March 13, 2024
15 minutes read
Information Insight

The Kenway Guide to Data Governance

Despite the fact that 67% of executives recognize the value of data governance, only 42% say they have an actual strategy in place. Organizations without defined data governance strategies are almost certain to fall far behind their peers. The age where data-driven strategies were considered groundbreaking has passed; it’s now a prerequisite for survival. However, as organizations strive to embrace this new era, they grapple with the realization that achieving true data-driven prowess is no small feat. 

The key to success lies in the seamless integration of people, processes, and technology. An advanced data governance framework is critical to enabling an organization to be able to store and access its data, but the technical framework is not enough. To fully leverage data, an organization needs to look beyond raw information and focus on empowering decision-makers, streamlining processes, and leveraging cutting-edge technology to build a comprehensive data governance strategy.

Data Governance Definition 

What is data governance? Data governance is defined as the collection of clearly defined policies, procedures, standards, processes, roles, and responsibilities that ensure the effective and efficient use of data in enabling an organization to achieve its goals. Your data governance strategy defines who can take action on data, the data they can act upon, and the methods they can use. A data governance strategy is made up of the following elements:

  • A vision for how data accomplishes and supports the business strategy
  • Policies and Standards that guide how data is acquired, transformed, and connected
  • Procedures required to cleanse historical data, update systems, and deploy operational processes to adhere to policies
  • Roles and Responsibilities for the people tasked with sponsoring, directing, and providing operational support for data governance procedures
  • Education, communication, and data literacy to engage the entire organization in advancing data
  • Technology that supports the operationalization of data governance policies and processes

The data governance strategy must be defined by business drivers. Good data governance is a strategic tool that can be applied judiciously where it adds value and aligns with organizational goals while ensuring that we maintain the balance of what to govern and what not to govern. In doing so, we allow decision-makers the flexibility to be creative with fit for purpose governance and without diminishing the accuracy or access to their consistent, enriched, and mastered data through dashboards and AI algorithms. Without this connection, it has a tendency to be bureaucratic and stifling and can even result in poor Master Data Management.

Why Enterprise Data Governance is Essential for Digital Transformation

As every aspect of society rapidly digitizes, being able to navigate the ever-evolving landscape of digital transformation has become essential for every business in every sector. Success depends on the business’ ability to leverage data effectively. On its own, raw data doesn’t have much value. However, when you learn how to refine it to information or knowledge, it becomes a valuable tool for decision-making, discovering insights, and collaboration. 

Achieving these objectives can be challenging. Even if you invest in the most innovative analytics platforms and technologies, data silos, quality issues, and inefficiencies will undermine their effectiveness. Data silos alone may cost employees to lose 12 hours per week because they’re chasing data.


Following data governance best practices ensures you take a more deliberate approach to data management so that your organization’s data is used in the right way, by the right people, at the right time. When the people and systems interacting with data across the enterprise work in harmony, you can overcome many of the common hurdles to realizing your digital transformation vision.

1. Improve Reporting and Dashboards

With data readily available, clear to understand, and easy to trust, it’s easier to manipulate and leverage data to perform advanced analytics and generate consolidated, up-to-date reports.

2. Use Resources More Effectively

By promoting a proactive approach to data management, data governance reduces the time spent on data cleanup and other reactive projects. It also supports self-service and automation tools, which frees up IT and data analytics teams to work on more productive tasks. 

3. Support Advanced Machine Learning

Machine learning simplifies and automates processes, but without clean and reliable data, the output adds no value.  Data governance supports generative AI systems by ensuring they are operating with great metadata and enriched, consistent, and mastered data to further enable the reliability of the output.

4. Improve Integrations

Standardizing and mastering data and how it’s handled throughout its lifecycle allows for more seamless system integrations, which supports better collaboration with internal teams and external partners. Additionally, it allows for fluid omnichannel customer experiences.

5. Future-proof Your Data 

Technology is changing so rapidly, it’s hard to predict which innovations you’ll implement in the future. Your data governance strategy ensures that you maintain data accessibility and usability so that you can onboard new tools quickly and confidently.

6. Reduce Risk

When your company has solid data governance practices in place, it can reduce the risk of data loss due to human error by ensuring employees follow established procedures for handling sensitive information. Demonstrating effective controls also allows you to reduce the risk of legal action from regulators.


What Is the Role of Data Governance for Different Industries?

Digital transformation has been top of mind for business leaders for years, but the COVID-19 pandemic sent digitization into overdrive. To pivot their business practices rapidly and leverage digital tools more effectively, businesses across sectors had to step up their data capabilities. Here are some of the unique data challenges that data governance can address for some of today’s largest industries. 


The healthcare industry generates 30% of the world’s data volume. A majority of healthcare executives say they can’t trust their data, and that interoperability is a major hurdle to implementing strategic data initiatives. Data governance allows healthcare organizations to take better control of the massive amount of data they manage to improve record-keeping, reduce treatment errors, deliver services more efficiently, and comply with regulations.


Telecom providers are at the forefront of many digital innovations, and they’re under immense pressure to optimize networks, protect data security, and deliver personalized customer experiences. To address these challenges, it’s important to be able to analyze complex data sets and create a 360-degree profile of your customers. By centralizing and standardizing data, you can ensure that analytics platforms receive reliable, up-to-date data. You can also develop a holistic view of your customers to deliver a customized experience. 

Financial Services

Financial institutions face an ever-changing slate of regulations, ranging from Know Your Client (KYC) standards to cybersecurity rules. Maintaining compliance has become a significant challenge. With data management practices backed by a data governance strategy, financial services firms can maintain and access the records needed to meet regulatory requirements and adapt as regulations change in the future.


How Data Governance Supports Data Privacy

Regulators and consumers have grown increasingly concerned with data privacy. After the passage of the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), states like Colorado, Virginia, and Connecticut have passed similar legislation. Each year, new data privacy laws are enacted or refined, and managing data privacy will continue to be a key focal point for all businesses in the years ahead. Data governance, especially data governance at an enterprise level, can help organizations avoid the legal consequences and public fallout of non-compliance and data privacy incidents.

Data governance also helps to prevent mishandling of protected information. When employees clearly understand data policies and can easily access the data they need, you can reduce the likelihood that they implement workarounds that may expose sensitive data.

As part of your data governance program, you can identify high-risk, high-value data so you can ensure that it’s properly protected according to its level of sensitivity. This allows you to implement data privacy tools like OneTrust more effectively. Data must be inventoried, classified, and follow well-defined workflows to ensure your data privacy solution is truly compliant.

How to Build a Data Governance Program

Once an organization recognizes the need for a data governance strategy, it’s time to create a plan. Whether you are starting from scratch or revamping your enterprise data governance strategy, these steps will help you craft a well-defined plan.  

1. Define your goals.

What are the key business drivers (such as compliance, customer satisfaction, or innovation) for your organization? Defining your desired outcomes up front will impact the decisions you make about policies, procedures, and responsibilities as you create your program. 

2. Establish a clear business case.

Because data governance can take a backseat to other priorities, establishing the business case helps to align stakeholders on its worthiness as a project. Be prepared to demonstrate how data governance can help ensure the success of strategic initiatives and address current data frustrations. 

3. Choose your data governance model.

There’s no one-size-fits-all approach to data governance. However, there are three common frameworks used to implement data governance: centralized, hybrid, and decentralized. Your organization’s data governance model will depend on your company’s needs, objectives, and resources. 

4. Establish an invested team of senior-level stakeholders who have authority and credibility to ensure these rules are followed.

To be effective, data governance adoption requires buy-in and engagement from senior leaders. They help guide the data governance strategy so that it aligns with the overall corporate vision. Also, data governance needs to be ingrained into the business culture or it will undoubtedly fail. Leadership plays a vital role in ensuring that everyone, at every level of the organization, understands their role in data governance, is engaged with the program, and has the data literacy needed to use information effectively.  

5. Determine which team members will be involved in developing and enforcing the data governance plan.

Individuals from throughout the company should share responsibility for data governance. This can include subject matter experts (SMEs) with knowledge of specific areas, like human resources, or widely used tools, like Salesforce. They should also be supported by people with general expertise in areas such as IT or data management. You’ll want to include representation from across departments so that everyone has a stake in ensuring compliance throughout the organization’s data environment.

6. Define the scope of your data.

Where does your business generate, ingest, or share data? Before diving into policies, procedures, and roles, it’s important to understand your data sources and outputs. 

7. Determine the rules for your data.

Create policies defining how data will be managed. Policies should be set at both the organizational and data element levels. Organizational policies may include mandates for the creation of data dictionaries that include metadata, lineage, and data quality rules for all company data. Lower-level policies may include rules around the appropriate use of data fields, such as whether a field is required and what determines completeness. 

8. Determine which tools and resources are necessary for implementing policies and procedures.

Create and implement technical tools (such as front-end entry screens, required fields, and drop-down lists) employees will need to adhere to data governance policies. Technology is only part of the picture. Training, communication, and data reviews are also necessary to ensure that policies are executed and enforced.


Build Your Data Governance Program with Help from the Experts

By taking the time to implement and refine your data governance program, you can lay the foundation for successful digital transformation. As you invest in advanced technologies and offer more digitally based services, your data governance strategy ensures that your most valuable asset—data—is always available and accurate. It removes many of the barriers to effective reporting, integration, and analytics so you can reduce risk and maximize your technology investments.

The key is to use best practices and a proven, methodical process and customize to your unique organization. If you are unsure of where to start and need support in developing, revamping, or integrating data governance best practices into your business processes, Kenway can help. We work with complex organizations, from startups to those in highly regulated industries, to implement data governance throughout the enterprise. With the right foundation set by data governance, they’re able to reap the full rewards of digital transformation and promote data-driven processes across the business. 

Schedule a consultation to learn how we can help you do the same.


Data Governance FAQs

What are the 4 pillars of data governance?

The four pillars of data governance are:

  • Data quality — the accuracy and completeness of the data
  • Data stewardship — the roles and responsibilities of those managing data
  • Data management — the practice of implementing data governance strategies
  • Data security and compliance — the proper protection and treatment of sensitive data

What are the 3 key roles of data governance?

Data governance serves three key roles:

  • Improves availability and usability of data
  • Promotes viability of data throughout its full lifecycle
  • Ensures compliance with regulatory, industry, and company standards

What are examples of data governance?

Example data governance programs include:

  • A framework for protecting personally identifiable information of customers
  • A strategy to improve the quality of customer data across go-to-market organizations
  • And enterprise-wide initiative to consolidate and organize financial data

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