Trench Tale #3: What Really Makes Data Governance Stick

At a recent AASCIF Data Track Zoom session, I had the opportunity to co-present with a State Fund colleague on a topic we’ve both lived deeply: how to build data governance that actually works. Not the kind that looks good in policy docs or gets announced at a town hall, but the kind that sticks. The kind that changes the way organizations use, trust, and think about data.

What became immediately clear in the session was this: nearly everyone had tried something related to data governance. And nearly everyone had seen it struggle. It wasn’t for lack of effort, or even executive interest. The issue, as always, was in the execution.

The Illusion of Starting with Tools

Most governance efforts start from the wrong end of the map. Organizations buy a shiny new tool—a data catalog, a quality dashboard, a metadata harvester—and expect it to create structure. But tools reflect structure; they don’t create it.

At Datagize, we anchor governance on three forces: People, Process, and Technology. But what makes that model work is the sequence in which they show up. It starts with principles: clear statements about how your organization believes data should be used, protected, and trusted. Then come the structures—your charter, your roles, your escalation paths. Only after that scaffolding is in place should technology enter the picture.

Skipping this sequence leads to shelfware, not stewardship.

What Actually Worked

In one engagement with a State Fund, we led with principles and purpose. That meant defining beliefs about openness, data risk, and accountability. We then co-created a governance charter and formed a data governance committee composed of business and technical leaders across the organization. The right people were critical—not just role-fillers, but passionate, well-positioned individuals who could influence change.

From there, we prioritized high-impact data domains, focusing first on areas like claims, underwriting, and policyholder services where inconsistencies had real-world consequences. Only once those foundations were in place did we select a data cataloging tool to support the structure we had already built.

The result? Shared definitions. Certified datasets. Self-service reporting. And most importantly, business ownership of data.

Common Pitfalls to Avoid

Governance fails when:

  • IT tries to own it instead of enabling it
  • The organization tries to boil the ocean instead of starting small
  • ROI is assumed, not shown

The big question to ask is: Where is data causing rework, disputes, or decision delays? That’s where governance needs to start.

Also: remember that governance committee members have full-time jobs. Respect their time. Use working sessions to energize documentation and decision-making.

What Made It Stick

What worked in this engagement wasn’t magic. It was:

  • Executive sponsorship
  • Tangible early wins
  • Clear roles and decision rights
  • Cross-functional collaboration
  • Alignment among governance, analytics, and modernization

The client embedded governance into the day-to-day. Jira tickets now include governance metadata. Tableau dashboards are tied to certified datasets. Governance isn’t a side job—it’s part of the workflow.

And trust? That grew because the people using the data were the ones shaping its meaning.

What’s Next

Governance isn’t standing still. AI and automation are pushing data to its limits. Regulators are shifting from trusting policies to requiring proof. Cloud platforms expect you to arrive with a model, not build one on the fly.

Governance must evolve. That means:

  • Investing in maturity, not headcount
  • Integrating DG into analytics and AI planning
  • Embedding governance into the tools people already use
  • Upskilling your people to steward data with confidence

Our Approach

At Datagize, we built the DG Accelerator for organizations that need to move fast. It’s a five-week sprint with structure, working sessions, and real decisions—not just theory. But we also offer a Collaborative Roadmap for those who need to move together, aligning gradually and building buy-in along the way.

Both work. The key is setting the right pace.

Let’s build governance that lasts—not as a checkbox, but as a competitive advantage.


Want to dig deeper? Reach out to start your own Trench Tale.

Why Data Governance is More Critical Than Ever in an AI-Driven World

Introduction: AI is Accelerating the Need for Stronger Data Governance

The rapid rise of generative AI is increasing the urgency for organizations to strengthen their data governance frameworks. According to McKinsey’s 2024 State of AI report, 65% of organizations are now using generative AI, nearly double the percentage from just 10 months ago. Gartner predicts that by 2026, over 80% of enterprises will have integrated Gen AI into their operations.

Yet, as AI adoption soars, many organizations are relying on unstructured, inconsistent, and poorly governed data to feed these models. The result? Misinformed AI outputs, regulatory risks, and compounding data integrity issues. Companies must act now to assess their governance gaps and strengthen oversight before AI-driven insights lead to unreliable decisions.


Governance Gaps in an AI-Enabled Data Landscape

Generative AI models are only as good as the data they’re trained on. If organizations lack governance, they risk:

  • Inaccurate & Biased Outputs – Poor data quality leads to AI “hallucinations,” generating false or misleading results.
  • Security & Privacy RisksUnprotected PII and proprietary data may be exposed through AI interactions.
  • Compliance Failures – Regulations like GDPR, HIPAA, and the EU AI Act demand transparency, fairness, and accountability in AI applications.
  • Lack of Data Trust – Without governance, companies struggle to ensure data quality, leading to poor decision-making.

As George Firican, a leading data governance expert, puts it:

“Data governance and data privacy go hand in hand. Without strong governance, organizations can’t ensure compliance, security, or data integrity.”

Organizations must rapidly evaluate their governance gaps to prevent AI from compounding data-related risks instead of solving them.


The Risks of Poor Data Governance in an AI-Powered World

The consequences of weak governance in AI deployment are already becoming evident:

  • Data Integrity Issues: Poorly governed data leads to inaccurate reporting, inconsistent business metrics, and flawed AI-driven insights.
  • Regulatory Violations & Fines: Organizations lacking structured data governance risk non-compliance with regulations like GDPR, HIPAA, and the EU AI Act.
  • Data Silos & Inefficiencies: Without governance, organizations struggle to maintain centralized, accessible, and high-quality data for enterprise-wide AI initiatives.

McKinsey warns that without strong governance, AI-driven decision-making can lead to reputational damage, legal challenges, and loss of customer trust.


Data Governance’s Role in Compliance & Risk Management

Governments worldwide are introducing stricter regulations to ensure responsible AI adoption, but data governance is the foundation of compliance. Organizations can’t meet regulatory requirements without structured governance ensuring data quality, lineage, and security.

Key ways data governance supports compliance:

  • Data Transparency & Auditability – Strong governance ensures organizations can trace data lineage and maintain records to prove compliance.
  • Access Controls & Data Classification – Enforcing role-based access and securing sensitive data helps meet GDPR and HIPAA standards.
  • AI Training & Data Ethics – Organizations with clear governance policies can mitigate AI bias and prevent the misuse of sensitive data.
  • Regulatory Alignment – Governance frameworks help companies adapt to evolving AI-related regulations like the EU AI Act without disruption.

Without proactive data governance, companies risk reactive, last-minute compliance efforts that lead to rushed implementations, costly fines, and reputational damage.


How Organizations Can Strengthen Data Governance in an AI-Enabled World

To future-proof data governance, organizations must embed governance into their overall data strategies by:

🔹 Standardizing Data Definitions & Business Rules – Ensure enterprise-wide consistency in how key metrics and terms are defined.
🔹 Implementing Data Lineage & Cataloging – Establish a clear understanding of where data originates, how it flows, and who has access.
🔹 Improving Data Quality & Master Data Management – Deploy processes that continuously validate and cleanse data before it enters AI models.
🔹 Enhancing Access Control & Security Policies – Ensure sensitive data is classified correctly and governed with role-based access.
🔹 Aligning Governance with Regulatory Compliance – Adapt governance frameworks to meet GDPR, the EU AI Act, and evolving global data laws.


Conclusion: Data Governance is the Foundation for AI Success

The AI revolution is here—but without strong data governance, it creates more problems than solutions. As companies accelerate AI adoption, they must ensure their governance frameworks evolve just as quickly to maintain trust, accuracy, and compliance.

📩 Want to assess and strengthen your data governance? Let’s strategize, energize, and datagize your governance framework today.

The Truth About Data Assessments

Why ‘You Don’t Know What You Don’t Know’

Introduction: A Common Analytics Challenge

Many organizations are investing heavily in data analytics, confident that their insights are driving smarter business decisions. Yet, a closer look often reveals a different reality—misaligned metrics, inconsistent definitions, and eroding trust in reporting.

Teams across different business units define and interpret the same KPIs differently, leading to executives receiving “directionally correct” (but ultimately unreliable) reports. The result? Decision-makers second-guess the numbers, and teams spend more time debating data than acting on it.

This isn’t a failure of technology or effort—it’s simply that many companies don’t have a clear, unified assessment of their data landscape. That’s where a structured data assessment comes in.


The Metrics Mismatch: When Data Doesn’t Add Up

One of the most common issues we see in organizations is metric inconsistency. Take a simple KPI like “customer churn.” Does it mean customers who canceled a subscription? Customers who stopped engaging? Those who downgraded a service? Depending on who you ask—marketing, finance, or customer success—the answer (and the calculation) may be different.

Without a unified definition, teams unintentionally create data silos, and executives receive reports that don’t match up. When leadership starts questioning reports instead of trusting them, data-driven decision-making stalls.

A data assessment identifies these gaps, helping organizations standardize key metrics and ensure alignment across teams.


The Hidden Costs of Spreadsheet Overload

Even when data alignment issues are addressed, many companies still face another major hurdle—the sheer manual effort required to compile reports. We see this all the time: analysts with MBAs spending 80-90% of their time pulling data from different sources, manually merging spreadsheets, and reconciling inconsistencies.

This problem—what we call “spreadsheet hell”—not only wastes valuable talent but also delays insights and increases the risk of human error. A data assessment can pinpoint these inefficiencies and lay out a roadmap for automating reporting workflows, freeing up analysts to focus on high-value analysis instead of data wrangling.


How a Data Assessment Uncovers Opportunities

A Datagize Data Assessment is designed to provide a clear, actionable understanding of an organization’s data health. It evaluates:

  • Metric & Definition Alignment – Are business metrics standardized and consistently defined across teams?
  • Data Governance & Security – Is sensitive data properly classified, protected, and accessible only to the right people?
  • Organizational Readiness – Does your team have the right skills and processes in place to scale data initiatives effectively?
  • Cloud & Architecture Health – Is your infrastructure optimized for performance, scalability, and cost efficiency?
  • Data Maturity Benchmarking – Where does your organization stand on the analytics maturity curve (Gartner, TDWI, etc.)?

Through this process, organizations gain a clear picture of their data landscape—where they’re strong, where there are gaps, and what steps to take next.


Conclusion: You Can’t Optimize What You Can’t See

The reality is that most organizations are further along in their analytics journey than they think in some areas—and further behind in others. The key is knowing where to focus to maximize the value of your data investments.

A structured data assessment helps organizations move forward with confidence, eliminating blind spots and setting the foundation for a truly data-driven future. If you’re looking to streamline reporting, improve data trust, and accelerate insights, let’s start with a conversation.

📩 Interested in understanding your data landscape? Reach out to Datagize for a comprehensive Data Assessment today.