Data Governance: Development, Roles & Benefits

What is data governance, how can it be implemented, and what are the benefits of strong data governance? Our data management experts provide deep insights and answers to frequently asked questions about data governance roles, mandates and responsibilities.

Definition: Data Governance
Data Governance Benefits
How to Develop a Data Governance Framework
Data Governance Roles
Data Governance Boards
Data Governance Mandate
Data Management Guidelines and Principles
Difference between Data Governance and Information Governance
References and Further Readings

What Is Data Governance?

In general, governance refers to the way the organization goes about ensuring that strategies are set, monitored, and achieved (Rau, 2004, p.35). Thus, governance implements a strategy by means of oversight and control mechanisms (Tiwana & Kim, 2015) and complements strategic as well as operational tasks: Strategy is doing the right things, operations are doing things right, and governance is ensuring that the right things are done right. In analogy, data governance links strategic data monetization initiatives with their daily execution to guarantee a return from data investments.

Usually, the following situations indicate that an organization suffers from unclear or non-existent responsibilities related to their data, meaning a lack of an effective data governance approach:

Data quality is poor (e.g. missing attributes, outdated values)
It takes a long time to find data and get access to it
It is not clear who is responsible for key data objects (e.g. customer, product, employee and their quality)
Data management activities are performed ad-hoc and are not documented
It is unclear how data is onboarded into data lake
Data lakes have become data "swamps"
Documenting data in a data catalog takes a long time

The problem with the term "governance" is that asking three different people what the word means, there will be at least four answers. The following definition reflects the real-world experiences from many companies and data initiatives, and is grounded in CC CDQ data management research:

Data Governance defines who does what in data management

The CC CDQ defines data governance as follows:

Data governance defines roles and assigns responsibilities for decision areas to these roles. It establishes organization-wide guidelines and standards, and it assures compliance with corporate strategy and laws governing data. (Weber et al 2009)

Data Governance determines the activities that must be undertaken in order to manage data like a true asset
Governance defines who in the organization takes care of which data management activity
Solid data governance also determines the accountabilities, responsibilities and decision rights for the people who take care of data

Best Practices: After defining responsibilities on paper, select and train the people who will take care of the data management activities on a day-to-day basis. It is recommended to select these individuals with utmost care. All too often, the people already overloaded with work also end up taking care of data. If that is the case, it will be impossible to achieve the agreed objectives, for instance, related to data quality.

Why Do We Need Data Governance?

The benefits of an effective data governance framework are threefold:

Having clear responsibilities for data and data management speeds up the implementation of projects. Many projects depend on data, in particular the ones that push enterprise-wide business process integration, establish analytics, or address compliance requirements.
Data governance is also the foundation for robust and sustainable data quality improvements. If it is not defined what good quality looks like (i.e. creating governance policies and guidelines) and ensured that these standards are adhered to, data quality is left to chance
Implementing effective data governance also contributes to increasing data’s value. If data is not used, it has no value. Data governance ensures that data issues are solved, and that data is "fit-for-purpose" for being used in business processes, for decision-making and in business models

Data Excellence Model (DXM)

The Data Excellence Model developed by the Competence Center Corporate Data Quality can provide guidelines for the development of a data governance framework. The Data Excellence Model

Webinar: Data Governance 2.0 (August 18)

What if you knew how to establish a strong data governance in your organization? Register now for our upcoming webinar on August 18 and find out how CDQ's framework can support you! Webinar recording

How to Develop a Data Governance Framework (Checklist)

Data leaders face several challenges when setting up data governance. One of them is that there are only a few guidelines on implementing data governance and that knowledge remains mostly tacit. Here is the good news: Defining a data governance framework isn’t rocket science. CC CDQ's reference model for data governance can help in developing and implementing a data governance framework. A proven approach to follow: Firstly, defining which data management activities are to be undertaken by whom in the organization. Secondly, deciding how many people are needed to run the governance plan on a day-to-day basis. Thirdly, setting the mandate and decision-making rights for the people defining and monitoring the governance policies and standards. Fourthly, defining an implementation plan, including training, communication and change management activities. Finally, select the people executing data governance.

A data governance program must follow a clear goal. Hence, it is of utmost importance to clarify the deliverables in the beginning. The deliverables concern different data management related decision areas in the enterprise, most importantly the data strategy, the data management portfolio and roadmap, the data governance framework, data models and architecture, and the data lifecycles. Once the deliverables are set, the relevant data management activities must be defined.

The required activities are defined in CDQ's data management process map. The map includes operational activities (e.g., creating and maintaining data), tactical activities (e.g., building up and running data quality management) and strategic activities (e.g., developing a data management strategy). To be effective, the activities from the process map must be assigned to employees in the organization.

We do not recommend assigning the activities directly to individual employees. Instead, we advise to allocate certain sets of activities to specific data management roles (e.g., a Data Steward) and to assign these roles to individual employees. A proven tool that helps to assign the responsibilities to Data Governance Roles is the RACI matrix (i.e., Responsible, Accountable, Consulted, Informed).

To obtain management approval and budget, it is necessary to estimate how many employees are needed to execute effective data governance. The best way to determine that figure is to estimate the overall workload. In a typical data governance setup, there are some full-time positions, typically in a central data management team. In addition, there will also be some employees in the business units and support functions that cooperate with the central team. However, these are typically not full-time data management positions.

To be effective, the people executing the data management activities need a clear mandate. The mandate defines the authority of the data management roles. If it is left unclear what they can or cannot decide, the entire governance setup will not work. In addition, it is necessary to define decision-making boards where stakeholders of the organization come together and align on data management activities and issues. This is needed because data is a shared resource, that is used by various organizational units.

Implementing data governance requires an organization to change. Therefore, change management activities are required to support the implementation. These include an effective communication strategy tailored at different stakeholders. In addition, a plan to train the employees involved in data governance is also needed.

The best data governance concept is likely to fail if the wrong employees for the data management roles are selected. Business understanding is key and often more important than technical/IT knowledge. At the end of the day, data managers often bring people from diverse backgrounds together, so strong communication skills are a must.

What Are Typical Data Governance Roles?

Principles underlying data governance

Two important design principles are fundamental for any data governance design:

Enterprise-wide data governance requires collaboration between business, data and IT - Consequently, a federated approach is needed for enterprise-wide data governance. This implies that the roles and responsibilities can be assigned to employees who work in different parts of the enterprise.
Data management and analytics roles facilitate the information supply chain - While the data management roles emphasize the provision of data for different business purposes, the analytics roles endeavor to deliver analytics products throughout the enterprise and integrate data across the business units. Both roles clearly depend on each other and facilitate information supply chains.

Data Governance Roles
*Data definition includes business and quality rules, data access policies, data lifecycle and the conceptual data model. From the data point of view, it thus provides the input for the authorization concept, the risk management and compliance.

Typical data management roles

Data governance introduces new roles (or makes them explicit). The following role model is grounded in CC CDQ research and based on practical experiences in many companies and data initiatives:

The Head of Data Management (or Chief Data Officer) is responsible for supporting the strategic data management board in defining a data management strategy and setting up a data governance. This role also manages data stewards on a day-to-day basis.
Data owners specify the business requirements on data and on data quality. The data owner role is typically assigned to senior managers in business functions or division. Their role can be split into:
- Data definition owner is a strategic role and accountable for the data definition in specific areas of responsibility (e.g., a specific data domain). For example, the head of central procurement typically assumes the data definition owner role for supplier master data, and the head of HR is data definition owner for employee master data.
- Data content owner is an operational role and cccountable for data creation and maintenance (Data lifecycle) according to the data definition for a specific area of responsibility. For example, the sales manager for a specific region ensures that sales data is created according to a given data definition.
Data stewards support the business departments in the desired use of data. Their role is often organized by data domains (e.g. customer data, vendor data, material/product data). Data stewards evaluate requirements and problems with data, and support projects (e.g. SAP S/4HANA implementation) and digitalization initiatives (e.g. online shops, customer portals) as experts for their respective domain. Their role can be split into:
- Business data stewards are responsible for measuring and reporting the data quality, defining guidelines for creating and maintaining the data and documenting the data in a data catalog / glossary. The Data Steward's role is organized by data domains (e.g. customer data, vendor data, material/product data).
- Technical data stewards (often called data architects) are responsible for the data model and data lifecycle across IT systems. They provide standardized data element definitions and formats and profiles source system details and data flows between systems. They typically work across domains.

Optional additional roles

Data editors operate the data life-cycle based on the defined standards. They create and maintain data. This role is often assumed by employees in the business units, or in dedicated support functions, for example in shared service centers.
Executive sponsors provide sponsorship, strategic direction, funding and oversight for data management.

Role	Responsibilities	Organisational assignment
Chief Data Officer	Responsible for the overall data management & analytics strategy and accountable for its implementation Defines and communicates the overall data management & analytics strategy as well as roadmap and portfolio Coordinates data management and analytics product delivery at the enterprise level	Typically, a C-Level executive
Head of Data Management	Responsible for the data management strategy, portfolio & roadmap and accountable for its implementation Details and communicates the data management strategy Coordinates data management requirements, the data governance framework (incl. standards & guidelines) and high-level data architecture to ensure the successful implementation of the data management strategy Defines portfolio and roadmap for rollout	This role exists only in the case of a central data management group Reports to the CDO, or is covered by Chief Data Officer Senior manager with data management background
Data Definition Owner	Accountable for the data definition* in specific areas of responsibility (e.g. a specific data domain) Ensures that business requirements are fulfilled, and data are compliantly accessed and used: collects and defines data requirements, delegates the detailed data definition to a data steward *Data definition includes business and quality rules, data access policies, data lifecycle and the conceptual data model. From the data point of view, it thus provides the input for the authorization concept, the risk management and compliance.	This role is typically assigned to a senior manager, for example the head of procurement for vendor master data Typically located within business functions/divisions
Data Content Owner	Accountable for data creation and maintenance (Data lifecycle) according to the data definition for a specific area of responsibility Coordinates the creation and maintenance of data-by-data editors	This role is typically assigned to a senior manager Typically located within business functions/divisions
Data Steward (Business Data Steward)	Responsible for the data definition* in specific areas of responsibility (e.g. a number of data fields in a specific data domain) Details how data must be created and maintained to fulfill business requirements and is compliantly accessed and used Monitors data quality, defines quality targets, identifies business issues and initiates quality improvements Primary point of contact for questions and requests regarding data definitions. Approves data access requests in daily business, based on the data access policies *Data definition includes business and quality rules, data access policies, data lifecycle and the conceptual data model. From the data point of view, it thus provides the input for the authorization concept, the risk management and compliance.	This role is typically organized by data domains (e.g. customer data, vendor data, material /product data) It may be part of a data management group (central model) or a business function/division (decentralized model)
Data Architect (Technical data steward)	Responsible for designing, creating, deploying and managing conceptual and logical data models and for the mapping to physical data models Defines how data is stored, consumed, integrated and managed by different IT systems and applications Develops cross-functional data models and governs their reuse Consults data definition owner and data steward	This role may be part of a central data management group or the IT department
Data Quality Manager	Responsible for data quality metrics and methods Defines data quality methods and approaches Provides support to data stewards and in data quality projects	This role exists only in the case of a central data management group This role is usually part of a central data management group
Data Documentation Manager	Responsible for documenting data and data related standards and frameworks Provides standards to document data e.g., in the data catalog Documents the data governance framework including roles and responsibilities Provides support to data stewards and in data quality projects	This role exists only in the case of a central data management group This role is usually part of a central data management group
Data Editor	Responsible for data creation and maintenance (Data lifecycle) according to the data definition for a specific area of responsibility Creates and maintains data content according to the data definition	This role may often be assumed by employees in the business units, or bundled in dedicated support functions, for example in shared service centers
Data Expert	Communicates and provides training on the data definition to data editors and consults them in the use of the data definition Collects and communicates data requirements	This role may often be assumed by employees in the business units, or bundled in dedicated support functions, for example in shared service centers

Source: Based on Fadler & Legner (2020)

Assigning these data governance roles in an organization depends on many factors – most importantly, the maturity of the company and the mandate for data management. In practice, many variants can be found. But independently on how roles are assigned, three groups must very closely work together: data management teams, business functions/lines as well as the IT organization.

Chief Data Officer/Head of Data Management

With the increasing recognition of data as a strategic asset, the role of the Chief Data Officer (CDO) - also called head of data and analytics or chief data and analytics officer – is becoming of major importance in enterprises. This role creates the bridge between data management and analytics-oriented teams, while steering the overarching data strategy at an enterprise level and fostering alignment with business and IT stakeholders.

In case a CDO does not exist, typically the Head of Data Management can be found in enterprises who steers the central data management team, and is responsible for the data management strategy, portfolio & roadmap and accountable for their implementation (data governance framework and high-level data architecture).

Roles in a Central Data Management Team

A central data management team typically consists of several Data Stewards. The Data Steward is responsible for specific areas of responsibility’s data definition. The data definition includes business and quality rules, data access policies, data lifecycle and the conceptual data model. From the data point of view, it thus provides the input for the authorization concept, the risk management and compliance. The Data Steward is complemented with Data Architects, Data Quality Managers, and Data Documentation Managers.

The Data Architect supports the data steward by designing, creating, deploying, and managing conceptual and logical data models, as well as with mapping to physical data models. The Data Quality Manager is responsible for data quality metrics and methods and acts as a subject matter expert across data domains.

The Data Documentation Manager is responsible for documenting data and data related standards and frameworks. This role becomes an essential driver of data catalog initiatives, for instance.

Decentralized Roles in the Business Units and Support Functions

To be effective, any central data management team needs to be linked to the business units and support functions. After all, this is where most of the data is created and used. Most importantly an effective data governance design requires data ownership to remain with the business functions/departments. Data ownership clarifies the fundamental accountabilities for data in the organization. Two types of ownership can be distinguished here - the data definition owner and the data content owner. However, depending on the size of the company

In respect of the data in his/her domain, the Data Definition Owner is accountable for data definitions of business and quality rules, data access policies, data lifecycle, and the conceptual data model. This role is typically assigned to executive with strategic oversight, such as process owners.
The Data Content Owner’s role is usually assigned to executives with operational responsibilities (e.g. the head of sales of a specific country), who are accountable for creating data according to the relevant data definition.

Depending on the size of the company, it might be necessary to create one or more roles to ensure the link. Some organizations have established a Business Unit or Divisional Data Steward who ensure that the activities of the central team are aligned with the needs and activities of the business units and support functions. Other companies have defined Data Experts who are contact persons for the central team because they have very specific knowledge about certain data attributes (e.g. health and safety data, tariff numbers for customs). In most organizations, data is created and maintained in the business units and support functions by Data Editors under supervision of Data Content Owners.

Additional (Partner) Roles

Since data is processed and stored in IT systems, the link between the data management roles and the IT roles is important. Therefore, it is important to align the Data Steward roles with the IT roles, for example a Demand Officer or Solution Architect. Data Management roles also need to closely work with process management roles, for example a Process Owner and Process Manager. After all, business processes require high-quality data to operate efficiently. In addition, process changes typically have implications on the data that is used in the processes. Finally, with analytics becoming ever more important in most organizations, the data management roles must be linked to the Data Scientists and Data Analysts roles.

What Are Typical Data Governance Boards?

Data is a shared resource, that is used by several business units and support functions. Consequently, data-related priorities, requirements and issues must be aligned between them. This alignment happens in data governance boards. Most organizations require one board for strategic data management topics and one or more boards for tactical data management issues.

Strategic data management board

This board (the name varies across organizations) is the central decision-making board in data governance that aligns and approves the data management strategy, annual objectives and budget. It also tracks the progress of the data management activities and resolves issues that had to be escalated. Oftentimes, the board consists of data definition owners from the business units and managers of the support functions as well as the Head of Data Management / Chief Data Officer. It is possible that these roles already meet in other boards or committees. If so, it should be considered whether an additional strategic data management board is really needed or whether the topics to be discussed can be integrated into the other boards or committees.

Data governance council(s)

Data governance council(s) are needed to align more operative data management issues. These can include changes to the data model that affect several business units or the prioritization of data quality issues that must be resolved. Depending on the size of the firm, it might be necessary to have several councils. If that is the case, they are typically established per data domain (e.g. one council for customer data, one council for material/product data etc.) Data governance council(s) meet more frequently than the strategic board. Members are the Business Data Stewards, Data Quality Manager, Data Documentation Manager, and Data Architect from the central data management team as well as the Business Unit / Divisional Data Stewards.

What Is a Robust Data Governance Mandate?

The mandate defines the authority granted to data management roles within the organization, and from this, the responsibility. This is particularly critical for the central data management team. Are they allowed to enforce the standards and guidelines for data creation and maintenance or are they dependent on the goodwill of the business units and support functions? The type of mandate has a significant influence on the overall impact of data management on the organization. Like the mandate of a project management office (PMO), four types of mandates can be distinguished.

It is not uncommon that the authority of the (central) data management team is not defined. It is unclear what they can decide or how they can influence the organization to improve data management practices. If there is no clear mandate, discussions about data management priorities and initiatives tend to be endless. The problems with having no clear mandate for data management is often the trigger for defining and implementing a data governance.

If the mandate is to support, the data management team provides templates, best practices and access to information. However, the influence on the data management activities in the business units and support functions is very low. They get to choose if they want to cooperate with the (central) data management team or not. Since data is a shared resource that is used across organizational boundaries, the mandate to support is not very effective.

In this mandate, data management has the authority to align and coordinate all data management activities across the entire organization. This helps to reduce duplicate work. A central data management team with a mandate to coordinate can also advise on methods and best practices. However, it cannot enforce them.

In a directive mandate, data management has the authority to enforce data management standards through various means. Firstly, it defines the framework, methodologies and tools to be used for data management across the organization. Secondly, it also monitors whether the standards for creating and maintaining data are upheld. If not, it can sanction non-compliance, for example by revoking access to IT systems.

Best Practices: The mandate should be defined early in the data governance journey. If the difficult questions about authority and decision are not discussed upfront, they will return during the implementation of the data management initiatives. Since these discussions can’t be avoided, we recommend having them early in a data governance journey.

What Are Data Management Guidelines and Principles?

How and where is the required data quality defined? What are acceptable values for a specific data attribute and what aren’t? How should data be documented as metadata? Data Governance related decisions refer to some fundamental principles of data management. They include standards and guidelines, for instance related to data creation and maintenance, but also more general policies and principles that make employees understand the true importance and value of data.

Data management guidelines, policies and standards (data management rule book)

Guidelines, policies and standards (sometimes also referred to as data management rule books) define how quality is measured, how data is documented (metadata) and how the data life cycle managed. But most importantly, they comprise documents that define, how to create and maintain data correctly. For each data domain, they describe the key data objects with permitted values for the data attributes. For example, if we have a German postal address, is it permitted to shorten the word “street” to “Str.” or is it mandatory to write “Strasse”? Similarly, should the legal form of the company be part of the name or not? These guidelines are relevant for the entire data life cycle and for all systems in which the data is stored or used. Good guidelines, policies or standards are the foundation for high-quality data that is also harmonized across the company. The key to success is to train the organization and to monitor whether users adhere to the rules. Some rules defined in the guidelines can probably also be coded as validation rules into the IT systems.

Data management principles

Effective and efficient data management requires organizations to change. As known, change is hard. Many change initiatives fail simply because employees don’t understand why they need to change. This is where data management principles come into play. They describe the data management aspiration in easy to understand words and give enough concrete guidance on how to deal with data on a day-to-day basis. Principles also help to communicate the abstract topic of data management in a more tangible way.

Three examples for data management principles:

We manage data as an asset
Assets are valuable to any organization because they are critical to the business model. Therefore, they are managed with great care. What is true for assets like financial resources, employees or brand names is also true for data.
We share data
Data does not belong to a person or organizational function alone. It belongs to the entire organization. Consequently, when we manage data, we think and act in end-to- end processes and beyond organizational boundaries.
We are all responsible for data quality
Data has the greatest value when it is of high quality. Therefore, we ensure high quality from the earliest time possible (“first time right”). In addition, ensuring high data quality is everyone’s responsibility. It is not only the responsibility of the central data management team.

Best Practices: No one comes to work in the morning to create poor data quality on purpose, but data quality is often the result of ignorance or negligence. Guidelines, policies and standards define what good data quality looks like. Data management principles help to explain why data matters and why it is everyone’s responsibility.

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Data Governance: References and Further Readings

Toward big data and analytics governance: redefining structural governance mechanisms

Data ownership revisited: clarifying data accountabilities in times of big data and analytics

Designing data governance that delivers value

Data governance: from master data quality to data monetization

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