank by bank.
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A decade of a bank’s card book: 1.7m transactions, what was declined, and what turned out to be fraud. Two thirds of the file is adjudicated and the rest is not, so the obvious way to compute the fraud rate is wrong by a third; the report shows both denominators. The same rows are then modelled a second time, badly, and measured against this one.
Five years of HR records whose status field contradicts people’s own dates 1,146 times. The year-on-year page compares the same months: from January to July 2023, leavers rose from 216 to 570 and annualised turnover from 22.4% to 60.1%, in every department.
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Three years of a retailer’s orders to 164 countries: net sales, margin and delivery against the lead time each shipping mode promises. First Class is late on every order, and the mode that costs more delivers no faster; only the promise differs. The column called Sales isn’t revenue, and the last four months come from a different generator.
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Four years of a US retailer’s orders, rebuilt as a star schema whose product key turned out not to be one. The revenue page compares any year with the one or two before: 2024 grew 20.3% on more orders, not bigger ones. Every card is a measure and every figure was checked against pandas.
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A full profit & loss statement built from a real general ledger, and a second page that asks why. In 2020 revenue ran 3.6% behind budget but EBITDA 26.2% behind: costs didn’t fall with sales, and the USA and UK carry the miss.
Explore the data →Explore how executive KPIs, remediation actions and metric definitions work together. Switch reporting months, inspect overdue actions and trace each measure to its calculation, owner and threshold. A browser prototype of a Power BI MI design, built on synthetic data.
Explore the framework →Turns a Power BI workspace export into a governance audit in seconds: over-privileged access, admin hygiene gaps and orphaned workspaces, flagged and explained.
Open Pulse →