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Practical perspectives on data governance, Microsoft Fabric, Power BI and analytics strategy for UK organisations.
What the analytics consolidation wave means for your platform choices
Strong analytics platforms keep being acquired by the giants. Why that is a compliment to the category, why Pyramid is a winner in it, and how to choose well.
Business Central to Power BI: the complete guide to reporting beyond the built-in tools
If your finance team still exports Business Central to Excel every month, the problem is not the team - it is that Business Central's built-in reports were never designed for cross-functional management reporting. Here is every option for doing it properly with Power BI.
Context is the bottleneck, not the model
AI-built analytics rarely fails on the data. It fails on the things nobody wrote down. Why context, not the model, is the real constraint.
Pyramid Analytics is now part of ServiceNow - what it means if you run it today
ServiceNow's acquisition of Pyramid Analytics raised a lot of questions and answered very few. Here is the honest version: what has changed, what has not, and what a mid-market business running or evaluating Pyramid should actually do about it.
When analytics stops being a place you visit
Analytics has always been a destination. The shift to insight delivered inside the workflow is now underway, and the foundations it needs are the ones you should already be building.
Lakehouse or warehouse? The Azure architecture patterns that actually hold up
Most mid-market data platforms are not badly built — they are built twice. Here is how the Azure lakehouse patterns actually work, the medallion architecture, when a lakehouse genuinely beats a warehouse, and the anti-patterns we get called in to unpick.
Azure data engineering: what to hand a consultancy, what to keep, and what it should cost
Two ways mid-market businesses get Azure data engineering wrong: hand over everything and depend on a day rate forever, or hand over nothing and stall for a year. The right answer is a deliberate split. Here is what to outsource, what to keep, and what it should cost.
The pipeline ran is not the same as the data is right: observability for Azure data engineering
Most Azure pipelines are monitored the way a smoke alarm monitors a house: they go off once something has already caught fire. Observability is how you build an Azure pipeline that tells you the data is wrong before the board pack does. Here is how, in practice.
The Fabric rollout that quietly goes wrong: workspace sprawl, shadow BI, and the readiness checks nobody runs
Most Microsoft Fabric projects do not fail loudly. They succeed at the demo, then degrade over six months into workspace sprawl, shadow BI, and month-end capacity throttling. Here are the deployment risks, and the readiness checks that prevent them before go-live.
Fabric, Databricks, Snowflake: a capability-by-capability comparison, not a sales pitch
Every comparison of these three platforms collapses into it depends. True, but useless. Here is what it actually depends on: capability by capability, how the pricing and operating models really work, and which workload profile each is genuinely built for.
In the loop or on the loop: governing analytics that AI helped build
Teams adopted AI building far faster than they decided how to check it. The light, practical gates that keep AI-built reporting trustworthy.
Your data pipeline doesn't have an uptime problem. It has a trust problem.
Uptime tells you the server was on. It says nothing about whether Monday's board pack was right. Here is how we design Azure data pipelines around freshness, completeness and trust — with SLAs the business actually understands.
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