65% of CFOs say their organizations are under pressure to accelerate ROI across their technology portfolios. So it’s clear that finance teams are investing heavily in smart tools, from automated forecasting platforms to machine learning models, predictive dashboards, and more.

But there’s a catch: all that shiny new tech won’t deliver real ROI if it runs on messy, inconsistent data.

It’s like installing a high-performance engine in a car with four flat tires and no steering—powerful, but directionless and stuck.

Why is finance data so hard to trust?

Most finance teams aren’t suffering from a lack of data. More often than not, they’re overwhelmed by it. Critical information is spread across ERPs, CRMs, accounting platforms, bank feeds, and a patchwork of spreadsheets.

Each system speaks its own language, runs on its own schedule, and often requires manual reconciliation. That fragmentation creates a series of all-too-familiar headaches:

Conflicting reports erode confidence in your numbers.

When teams pull the same metric—say, revenue by product line—from different systems, the numbers don’t match. Meetings spiral into debates over who's got the “right” data. Instead of driving decisions, finance ends up stuck refereeing spreadsheet disputes. Confidence drops, and momentum stalls.

Analysts spend too much of their time cleaning data.

Your analysts are trained to surface insights, but all too often they’re stuck sifting through messy data before any real analysis can begin. It’s like hiring a team of chefs and handing them dirty dishes: by the time they get to the actual cooking, the kitchen’s closed. Every hour spent wrangling spreadsheets is an hour not spent identifying risks, spotting trends, or steering the business forward.

Manual workflows slow down reporting cycles.

Without automation, every reporting cycle becomes a race against the clock. Teams scramble to gather, clean, and validate numbers, often uncovering issues at the eleventh hour. This reactive approach burns time, causes stress, and leaves little room for strategy.

Inconsistent inputs throw off AI-powered forecasts.

AI is only as good as the data you feed it. Inconsistent historicals and incomplete inputs lead to forecasts that miss the mark. Instead of unlocking clarity, the models create confusion—raising red flags instead of confidence.

How do you build a centralized data hub that actually works?

Adding more tools won’t solve the problem if the underlying data is unreliable. Before you invest in another forecasting platform, focus on what really drives performance: a rock-solid foundation of clean, connected, consistent data.

That foundation is a centralized data hub. Not a dumping ground for raw exports, but a purpose-built engine that makes modern FP&A actually work.

To support today’s forecasting tools and AI models, your data hub needs five core capabilities:

1. Plug into all your key data sources

Finance data lives everywhere—from your ERP and CRM to payroll systems, bank feeds, and spreadsheets. A strong hub connects to all of them, reliably and automatically, so your team can move from chasing numbers to making sense of them.

2. Align metric definitions across the organization

If every department defines metrics differently, even basic questions become complicated. A centralized hub enforces shared definitions and business logic, ensuring everyone speaks the same financial language (no translation required).

3. Spot issues before they spread

Data issues tend to show up at the worst time. A robust hub runs automated checks as data flows in, surfacing anomalies and outliers before they skew your numbers. It’s like having a smoke alarm that actually works: you want to catch problems early, not after the fire starts.

4. Empower teams without losing control

Finance shouldn’t be a bottleneck. A modern hub gives business teams self-serve access to the data they need, while role-based permissions and audit trails keep everything secure and accountable. It’s freedom with guardrails.

5. Make every number traceable

Every data point should come with a backstory. The hub should show where it came from, how it was calculated, and when it was last updated. That kind of visibility turns audits into a formality and makes your numbers bulletproof in the boardroom.

Implementing the right system

You know what you need to do to clean your data. But how do you get started with the right AI-driven platform? Read the full article on Medium to learn more.

The bottom line: AI alone isn’t the answer

You can’t build a forward-looking finance function on backward data. If you want smarter forecasts, faster insights, and real ROI from your tech investments, start with your data infrastructure.

It’s not the algorithm that makes the difference. It’s the infrastructure underneath it. Build that right, and everything else—forecasting, reporting, strategy—starts to click into place.

Ready to make your data work for you?

At Preql, we don’t just automate and centralize your reporting: we make sure your data is clean, consistent, and ready to power the tools you’ve already invested in. Our white-glove onboarding and support ensures your metrics are aligned, your definitions are standardized, and your team isn’t stuck untangling spreadsheets.

If you’re ready to stop fighting your data and start getting value from your finance stack, we’re here to help. Learn more.

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