If management reporting depends on fragile spreadsheets, manual consolidation or logic that only one person fully understands, the reporting process has become a business problem — not just a finance inconvenience. One critical management report can be a sensible first project.
You do not need a failed ERP programme or a broken dashboard to justify fixing the process. Often the warning signs are much more ordinary.
Files arrive from different places, formulas are refreshed, links are checked and the same pack is rebuilt every reporting cycle.
Important mappings, exceptions and workarounds have accumulated over time without becoming part of a documented reporting design.
Different extracts, definitions or calculation paths produce competing versions of the same number, undermining confidence in the final output.
The process scales by adding tabs, columns and another person to the chain instead of making the underlying reporting system more capable.
The goal is not simply to redraw the current spreadsheet in Power BI. It is to separate source data, business logic and presentation so each part can be managed properly.
Identify where each number originates, how it can be accessed and which source owns each part of the reporting truth.
Understand mappings, hierarchies, time logic, adjustments, exceptions and calculations currently embedded in the process.
Translate that logic into a maintainable Power BI model with reusable measures and clear relationships.
Deliver management reporting that refreshes reliably and gives finance more time to interpret the numbers rather than assemble them.
In an in-house role, StrategiQ Analytics’ founder independently handled the Power BI side of a complex P&L reporting rebuild. The work involved long-established spreadsheet logic and reporting requirements across multiple business functions.
The value is in changing what the finance team has to do every reporting cycle — and making the numbers easier for the business to trust and use.
Recurring collection, transformation and calculation work can be automated where the source environment allows.
Important measures and business rules live in a model that can be inspected and explained rather than scattered across workbook formulas.
Documentation and handover reduce dependence on the person who originally built the reporting process.
The team can spend more of the reporting cycle analysing performance and answering questions instead of rebuilding the pack.
You do not need to write a data specification. The reporting process itself is the starting point.
Understand what is produced, who uses it, where time is lost and which outputs are genuinely important.
Work backwards through sources, spreadsheets, mappings and business rules until the process can be explained.
Develop the reporting model and validate the numbers with the finance people who understand what correct looks like.
Put the solution into production with refresh, access, documentation, training and support appropriate to the organisation.
No. In most cases the job is to work with the systems you already have, establish reliable access to the relevant data and improve the reporting process around them. If a source-system limitation genuinely prevents the required outcome, that becomes part of the recommendation rather than an assumption made at the start.
Yes. A focused reporting process can be a very good starting point, particularly when it is business-critical and manually intensive. The architecture should match the problem rather than forcing every engagement into an enterprise-scale programme.
That is exactly the kind of environment this work is designed for. The first task is to understand the formulas, mappings, exceptions, manual interventions and source dependencies before deciding what should move into the new model.
Yes, with appropriate project terms and data-handling arrangements. Please do not send confidential datasets through the public website form. Once the engagement is being discussed, a suitable transfer method or client-controlled environment can be agreed.
That is where a paid discovery or Data Audit is useful. The existing process can be mapped first, with the output used to define a sensible implementation scope rather than guessing at the complexity.
You do not need a finished requirements document. Show us the report, explain how it is produced and tell us where the process hurts. We can work backwards from there.