An Essential Guide to Data Governance: What It Is and Why You Need It

The mere mention of data governance can cause a slew of reactions: worry, confusion, avoidance, or even the misguided smugness of thinking “I’ve done that once and don’t need to think about it again.” Overall, there’s a real tendency to misunderstand what data governance means and why it matters. We’re not speaking hyperbolically when we say it’s the most important process to get right for enterprises that increasingly rely on data to make smart business decisions—and that’s every organization who wants to remain competitive today.



We'll explore

  • Why data governance is a process, not a project
  • The difference between data management and data governance
  • A holistic approach to data governance
  • The consideration of decisions, people, and processes
  • The internet of things and data governance frameworks
  • The Govern by Design approach
  • How an expert team improves your performance


The mere mention of data governance can cause a slew of reactions: worry, confusion, avoidance, or even the misguided smugness of thinking “I’ve done that once and don’t need to think about it again.”

Overall, there’s a real tendency to misunderstand what data governance means and why it matters. We’re not speaking hyperbolically when we say it’s the most important process to get right for enterprises that increasingly rely on data to make smart business decisions—and that’s every organization who wants to remain competitive today.

Let’s clear up several of the misconceptions about data governance: The risks of bad governance policies, the difference between data governance and data management, why you need data governance, and how to approach and maintain it.

A process, not a project

According to Gartner, poor data quality costs organizations an annual average of $12.9 million. Not only does a lack of good data governance immediately impact revenue, but over time that suboptimal data quality further complicates data ecosystems and leaves leadership making decisions based on faulty data.

Gartner estimates that 60 to 85% of organizations fail in their big data analytics strategies annually. This factor may point to why: While 80% of organizations say data governance is important to enabling business outcomes, nearly half don’t assess, measure, or monitor their data governance programs. That fewer than a third expect to exceed their data and analytics ROI in the next two years is unsurprising.

Meanwhile, public sector and healthcare organizations have been dealing with changing regulations around constituent and patient data for some time, while private sector companies have come face-to-face with compliance mandates more recently. Public sector entities primarily focus on changes pertinent to their own state, while private sector companies tend to operate via the internet across the state and even international lines, and therefore must be compliant in all places where they conduct business.

Companies are now called into question about their data governance practices and compliance. Experience shows that companies who end up with steep fines and reparations are those without a defensible posture or data governance policy. They can’t show the exact steps of how their data governance meets or exceeds regulations; there are too many questions left unanswered, too many opportunities for data breaches.

Paola Saibene

Principal Consultant ,   Resultant

Consistently, this happens when data governance is treated as a project rather than a process.

Companies who come through relatively unscathed may not have done everything perfectly, but they thought through every step of their data governance process. They documented roadmaps of the full data lifecycle and had the right measures in place to course correct and carry on.

The difference between data management and data governance

Data management and data governance sound like the same thing, but they’re not. Information and data management are an IT practice; information and data governance are a business strategy. Data management is but a part of data governance. Data management focuses on data quality—eliminating silos, improving accuracy, and protecting against errors. Data governance also includes the people, processes, and systems that interact with data to continue to ensure accuracy and security while helping your organization use it to its maximum value. It takes data integrity to the next level: Not only is the data high quality and reliable, but it is stewarded and utilized with integrity by stakeholders across the organization.

The ultimate goal of data governance is to make sure your data is complete, accurate, and secure so that it is:

  • Accessible and strategically aligned, providing valuable information and insight to high-priority business needs.
  • Accessed through secure protocols that ensure it doesn’t change during access, nor is open to unauthorized access.
  • Accurate at every step of its lifecycle.

A holistic approach to data governance

Data governance is a function. A process. An ongoing, evolving, and sustainable methodology.

Treating data governance as merely a project isolates it from the business areas it impacts, shortchanging strategy and resulting in unsustainable choices.

Companies sometimes seek data governance solutions because they know they have a problem to fix, such as becoming compliant with new data use regulations. The tendency is to look for a single, out-of-the box data governance framework that addresses that concern. But that’s project-oriented thinking when successful data governance requires a holistic approach.

The holistic data governance approach takes data governance as a concept out of isolation and considers is in conjunction with all the business areas it impacts—which includes all that use data in any capacity, even indirectly.

While IT may have a limited data governance budget, its impact on other business units can free budgets designated for:
  • Legal
  • Risk management
  • Business intelligence
  • Data analytics
  • Customer/patient experience
  • Compliance
  • Data management
  • Revenue generation
  • AI and ML initiatives
  • Data monetization
  • Cloud migrations
  • Security and privacy
  • Digital transformations
  • Modern architectures like data mesh and data fabric
  • Records management
  • Business continuity management

Privacy is at the core of what we do in our Govern by Design methodology because we really do care about the people inside the data.

- Paola Saibene, Principal Consultant at Resultant

Considering decisions, people, and processes

Data governance is the entire system of rights and decisions about data and information: Establishing those rights and decisions, developing the processes around how the data moves through each part of its lifecycle, choosing and maintaining the technologies that facilitate access while preserving protocols, and strategizing to ensure your organization can use data to its maximum value. When evaluating the stages of the data lifecycle, executing a data governance calibration process brings great value by being intentional about every step and leaving nothing to chance.

Lifecycle through governance

1. Why are we collecting it?

2. Who touches it?

3. Who modifies it and derives from it?

4. Where is it reported? Where is it stored?

5. What conclusions are derived from it?

6. Does the meaning of a field change from unit to unit?

7. Are those seeing it supposed to see it?

8. How does it connect to other data sources?

9. Do vendors use it? How long should we keep it?

10. How healthy is it? How do we dispose of it?

Data Governance and the Internet of Things (IOT)

“We’re generating more data than ever, and that trend will only continue,” says Katie Hughes, manager of the data governance team at Resultant. “It’s the way of our world today and for the foreseeable future.”

The amount of data generated and stored worldwide is expected to reach 180 zettabytes by 2025. A zettabyte is a thousand exabytes, a billion terabytes, or a trillion gigabytes. One zettabyte could store 7.5 trillion MP3 songs, 60 billion video games, or 30 billion 4k movies.

By 2025, 30% of new industrial control systems will include analytics and AI capabilities, up from less than 5% in 2021— that’s a significant increase in just four years. Likewise, connected passenger vehicles are expected to generate 10 exabytes of data per month.

It’s estimated that companies don’t use up to 73% of their data. That’s a rich source of insights left unmined, but if that data isn’t governed properly, untapped resources aren’t your organization’s only concern. The mere act of collecting data demands that you be a proper steward of it and if you’re not, you’ll likely face consequences of regulatory noncompliance.

Going beyond data governance frameworks

When addressing data governance issues, data companies often speak about data governance frameworks, a term which carries its own misunderstandings. Effective data governance is rarely contained within a single framework. Some frameworks effectively address one problem while another is best addressed with a different solution.

“It’s important to understand that data governance without a strategy isn’t going to provide lasting solutions,” says Hughes. “Tailoring for each client means we’re not just making up frameworks. We’re applying what we know they need based on their industry and regulatory implications they’re likely to face.”

In that sense, data governance framework means a holistic approach that uses what is relevant and valuable from existing data governance tools, leaves behind what isn’t, and creates solutions for the remaining gaps.

“It’s right-sizing,” continues Hughes. “Choosing the best and right combinations of frameworks to build and infuse into their roadmap.”

A modern, dynamic data governance methodology— strategized and implemented with the whole organization in mind—applies value equations, advanced risk calculations, and enlightened insights to industry and sector demands while tailoring the details of what each organization needs.

A thoughtful, holistic data governance approach is the only thing that stands between your company and absolute data chaos

- Katie Hughes, Data Governance Manager, Resultant

Govern by Design: A design thinking approach to data

Executing data governance changes without disrupting current operations sounds like a pipe dream, but incorporating design thinking principles to data governance implementation does exactly that.

By not limiting solutions to isolated, out-of-the-box data governance frameworks, this holistic approach delivers more reliable, better-quality data and iteratively builds an organizational culture that trusts and embraces data.

An organization determines exactly which data governance areas and other peripherally related business needs they’d like to prioritize, such as

  • Business continuity
  • Data model governance
  • Third-party risk management
  • Metadata enrichment
  • Data monetization

This approach implements changes a bit at a time in ways that don’t stop your current operations, which helps your organization support the initiatives from the beginning— an essential factor to success. The formula is outcomes-based and targets visible tangible progress, producing benefits quickly like

  • Bringing in more revenue
  • Improving the quality of services for customers, patients, or constituents
  • Streamlining processes for employees
  • Meeting constantly changing compliance regulations

Data governance is essential for better data, better outcomes

Data governance is like putting on the right tires to better handle your race’s terrain. Everything you want to do in the future—both known and unknown—depends on it.

The amount of data your organization collects will continue to increase. Regulations will keep changing in ways not everyone anticipates. Tools that provide previously unimagined insights will continue to evolve. And there will always be one more unknown lurking in the wings, waiting to make its big entrance.

But good data governance will carry you through all of it. It’s adaptable and scalable, keeps you compliant, grows along with your organization, and enables you to execute good data stewardship while deriving maximum value from this most valuable asset.

Improve your ride, and your pit crew

If data governance is like the right tires for your race, the team helping you implement it is your pit crew.

A Formula 1 pit stop can make or break the race. The pit crew must possess advanced skill, exacting accuracy, an incredible speed to make the correct necessary changes to the car, fast. Surprisingly recently, these pit stops averaged four minutes—an eternity in races  often won by margins of less than a second. Today, the record is 1.82 seconds.

Carrying on with the analogy, the driver is your CEO; the racecar is your well-funded company, expected to perform at its highest ability and to keep going. Market and industry demands are the track conditions and other racers, pushing your company toward relentlessly accelerated digital transformation and innovation strategies or risk losing the race: market share loss, missed opportunities, and diminished competitive advantage.

The team performing modern, dynamic enterprise data governance is the highly skilled, thoroughly vetted pit stop crew, performing near-magic fast, right in the middle of the competition. An experienced pit crew understands how your race is run, what factors improve your performance, and how to execute maneuvers quickly to get you back on the track as fast as possible.

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