Build the Data You Wish You Had: A CFO’s Guide to Dismantling Silos and Structuring for Insight

Modern finance teams carry a bigger mandate than ever. You must control cost, protect cash, sharpen forecasts, guide pricing, manage risk — and do it with leaner teams. Talent is tight, productivity must rise, and leadership expects better decisions sooner.

If you’re like many CFO’s we speak with, the blocker you feel most is the silo pattern: acquisitions add new ledgers, point tools sit off to the side, and planning lives on islands in Excel. You pay for it with conflicting definitions, slower cycles, and higher risk.

The fix is a shared model of the business — one place where transactions land, master data stays governed, and every report or analysis pulls from the same meaning, so the path from transaction to decision gets a whole lot shorter.

Start with the Decisions That Stall

Make breaking silos part of the scope. Map where today’s numbers actually live (ERP, AP, payroll, CRM, planning workbooks), choose the authoritative source for each object (company, account, customer, project), and route everything into one model with a single calendar and exchange-rate policy. When definitions travel with the data, debates fade and momentum builds.

A practical first move is close and reconciliations. It’s where quality problems surface in daylight and where cycle-time wins are most visible. With consistent entities, dimensions, calendars, and exchange rates — and clear lineage — your team spends less time arguing the number and more time acting on it.

From there, target the decision arenas that slow you down. For example, executives we work with cite cost containment, cash flow management, reliable forecasting, and data timeliness as ongoing priorities. Define what “fast and reliable” means for each, then sequence work to hit those marks week after week.

Treat Data as a Finance Product, Not a Side Project

Successful teams run data like a product the business consumes: it has an owner, a purpose, and a roadmap. In finance, that “product” mirrors how you manage the company: multi-entity ledgers, a governed chart of accounts, dimensionality for customers, projects, cost centers, and channels, and transaction-level detail with a clear roll-up path.

Publish that structure as a shared semantic layer — the antidote to silos — so reporting, analysis, and planning use the same dimensions and measures, and finance moves from reconciling versions to steering outcomes.

Sequence for Outcomes, Not Perfection

Perfection stalls momentum. Take a 90-day run at one concrete outcome — for example, close the month within four business days and publish one consistent P&L by entity and channel. Define what ‘done’ means, set up a single place for the data to land, build the core metrics you rely on, surface them in the tools your team already uses, and add simple monitoring so exceptions light up before they slow you down.

From there, extend the same pattern to working capital, project profitability, or inventory health. Each expansion reuses the master data, controls, and pipes you already built, so the wins compound.

How to Get There

There are plenty of ways to execute a data-first finance strategy. We favor Microsoft because it helps you end the silo pattern without adding new ones.

• Dynamics 365 Finance (or Dynamics 365 Business Central) gives you the multi-entity control plane and dimensional structure that finance relies on.
• Fabric brings a governed landing zone (OneLake), pipelines, and a shared semantic model so data lands once and feeds both Power BI and Excel without copy-pasting extracts.
• Power BI puts your model in play (embedded in Dynamics and available in Excel) so leaders explore the same definitions without spinning up new spreadsheets. As questions get sharper, you refine the model once and every report reflects it.

With these tools, you’ll move faster today, and you have a clear on-ramp for Copilot and AI agent-style automation when you’re ready—on your terms, with governance intact.

A quick word on AI maturity: Deloitte reports that while a majority of finance functions are piloting or using AI (63%), only about one in five say it’s already delivering clear, measurable value — and fewer still have fully integrated AI agents into finance workflows. That gap argues for our approach: clean definitions first, embed insights where work happens, then automate specific tasks with controls.

Executive Proof Points

You’ll feel progress in how the week runs. Close checkpoints get quieter, FP&A moves from wrangling extracts to mid-month scenarios, and leaders ask “what if” in the meeting because answers arrive while the discussion is live. Controllers can trace a figure back to the source without a side quest through email threads. Audit requests turn into self-serve pulls with clear lineage. When a new entity or product launches, you plug it into the model without rebuilding reports.

These are the proof points that matter: less debate, faster cycles, cleaner handoffs, and decisions made in the moment — visible signals that the foundation is working and the finance function is adding more value with the same team.

The Takeaway

Your mandate is clear: do more with less while raising the quality of decisions. You get there by shortening the path from transaction to action — one decision arena at a time — on a governed data foundation that your whole team trusts. Treat data as a product owned by finance, operate it with IT, and sequence work for visible wins in 90 days.

The result is a function that meets today’s pressures — cost, cash, speed, and risk — while setting you up to take advantage of the next wave of tools on your terms. That’s how you build the data you wish you had — and use it to lead. Velosio is here to help.

This blog post is from our webinar, “Top Five Ways to Solve Data Management Issues in Finance Departments.” For more information on the subject, watch the webinar below: