Why Finance Leaders Must Build Unified Data Foundations to Drive Capital Efficiency and Tariff Readiness

As 2026 approaches, CFOs are having to operate and lead finance differently. They are navigating unpredictable tariffs, enabling automation, and delivering insights faster than any year before. These expectations are heavy, so they must proactively address complexities that risk undercutting their strategic agenda, such as inconsistent, fragmented data.

Without a unified foundation of truth, even the most sophisticated planning tools can become expensive calculators and forecasts can lag behind reality. Teams may argue over whose spreadsheet is correct. Strategic decisions may be delayed, not due to lack of insight, but due to lack of alignment. And the root cause can live upstream in other teams, like Procurement. As an example, inaccurate or missing supplier data, mismatched sourcing records, and conflicting capital project identifiers can derail tariff monitoring and cost optimization efforts.

Here we will unpack why the path forward doesn’t always start with AI or dashboards as one might expect, it starts with data architecture. And we offer a real-world example of how one Fortune 500 manufacturer confronted the chaos and rebuilt data clarity.

The Problem: Strategic Finance Can’t Thrive on Dirty Data

Recent trend analyses point to several converging forces that magnify the need for trusted data:

• Tariffs have been front page news. While the exact tariff percentages fluctuate, sourcing decisions are once again being shaped by shifting rules of origin, import classifications, and trade relationships.
• AI and automation are now table stakes for finance organizations, but require clean, structured data to deliver real value.
• Capital allocation scrutiny is intensifying, as investor expectations grow, and cost of capital fluctuates.

But here’s the disconnect: most teams still operate across siloed systems such as a Excel trackers, SharePoint lists, outdated ERPs, and half-implemented cloud platforms.

This issue came to a head in a recent engagement where a Fortune 500 company needed help with supplier outreach tied to large-scale capital expenditures projects. Procurement leaders were struggling to reconcile tariff codes, project names, and vendor classifications across multiple systems. As a result, tariff modeling was impossible, project delays were mounting, and compliance risk was escalating.

We discovered there were:

• Over 500 capital projects out of sync between their cloud-based business planning and performance management platform and their “master” Excel tracker
• Dozens of duplicate or missing projects, with conflicting naming conventions
• No centralized source of truth for HTS codes or country-of-origin, despite growing tariff exposure
• No relational logic, just flat files shared through email

This is not a rare issue. 

Build the Backbone Before the Tools

Too many finance teams leap into analytics, automation, and AI before cleaning their house, with the result being dashboards no one trusts, forecasts built on shaky assumptions, and “planning” that is mostly reactive.

Here’s how we recommend CFOs tackle the issue:

Phase 1: Foundation

Inventory all data sources across finance, procurement, supply chain, and ops. Don’t forget hidden Excel trackers. Then define standard taxonomies, naming conventions for projects, vendors, locations, divisions, and financial attributes.

Pro Tip: Create a dictionary of agreed terms and attributes. If one team says “Houston Branch” and another says “H-01,” align them.

Phase 2: Reconciliation

Cross-reference all datasets. Identify mismatches, gaps, or duplications. Create a clear “golden record” for every key entity, especially capital projects, vendors, and tariff identifiers (HTS + country of origin).

In the real case noted earlier, 33 projects existed in a Project Management tool that weren’t in the “master” list, and hundreds the other way around.

Phase 3: Relational Modeling

Use SQL or your preferred tool to build a relational architecture. Move away from flat spreadsheets. Think in terms of tables: Projects, Financials, Vendors, Attributes. Normalize and link them.

This allows you to run queries like: “Show me all active projects with country-of-origin = China and tariff impact over 12%.”

Phase 4: Enable Use Cases

Now that you trust the data, reconnect your systems (cloud platforms, dashboards, ERP). Layer in your value-add tools: forecasting engines, tariff models, capital ROI calculators, audit prep tools.

Never plug dirty data into a smart tool because it just automates chaos.

Real-World Example

A manufacturer had recently migrated their capital project planning to a cloud-based performance management platform, but their procurement data was still scattered across legacy spreadsheets. As supplier information came in tied to CapEx projects, the gaps in country-of-origin and HTS code tracking became impossible to ignore. Vendor outreach was inconsistent, and tariff forecasting stalled because no two systems matched. What started as a data quality issue quickly became a compliance risk. 

• New project requests weren’t showing up in their new platform
• Tariff exposure by HTS code was impossible to model with confidence
• Project teams were manually entering country-of-origin in two systems
• Stakeholder meetings spent 30 minutes debating which version of the file was “right”

The turning point came when a global VP admitted, “We don’t need a new tool. We need to agree on what’s real.”

That sparked a full reconciliation initiative. In six weeks, focused on clean data architecture, they were able to:

• Consolidate project records across sources
• Normalize vendor and tariff fields
• Build a SQL-based relational dataset to serve as the backbone
• Automate reporting feeds into the new platform and dashboards
• Surface early wins like de-duplicated projects, flagged anomalies, and quick-hit tariff mitigations

The Result: No more hours of Excel wrangling. And project planning meetings now start with strategy, not data triage.

What This Enabled: Strategic Finance Without the Static

With a clean, centralized data foundation, this manufacturer unlocked:

• Smarter sourcing decisions, based on enabling faster evaluation of supplier responses 
• Faster planning cycles, since teams trusted the data
• Tariff simulations based on real HTS + COO pairings
• Audit readiness, thanks to traceable data sources
• Capital efficiency, by eliminating duplicates and prioritizing high-ROI investments

And, most critically, they rebuilt cross-functional trust. Finance, Procurement, and Operations were finally speaking the same language.

Checklist: Are You Building a Backbone or a Bottleneck?

Ask yourself:

• Do our systems share a common naming convention for projects, vendors, and financial attributes?
• Can we trace each capital investment through its full lifecycle, from request to execution to ROI?
• Can we simulate changes in tariffs or sourcing and see the financial impact in real time?
• Are cloud platforms, Excel, ERP, and other tools synced, or manually reconciled each quarter?
• Can I say with confidence: “We have one system of truth”?
• Are we capturing supplier data in a way that informs sourcing and capital spend decisions?

If you answered “no” to more than one, your strategy may be built on sand.

Final Thought: This Isn’t a Systems Project. It’s a Leadership Imperative.

CFOs can’t afford to be stuck in the weeds, but they also can’t delegate data quality or supplier data integrity to IT and Procurement alone. Building a unified data foundation is not a side project. It is a core competency of modern finance leadership.

Strategy changes fast, investor expectations are higher than ever, so clarity is your edge.

And clarity begins with clean data.