Data Governance
The Strategic Blind Spot That’s Costing You Growth
The Data Standoff
There’s a moment in nearly every engagement where the energy shifts. Momentum stalls.
Sales swears the CRM is “mostly clean.” Marketing insists the email list is fine, just needs “some tagging.” RevOps is running reports, but nothing adds up. Leadership wants attribution. Marketing wants clarity. And nobody wants to own the mess.
Welcome to the Data Standoff ! One of the most quietly expensive problems mid-size companies face. Especially those that have grown quickly, merged teams, or layered tools on top of one another like sediment. Everyone agrees the system is messy, but nobody claims responsibility.
Dirty data doesn’t just slow growth. It erodes it from every angle.
The Ownership Vacuum
Everyone touches the data. Few own its integrity.
Sales calls their CRM “the truth.” Marketing builds lists and segments. IT controls infrastructure and integrations. Finance depends on reports for forecasting. Each assumes someone else is keeping things accurate. In reality, definitions drift, duplicates multiply, and accountability disappears.
The result? Thirty-six versions of the same customer, shadow spreadsheets hidden on desktops, dashboards that don’t match, and a leadership team unsure which numbers they can actually trust. The root of the problem? No one has defined a system of record: the official home for each type of data.
What Is a System of Record?
A system of record (SOR) is the officially designated home for a type of data. Salesforce might be the SOR for accounts and opportunities. HubSpot might be the SOR for campaigns. Without it, every department creates its own version of the truth.
The problem isn’t just that ownership is unclear; it’s that the very structure of where data lives has shifted. What used to be a single, central database is now a patchwork of systems, each holding its own version of the truth.
Then vs. Now: Where Data Lives
In the past, most organizations relied on a central relational database. All tools - marketing, sales, finance, pulled from and updated that single source. Governance was still an issue, but at least there was one hub to manage. Everyone knew where the data lived, and the challenge was simply keeping it clean¹.
Today, that structure has splintered. CRMs like Salesforce or HubSpot claim to be the customer truth. Marketing automation platforms maintain their own records. Finance and ERP systems hold billing data. Data warehouses such as Snowflake, Redshift, or BigQuery try to unify. And dozens of SaaS tools each capture a slice of the customer relationship².
Instead of one master database, we now talk about declaring systems of record by domain - Salesforce for accounts, NetSuite for finance, HubSpot for campaigns, so each type of data has an authoritative home. Without those designations, companies end up with multiple versions of the truth and no reliable foundation³.
Some firms are trying to swing back toward centralization using modern cloud data warehouses as the hub. Everything pipes into Snowflake, Databricks, or BigQuery, and downstream tools consume clean data from there. In theory, it’s the best of both worlds. In practice, very few mid-market companies execute this well. Without strong governance and data engineering discipline, the warehouse simply becomes another silo⁴.
Yesterday’s single source has become today’s patchwork of truths.
The Real Cost of Bad Data
The costs of poor data quality don’t show up as a single budget line item. They leak out everywhere.
Gartner estimates the average organization loses $12.9 million annually⁵ to bad data quality. IBM put the U.S. economy’s loss at an astonishing $3.1 trillion per year⁶. Employees themselves feel the drag: as much as 27% of their time is wasted⁷ dealing with bad data instead of adding value.
Forrester found that more than a quarter of analytics professionals say their company loses over $5 million per year⁸ to poor data quality, and 7% report losses above $25 million. Other studies suggest it eats up 15–25% of the average company’s operating budget⁹.
The consequences aren’t just financial. One survey found that 86% of executives admit they’ve made wrong decisions because of inaccurate data¹⁰. When decisions, strategies, and forecasts rest on a shaky foundation, the business as a whole is at risk.
Bad data isn’t a side expense. It’s a silent tax on every decision you make.
From Hoarding to Harnessing
For many companies, the instinct is still to collect more. More integrations, more custom fields, more dashboards. The thinking goes: if we capture everything, insight will follow.
But the reality is the opposite. More data without governance creates more noise. CRMs turn into junk drawers, filled with fields nobody uses, workflows nobody monitors, and free-text entries that mean nothing.
Executives are beginning to recognize the problem. In a 2025 survey, 64% of leaders said data quality is now their top data integrity challenge¹¹, up from 50% just two years prior.
Harnessing data doesn’t mean gathering more. It means stewarding what matters. Cleaner fields. Standardized definitions. Clear ownership. A smaller, sharper picture of the customer … not a bloated, unreliable one.
AI Is Pulling the Curtain Back
For years, companies could get away with messy data. A clever analyst could massage a report, a sales leader could adjust a forecast, and instinct could fill in the blanks. The cracks were there, but they stayed hidden beneath layers of manual workarounds and selective visibility.
AI has changed that. Models and automations don’t gloss over inconsistencies, they expose them. A chatbot trained on duplicate records delivers conflicting answers. Predictive models built on incomplete inputs generate the wrong customer segments. Generative tools pulling from enterprise systems repeat errors with authority, spreading misinformation faster than ever.
Instead of covering governance problems, AI magnifies them. What once felt like small operational headaches are now reputational and strategic risks. Leaders can no longer paper over bad data — because AI is pulling the curtain back.
AI doesn’t fix bad data. It spotlights it.
Tool Reality: Where It Breaks Down
Technology is often treated as the solution. Add another tool. Layer in another integration. Buy the newest data platform.
But tools don’t fix bad data. They amplify whatever processes you already have, or don’t.
In Salesforce and HubSpot, lifecycle stages often fall out of sync. Leads appear in one system but vanish in the other. Both systems try to be authoritative, and duplicates pile up. Alignment requires strict governance: mapped picklists, controlled field creation, even a “field graveyard” to retire outdated values.
Sales Navigator introduces another crack. Reps export lists and re-upload them into the CRM with inconsistent formatting and ownership. Without a defined integration path and clear enrichment rules, records fragment.
Event platforms like Cvent or Eventbrite create their own brand of chaos. Attendees type company names however they like, producing multiple versions of the same organization. Without validation at entry, “IBM,” “I.B.M.,” and “International Business Machines” live as separate entities.
Even the humble web form becomes a liability. Entries like “abcdef” in the company field or “test@test.com” in the email slot aren’t rare. Progressive profiling and real-time validation are essential to keep garbage out.
And when marketing automation syncs with CRM, duplicates multiply if both systems think they’re the “system of record.” Without clear governance, you end up with two versions of every contact.
Without governance, your tech stack is like letting every contractor remodel your house without a blueprint. You’ll end up with ten kitchens, six bathrooms, and no plumbing that connects.
The Brand & Business Blind Spot
It’s tempting to see bad data as an internal operations problem. In truth, it’s a brand problem.
Customers don’t blame your CRM for the misfire. They blame you. The email that arrives after they’ve canceled. The renewal notice addressed to the wrong contact. The “personalized” offer that shows you don’t know who they are. Each erodes trust.
Inside the business, attribution becomes unreliable. Marketing can’t prove ROI without clean data. Budget conversations turn skeptical. Leaders hesitate to act because nobody can agree which numbers are real. Strategy slows to a crawl.
Governance, put simply, is brand governance.
The C-Suite Playbook
The good news: governance doesn’t have to be complicated. It requires clarity, ownership, and habit more than infrastructure.
A growing number of leaders are realizing this. One survey found that 61% of data professionals said improving data quality and trust is now their top governance priority¹². Another reported that 65% of data leaders cite governance as their main focus in 2024¹³.
And the results are measurable. Organizations with formal governance see 41% fewer data-related incidents and 29% higher regulatory compliance¹⁴. Governance works when leadership commits.
So where should the C-suite start?
Inventory: Map systems, integrations, and critical fields.
Define: Assign systems of record, draft a data dictionary, and freeze unnecessary field creation.
Clean: Deduplicate, normalize, and repair broken reports.
Govern: Establish monthly hygiene, quarterly audits, and annual reviews.
Field Change Request Form
To prevent chaos, every new field request should answer:
Who is requesting it?
What’s the definition?
Why is it needed?
What type is it (picklist, text, numeric)?
Who owns it?
When should it be reviewed or retired?
Proof of Perspective
My perspective comes from doing this work at depth. In the first decade of my career, I wasn’t just observing database challenges - I was in the trenches with them. I spent years working directly with marketing databases: segmentation, modeling, and even cleaning client data before it ever entered our systems.
One of my earliest milestones was leading the transition from mainframe marketing systems to one of the industry’s first standalone platforms (Group 1 Software). What once took days of manual coding and batch processing to segment and model could suddenly be done in hours, with sharper accuracy and far more intelligence.
That early immersion taught me a truth that has guided my work for three decades: governance isn’t IT housekeeping. It’s the foundation of marketing effectiveness. Clean data isn’t just operationally efficient; it’s what makes strategy, measurement, and customer connection possible.
Case Snapshots
These aren’t theoretical. Here’s what governance in practice looks like:
A B2B SaaS company with $50M ARR cut its duplicate contacts from 18% to under 3% in eight weeks, reducing forecasting variance by 40%.
A multi-division consumer brand standardized retailer IDs across systems, enabling clear post-promotion analysis.
A professional services firm restored deliverability after merged lists tanked its email reputation, cutting bounce rates with stricter permissions and validation.
Each case reinforces the same lesson: when governance takes hold, growth accelerates.
Conclusion: Data Doesn’t Clean Itself
For most organizations, data problems aren’t caused by a lack of tools or even a lack of effort. They come from neglect - the quiet decay that happens when no one owns the foundation. Left unchecked, that neglect compounds until it erodes trust, slows growth, and blinds leadership.
Governance isn’t bureaucracy. It’s clarity. Data doesn’t just tell your story … it builds or breaks it. Treat it like infrastructure, not afterthought.
Data governance isn’t a one-time project. It’s a habit. And it’s everyone’s job.
Good governance doesn’t have to be complicated. Let’s start simple.
Sources
Kovair – Database Management: Centralized vs. Decentralized (2023). https://www.kovair.com/blogs/database-management-centralized-vs-decentralized/
Tamr – Centralized vs. Decentralized Data Management (2023). https://www.tamr.com/blog/centralized-decentralized-data-management/
Alation – Cloud Data Warehouse Migration 101 (2024). https://www.alation.com/blog/cloud-data-warehouse-migration-101/
Dataversity – Putting a Number on Bad Data (2023). https://www.dataversity.net/putting-a-number-on-bad-data/
IBM estimate via Segment – The Cost of Poor Quality Data (2023). https://segment.com/blog/cost-of-poor-quality-data/
Monte Carlo Data – The Cost of Poor Data Quality (2023). https://www.montecarlodata.com/blog-the-cost-of-poor-data-quality/
Forrester – Millions Lost in 2023 Due to Poor Data Quality (2023). https://www.forrester.com/report/millions-lost-in-2023-due-to-poor-data-quality-potential-for-billions-to-be-lost-with-ai-without-intervention/RES181258
ResearchGate – The Costs of Poor Data Quality (study). https://www.researchgate.net/publication/277237089_The_costs_of_poor_data_quality
FirstEigen – Cost of Bad Data (2024). https://firsteigen.com/blog/cost-of-bad-data/
Precisely – 2025 Planning Insights: Data Quality Remains the Top Data Integrity Challenge (2025). https://www.precisely.com/data-integrity/2025-planning-insights-data-quality-remains-the-top-data-integrity-challenges/
Secoda – Data Governance Survey (2024). https://www.secoda.co/blog/data-governance-survey
Atlan / Humans of Data – Future of Data & Analytics 2024 (2024). https://humansofdata.atlan.com/2024/03/future-of-data-analytics-2024/
13. Secoda – Data Governance Survey (2024). https://www.secoda.co/blog/data-governance-survey
14. Atlan / Humans of Data – Future of Data & Analytics 2024 (2024). https://humansofdata.atlan.com/2024/03/future-of-data-analytics-2024/
Feel free to share this with your social networks!