Data Quality & Trust Framework 2025: Why Executives Still Default to Spreadsheets
Even accurate data can fail if no one trusts it. Learn a proven 6-step data quality and governance framework to build adoption, trust, and AI readiness.
Ali Z.
𝄪
CEO @ aztela
Table of Contents
Excellent — this draft is strong but, as with the last ones, it needs to be cleaned and packaged into Aztela’s final blog format. I’ll:
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Meta Title
Data Quality & Trust Framework 2025: Why Executives Still Default to Spreadsheets
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Introduction: The Data Quality Paradox
Even accurate data can be wrong.
We’ve seen it across financial services, healthcare, pharma, biotech, manufacturing, and technology.
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, executives stopped using it. They went back to 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
This is 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.
See: Aztela 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.
See: Aztela Data Governance Framework
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.
Run 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.
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 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.
Retired shadow spreadsheets in under 90 days.
Result: Executives trusted dashboards, adoption increased, and predictive risk models moved forward.
The Blunt Bottom Line
Without trust, even “perfect” data fails.
If executives are still defaulting to spreadsheets, your governance isn’t working.
And if your data isn’t trusted, your AI and analytics investments are dead on arrival.
Schedule a Data Strategy Assessment to see if your executives truly trust the numbers they’re using.
FAQs
What is a data quality framework?
A structured approach to ensuring data is consistent, accurate, transparent, and trusted by business stakeholders.
Why do executives still default to spreadsheets?
Because spreadsheets feel “closer to the truth.” Without KPI ownership, reconciliation, and transparency, executives won’t trust dashboards.
What industries face the biggest data trust issues?
Financial services, healthcare, and regulated industries where compliance and reporting accuracy are critical.
How do you measure data trust?
Track adoption, reconcile shadow systems, and run stakeholder trust surveys alongside technical data quality checks.
Why is data trust important for AI?
AI amplifies bad inputs. Without trusted, reconciled data, AI models produce confidently wrong outputs that erode adoption.
