Deploying a data strategy in an outsourced finance function

In the transformation of finance functions, few topics are more strategic – and more operationally complex – than data. As real estate managers increasingly outsource elements of their finance operations – from SPV bookkeeping to consolidation and reporting – the need for a clear, pragmatic data strategy becomes central. Data is no longer a technical afterthought. It’s the infrastructure of a modern, insight-driven finance function.

For decades, finance was document-driven: invoices, reconciliations, reports. Today, the tools have changed. Platforms and automation solutions have shifted the landscape. Excel-based models and static reports are giving way to structured, real-time data flows. In this new environment, both finance teams and their service providers must collaborate to ensure that data is clean, connected, and consistent across systems, jurisdictions, and reporting layers.

This evolution changes what’s expected from outsourcing providers. No longer just processors or record-keepers, they are now stewards of financial data. Their value is measured not only in service levels or cost control, but in their ability to collect, structure, validate, and deliver high-quality data across every touchpoint.

To make this work, the fund manager and service provider must align on the vision for the data strategy. This alignment isn’t just about agreeing to use the same system – it means jointly defining the purpose of the data model (performance analysis over compliance), the level of data granularity required, and shared definitions through a consistent data dictionary. Without that common foundation, misunderstandings emerge quickly: what one side considers “good enough” might fall short of the other’s analytical or reporting needs.

The challenges of building a data-driven model in the outsourced back office

For real estate managers with multi-jurisdictional structures, deploying a data-driven approach in an outsourced model comes with real-world obstacles. Local accounting standards vary. Legacy systems often lack integration. And each structure might be interpreted slightly differently by different stakeholders. This results in a fragmented data landscape – where providers inherit inconsistent or incomplete data sets, slowing onboarding and introducing errors down the line.

Ownership is another sticking point. When multiple parties feed into the same dataset, accountability can blur. Who owns the vendor master? Who validates fund hierarchies? Without clear definitions, problems aren’t discovered until late in the cycle – during reporting, or worse, during an audit.

Integration challenges add friction. Even when the primary ERP system is consistent, peripheral systems – AP automation, banking, tax tools – often operate in silos. The result: teams spend more time reconciling data than analyzing it.

A major blind spot is often the property manager. Their systems sit at the source of many core data flows: rent rolls, service charges, recoveries, maintenance costs. When those systems are misaligned or poorly integrated with the fund manager’s platform, gaps emerge. For example, if rent roll data is exported manually each month with inconsistent naming conventions, reconciliation becomes a recurring pain point. Or if service charge accruals are booked differently across properties, fund-level reporting lacks comparability. These inconsistencies introduce risk and erode trust in the numbers.

These issues can be solved, but not with technology alone. Strong data governance, clear ownership, smart system architecture, and early involvement from controllers all play a role. When data is treated as infrastructure – not just as an output – outsourced models become faster, more reliable, and more scalable.

Why it matters: the value of a data strategy for real estate managers

A service provider with a mature data strategy creates tangible benefits. For real estate managers, it means more than operational efficiency. It can unlock competitive advantage.

It starts with transparency. High-quality, real-time data enables faster responses to investor queries, clearer oversight, and sharper decision-making. Operational risk drops. Clean, traceable data makes audits smoother and supports regulatory compliance.

Reporting accelerates. With unified data models and automated consolidation, fund managers can close books faster, meet deadlines more consistently, and generate forward-looking insights. This is especially critical as the demand for non-financial reporting – like ESG disclosures – continues to grow.

There's also the benefit of perspective. When providers bring industry-standard models and benchmarking capability, data becomes more than a record of past activity. It becomes a lens for understanding portfolio health, operational performance, and emerging risks. Managers can engage investors and investment committees with confidence, backed by structured analysis and comparative insight.

The CFO agenda increasingly centers around cost efficiency. With rising expectations around reporting speed, transparency, and ESG data, many CFOs are being asked to do more with less. A strong data strategy supports that goal. Clean, connected data reduces time spent on manual checks, accelerates close cycles, and minimizes rework. It lowers the total cost of reporting, both internally and at the service provider level. That efficiency often translates directly into a more favorable Total Expense Ratio (TER) at the fund level – helping to deliver better outcomes for investors without adding headcount or complexity.

Perhaps most importantly, a sound data strategy enables integration across the broader CFO agenda. Consolidation, FP&A, treasury, tax, ESG reporting, investor relations – they all draw from the same source. A consistent foundation removes duplication, aligns timelines, and helps the CFO move from reporting the past to shaping the future.

Controllers benefit too. Freed from chasing reconciliations and patching reports, their role expands. They become managers of data flows, monitors of KPIs, and business partners with a seat at the table. With the right data, they can focus on what matters: oversight, insight, and continuous improvement.

And it all scales. With the right data infrastructure, onboarding a new fund or adjusting to new regulations becomes smoother. The provider stops being a bottleneck – and becomes a platform for growth.

Building the foundation for AI

A strong data strategy also lays the groundwork for responsible, high-impact AI adoption. AI doesn’t work well with bad data. It needs structure, consistency, and governance to deliver real value.

When the foundations are in place, AI can go further: flagging anomalies, predicting late payments, classifying transactions, identifying trends, and even generating draft reports. Predictive models can support cash flow forecasting, valuation scenarios, or macroeconomic stress testing.

But AI can’t compensate for a weak data backbone. In a fragmented environment, it amplifies noise. In a governed environment, it becomes a true enabler – reducing manual work, surfacing insights, and expanding the role of finance teams without compromising on control.

What makes it work: key requirements for success

Even the best strategy will fail without careful execution. Success isn’t about ticking boxes. It’s about alignment – between systems, people, and expectations.

It begins with shared understanding. Fund managers and providers must agree on data ownership and responsibilities. Who controls the chart of accounts? Who manages vendor records? These are not minor details – they shape everything downstream.

Consistency is essential. Without common naming conventions, dimensional structures (like fund, property, investor), and clear reporting hierarchies, automation can't deliver on its promise.

Technology must be both robust and open. A central data hub is critical, but it must connect easily to other tools across the finance stack. Manual interventions should be the exception, not the rule.

People remain the bridge. Controllers need visibility across systems, the ability to trace data back to source, and the mandate to oversee exceptions. Their involvement ensures that insight stays aligned with oversight.

Outsourcing providers must bring more than just capacity—they need to bring expertise, tested frameworks, and a deep understanding of how the industry operates. That includes engaging with upstream stakeholders like property managers. Standardizing data handoffs – such as how rent roll files are formatted, how maintenance costs are classified, or how vacancy data is tracked – reduces downstream rework and improves reporting speed.

A well-designed interface between the property manager’s system and the fund’s reporting stack turns operational data into financial insight. Without that, controllers are stuck filling gaps instead of overseeing performance.

A shared vision between fund manager and provider doesn’t just clarify responsibilities – it creates space for smarter decisions about what data to collect, how much detail is needed, and what’s worth automating. Without that alignment, discussions on tools or architecture quickly become tactical rather than strategic. When both sides agree the goal is actionable insight – not just compliance – the data model becomes a real asset.

And the strategy can’t sit still. It has to evolve. Data quality metrics, close times, reconciliation rates – all must be tracked and improved continuously. A static strategy is a fragile one.

Conclusion: data as infrastructure for growth

A finance function that treats data as an output will always be behind – reactive, manual, and under pressure. One that treats data as infrastructure becomes something else entirely: strategic, scalable, and ready for what’s next.

A well-aligned data strategy also delivers what CFOs care about most: control and cost. It reduces inefficiency, scales without complexity, and helps keep TER in check – all while improving the quality and speed of reporting. In that sense, it doesn’t just support finance. It strengthens the entire investment platform.

In today’s real estate environment – where complexity is rising and expectations aren’t slowing down – the ability to rely on your data isn’t just helpful. It’s how you stay ahead.