How Data Governance Improves Business Decisions and ROI | ProArch

Poor data quality isn’t just an inconvenience— it stalls AI and analytics initiatives, erodes trust, increases compliance risk, and leads to bad decisions. Gartner estimates bad data costs organizations roughly $15 million per year on average.

The strategic impact is clear: it affects profitability and competitiveness. Data-driven organizations acquire customers faster, compounding that advantage over time, so the real question is: how do you put the right guardrails in place to make data work for the business?

To answer that, we spoke with ProArch’s data experts, who have led governance initiatives across industries. They shared why governance often fails, the business risks of poor data, and the practical steps to make governance stick.

What Is Data Governance, And Why It Matters

Data governance is the set of policies, roles, processes, and controls that turn raw information into a reliable business asset.

It defines:

• Who owns data
• Who can access it
• How it’s protected
• How it’s used

With governance in place:

• Decisions are made with confidence
• Compliance risk is reduced
• Customer trust is protected

The Core Pillars of Data Governance

• Data Quality: Ensuring data is accurate, complete, consistent, relevant, and timely.
• Security and Privacy: Protecting data from unauthorized access, use, disclosure, disruption, modification, or destruction while ensuring compliance with privacy regulations.
• Data Stewardship: Clear roles and responsibilities for data ownership and accountability. Watch our recent webinar on this.
• Data Management: Oversight across the entire lifecycle of data, including collection, storage, processing, distribution, and disposal.

How Governance Improves Business Outcomes

When there is a data governance framework in place, it significantly improves your company’s ability to manage and leverage data.

• Improved Compliance → Policies and access controls reduce risk and enable secure practices
• Reliable Data → A single source of truth drives accurate reporting and confident decision-making
• Operational Efficiency → Less time reconciling reports and cleaning bad data
• Better Customer Experiences → Coordinated, personalized services that build loyalty
• Future-Ready Analytics → A strong foundation for predictive and AI-driven insights

Why Governance Programs Lose Momentum (and How to Avoid It)

The bigger problem with bad data isn’t volume — it’s trust. A recent Salesforce survey found confidence in data accuracy is dropping, and fewer than half of leaders say their data strategy aligns with business priorities.

Ask yourself:

• Is your team arguing over which sales numbers are correct?
• Do analysts spend more time cleaning data than analyzing it?
• Do you know who owns your business-critical datasets—or who last accessed it?

These everyday problems explain why governance programs often lose momentum.

3 Data Governance Pitfalls to Avoid:

1. Technology-First Approach (45% of failures)

Organizations rush to buy tools before defining their processes or assigning ownership. Programs end up IT-led, compliance-heavy, and disconnected from business value.

One customer had invested in a licensed model data platform, only to find within a year that it didn’t meet their needs. Adding more data drove costs up, their information was locked into the vendor’s platform, and switching was expensive due to annual commitments.

The lesson: Tools should follow process and people, not the other way around.

2. Lack of Executive Commitment (35% of failures)

Without top-down sponsorship, governance gets treated like a side project—no budget, no momentum, no accountability..

The lesson: Governance is an organizational transformation, not an IT task.

3. Boil-the-Ocean Syndrome (20% of failures)

Enterprise-wide rollouts create endless discussion and documentation without visible progress..

The lesson: Start small, deliver quick wins, and scale over time.

Choosing the Right Data Governance Model

To make governance effective, organizations need the right model to define how decisions are made and how policies are applied across the enterprise. Most follow one of three approaches:

Centralized

• What it is: Single authority, standardized policies, centralized data management.
• Best for: Highly regulated industries and organizations where data currency and control are critical — e.g., banking, insurance, healthcare, government, and large utilities.
• Pros: Easier to enforce standards and audit trails; faster to implement consistent controls.
• Cons: Can be slower, less flexible for local business needs.

Hybrid

• What it is: Central standards for critical areas with decentralized execution for business units.
• Best for: Large enterprises with diverse business units and mixed regulatory needs — e.g., multinational retailers, manufacturing companies with multiple product lines, pharmaceuticals (central control for safety/regulatory data, local flexibility for operational data).
• Pros: Balances control and agility; centralizes sensitive datasets while enabling local innovation.
• Cons: Complexity in coordination; risk of multiple “points of truth” if not well governed.

Decentralized

• What it is: Distributed decision-making and local ownership.
• Best for: Fast-moving, product-led or innovation-first organizations where speed and autonomy matter — e.g., startups, digital-first tech firms, R&D organizations, franchise models where local operators must act quickly.
• Pros: Faster decisions, closer alignment to local needs.
• Cons: Harder to get consistent definitions and compliance across the enterprise.

How to Choose Your Data Governance Model

• Heavy regulation or sensitive data → Centralized or Hybrid
• Global with strong local autonomy → Hybrid
• Fast-moving, product-led orgs → Decentralized (with lightweight guardrails)
• Low maturity → Start centralized/hybrid, evolve as capability grows

How to Get Started with Data Governance

Strong data governance turns information into a reliable business asset—defining ownership, access, and accountability so teams can trust their data.

Here’s how you can start:Spot where poor data quality is draining time, trust, and money.

• Start with people—get buy-in and define ownership. Watch our recent webinar on this!
• Build simple practices first, add tools later.
• Create a framework that balances compliance, speed, and flexibility.

Explore ProArch’s Data Governance Services to get started.