Editorial
There's so much power in seeing governance as an exciting process for the future. With both the hype and reality of our AI future, governance is the foundation of success. Governance, originally considered necessary for compliance, is now the center of building a data-driven organization. It's at the forefront of value realization. The ability to connect technology and be compliant is not enough; governance must have a strategy.
When people hear "data governance," they often picture compliance paperwork or IT gatekeepers. Leading worldwide organizations and governance teams, however, describe their work as "strategic" and "democratizing." Here's why treating governance as overhead is costing your business revenue and customer advocacy.
Table of Contents
- Monitoring Isn't Governance: Understanding the Distinction
- Why Governance Matters Now More Than Ever
- From Business Silos to Strategic Assets
- Strategy From the Field
- Strategic Value Positioning
- Challenges Identified Through the Process
- My Vision for Hope: Assisting in Humanizing AI and Raising Ethical Consciousness
- Team Orchestration for Success
- Developing the User Experience for Adoption
- Quick Thoughts on Driving Engagement
Monitoring Isn't Governance: Understanding the Distinction
Many enterprises believe they're practicing governance when they're actually just monitoring. This confusion is costing them dearly.
Monitoring tracks what's happening and is conveyed within dashboards showing data quality scores, compliance metrics and system performance.
Governance establishes the frameworks, accountability structures and decision rights that determine what should happen and who's responsible when it doesn't.
Low-quality data and structures produce low-quality outputs, which breeds distrust in decision-making. In today's complex enterprise landscape with multiple data sources, interconnected services and workflows spanning organizational boundaries, this fundamental distinction between monitoring and governing determines whether digital transformation succeeds or stalls.
The good news? Governance done right creates interesting work, drives innovation and delivers measurable returns.
Related Article: The Blueprint for Building Enterprise-Grade AI Governance
Why Governance Matters Now More Than Ever
AI Imperative – According to Gartner, 30% of GenAI proof of concept projects are abandoned due to poor data quality. As a consultant, more than half of the clients I have worked with over the years have data quality issues. I’ve spent many valuable hours rectifying this gap before being able to address the original transformation scope to personalize at scale and improve organizational efficiency.
From Business Silos to Strategic Assets
I have observed several practical practices over the years that can enhance governance, ultimately leading to better profitability and customer experiences. Full data integration is said to occur at less than 5% of enterprises. The initiative to put together the right accountability, build an effective framework and ensure ethical responsibility is taken seriously, is essential.
Ultimately, the goal is to make data widely available while breaking organizational silos and improving the customer experience. The best part is that, as an antidote, it encourages company innovation, builds trust and drives growth.
Strategy From the Field
Here are practical tools proven in the field to evolve current data governance:
- Discovery-first governance
- Measuring governance ROI
- Starting with critical datasets
- Piloting one automation tool
- Implementing key metadata/labeling tags (purpose, owner, sensitivity)
- Establishing a single source of truth to enable data fusion and responsible data usage
- Embedding governance into the culture with internal road shows and publicized examples
The above goes beyond governance and could create an adoption hub for users to learn how to find and use data.
Strategic Value Positioning
CEOs now view data governance as "foundational and crucial to the business" — a transformation from the regulatory compliance view.
Gartner conducted a study in 2023 that substantiates this. The data democracy vision unlocks "an enormous amount of value across the company." The hybrid infrastructure approach addresses both regulatory compliance and commercial constraints.
Currently, the most progressive industries seem to be financial and healthcare institutions. Though not surprising given regulations, they are realizing that when governance is prioritized and in alignment, it leads to growth. I once had a less progressive client comment that they feared I might be "slowing them down" when in fact, the evidence shows that it accelerates the enterprise in terms of not only control but also alignment and confidence. There's more collaboration, data visibility and increases in adaptability. It's a clear path to building trust and how the company moves forward in this dynamic environment.
Challenges Identified Through the Process
Here are top challenges I’ve consistently seen when there’s lack of AI-ready data:
- High-quality metadata population is time-consuming and resource-intensive
- Different user personas require tailored experiences
- Metadata enrichment requires automation
- Organizations must establish a single source of truth to enable data fusion and responsible data usage
- Essential people and processes are not included in the strategy
According to Roxane Edjlali, senior director analyst at Gartner, organizations will abandon 60% of AI projects unsupported by AI-ready data through 2026.
Related Article: Why Bad Data Is Blocking AI Success — and How to Fix It
My Vision for Hope: Assisting in Humanizing AI and Raising Ethical Consciousness
As I matured in this industry, there have been so many developments and individuals that excited me over the years. There have also been rooms where, walking in, I admired particular tech group leaders or products until I actively listened. I found many decision makers didn’t look at the big picture, didn’t consider the greater good, weren’t as knowledgeable to make things happen, nor did they care that they weren’t. AI POC is no different.
However, there’s a twist where the human factor matters more than ever. For companies interested in the greater good of the world, there’s a lot of opportunity for growth and advocacy through AI data governance. From my perspective, great opportunities exist for those organizations that care for people, believe in guardrails, want to win over more than just target markets and contribute to future paradigm shifts in a good way.
I suggest leveraging governance opportunities in areas that seem to be addressed less often. They include:
- Better understanding of large language models (LLMs), machine learning (ML), tokens, etc., and their evolution, training and ongoing changes that affect data guardrails, information quality and integrity, as well as establishing clear ways of working
- Addressing bias and making adjustments for the betterment of society and effective human-AI collaboration
- Easing the feasibility of understanding human commonalities to solve for friction and anticipating societal fears
- Addressing the possibility of giving value to human daily life, creativity and overall well-being in the midst of AI chaos, where livelihood is questioned
- Avoiding the social media influence conundrum in AI, where limiting access and falling victim to curated algorithms, where limiting points of view is common and replacing it with more democratic access and information
- Influencing where we go as a society and shifting towards more unity and community focus
- Build education platforms to provide self-sufficient learning to those who don’t have the resources
Team Orchestration for Success
Policy documents are a partial requirement for effective governance. Beyond mandated roles, GDPR officers and managers under CCPA, consider these positions for monetization and value realization:
- Data Governance Lead: Owns lifecycle rules, including versioning, retention, lineage and provenance for every knowledge asset. This role bridges business strategy and technical implementation, ensuring governance serves operational needs.
- Data Stewards (By Domain): Business-side experts (SMEs) who understand how data is created, used and what quality means in context while maintaining standards.
- QA/Evaluation Engineers: Automate evaluations for faithfulness, toxicity, bias and accuracy. Gate releases based on objective criteria and feed failure cases back into retrieval systems or prompt refinements. This team should work closely with prompt/interaction designers and regularly report on system performance against "must-haves" vs. "nice-to-haves."
- Data Product Managers: Treat governed data as products with roadmaps, user research and success metrics. They ensure governance investments align with stakeholder needs and business priorities
Definitely similar to security and compliance, but closer to the specific data being fed into this instance of the model. These roles should be governed by the overarching data strategy but hyper-aware of what's in this specific system.
Developing the User Experience for Adoption
Here’s an uncomfortable truth: most governance initiatives fail not because of poor frameworks, but because no one uses them and collaboration is not built into daily workflows. Treating internal teams as customers transforms compliance burden into a competitive advantage.
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By providing unified visibility across departments and system, silos breakdown. When a marketing analyst can discover customer data governed by sales and enriched by customer service, innovation happens. Ensure data engineers focus on sustainable infrastructure rather than constant firefighting. This requires executive support for technical debt reduction and architectural improvements.
Related Article: How to Tell If Your Company Is Truly Data-Driven — and What to Do If It’s Not
Quick Thoughts on Driving Engagement
- Tool Integration and Quality Dashboards: Provide portal access with real-time data quality visibility. Users should see quality scores, lineage information and compliance status at a glance.
- Personalized Discovery Hub: This interface adapts to user roles, showing relevant datasets, recent searches, schemas and recommended assets based on usage patterns. Personalization drives adoption; generic interfaces get ignored.
- User Ratings and Comments: Enable dataset ratings, usage notes, risk avoidance and quality feedback. Let community input influence search rankings to surface the most valuable assets naturally.
- Collaborative Features: Support team discussions about data meaning, quality issues and usage patterns directly in the governance platform. Governance shouldn't be a one-way communication channel.
Data governance, often overlooked amidst the digital transformation’s spotlight, plays a pivotal role in driving financial and sustainable results. By proactively rethinking governance within the enterprise and establishing a structure that fosters agility while safeguarding trust, organizations can enhance adoption rates and develop change agents within their corporations.
While data governance may not generate headlines like AI breakthroughs or create viral demos, its consistent growth engine outperforms peers in time-to-market, operational efficiency, risk management and customer satisfaction. Moreover, adopting a strong stance on human-AI collaboration and recognizing its societal benefits will yield positive ancillary effects.
Organizations with mature, strategic governance frameworks and adaptive learning environments are the key to unlocking this potential.
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