Where finance reporting usually goes wrong
Most finance teams already have dashboards. The problem is that nobody trusts them. The revenue figure in Power BI does not match the one in the management accounts. The refresh breaks when someone renames a column. Three versions of gross margin circulate because three people built three measures. So the team falls back to the Excel pack, and the dashboard becomes decoration.
The root cause is almost always the same: the report was built by someone who understood Power BI but not finance, or by someone who understood finance but not data modelling. Finance reporting needs both. You have to know why deferred revenue moves the way it does, and you have to know how to build a star schema that models it correctly.
What we build
We design reporting around the questions your leadership team asks, then engineer the data layer so the answers are right every time. A typical business intelligence engagement includes:
- Management reporting dashboards: P&L, balance sheet and cash flow by entity, department and period, with budget and prior-year comparison and drill-through to transactions
- KPI reporting for the board: revenue, margin, cash, working capital, headcount and the operational drivers behind them
- A governed semantic model in Power BI with a single definition of every measure, so gross margin means the same thing on every page
- Automated refresh from your accounting system, CRM, billing platform and operational tools, so the pack is ready on the morning after close
- Row-level security so budget holders see their own numbers and the executive team sees everything
- Paginated and printable reports where a board or a lender still wants a PDF
- Custom dashboards and web reporting where Power BI is not the right fit
The data engineering underneath
Good reporting is mostly plumbing. The dashboard is the last ten percent. Behind it we build the pipelines that extract data from Xero, QuickBooks, Sage, Business Central, HubSpot, Salesforce, Stripe and the rest, land it in a warehouse or lakehouse on Azure, model it into a clean finance schema and reconcile it back to source.
That reconciliation step is where finance experience pays for itself. We build control totals into every pipeline so the data engineer, not the finance manager, is the first to know when a number does not tie.
Where you already have a data platform, we work within it. Where you do not, we start small: a SQL database or Microsoft Fabric workspace sized for a finance team, not an enterprise data programme.
Financial reporting automation
The highest-value outcome of a BI engagement is often the time it gives back. A month-end pack that took four days of copying and pasting can be a refresh button. Variance commentary can be drafted from the data. Board slides can pull live figures rather than screenshots.
We approach reporting automation as a finance process problem first. We map how the pack is produced today, remove the manual steps that do not add judgement, and keep the ones that do. The result is a close that finishes earlier and a finance team that spends its time on analysis rather than assembly.
How we work
A reporting engagement usually starts with a two to three week discovery and design phase: we interview the people who produce and consume the reports, agree the measure definitions, and map the data sources. We then build in short increments, putting a working report in front of users every week or two and refining it against real questions.
You own everything we build. The semantic model, the pipelines and the documentation sit in your Azure tenant and your Power BI workspace, and we train your team to maintain and extend them.
Why finance teams choose us for Power BI
Plenty of consultancies can build a Power BI dashboard. Few can tell you why your revenue measure is wrong before you notice, or design a model that handles multi-currency consolidation, intercompany eliminations and accruals correctly. We can, because we come from finance and build the technology ourselves.
If your current reporting is not trusted, not used, or still assembled by hand, we should talk.
Good questions, straight answers
Can you fix an existing Power BI report rather than start again?
Often, yes. We start with a health check of the model, measures and refresh, and tell you honestly whether it is quicker to repair or rebuild. Many problems come down to a poorly structured model and inconsistent measure definitions, both of which can be fixed in place.
Which accounting systems can you connect to Power BI?
Xero, QuickBooks Online, Sage 50 and Sage Intacct, Dynamics 365 Business Central, NetSuite and most systems with an API or database access. Where there is no API we build a reliable file-based pipeline.
Do we need a data warehouse?
Not always. A single-entity business on one accounting system can often report directly. Once you have several sources, several entities or several years of history, a small warehouse or Fabric lakehouse makes the reporting faster, more reliable and easier to extend.
How do you make sure the dashboard matches the accounts?
We build reconciliation into the pipeline: control totals from the source system are compared with the model on every refresh, and any difference is flagged before anyone opens the report.
Sectors where this work lands
- Accountancy practices
Automated bookkeeping and document processing, client reporting dashboards and the advisory tooling that lets a practice sell insight rather than hours.
- Education groups and schools
Consolidated reporting across sites, pupil-driven budget forecasting, school-by-school benchmarking and finance automation for academy trusts, school groups and independent schools.
- IFAs and wealth managers
Client reporting, fee and revenue reconciliation, management information and governed AI for advice firms that want their data to work as hard as their advisers.