Studies reveal a startling fact: an estimated 85% of all enterprise data is “dark” – redundant, obsolete, or unclassified information that creates a massive, hidden liability.
For a company like GE, this translated into petabytes of unidentified data driving up storage costs.
By implementing a governance-led initiative to identify and manage this data, they were able to achieve an estimated $30 million in savings. This demonstrates that a lack of data governance isn’t just a risk; it’s a direct drain on financial resources.
A data governance framework is the strategic document for shining a light on this dark data. It establishes the rules for data lifecycle management, ensuring you aren’t paying to store and secure information that provides no value.
This article is a practical guide for building a framework that not only improves data quality and security but also delivers a tangible return on investment by reducing waste and optimizing resources.
Key takeaways
- A lack of data governance is a direct financial drain; an estimated 85% of enterprise data is redundant or obsolete, creating hidden liabilities. By implementing a governance framework, companies like GE have realized tangible savings, in their case an estimated $30 million.
- Effective data governance is a fundamental business strategy that treats data as a core asset, similar to financial capital. Its primary goal is to enhance decision-making, increase efficiency, manage risk, and reduce operational costs.
- The most effective approach is a phased implementation that begins by solving a specific, high-impact business problem. This strategy allows the program to demonstrate tangible ROI quickly, securing the long-term buy-in needed for enterprise-wide success.
The critical importance of data governance
Before diving into the “how” of building a framework, it’s essential to understand the “why.”
Effective data governance isn’t a bureaucratic exercise or an IT-centric project; it’s a fundamental business strategy that directly impacts efficiency, risk, and revenue.
In today’s economy, organizations that fail to manage their data as a strategic asset are not just falling behind—they are actively taking on unnecessary costs and liabilities.
Understanding the importance of data as a strategic asset
In the modern economy, data is a core business asset, just as valuable as financial capital or intellectual property. However, without structure, this asset can quickly become a liability.
Think of ungoverned data as a massive, disorganized warehouse. You know valuable things are inside, but you can’t find what you need, you don’t know what’s obsolete, and the clutter creates a significant safety hazard.
A data governance framework provides the inventory system, the organizational structure, and the safety protocols for that warehouse. It transforms data from a source of chaos into a well-managed, reliable asset that the entire organization can use with confidence to create value.
The tangible benefits of a data governance program
When implemented correctly, a governance program delivers clear, measurable business outcomes across the enterprise:
| Benefit | Business impact | Example |
| Enhanced Decision-Making | By providing trustworthy, accurate, and consistent data, governance enables leaders to make more informed strategic and operational decisions with greater speed and confidence. | A retail company uses governed data to analyze customer buying patterns and preferences, allowing them to optimize product offerings and pricing strategies to increase sales. |
| Increased Operational Efficiency | Governance eliminates redundant data, streamlines access, and automates manual processes, which reduces waste and frees up analysts and knowledge workers to focus on high-value tasks instead of data cleanup. | A healthcare organization implements standardized data definitions and quality rules, reducing manual data cleansing efforts and ensuring that analysts work with consistent, reliable data to improve patient care. |
| Improved Compliance & Risk Management | A formal framework embeds compliance into daily data processes, creating clear audit trails and controls that reduce the risk of costly fines, data breaches, and reputational damage. | Murdio partnered with a leading Swiss bank to implement a Collibra-based framework, allowing them to catalog sensitive data and automate workflows to achieve full compliance with FINMA regulations ahead of schedule. |
| Cost Reduction | By identifying and managing redundant, obsolete, or trivial (ROT) data, governance directly lowers operational expenses by minimizing unnecessary data storage, avoiding duplicate tools, and reducing errors that require costly fixes. | A retail enterprise implemented governance policies to flag and archive unused datasets, cutting unnecessary cloud storage expenses while ensuring compliance with data retention rules. |
Deconstructing the data governance framework
A successful data governance framework isn’t a single document or a piece of software; it’s a living system built on three interconnected pillars.
Like the legs of a stool, if one is weak, the entire structure becomes unstable. You need engaged people to follow processes, clear processes to guide technology, and the right technology to empower your people.
Neglecting any one of these areas is a common reason why governance initiatives fail to deliver value.
The three pillars of a data governance framework
A strong framework is built on three interconnected pillars that must be developed in concert: people and culture, process and policy, and technology and tools.
1. People & culture
A data governance program is fundamentally a human endeavor. Success depends on moving away from the outdated idea that “IT owns the data” and establishing a culture of shared responsibility.
This starts with defining clear roles so everyone understands their part:
- Data owners (the strategists). These are senior business leaders who are ultimately accountable for the data within their specific domain (e.g., the VP of sales owns the customer relationship data).
- Data stewards (the experts). These are the hands-on subject matter experts, often within business teams, who are responsible for the day-to-day management of data, including defining terms and ensuring data quality.
- Data custodians (the technicians). These are the IT professionals who manage the technical infrastructure—the databases and systems—where the data is stored and secured.
2. Process & policy
This is the “rulebook” for your data. The goal isn’t to create bureaucracy, but to establish clear, practical guidelines for how data is managed throughout its lifecycle.
These rules are critical for maintaining data quality, ensuring security, and complying with regulations.
Developing these rules is a critical step, which we cover in-depth in our guidesto creating a data governance policy.
3. Technology & tools
Technology is the engine that enables and automates your governance processes at scale. While a tool alone won’t solve governance challenges, the right platform is essential for enforcement and efficiency.
Key technologies include:
- Data catalog. This acts like a library catalog for all your data assets, making it easy for users to discover, understand, and trust the data they need.
- Master data management (MDM). These systems are used to create a single, authoritative “golden record” for critical data entities like customers or products, eliminating inconsistencies across the organization.
How to implement a data governance framework: a phased approach
Implementing a data governance framework can feel like a monumental task, but the most successful programs don’t happen all at once.
A “big bang” rollout is often a recipe for failure, as it requires a significant leap of faith from leadership without any prior proof of value.
A far more effective method is a phased, iterative journey that starts small, solves a real business problem, and uses that success to build momentum.
This approach allows you to demonstrate tangible ROI quickly, which is critical for securing the long-term buy-in and resources needed for an enterprise-wide program.
Step 1: start with a business problem, not a technical one
The most common mistake is to start with a vague, technical goal like “improve data quality“.
Instead, anchor your initiative in a specific, high-impact business pain point. Identify an area where poor data is creating obvious friction, such as the marketing team wasting ten hours a week manually reconciling duplicate customer lists.
By focusing on a single, measurable problem, you create a clear scope for a pilot project that can deliver a visible win. However, it is crucial to remember that a data governance framework cannot stand alone; it must be built upon a previously defined strategy and policy to be truly effective.
Step 2: assemble your governance program team
With a clear business case, the next step is to formalize the team for your pilot project. This involves officially assigning the roles discussed earlier.
Identify a business leader to be the data owner for the pilot’s data domain (e.g., the VP of Marketing for customer data), and empower a subject matter expert to act as the data steward.
Most importantly, secure an executive sponsor—a respected leader with the authority to champion the initiative, remove organizational roadblocks, and advocate for the program at the highest levels.
Step 3: define the “rules of the road” for your pilot
Avoid the trap of writing long, complex policies for the entire organization at the outset. Instead, work collaboratively with your pilot team to define the essential rules and standards needed only to solve the specific problem at hand.
For the customer data example, this would mean agreeing on the official data sources, defining what constitutes a “complete” customer record, and establishing the process for merging duplicates.
Policies created with the business are far more likely to be practical and adopted than those dictated by IT.
Step 4: choose the right tools for the job
Technology should always follow strategy, not lead it. Once your pilot’s processes and rules are defined, select the technology that directly supports those needs.
This prevents over-investing in a complex, expensive platform before your organization has a clear understanding of its requirements.
For a pilot focused on data quality, this might mean implementing a targeted data cleansing tool rather than a full-scale master data management suite.
The right data quality tool should be an enabler, not the entire focus of the project.
Step 5: measure, communicate, and expand
Data governance is an ongoing journey of continuous improvement, not a one-time project. Once your pilot is launched, rigorously track its success using the business metrics you established in the first step (e.g., “we reduced time spent on data reconciliation by 80%”).
Widely publicize this success story within the organization. A tangible win, communicated in clear business terms, is the most powerful tool you have for building credibility and securing the support needed to strategically expand the governance program to other high-value areas across the enterprise.
Step 6: partnering for success – accelerating your implementation
While these steps provide a clear roadmap, turning a plan into a successful, enterprise-grade reality requires deep expertise in process design, change management, and technology implementation.
The complexities of integrating tools, training teams, and driving cultural adoption can slow down even the best-laid plans. For organizations looking to accelerate their journey and ensure a successful rollout, partnering with specialists is key.
This approach allows you to leverage proven methodologies and avoid common pitfalls, ensuring your framework is built on a solid foundation. For example, Murdio helped an international retail chain deploy a dedicated Collibra implementation team to establish a unified framework, rapidly driving adoption and enabling data-driven decisions across the enterprise.
At Murdio, we specialize in turning data governance plans into reality. As implementation experts for the industry-leading Collibra Data Intelligence Platform, we help organizations build, automate, and scale effective governance programs that drive real business value. If you’re ready to move from plan to action, book a consultation and let’s discuss your project.
Murdio in action: real-world data governance examples
Theory and implementation steps provide a valuable roadmap, but seeing a framework in action demonstrates its true business impact.
At Murdio, we partner with organizations to turn governance plans into tangible assets.
These examples showcase how a well-executed framework, powered by the right expertise and technology like Collibra, solves critical business challenges across different industries.
Data governance framework examples from our clients
Driving compliance in finance
A leading Swiss private bank was under pressure to meet strict FINMA regulations for managing sensitive data scattered across more than 100 applications.
Their existing Collibra instance was underutilized, and manual processes were inefficient and risky.
Murdio implemented a centralized Collibra data catalog and automated critical workflows for data reviews and ownership changes.
This transformed their governance process, provided full visibility into their most critical data, and ensured they achieved complete compliance with regulatory deadlines ahead of schedule.
Full Swiss private bank case study here.
Enabling a global retailer
An international retail chain with a massive, decentralized data landscape struggled to create a unified view of its operations.
This lack of consistency hindered its ability to make cohesive, data-driven decisions. Murdio provided a dedicated Collibra implementation team that worked on-site to establish a single, unified data governance framework.
By driving the technical implementation and fostering adoption across business units, we helped them create a single source of truth, enabling smarter analytics and more effective enterprise-wide decision-making.
Full international retail chain case study here.
Rescuing an energy giant’s program
An energy company’s ambitious Collibra implementation had stalled due to a lack of clear direction and technical leadership, failing to deliver its expected value.
The program was at risk of being abandoned. Murdio revitalized the initiative by embedding an expert Technical Product Owner into their team.
This leader provided the necessary strategic oversight and technical guidance to bridge the gap between business needs and the IT development team, successfully getting the program back on track and demonstrating how having the right expert in the right role is critical for success.
Full Energy large enterprise case study here.
Building an effective enterprise data governance framework for the future
Implementing a data governance framework is not the end of the journey; it’s the beginning of a continuous process of improvement and adaptation.
A successful program must be able to demonstrate its value over time and evolve to meet the future demands of the data landscape.
This means establishing clear measures of success and embracing new technologies that can automate and scale your governance efforts.
Measuring success for effective data governance
One of the most significant barriers to long-term success is the difficulty in quantifying the business benefits of data governance.
To overcome this, your program must move beyond operational metrics and track Key Performance Indicators (KPIs) that are directly tied to strategic business objectives.
This transforms measurement from a simple reporting exercise into a powerful tool for demonstrating ROI and justifying continued investment.
Before you begin, it’s crucial to establish a baseline for these KPIs; without it, you cannot prove the value of your improvements.
| Business goal | KPI category | Example KPI to track |
| Increase Marketing ROI | Data Quality & Trust | Percentage reduction in duplicate customer records in the CRM. |
| Improve Operational Efficiency | Operational Efficiency | Reduction in hours spent by analysts on manual data discovery and preparation. |
| Reduce Compliance Risk | Compliance & Risk Management | Percentage of critical data elements with documented ownership and lineage. |
| Foster a Data-Driven Culture | Data Usage & Adoption | Increase in the usage rate of certified, governed data assets in business intelligence reports. |
AI’s role in your governance program
Historically, data governance has been a highly manual, reactive discipline. This approach is no longer sustainable given the exponential growth in data volume and complexity.
Artificial Intelligence (AI) is fundamentally transforming this landscape, enabling a shift from reactive firefighting to proactive, automated governance. AI provides the scalability that manual human effort simply cannot match.
Simple examples of AI’s impact are already in practice:
- Automated data classification. AI algorithms can automatically scan vast datasets to identify and tag sensitive information, such as Personally Identifiable Information (PII), ensuring security policies are applied consistently and accurately.
- Anomaly detection. By learning normal data access patterns, AI can instantly detect unusual activity that could signal a security breach, providing an early warning system that is far more sophisticated than traditional rule-based alerts.
This forward-looking approach is not just theoretical. Murdio’s expertise is already helping clients navigate this new frontier.
We are actively partnering with organizations like a global bank to strengthen their AI governance, proving that a solid data governance framework is the essential foundation for building trustworthy and ethical AI systems.
Conclusion: from a set of rules to a competitive advantage
Building a data governance framework is not about creating a restrictive set of rules or launching another IT project.
As we’ve explored, the path to success begins with a strategic shift in mindset: from viewing governance as a cost center to recognizing it as a value driver. The journey starts by anchoring your efforts in a tangible business problem, building a stable foundation on the three pillars of people, process, and technology, and implementing the framework iteratively to demonstrate value quickly.
By measuring your success with clear, business-focused KPIs, you can transform your data from a problematic liability into a reliable, high-value asset.
Ultimately, an effective data governance framework is not a burden that stifles progress. It is a foundational business capability that builds trust, accelerates innovation, and turns your data into a true, sustainable competitive advantage.
Frequently Asked Questions
1. What is a framework in data governance?
A data governance framework is a comprehensive system that defines how an organization can manage data as a strategic asset. Think of it as the blueprint for your company’s data, outlining the necessary people, processes, and technology. A well-structured governance framework helps transform data from a potential liability into a reliable, well-organized resource that drives value. It establishes the rules and structure needed to ensure data is accurate, secure, and used effectively across the business.
2. How do you build a data governance framework?
Building a successful framework is best done through a phased, iterative approach rather than a “big bang” rollout. The article outlines a practical, six-step journey:
- Anchor your data governance initiative in a specific, high-impact business pain point, not a vague technical goal.
- Formalize key roles like data owners, stewards, and custodians, and secure an executive sponsor.
- Collaboratively create the essential data governance processes and standards needed to solve the initial problem.
- Select technology that supports your defined processes, such as a data catalog or Master Data Management (MDM) system.
- Track success using business-focused KPIs and use those wins to build support for expanding the program.
- Leverage expert partners to accelerate implementation and avoid common pitfalls.
3. Who needs a data governance framework?
Any organization that collects, stores, or uses data as a critical asset needs a data governance framework. This is especially true for businesses in today’s data-driven economy that want to improve efficiency, reduce risk, and make more informed decisions. As shown in the article’s examples, this includes industries like finance, retail, healthcare, and energy, where the ability to properly use data is directly tied to operational success and regulatory compliance.
4. What are the benefits of a data governance framework?
A well-implemented framework delivers tangible business benefits by establishing effective governance practices. The primary advantages include:
- Provides leaders with trustworthy and consistent data, leading to faster, more confident strategic decisions.
- Streamlines processes and eliminates redundant data, freeing up employees to focus on high-value tasks instead of manual data cleanup.
- Embeds compliance into daily operations, creating clear audit trails that reduce the risk of fines and data breaches.
- Directly lowers expenses by identifying and eliminating redundant, obsolete, or trivial (ROT) data, which cuts unnecessary storage and maintenance costs.
5. Is GDPR a data governance framework?
No, GDPR is not a data governance framework, but the two are closely related. GDPR (General Data Protection Regulation) is a set of legal regulations that dictate the rules for data privacy and how to regulate data belonging to EU citizens.
A data governance framework, on the other hand, is the internal, operational system – the people, processes, and tools – that a company builds to ensure it can comply with regulations like GDPR while also achieving its own data-related goals. In short, GDPR sets the “what” (the rules), while your data governance framework provides the “how” (the method for following those rules).
