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Practical perspectives on data governance, Microsoft Fabric, Power BI and analytics strategy for UK organisations.
From day seven to day two: automating the monthly reporting pack
If the board sees the numbers a week after month end, they are steering last month's business. Automating the monthly pack is one of the highest-return jobs in a data estate.
Eight of us are at FabCon Europe this week. Here is what we are listening for.
Eight of the Hopton team are at FabCon Europe in Barcelona this week. The brief is simple: bring back what is real, and leave the rest on the stage.
Row-level security done right: when everyone should see only their own numbers
One report, many audiences, and each one allowed to see only their own slice. Row-level security is how you do it without building the same report ten times.
Testing a data pipeline: the checks that catch wrong numbers before the board does
A pipeline that runs is not a pipeline that is right. A small set of automated checks, run on every load, catches most wrong numbers before anyone outside the data team sees them.
Fund the outcome, not the platform
A board does not buy a medallion architecture. It buys control, faster decisions and numbers it can trust. Here is how to put a data platform investment the right way up.
Late-arriving data: what to do when facts turn up before their dimensions
Data does not always arrive in the order a model expects. A sale can turn up before its customer, or a week after the month closed. Handling both properly keeps totals right without losing rows.
Natural keys, surrogate keys and hash keys: what joins your data together
Every join in a data model depends on a key. Source systems bring their own, reporting models usually add another, and getting the difference wrong is how the same customer ends up counted twice.
Power BI relationships and cardinality explained: one-to-many, many-to-many and filter direction
Relationships decide how a filter on one table reaches another. Get cardinality and filter direction right and a model behaves predictably. Get them wrong and totals quietly change.
Microsoft's New Power BI Agentic Tooling Is Less About AI and More About Discipline
Microsoft's new Power BI Agentic tooling isn't really an AI upgrade. It's a codified standard for what good semantic model and report delivery looks like, enforced automatically whether a person or an agent is building it.
The date table: why every Power BI model needs one, and what should be in it
Time is in almost every business question. A proper date table makes financial years, prior-year comparisons and year-to-date totals simple, and turning off auto date/time makes models smaller.
AI delivery is not magic. It is disciplined execution.
AI solution delivery still follows a familiar enterprise path: problem, requirements, build, test, launch, monitor. What changes is the number of leadership judgement calls packed into every stage. Six questions decide whether that discipline is actually there.
Change data capture explained: catching inserts, updates and deletes
Change data capture reads the changes a database records about itself, including deletes. It is the most reliable way to keep a copy in step with a busy source, as long as you plan for what it cannot do.
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