Financial Systems & Data Architecture
Use the systems you have more effectively before committing to major change.
In many growing organizations, the immediate answer is not a large ERP implementation. A better first step may be to extract useful data from existing systems, standardize it centrally and build a practical management reporting layer.
Common client situations
- Business has grown but finance data sits across multiple local accounting systems with no consolidated view.
- Management reporting depends on manual data extraction and consolidation every period.
- Leadership is considering an ERP implementation but is unsure whether it is the right next step.
- Finance team cannot easily answer basic questions about costs, margins or entity performance.
- Existing systems contain useful data but there is no reporting layer to make it accessible.
- Local teams use different tools, chart of accounts and naming conventions, making consolidation slow.
What we help with
We start with the business questions management needs to answer. Then we identify what data exists, where it sits, how reliable it is and what structure is needed to make it usable.
Local versus centralized architecture
Assess whether local accounting systems should feed a central consolidation layer or whether a single ERP is the right direction given the organization's structure and complexity.
Data extraction and mapping
Extract data from existing accounting, CRM, billing and ERP tools. Define chart of accounts mapping logic, entity structure and intercompany treatment.
Reporting layer design
Design a practical management reporting layer using Excel, Power Query, Power BI or structured data models to create usable management views without replacing source systems.
System readiness assessment
Evaluate whether existing systems are configured correctly, whether data quality is sufficient and what needs to be addressed before a larger system investment.
Vendor and solution evaluation
Assess finance system options based on business requirements, without unnecessary customization or implementation risk. Separate real needs from vendor promises.
Typical outputs and deliverables
A clearer view of what can be improved quickly, what should be automated later and which system decisions should wait until the facts are clearer.
- Data architecture assessment
- Chart of accounts and mapping documentation
- Reporting layer prototype or design
- Power BI or Excel reporting model
- System readiness evaluation
- Vendor and solution comparison
- Finance data quality assessment
- Implementation readiness roadmap
- Local versus central architecture recommendation
Create visibility quickly, then make better system decisions based on evidence.
An initial assessment can clarify what data you already have, what structure it needs and what can be improved without a major system replacement.
Start with a diagnostic discussion