Building modern finance teams: common bottlenecks with data foundations
Finance teams are under growing pressure to deliver faster, more forward‑looking insight, often with data and systems that were never designed for that purpose.
Across the market in the Netherlands, finance functions are moving away from manual, backward‑looking processes towards more structured, automated ways of working. But for many teams, progress is constrained by weak data foundations and growing complexity across systems.
From our perspective, these challenges are increasingly shaping hiring decisions. Finance leaders are looking for people who can operate confidently across finance, data and technology, not just to produce outputs but to underpin better decision‑making.
This article is the first in a joint series with Finthera, a Netherlands‑based consultancy founded by Tim Hoogeveen and Djurre Heemskerk, working at the intersection of finance and data
Drawing on their experience across Excel models, data warehouses, business intelligence and AI, we explore the structural issues holding finance teams back and what strong data foundations mean in practice.
Data quality
Many finance teams still don’t have a structured database and rely heavily on Excel. Excel works well for many finance tasks, but problems arise when teams become overly dependent on it.
Files are often stitched together using XLOOKUPs and references, with limited automation. Every month follows the same routine of opening files, refreshing, manually checking outputs and forwarding spreadsheets.
At entity level, this can work reasonably well. At regional or group level, it becomes more difficult. Without consistent master data, uniform mapping is hard to achieve.
The typical response is to build increasingly complex Excel models to bring everything together. Before long, significant time is spent each month maintaining spreadsheets rather than analysing results.
The data itself doesn’t need to change, but the way teams work with it does. With the right structure and validation checks, much of this process can be automated which improves speed and quality.
Data access and infrastructure
What often appears to be a technical problem is usually an organisational one.
Across software development, data teams and finance, people work with code. Excel formulas, DAX and SQL are all forms of code, but they are governed very differently.
A data engineer commits code to Git with testing and version control. A finance professional typically circulates an Excel file via email. The activity is similar, but the organisation around it isn’t.
IT teams focus on systems, security and standards. Finance teams focus on deadlines, numbers and output. IT asks for processes to be followed. Finance needs answers quickly. Both perspectives are valid, but there’s no shared language.
Under pressure, finance teams start building their own solutions. This might be Excel, Power BI models or tools purchased directly by the team. This shadow IT is a logical response to business timelines.
The risk is that these solutions are often undocumented and built around exceptions. When the person who created them leaves, knowledge is lost and governance issues can follow.
Fragmentation of systems
Most organisations run an accounting system alongside a CRM, HR system, possibly a planning tool and a treasury system.
Individually, these systems are not the issue. The challenge comes when everything needs to be brought together for reporting, which still often happens manually.
Every month, data is exported from one system, pasted into Excel, matched with another system and manually adjusted. Errors can be introduced easily and discovered late.
Power BI built on Excel files is a common and sensible first step. Over time, data volumes grow, refreshes slow and complexity increases. At that point, a proper database becomes necessary.
Legacy ERP and system landscapes
Legacy ERP is not simply about running old software. It often emerges through growth and acquisitions.
One acquisition may be partially migrated, with historical data sitting in a different system. Another may still be operating entirely on its own ERP. All this data still needs to come together for consolidated reporting, every month.
For finance teams, this adds complexity and places greater reliance on individuals who understand how the pieces fit together.
Solution
Addressing these bottlenecks means moving away from fixing individual issues and towards building finance environments that are repeatable, transparent and scalable:
- Clear data foundations ensure consistent definitions, controlled data flows and reporting logic that is not dependent on one individual
- Designing foundations around how finance teams actually work reduces manual intervention and reliance on spreadsheet‑based workarounds
- Exceptions become easier to identify and manage, improving confidence in numbers
- Automation, business intelligence and AI can be introduced incrementally, rather than through disruptive overhauls
- Finance roles shift away from maintaining data towards analysis, insight and commercial decision‑making
In our next articles, we’ll explore where finance data initiatives fail, how to build foundations that work and what this means for the future shape of finance teams.
Get in touch today whether you’re reviewing reassessing team capability or planning future hires.
Author
Frequently asked questions
This section provides clear, concise answers to the most common queries from finance leaders.
Finthera is a Netherlands‑based consultancy founded by Tim Hoogeveen and Djurre Heemskerk, working at the intersection of finance and data. Their experience spans financial analysis, data engineering, business intelligence and machine learning, across environments ranging from spreadsheets to modern data platforms.
They focus on building scalable data foundations that improve data quality, support reliable financial reporting and enable finance teams to operate more effectively in real‑world settings.
As workflows become more automated and data is increasingly real‑time, finance roles are shifting away from manual processes towards enabling informed decision‑making.
Finance professionals are now expected to work confidently with dashboards, business intelligence and financial data models, translating outputs into actionable insights that support business objectives rather than maintaining spreadsheets.
Data silos, weak data governance and inconsistent datasets often expose capability gaps within finance teams. Common issues include limited understanding of data dependencies, validation and data integrity.
As a result, CFOs increasingly prioritise finance professionals who understand data management, single source of truth principles and how metrics are generated, even if they are not hands‑on engineers.
As artificial intelligence, machine learning and advanced analytics become part of finance workflows, judgement becomes more important.
High performing finance teams include people who can assess realistic use cases, challenge outputs, explain insights to stakeholders and work comfortably with data models, metadata and automated processes. These individuals act as enablers of better strategic decisions.
Few organisations have a fully mature data ecosystem. Many are managing ERP change, CRM integration or evolving data infrastructure alongside ongoing reporting demands.
During these periods, finance leaders benefit from hiring people who can work with imperfect data, streamline workflows and maintain decision‑making momentum while longer‑term initiatives progress.
