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Reporting people open on Monday, not just at audit time

We turn the data you already hold into reporting that changes what people do — built around the decisions each role has to make, not around how many charts fit on a page. Power BI, the Microsoft BI stack and SSRS, delivered with the adoption work that decides whether any of it gets used.

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A senior engineer replies within 24 hours — not a sales rep.

Why your dashboards are not being used

The dashboard was delivered and the usage log flatlined

Twelve visuals, eight filters, and it answers a question the requester was asked once in a board meeting. Nobody's Monday starts there because nothing on the page tells them what to do differently. Adoption is not a training problem here — the report was scoped from a wish list rather than from a decision.

Somebody found a wrong figure and now nothing is believed

One number was off by a fortnight because a refresh had failed quietly. That was six months ago and the sales director still exports to Excel and recalculates. Trust in reporting is expensive to build and cheap to lose, and no amount of visual polish gets it back.

Every report request goes into a queue

The BI team is two people and the backlog is eleven weeks. Business users cannot answer their own questions because the model is a set of flat extracts with no relationships, so anything not already on a page requires an engineer. Meanwhile shadow reporting grows in spreadsheets nobody governs.

The workspace is a graveyard of near-identical reports

Forty-odd items, several called some variant of Sales Summary, each built by whoever needed it that quarter. No one is sure which is current, so people pick the one they were sent last. Refreshes run on all of them and the capacity is permanently under pressure.

How we approach BI

We start from the decision, not the dataset. If we cannot name who acts on a report and what they will do differently, we do not build it — and that question kills more requested dashboards than any technical constraint.

Scoped from decisions, built for a named audience

Each report gets an owner, an audience and a stated decision it supports. A regional manager checking pipeline health needs three numbers and an exception list, not the same canvas an analyst needs for root-cause work. Separating those two audiences is usually the single biggest lift in adoption.

A semantic model, so people can answer their own questions

One governed model with relationships, hierarchies and centrally defined measures, rather than a fresh extract per report. Business users then slice it themselves without inventing their own version of gross margin, and your BI team stops being a request queue for questions the model could already answer.

Refresh health treated as a visible thing

Failed refreshes get alerted, and every report carries a plainly worded as-at timestamp. When data is stale the page says so rather than showing yesterday's figures as if they were current. That single habit does more for confidence in the numbers than any redesign.

Rollout, tidy-up and the training that makes it stick

We audit what already exists, retire duplicates with the people who use them, and set up workspaces with sensible promotion between development and production. Training is by role and short, run against live data, and repeated a few weeks in when the real questions have surfaced.

What this covers

Pick the piece you need, or bring us the problem and we'll tell you which applies.

Power BI Development

End-to-end Power BI work: the semantic model underneath, the DAX measures, the report design and the workspace governance around it. Most Power BI performance complaints trace back to the model rather than the visuals, so that is where we spend the effort. Suited to organisations already in Microsoft 365 that want reporting people will use unprompted.

Star-schema semantic models with a proper date table and centrally defined DAX measures
Row-level security so one report serves every region without building a copy per manager
Incremental refresh and query folding tuned to keep refresh windows and capacity use predictable
Deployment pipelines across development, test and production workspaces with sensitivity labelling

Microsoft BI (MSBI)

The wider Microsoft stack that feeds and structures your reporting — SSIS for integration, SSAS models for shared calculation logic, and the migration path from on-premise servers to Azure or Fabric when it is time. Relevant if your reporting already sits on SQL Server and you need it modernised without discarding what works.

SSIS packages for scheduled integration, with logging and restartable checkpoints on long-running loads
SSAS tabular models that hold shared business logic once for Power BI, Excel and paginated reports
Migration of on-premise SQL Server BI workloads to Azure SQL, Synapse or Microsoft Fabric
Gateway configuration, service account hygiene and refresh scheduling across hybrid environments

SSRS & Paginated Reporting

Some outputs have to be exact and printable: an invoice run, a regulatory return, a statement pack that goes to a customer. Dashboards are the wrong tool for those, and SSRS or Power BI paginated reports are the right one. We build, migrate and modernise this layer, including the estates still running on unsupported server versions.

Pixel-accurate multi-page layouts for statements, invoices and regulatory submissions
Scheduled subscriptions delivering PDF or Excel output to inboxes and file shares on a defined calendar
Parameterised reports with cascading selections and per-user data filtering
Upgrades from legacy SSRS instances to supported versions or Power BI Report Server

The stack we build on

Chosen to fit the problem — not because it's what we used last time.

Reporting & visualisation

Power BI
Power BI Report Server
SSRS
Excel
Tableau
Looker Studio
Metabase

Microsoft data platform

SQL Server
SSIS
SSAS
Azure SQL
Azure Synapse Analytics
Microsoft Fabric
Azure Data Factory

Modelling & query

DAX
Power Query (M)
T-SQL
dbt
Tabular Editor
DAX Studio

Governance & delivery

Microsoft Entra ID
Row-level security
Azure DevOps
Git
Power BI deployment pipelines

How we'll work together

Every stage ends with something in your hands — not a status update.

01
Decision mapping and report audit
We interview the people who are meant to act on the numbers and write down the decisions each role owns. In parallel we inventory the existing reports, including usage figures where the platform records them, which usually reveals that a small handful carry all the value.
You get: A decision-to-report map by role, an audit of current reports with a keep, merge or retire recommendation for each.
02
Semantic model and a first working report
We build the shared model — relationships, hierarchies, measures — and one report on top of it against real data. Putting something in front of the actual audience early is the fastest way to find out that what they asked for is not quite what they need.
You get: A documented semantic model with a measure dictionary, plus one production-ready report reviewed by its named owner.
03
Build out, secure and schedule
The remaining reports are built against the same model, with row-level security, refresh schedules and workspace promotion configured. We test security by signing in as real roles rather than trusting the configuration screen.
You get: The full report set deployed across development and production workspaces, security roles tested per user group, refresh monitoring in place.
04
Adoption, then a review with usage data
Short role-based sessions run against live data, followed a few weeks later by a review of what people actually opened. Reports nobody used get fixed or retired — leaving them in place is how workspaces turn into graveyards again.
You get: Role-based training materials, a usage review with recommendations, and a governance guide covering who publishes what and where.

Where we've applied this

Retail

Store and channel performance with like-for-like comparisons and stock exceptions surfaced daily, so area managers see what needs action before the weekly pack arrives.

Healthcare

Capacity, waiting list and utilisation reporting with role-based access, keeping identifiable detail visible only to the staff who are entitled to it.

Logistics

On-time performance, cost per consignment and exception queues by lane, with paginated carrier statements produced from the same governed figures.

Finance

Management reporting where the audit trail matters as much as the visual — versioned figures, defined measures and paginated regulatory output from one model.

Manufacturing

Output, scrap and downtime reporting per line and shift, framed so a supervisor can see the shift's exceptions without waiting for a monthly analysis.

Professional services

Utilisation, realisation and project margin reporting that partners can slice by client or team without asking an analyst to rebuild the view.

Clients we've built for

Real products, in production, with real users on them.

National Industries Company

Jim's Group

Prime Selection

Mobile Travelers

Questions buyers ask us

Do we need a data warehouse before we start on BI?

Not always. If your reporting draws on one or two clean systems, a well-built semantic model over them will get you a long way and costs far less. The warehouse becomes necessary when figures have to be joined across systems that disagree, or when you need history the source applications overwrite. We will tell you which situation you are in rather than selling the larger project by default.

What makes a BI engagement expensive?

The state of the data underneath, almost every time. Reports on a modelled source are quick; reports over systems with no reliable join key mean cleansing work before a single visual exists. The other driver is audience breadth — one team's reporting is a short engagement, while rolling out to several departments with different security requirements is a programme.

How do you actually get people to use the reports?

By scoping each one to a decision someone owns, and by involving that person during the build rather than at handover. Practically: fewer visuals, exceptions surfaced instead of buried, a visible data-as-at stamp, and training run in short role-specific sessions against live data. Then we look at usage a few weeks later and fix or retire what nobody opened.

Can you take over reports that another team or agency built?

Yes, and it is a common starting point. We review the existing models and measures, document what we find, and give you an honest assessment of what to keep. Sometimes the right answer is remedial work on a model that is fundamentally sound; sometimes a rebuild costs less than untangling accumulated logic. We would rather say so up front.

Should we still use SSRS, or move everything to Power BI?

Keep paginated reporting for anything that must be exact and printable — invoices, statements, regulatory returns. Power BI is the better tool for exploration and monitoring. Most organisations end up running both, ideally over one shared model so a figure on a dashboard matches the same figure on a PDF. Moving statement runs into dashboards is a mistake we have seen made more than once.

Who owns the reports and can our team maintain them?

You do — everything is built in your tenant, in your workspaces, with the model definitions and measure documentation handed over. We build so that a competent internal analyst can extend the work, and we usually train that person during the project rather than after it. If you prefer to retain us for new reports and model changes, that is available, but it is not a dependency we design in.

Tell us which decision is being made without data

Send us a note on what your teams are reporting on today and where they still fall back to a spreadsheet. A senior BI engineer replies within one business day with a view on what would actually get opened.

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