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This piece builds on a recent analysis from A.I.N.S.T.E.I.N. on the ROI case for data governance, extending its argument to explore how the right planning infrastructure can prevent these costs from accumulating in the first place.
Here is a scenario that will sound familiar to anyone who’s worked in finance or data. Someone runs the numbers on their organization’s data quality costs. They discover the damage: nearly $800,000 bleeding out annually through rework, failed projects, and decisions made on flawed information. They bring this to their CFO, expecting to unlock budget for a fix.
Instead, they get two questions: “How did we let this get so bad?” and “How much will it cost to fix?”
The core insight is sharp: executives don’t fund problems. They fund solutions with returns. Framing data quality as a 225% ROI opportunity works better than presenting it as a disaster.
There’s a question worth asking here, though: What if the conversation never had to happen in the first place?
The Real Cost of Bad Data
Data quality costs typically fall into four buckets that most organizations will recognize:
- Rework costs: These costs hit hardest. Data teams spending their time cleaning, reconciling, and validating instead of actually analyzing. That’s not just a labor expense. It’s expertise you’re paying for but not using.
- Failed project costs: Initiatives that never delivered because the underlying data couldn’t support them. Three projects that stall out can easily run into six figures.
- Bad decision costs: These are the trickiest to quantify but often the largest. Choices made on flawed data don’t announce themselves as mistakes until the damage is done.
- Compliance documentation time: It sounds minor until you’re scrambling before an audit, manually reconstructing data lineage that should have been automatic.
The hidden costs compound even faster. When data scientists spend half their time cleaning data, that’s innovation that never happens or products that never launch or insights that never surface. When leaders don’t trust the numbers, they delay decisions. They ask for “one more analysis.” In fast-moving markets, slow decisions are expensive decisions.
And here’s one that doesn’t show up on any calculator: good data professionals don’t stay at organizations with broken data infrastructure. Replacing a senior data scientist can easily run into six figures when you factor in recruiting, onboarding, and lost productivity.
Prevention vs. Remediation: A Different Question
Building an ROI case to fix data quality issues after they’ve accumulated is necessary advice when you’re already deep in the problem.
Organizations choosing or modernizing their planning and analytics infrastructure today have a different opportunity, though. They can build it right from the start.
Every dollar spent on data quality remediation is a dollar that didn’t go toward growth. Every hour your analyst spends reconciling spreadsheets is an hour they didn’t spend finding the insight that changes your Q3 strategy.
Instead of asking how to justify fixing this, ask how to build infrastructure that makes these problems hard to create in the first place.
What Prevention Looks Like
Infrastructure that prevents data quality costs rather than requiring remediation shares a few common characteristics.
A genuine single source of truth. Not a dashboard that pulls from multiple conflicting sources, but a unified data environment where everyone works from the same governed dataset. The “which spreadsheet is current?” problem disappears because there’s only one place where planning happens.
Automated data integration. One-click connections to your ERPs, CRMs, and accounting systems. Structured workflows instead of ad-hoc exports and manual imports. Every time someone copies data from one system to another by hand, errors creep in. Automation eliminates the handoffs where quality degrades.
Write-back capabilities. This is the piece most reporting tools miss entirely. Traditional BI is one-way: data flows in, reports flow out. Planning requires two-way communication. Changes need to flow back to source systems with proper controls. Without write-back, you end up with a gap between your planning environment and your execution data. That gap is where data quality problems breed.
Built-in governance and audit trails. Version control on every change. User rights management that ensures the right people can edit the right data. Complete traceability so when the auditors ask “where did this number come from,” the answer takes seconds, not days.
Real-time collaboration without chaos. Multiple users planning concurrently without data conflicts. No more waiting for your turn with the master spreadsheet. No more emailing files back and forth and hoping you’re working on the latest version. Changes visible immediately across the organization, with proper controls on who can approve what.
The Microsoft Ecosystem Advantage
For organizations already invested in Microsoft tools, the infrastructure choice becomes clearer. Your teams already know Fabric, Power BI and Excel and are productive in those environments. The question is whether those tools can support governed, unified planning or whether they’ll remain one-way reporting instruments.
This is where platforms like Acterys fit. Rather than asking finance teams to learn entirely new systems, Acterys transforms Power BI and Excel into a two-way planning environment with enterprise-grade write-back and workflow capabilities. The familiar interface stays. The governance layer that native tools lack gets added on top.
The data warehouse, the planning models, the consolidation workflows, the audit trails: they all live in one place. Data doesn’t need to be exported, transformed, and imported between systems. The integration is structural, not stitched together with manual processes.
The Conversation That Changes
When planning infrastructure enforces data governance by design, the CFO conversation shifts entirely.
The “how did we let this get so bad” discussion doesn’t happen because the problems don’t accumulate. Data teams analyze instead of clean. Decisions happen faster because leaders trust the numbers. Compliance documentation becomes a byproduct of normal operations, not a fire drill before every audit.
The conversation shifts from “how much to fix this?” to “what’s possible now that we trust our data?” Much better place to be.
Build It Right or Fix It Later
If you’re already dealing with significant data quality costs, framing the fix as an ROI opportunity is how you get budget. That advice holds.
For organizations building or modernizing their planning infrastructure, though, prevention beats remediation. Choose platforms that make data quality problems structurally difficult to create. Build governance into the foundation instead of bolting it on later.
The cheapest data quality problem to fix is the one that never happens.
Learn how Acterys helps CFOs build governed, unified planning environments →
