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Data is the lifeblood of today’s world. It’s essential to running key company operations, driving decisions, and informing responses in almost all industries. As its scope expands, the organizations that manage it must improve and adapt. The ability to intentionally align your organization around data through policies, procedures, and defined responsibilities will lead to sustainable success in any organization. Some of the benefits are reducing the operational costs of managing data, mitigating the risks associated with incomplete and inaccurate data, enabling utilization of data as information to drive decisions, and setting your organization up for success with AI.

Many organizations face predictable data challenges. These might include inconsistently defining data attributes across business units, struggling to find the data needed to quickly respond to regulatory requirements, not being able to create timely and consistent reporting for leadership, and an overall lack of understanding of enterprise data available to employees. Because of these barriers, 43% of data remains largely unleveraged. 

But there are ways to reduce the chances of errors in data and improve the organization and usability of data. A well-crafted data governance strategy and related organizational framework can help companies capture large volumes of diverse data and capitalize on it. Well-defined policies, roles, and responsibilities, along with consistent practices, improve the data culture throughout the organization and lay the foundation for advanced use cases, such as machine learning and artificial intelligence.

Here is an introduction to data governance, along with best practices for developing a framework that supports data accuracy, usability, and scalability. 

What Is Data Governance?

Data governance is the collection of clearly defined policies, procedures, standards, and/or roles that ensure the effective and efficient use of data in enabling an organization to achieve its goals. At its core, data governance defines who can take what action, when, and how, based on the data itself.

It is important to understand the fundamental difference between data governance and data management. These two terms are often interchanged when addressing data strategy but, while the two are interconnected, they are not the same. Data governance is the high-level oversight of data providing the policies and procedures required to deliver complete, accurate, consistent, and timely data. 

Data management, on the other hand, is the day-to-day execution of data governance policies, and includes the processes people follow as well as the tools and technologies they need to help them comply with the mandates of the data governance program. Data management cannot succeed without data governance, which is why high-quality implementation of a data governance program is crucial to any long-term success. As the volume and complexity of data increases, so does the gap in data management capabilities when there is a continued absence of data governance. 

A data governance program should enforce the integration of strategy, standards, policies, and communication to deliver optimally. Regardless of the size of an organization’s data scope, preparedness is crucial to ensure the quality delivery of data. 

The Benefits of Active Data Governance

Data is now a pervasive and mandatory aspect in any organization, and the importance of leadership in its understanding and investment cannot be downplayed. High-quality data is an asset which can be utilized to achieve a multitude of goals. Proper governance and management of data will ensure accuracy. This will not only manage risks but allow organizations to transform opportunities into advantages. By introducing data governance into an organization’s corporate structure, companies can achieve the following:

• Increased trust in an organization’s data through improved integrity and a reduction in data errors
• Future-proofing in order to allow easy introduction of new technologies and data tools that further enhance data utilization and its value to an organization
• Minimized costs through the reduction of effort needed to correct and maintain data 
• Maximized profits with better visibility into customers, providing insights for enhanced customer satisfaction and increased revenue through cross-sell, up-sell, and enhanced marketing capabilities
• Improved efficiencies by reducing duplicative work for data feeds, stores, and reports
• Improved effectiveness of data to measure the risk and returns of strategic initiatives
• Effective and simple use of data in AI

Use Cases for Adopting a Data Governance Framework

One example of how introducing data governance can help to work toward achieving these goals is by defining data policies that standardize key data attributes for greater data consistency and

understanding across an organization. If an organization does not have standardized definitions for its data elements, data becomes convoluted and processes become inefficient. 

For something such as inventory management, having a standardized definition across the organization is crucial to ensuring product inventory is both complete and accurate, and that there are metrics available around sales, usage, age, etc. Ensuring this accuracy will allow for precise strategic planning and portfolio management downstream. Through data consistency, an organization can reduce cost and increase operational effectiveness by automating data reporting and informing strategic planning.

Another potential outcome of employing an effective data governance program is improved customer satisfaction resulting from data procedures and clean data. Today, people want clear visualizations of data that are digestible, and delivered quickly and regularly. In the healthcare industry, for example, this might come in the form of patient experience (PX) dashboards to display health facility and provider performance statistics along with various PX metrics that will inform improvement of care.

A data governance program can also empower the creation of business dashboards in order to gauge opportunity streams or customer profitability profiles. This is done by leveraging consolidated data from across the organization which helps to recognize pain points as well as identify opportunity pipelines. To achieve this, an organization’s data structures must be seamless across platforms to allow the data to be linked. Having clear policies and procedures in place will

make an organization’s data easy to manipulate and leverage into consolidated reporting and analytics platforms to provide up-to-date reporting. 

This will increase organizational capacity, alleviating the need for data cleanup and allowing an organization to effectively utilize data visualization and maximize understanding of its data. This provides insight on organizational direction, performance, and profits, which ultimately will inform downstream decision-making.

In addition to facilitating quality data visualization, which broadens the scope of data understanding, data governance also allows for the consolidation of a user’s experiences. As data and systems expand and merge, bringing data together across systems becomes challenging. Data governance programs support standardization across these systems which allows for a more

seamless user experience. With technology platforms like web portals and applications, a consolidated user experience/customer experience (UX/CX) will improve overall experience and efficacy. Whether this is merging multiple login systems to work under one centralized hub or improving customer support and relationship managers (RMs), data governance can streamline messy systems and help with marketing and sales interactions. Getting started in implementing these programs can be a daunting task. However, the first step is choosing a data governance framework that will guide how policies are created, procedures are implemented, and how the structure of the teams that will lead the program are determined.

If you read the list of benefits of a data governance framework above and feel as though perhaps your company is not organizing or leveraging its data effectively, then you might need a more resilient data governance framework. In addition, if your organization is experiencing any of the below pain points, that is a further indication that your company could benefit from improved maturity in its data governance practices.

How To Develop a Successful Data Governance Framework

It is important to note that the size, complexity, and goals of your organization will dictate the details of your framework. It is important for it to define the scope of its data and to choose the right framework that meets its needs. This will determine how successful both the implementation and ongoing maintenance of the program will be. An organization must minimize data risk and create effective countermeasures against potential threats, while maximizing the benefits from data usage.

Setting objectives and goals is the first measure to ensure a data governance program will meet the requirements for organizational success. Once policies are created and the procedures are in motion, there must be a team that will effectively manage and maintain those rules post- implementation. The structure of this team will vary based on an organization’s unique structure and needs. There are a few approaches to resourcing a data governance program, however, there is no right answer that works best. 

In a case study examining two comparable telemarketing firms, each firm chose differing frameworks and configurations of data governance based on factors unique to the organization, including goals and structure. While different approaches were taken, both firms had successful data governance programs, demonstrating there is no “one size fits all” solution when it comes to data governance frameworks.

Data Governance Operating Models

There are three common frameworks that are utilized when executing a data governance program.

• Highly centralized approach: This involves fully allocated resources whose sole responsibility is the data governance program. This approach is typically seen in large organizations or those with complex governance and data issues that necessitates a team fully dedicated to managing data policies. 
• Decentralized (or federated) approach: This involves partially allocated resources with no fully allocated resources existing solely for data governance. Instead, resources have data governance roles alongside other job responsibilities. This approach is typically seen in smaller organizations or those in the infancy of a data governance initiative.
• Hybrid approach: This approach involves a mix of fully allocated and partially allocated resources in which some resources are fully dedicated to managing the program while others are partially allocated to the program alongside other responsibilities. The typical arrangement for this method is for the Data Governance Office to be instantiated with fully dedicated resources while a Steering Committee and Working Group are comprised of partially dedicated resources. As stated before, the approach selected by an organization will be dependent on an organization’s unique attributes and needs.

The framework that has been selected will inform the roles that must be assigned to carry out the various responsibilities. Defining these roles and responsibilities is a critical aspect that will ensure the smooth running of any program. Core roles and groups include executive sponsors and stakeholders, a Steering Committee, an Office of Data Governance, a Working Group, data stewards, data creators, and data consumers.

In each of the frameworks, the data creators, stewards, and consumers will lay down the groundwork and maintain the underlying data for an organization. First, data creators will enter or extract data into/from a source system. The data stewards will then ensure the quality of that data by performing data review and cleansing activities. These individuals will serve as liaisons to business units regarding governance policy and procedure items, consolidation of systems, data consumers and originators, and IT support. 

The final aspect behind the data in an organization will be the data consumers who utilize the data for both operational systems, tasks, and analytics. As the data flows downstream, systems and employees will use this data for process support and organizational engagement.

While the data teams are critical, executive sponsorship and stakeholder buy-in are crucial for the success of any data governance program. These are the individuals who understand corporate strategies and working with them will ensure the program is built to align with these strategies. This will guarantee that stakeholders remain committed to the program and understand its overall business importance. The most common reason for failure in data governance initiatives is a lack of executive sponsorship. These sponsors are typically organization leads who will ensure funding and resources are available to execute the data governance program.

Introducing Data Governance to Your Organization

After a framework is chosen and roles and responsibilities have been defined, the implementation of a data governance program can begin. A data governance framework has three general pillars: 1) Policies and standards, 2) processes, and 3) roles and responsibilities. There are many ways to implement a data governance framework, but all data governance frameworks have this same basic bone structure. 

The approach can vary across organizations depending on the desired breadth for the data governance framework (whole company vs. single business unit/department), big bang vs. iterative (we recommend the latter), or the formality of the policies and standards being sought. For example, highly regulated companies will have a more rigorous approach to defining and implementing policies than non-regulated companies. Smaller companies might seek to institutionalize the data governance standards through more informal but equally effective channels.

Policies and Standards

Having clear policies and standards in place is one of the most important aspects of a successful data governance framework. Effective policies make an organization’s data easy to manipulate and leverage into a consolidated reporting and analytics platform that provides up-to-date information insights.

Furthermore, policies and standards will ensure your company’s data is “fit for use,” whether that use is reporting, analytics, AI, and/or distribution to downstream systems.

By creating these policies, organizations create rulebooks and statements that define how data should be governed and managed, how it should be used, who can use it, and what the roles and responsibilities are for those who are accountable for said data. Additionally, putting these policies and standards into place allows for more effective data processes, which alleviates the need for data cleanup and simplifies data visualization and analytics.

Processes

Once policies and standards are put into place, the next step in improving data governance is to establish how the implementation of policies and standards will work. Processes need to be defined to ensure the policies and standards are followed throughout the organization. Some of these processes will deal directly with the data while some will relate to how each area will define and govern their critical data. 

Additionally, as the processes are developed and implemented, it is critical that appropriate change management practices, including training, are included in order to ensure adequate organizational adoption of the processes, policies, and standards.

Roles and Responsibilities

Designated roles with predetermined responsibilities help make a data governance framework a reality. These individuals can then become members of data governance specific key forums which form the foundation of the data governance framework for the company. Examples of these forums include a Steering Committee, a Working Group, and the Office of Data Governance.

1. The Steering Committee is responsible for sponsoring and funding data governance activities and enforcing all individual data governance accountabilities across the company.
2. The Data Governance Working Group is the primary decision-making body and oversees the more general, day-to-day execution of a data governance implementation and processes. They promote training and education efforts and awareness of data governance practices and standards. This group consists of key stakeholders across the organization or area being governed.
3. The Office of Data Governance is typically composed of employees dedicated to the task of data governance. They are responsible for defining the standards, metrics, and procedures across the organization. Metrics should include information about the benefits derived from maturing data governance, including improved data quality, reduced reporting costs, and efficiencies/communication improvements across the organization.

Best Practices for Data Governance Quality Assessment and Improvement

Another important practice that should underpin any program is data quality reporting to demonstrate that the data governance program is successful and useful. By periodically measuring data quality from the start of the data governance program, organizations can provide insights into how the program is improving data quality, as well as highlight areas that need more improvement. 

Key metrics and key performance indicators (KPIs) for data governance programs are:

• Program Performance 
  • Number of supported groups and users
  • Issue metrics: Number of issues, resolution time, issue status
  • Qualitative engagement scores
  • Data steward KPI: Number of items for review, resolution time

• Data Quality
  • Record error / review required alerts
  • Duplicate record check, orphan record alerts
  • Data integrity metrics: Record completeness, field uniqueness, data consistency, and data connection density

• Business Value
  • Regulatory compliance status
  • Risk mitigation (i.e., costs avoided)
  • Cost savings: Resource time, software costs, maintenance costs
  • Value creation: Lift in cross-sell /up-sell revenue, customer retention, customer satisfaction, resource allocation

Procedures must also be put in place to account for issue management. Constant monitoring and management of the program while intermittently making tactical fixes will ensure the data retains the appropriate level of quality and consistency. These fixes might include data governance procedures such as creating data definitions, defining attributes, amending attributes, and retiring attributes. Issue management will also entail revising the procedures so that a data governance program continues to evolve to best meet the organization’s needs.

Overall, a successful program will have policies and procedures working in tandem, with an organized team that will implement and enforce the data governance program.

Once an organization has chosen the Data Governance framework that will best suit its needs and initiated Data Governance deployment, it should then define the methods for ongoing monitoring and maintenance of the program. As discussed above, adding accountability to resource roles and responsibilities is key to ensuring the ownership and implementation of data governance policies. By doing this, it will guarantee procedures become a part of day-to-day operations. Furthermore, actively monitoring the performance of the data governance program both in terms of data quality improvements and the attainment of business values (e.g., lower costs, operational efficiency, increased sales) is critical to continually improving an organization’s data governance operating model. Creating reporting assets, either through the purchase of a data governance tool or through an internal build, is one way to address these monitoring tasks. Analytics dashboards, scorecards, and status updates provide managers and operators with insights into the overarching performance of a data governance platform.

It is of note that a large component of implementation is change management. The single largest reason for failure of Data Governance implementations is poor change management. There is a need to align both people and processes to strategic initiatives. By applying tenets of change management, an organization can ensure there is understanding and buy-in from both stakeholders and all impacted resources which will aid in the successful rollout of the program. By setting incremental goals and including team members who will be impacted by the new program, the organization will build momentum within the program, allow for feedback and increased participant buy-in, and create a sense of accountability in the successful rollout.

As the process continues, there is a need for continuous evaluation to understand which components are successful and which are not. Measures to gain stakeholder trust include setting success measures followed by evaluation, recommendations for improvement, and broadcasting success. This trust and collaboration are critical as stakeholders will be the ones to advocate for and expand data governance programs throughout an organization. By utilizing change management and broadcasting success, an organization can minimize risks associated with significant culture shifts and guarantee governance thrives instead of falling to the wayside.

Data Governance Maturity Curve

Overall, it is good practice to periodically assess the maturity of data governance in an organization. Quantifying and defining scope and ability lets an organization assess the state of its program, understand its data capabilities, identify vulnerabilities, and know which areas need improvement. The level of maturity and complexity of an organization’s data governance monitoring capability, as well as its overall data governance program, helps to identify where it falls on the Data Governance Maturity Curve. 

When an organization’s data governance maturity is at the Unaware stage, there are no policies or procedures in place to ensure the enterprise-wide or even cross-functional quality of data. This often arises as organizations grow, while early-stage organizations can afford to allow distinct groups and business units to function separately. As the utilization of data across an organization increases, the organization moves to a higher level of maturity, the Aware stage, where there is some siloed data governance to support its growing capabilities. These organizations typically require independent efforts to create consistent data. For example, a sales team may require its users to enter an email address for all prospects in order to tie customer interactions to orders. This would likely require some manual reviews of records and addressing missing data on an ad-hoc basis without the appropriate data governance in place.

As an organization begins to formalize data governance, it moves into the Defined & Managed stage of the Maturity Curve. Here, standards and policies are in place across the organization which allows for a more robust connection of information across all systems. The formalization of these standards and policies requires the creation of dedicated roles and responsibilities for data governance. As discussed, data governance frameworks can vary by organization, but they each fundamentally involve a core governance team and ties to resources’ job descriptions. Finally, the organizations with the most mature data governance optimize their existing operating framework to allow for the enhancements of strategic assets throughout the organization. These companies factor in data governance as they deploy new services, product features, and incorporate new data sets. This allows them to quickly and efficiently integrate data into their current operations, minimizing the time to value and costs to manage the data.

Wherever on the maturity spectrum an organization may be, understanding the changes required to reach the next step will be vital to successful implementation. As the complexity of systems increase and an organization progresses along the maturity curve, it will find that it needs to address the shifting landscape of its organization which often comes in the form of organizational change management. Whatever the method, as the organization evolves, it must continue to optimize its processes to realize the highest level of benefits and remain competitive on the market. By continuously assessing capability and utilizing a maturity curve, an organization can achieve a successful and long-lasting data governance program.

To learn more about this topic, download An Introduction to Data Governance. 

Kenway’s Data Governance Methodology

Here at Kenway, we have the passion, expertise, and skills needed to effectively partner with organizations at any point in their data governance journey to begin maximizing the value of their data.

As we walk through the process of developing or improving a data governance framework for a company, we start by aligning company objectives. We identify the most critical pain points and high-value use cases while still establishing a data governance mission and vision that aligns with corporate objectives.

Then we establish business value opportunities. After understanding where a data governance program will drive the most value for a business, Kenway can assess the organization’s data governance maturity.

Based on the company’s data governance maturity, we build a data governance roadmap to determine the actionable steps necessary to begin implementing a data governance framework. We often partner with organizations to translate the roadmaps into results, from 1) writing anchor and identifier policies, to 2) designing and implementing a data governance framework specific to the company’s needs, to 3) developing data governance tools, processes, and technologies. 

If you’re ready to take the next step in your data governance journey, connect with one of our consultants to learn more.