Data Quality & Trust Framework 2025: Why Executives Still Default to Spreadsheets

Introduction: The Data Quality Paradox

Even accurate data can be wrong.

We’ve seen it across all industries from fintech, healthcare, pharma, biotech, manufacturing, tech and so on.

A company spends months building the “perfect” data product:

• Dozens of stakeholder interviews.

• Agreed definitions across every metric.

• Unified data foundation.

• Beautiful self-service dashboards.

• Governance program in place.

It should have worked. But within weeks, no one trusted it.

Executives went back to their old, broken spreadsheets.

Why? Because no one asked the most important question:

“What would it take for you to trust this?”

We assumed “accurate” = “trusted.” It doesn’t.

Even the most technically perfect stack will fail without trust.

Why Data Quality Efforts Fail

Most organizations focus on tools and pipelines:

• Warehouses like Snowflake, BigQuery, or Databricks.

• Catalogs like Collibra, Alation, Informatica.

• Policies written in 200-page PDFs.

And yet, CFOs, COOs, and business leaders still default to spreadsheets.

Because governance isn’t about tools. It’s about human trust.

The truth: Data quality is a business adoption problem, not just a technical one.

The 6-Step Framework for Building Data Trust

Here’s the playbook we run with mid-market and enterprise clients.

1. Uncover Trust Triggers Early

Don’t assume logic will win. Ask explicitly:

• “What would it take for you to trust this number?”

Capture these trust triggers and bake them into your quality checks.

-> Related: Data Strategy Roadmap

2. Surface and Reconcile Shadow Systems

Spreadsheets, rogue dashboards, “secret” CSVs — they aren’t wrong. They’re reference points.

• Gather them.

• Compare with warehouse outputs.

• Explain differences transparently.

Kill myths openly, don’t sweep them under the rug.

3. Assign a Single Owner Per KPI

Governance fails without accountability.

• Each KPI = one owner.

• Ownership visible in dashboards.

• Owners responsible for freshness, accuracy, adoption.

This creates accountability loops instead of finger-pointing.

-> Related: Data Governance Framework 2025

4. Make Logic Transparent

“Trust” isn’t just about accuracy — it’s about lineage clarity.

Executives don’t care if it comes from raw_crm_v24_final. They care about:

• Which system it came from.

• How it was transformed.

• Who owns it.

Transparency kills skepticism.

5. Prioritize Ruthlessly

Don’t try to “fix all data.”

• Focus only on metrics tied to business value.

• Sequence by ROI × Complexity.

• Kill vanity metrics.

Every initiative must tie back to revenue, cost, or risk.

6. Feedback Over Policies

Governance isn’t paperwork. It’s adoption.

• Weekly truth checks with stakeholders.

• Ask: “Are you using this? Do you trust it? What’s missing?”

• Adjust based on usage, not policy.

Governance must evolve like a product, not a document.

-> Related: Is Your Data AI-Ready?

Why This Matters for AI

AI doesn’t care if your data is technically accurate. It cares if your data is consistent, trusted, and transparent.

If Finance and Sales don’t agree on revenue, your AI model will just amplify bad inputs faster.

That’s why trust and quality are not optional — they’re the prerequisite for AI readiness.

Case Example: A Fintech Data Team

A fintech lender had invested heavily in a modern data stack:

• Snowflake + dbt + Power BI.

• Centralized pipelines.

• 150+ defined metrics.

But in board meetings, leadership still asked Finance to “pull numbers from Excel.” Why?

• No single KPI owners.

• Conflicting definitions between Finance and Risk.

• No reconciliation with shadow spreadsheets.

After running our 6-step trust framework:

• They standardized 15 business-critical KPIs.

• Aligned Finance + Risk reporting.

• Built lightweight data quality checks directly in dbt.

• Shadow spreadsheets were retired in under 90 days.

Result: executives trusted dashboards, adoption increased, and predictive risk models moved forward.

Summary

• Ask for trust triggers up front.

• Reconcile shadow systems.

• Assign KPI ownership.

• Document logic transparently.

• Prioritize by business impact.

• Run feedback loops, not just policies.

Without trust, even “perfect” data fails.

Want to know if your executives actually trust the data they see?

👉 Take our Data Quality & Trust Assessment (free 10-min diagnostic).