Data Governance: Definition, Challenges & Best Practices [Interactive] (2022)

Data Governance: Definition, Challenges & Best Practices [Interactive]Nikolai Janoschek2020-11-23T15:20:28+01:00

Data governance forms the basis for company-wide data management and makes the efficient use of trustworthy data possible. The efficient management of data is an important task that requires centralized control mechanisms.

To help end users gain a better understanding of this complex subject, this article addresses the following points:

  • What is data governance?

  • Why data governance matters

  • How much importance do companies attach to the issue?

  • (Video) What is Data Governance?

  • Challenges

  • Best practices

  • Data Governance: Definition, Challenges & Best Practices [Interactive] (1)

    What Is Data Governance?

    Data Governance includes the people, processes and technologies needed to manage and protect the company’s data assets in order to guarantee generally understandable, correct, complete, trustworthy, secure and discoverable corporate data.

    The topics encompassed by data governance are:

    At its core, data governance is about establishing methods, and an organization with clear responsibilities and processes to standardize, integrate, protect and store corporate data. The key goals are to:

    • Minimize risks

    • Establish internal rules for data use

    • Implement compliance requirements

    • Improve internal and external communication

    • Increase the value of data

    • Facilitate the administration of the above

    • Reduce costs

    • Help to ensure the continued existence of the company through risk management and optimization

    Data governance programs always affect the strategic, tactical and operational levels in enterprises (see figure below). In order to efficiently organize and use data in the context of the company and in coordination with other data projects, data governance programs must be treated as an ongoing, iterative process.

    Data governance levels

    In addition to the responsibilities, the following aspects of any data governance program must be clarified (see figure below).

    Aspects of data governance

    • The organization (the “where” and “who”)

    • Business aspects (the “what”)

    • Technical aspects (the “how”).

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    Why Data Governance Matters

    Most companies already have some form of data governance for individual applications or business departments, although it is not necessarily comprehensively institutionalized. The systematic introduction of data governance is therefore often an evolution from informal rules to formal control.

    Formal data governance is normally implemented once a company has reached a size at which cross-functional tasks can no longer be implemented efficiently.

    Data governance is a prerequisite for numerous tasks or projects and has many clear benefits:

    • Consistent, uniform data and processes across the organization are a prerequisite for better and more comprehensive decision support;

    • Increasing the scalability of the IT landscape at a technical, business and organizational level through clear rules for changing processes and data;

    • Central control mechanisms offer potential to optimize the cost of data management (increasingly important in the age of exploding data sets);

    • Increased efficiency through the use of synergies (e.g. by reusing processes and data);

    • Higher confidence in data through quality-assured and certified data as well as complete documentation of data processes;

    • Achieving compliance guidelines, such as Basel III and Solvency II;

    • Security for internal and external data by monitoring and reviewing privacy policies;

    • Increased process efficiency by reducing long coordination processes (e.g. through clear requirements management);

    • Clear and transparent communication through standardization. This is the prerequisite for enterprise-wide data-centric initiatives;

    • Further, specific benefits result from the specific nature of each data governance program.

    More than ever, data governance is vital for companies to remain responsive. It is also important to open up new and innovative fields of business, for example by big data analyses, which do not permit the persistence of backward thinking and overhauled structures.

    At the moment, the most important drivers that lead companies to rethink their current approaches are:

    • Establish data-centric views to support digital business models

    • Enterprise-wide data quality and master data management

      (Video) Data Governance - Best Practices

    • Manageability of data in big data environments

    • Creation of standards to increase the ability to react to external influences (e.g. M & A)

    • Self-service BI (SSBI): Users want to carry out analyses independently of IT

    • Compliance: transparent and understandable data processes to comply with legal requirements

    In addition to these drivers, there are a number of other developments and requirements that make data governance more and more relevant.

    Examples include operational BI, advanced analysis, social media, a 360° degree customer view, BI in the cloud or as a service, information strategies, and compliance with data protection guidelines for internal and external use of data (SCM, CRM).

    Data Governance: Definition, Challenges & Best Practices [Interactive] (6)

    The BI Professionals’ View on Data Governance

    Data from BARC’s BI Trend Monitor confirms the importance of data governance.

    Importance of Data Governance in 2017 (n=2,661)

    Data governance is most relevant in large enterprises, in the financial sector and in the UK & Ireland.

    It is less popular with business users and mid-sized/smaller companies.

    Importance of Data Governance (Timeline)

    Data Governance: Definition, Challenges & Best Practices [Interactive] (7)

    Data Governance Challenges

    The relevance of data governance is obvious. Nevertheless, despite its advantages, many companies are afraid to implement data governance programs – either because of the assumed complexity or due to general uncertainty.

    Implementing data governance programs is by no means a trivial undertaking. The following are some of the biggest hurdles in the implementation phase:


    Data governance requires an open corporate culture in which, for example, organizational changes can be implemented, even if this only means naming roles and assigning responsibilities. As a result, data governance becomes a political issue, because this ultimately means distributing, awarding and also withdrawing responsibilities and competencies. A sensitive approach is needed here.

    Acceptance and Communication

    Data governance needs acceptance by means of a working communication between all parties by suitable employees in the right places. Project managers in particular need to have an understanding of the technical as well as business aspects, the jargon and preferably an overarching conceptual view of the company.

    Budgets and Stakeholders

    It is often still difficult to convince stakeholders in the organization of the need for data governance programs and to get budget. In addition, changes are often hindered by ingrained, but functioning processes and deficiencies in information processing are compensated by not directly visible resources in business departments.

    Standardization and Flexibility

    Businesses need to be flexible to address fast-changing requirements. However, it is vitally important to seek the right balance between flexibility and data governance standards according to each individual company’s business requirements.

    Balance between chaos and repression

    Data Governance: Definition, Challenges & Best Practices [Interactive] (9)

    Data Governance Best Practices and Success Factors

    (Video) Real World Data Governance: Data Governance Best Practices

    Implementing a Data Governance Initiative

    Data governance is not a big bang initiative and would not work in this fashion. Instead, global initiatives are highly complex and long-term projects. They therefore run the risk that participants might lose trust and interest over time.

    It is therefore recommended to start with a manageable or application-specific prototype project and to continue iteratively. In this way, the project remains manageable and experience can be used for more complex projects or to expand the data governance programs in the company.

    Typical project steps are:

    • define goals and understand benefits;

    • analyze current state and delta analysis;

    • derive a roadmap;

    • convince stakeholders and budget project;

    • develop and plan the data governance program;

    • implement the data governance program;

    • monitor and control.

    These steps are not only to be repeated for each new program, but they also need to be repeated if changes are made.

    Before the start of any data governance program, questions about the reasons for the project should always be answered in order to avoid unnecessary additional work. Similarly, existing processes should be evaluated to determine whether they can be adapted to the new requirements within the framework of a data governance program, instead of starting with the perhaps unnecessary development of new processes.

    The following tools provide assistance with the implementation of a data governance program:

    Data Management (DAMA) Framework

    The DAMA Framework provides orientation towards identifying disciplines and functional groups – see

    BARC 9-Field Matrix

    BARC’s “9-Field Matrix” is designed to determine the current state of an organization’s approach to data management and derive a roadmap from it.

    The three company levels (strategic, tactical and operational) and the organizational, business and technical aspects thereof form the basis of the matrix. With its structure, data management projects can be fleshed out with specifications of the topics, processes, roles and tasks involved.

    It should be noted that the projection of the levels, the organizational, business and technical aspects, and also the roles in the company should be very specific. The matrix is nevertheless suitable for any topic in the field of data management.

    BARC 9-Field Matrix

    The DAMA framework provides all the relevant data management topics with documented criteria. They are assigned to a field in the BARC 9-Field Matrix.

    In this way, the current state for each field can be compared in a structured manner against the target state. In doing so, the delta can be identified, priorities can be set and a roadmap with concrete actions can be derived.

    Role Models

    Roles are essential to every data governance program. These days, software tools provide data governance templates for metadata management, data quality, master data management and data integration.

    The roles vary slightly, but the core ones are always as follows:

    • Data Governance Council (steering committee / strategic level)

    • Data Governance Board (tactical level)

    • Data Manager

    • Data Owner

    • Data Steward

    • Data User

    Templates and Libraries

    Templates go a step further than role models. Among other things, they also include best practice processes, decision-making rules, data quality rules, key figures and task types.

    ”Data Governance“ Platforms

    Data governance platforms offer different functional blocks for data quality, master data management, data integration, metadata management and data protection.

    Data Governance: Definition, Challenges & Best Practices [Interactive] (11)

    BARC Recommendations

    The following tips will help you implement your data governance initiatives or programs:

    • Never launch a data governance program without management support;

    • Do not start big bang initiatives, but understand data governance as a continuous, iterative process consisting of sub-projects;

    • Start with small pilot projects and bring the experience of these into the company;

    • Data governance programs can run for years. However, individual projects should not last longer than 3 months;

    • Set well-considered and clear targets;

    • Winning acceptance is the top priority. Involvement of stakeholders and transparency of the process are key. An open and transparent communication with all stakeholders with no hidden agendas is recommended;

    • Do not re-invent the wheel but use templates, models and best practices that are already available on the market, whether it’s through software tools, frameworks and libraries, or consultants;

    • Appoint the roles in the company properly. Particularly critical are the communication skills of the program manager, who has to bring the data governance program into the company, taking into account political issues and sensibilities;

    • Carefully examine and consider why established processes and solutions have not been sufficiently streamlined;

    • Evaluate data governance platforms;

    • Create clear structures and responsibilities;

    • Establish a thorough methodology for documenting organizational best practices.

    Data Governance: Definition, Challenges & Best Practices [Interactive] (12)

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    What are the challenges in data governance? ›

    4 Data Governance Challenges for Organizations
    • Data Silos with No Single Source of Truth. Countless organizations struggle with data silos. ...
    • Lack of Trust and Ownership of The Data. Data quality is the key to actionable business insights, reliable reporting, and better results. ...
    • Poor Leadership. ...
    • Misallocation of Resources.
    14 Mar 2022

    What are data governance best practices? ›

    Check out our top six data governance best practices to help you start collecting, storing, and using your data more effectively.
    • Start small and build to the big picture. ...
    • Get business stakeholders on board. ...
    • Define data governance team roles. ...
    • Use metrics to measure progress. ...
    • Encourage frequent communication.
    19 Oct 2020

    What is data governance defined as? ›

    Data governance defined

    Data governance is everything you do to ensure data is secure, private, accurate, available, and usable. It includes the actions people must take, the processes they must follow, and the technology that supports them throughout the data life cycle.

    What are the 4 pillars of data governance? ›

    There are four pillars to the data governance framework to enable organizations to get the most out of their data.
    • Identify distinct use cases. ...
    • Quantify value. ...
    • Improve data capabilities. ...
    • Develop a scalable delivery model.
    9 Mar 2022

    What are the 3 pillars of data governance? ›

    3 Pillars of Data Governance: Who should own, secure, and access your data?

    What are the types of data governance? ›

    Let's take a look at four of the most common data governance models:
    1. De-centralized Execution – Single Business Unit. ...
    2. De-Centralized Execution – Multiple Business Units. ...
    3. Centralized Governance – Single or Multiple Business Units. ...
    4. Centralized Data Governance & Decentralized Execution.
    8 Aug 2016

    Why do we need data governance? ›

    Data governance is important because it brings meaning to an organization's data. It adds trust and understanding to an organization's data through stewardship and a robust business glossary, thus accelerating digital transformation across the enterprise.

    What is data governance tools? ›

    A data governance tool is defined as a tool that aids in the process of creating and maintaining a structured set of policies, procedures, and protocols that control how an organization's data is stored, used, and managed.

    What is another name for data governance? ›

    Macro level. On the macro level, data governance refers to the governing of cross-border data flows by countries, and hence is more precisely called international data governance.

    What are the key attributes of effective data governance? ›

    Increasing the privacy and security of data. Access to sensitive data is regulated and monitored. Using timely data analytics to improve operations and corporate decision-making. Obtaining and ensuring compliance with data privacy and security standards on an ongoing basis.

    How do you implement data governance and best practices? ›

    BARC recommends the following steps for implementation:
    1. Define goals and understand benefits.
    2. Analyze current state and delta analysis.
    3. Derive a roadmap.
    4. Convince stakeholders and budget project.
    5. Develop and plan the data governance program.
    6. Implement the data governance program.
    7. Monitor and control.
    18 Mar 2021

    What skills are needed for data governance? ›

    Excellent communication skills, both written and verbal. Communication with staff at all levels within the organization (including upper management, and ability to make presentations) Ability to be a leader, develop relationships, provide direction and oversight, make decisions, and educate others.

    What is the difference between data governance and data management? ›

    In the simplest terms, data governance establishes policies and procedures around data, while data management enacts those policies and procedures to compile and use that data for decision-making.

    What is a data governance operating model? ›

    What is an operating model in data governance? An operating model outlines how an organization defines roles, responsibilities, business terms, asset types, relations, domain types, and more. This, in turn, affects how workflows and processes function; it impacts how an organization operates around its data.

    What is data governance interview questions? ›

    'Data Governance and you' questions

    How did you come to be in the Data Governance arena? Give me your Data Governance elevator pitch. How do you measure the success of Data Governance initiatives? Give me an example of how you went about implementing governance before and what you would do differently this time.

    Is data quality part of data governance? ›

    Data quality is an important pillar in the data governance framework and plays a vital role in an organization's ability to meet established governance standards.

    What does a data governance team do? ›

    The data governance team is typically responsible for gaining budget approval, setting governance goals and priorities, architecting the data governance model, selecting technologies to adopt, and evangelizing the program.

    What is the risk of poor data governance? ›

    Short-Term and Long-Term Impact

    Ultimately, poor quality data can lead to a decrease in the trust level of the data in the system, which in turn can lead to the abandonment of the system by business users.

    Why is data governance so hard? ›

    Challenges to Data Governance

    Conflicting data flows and a lack of data ownership can lead to a lack of trust in information, he said, and an inconsistent understanding of that information. According to Dye, challenges come from a variety of sources: Limited funding and resources, or competition for them.

    What is the impact of poor data governance? ›

    Poor data governance can weaken your company's security infrastructure, pretty much sounding the alarm for cybersecurity threats to swarm in to steal sensitive data or hold it for ransom.

    Who is responsible for data governance? ›

    Chief data officer (CDO)—a senior executive responsible for overseeing the data governance program. CDO responsibilities typically include securing approval and funding for the program, hiring key staff members, as well as leading the program. Data governance manager—a program manager leads the data governance team.

    How do you start data governance? ›

    Take this report and follow these six steps to start a data governance program that will allow you to scale systematically and swiftly:
    1. Identify roles and responsibilities. ...
    2. Define your data domains. ...
    3. Establish data workflows. ...
    4. Establish data controls. ...
    5. Identify authoritative data sources. ...
    6. Establish policies and standards.
    29 Dec 2020

    What are examples of governance? ›

    Governance is defined as the decisions and actions of the people who run a school, nation, city or business. An example of governance is the mayor's decision to increase the police force in response to burglaries. The process, or the power, of governing; government or administration.

    What is data governance use cases? ›

    Data governance is the process of organizing, securing, managing, and presenting data using methods and technologies that ensure it remains correct, consistent, and accessible to verified users.

    What is data governance solutions? ›

    Data governance solutions and tools provide understanding, security and trust around an organization's data. As companies scale and accumulate more data sources and assets, they must determine the appropriate big data environments for storage and access purposes.

    What is the difference between master data management and data governance? ›

    Data Governance and Master Data Management

    Master Data Management includes processes from the creation of master data through to its disposal. Data Governance creates the rules and adjudication of the operational processes that are executed within those processes.

    What is not data governance? ›

    Data governance isn't about data protection, data privacy or data security. It's not about data retention or records management, and it has no relation whatsoever to Big Brother.

    What are three benefits of data governance? ›

    Here are the key benefits that a successful data governance program can produce in an organization.
    • Greater efficiency. ...
    • Better data quality. ...
    • Better compliance. ...
    • Better decision-making. ...
    • Improved business performance. ...
    • Enhanced business reputation.
    26 May 2022

    How do you master data governance? ›

    Key elements of master data governance

    These include transparency, maintenance, data ownership, change management, compliance, accountability, authority, auditability, data stewardship, standardization, and education.

    Is data governance part of data analytics? ›

    The Relationship Between Data Governance and Data Analytics

    Together, data governance and data analytics make each other stronger. Data governance ensures that decision-makers can rely on analytics for actionable insights.

    What is the data life cycle? ›

    The data life cycle is the sequence of stages that a particular unit of data goes through from its initial generation or capture to its eventual archival and/or deletion at the end of its useful life.

    How do you create a data strategy? ›

    Our 7 Elements of a Data Strategy
    1. Business Requirements. Data must address specific business needs in order to achieve strategic goals and generate real value. ...
    2. Sourcing and Gathering Data. ...
    3. Technology Infrastructure Requirements. ...
    4. Turning Data into Insights. ...
    5. People and Processes. ...
    6. Data Governance. ...
    7. The Roadmap.
    29 Apr 2022

    What is in a governance model? ›

    A governance model outlines how people in authoritative positions hold themselves accountable to their stakeholders. Governance models incorporate ethics, integrity, and a responsible code of conduct for all leaders, volunteers, and workers.

    How do I create a SaaS governance framework? ›

    How to Develop a SaaS Governance Framework
    1. SaaS Governance Approach.
    2. Create Policy. Approve All SaaS Use Through a Defined Process. ...
    3. Analyze Control Requirements.
    4. Assess Risk to Select Your SaaS Provider.
    5. Position and Policy.
    6. Initiate SaaS Service. ...
    7. Perform Continuous Management. ...
    8. Manage End of Life (Planned and Unplanned)
    11 Feb 2020

    What is the difference between operating model and delivery model? ›

    A business model outlines how a company captures and offers value through its products/services, value proposition, customer segments, key partners, etc. An operating model, on the other hand, lays out how a company will run in order to deliver that value.

    Why is data governance so hard? ›

    Challenges to Data Governance

    Conflicting data flows and a lack of data ownership can lead to a lack of trust in information, he said, and an inconsistent understanding of that information. According to Dye, challenges come from a variety of sources: Limited funding and resources, or competition for them.

    What is the risk of poor data governance? ›

    Short-Term and Long-Term Impact

    Ultimately, poor quality data can lead to a decrease in the trust level of the data in the system, which in turn can lead to the abandonment of the system by business users.

    Why do data governance programs fail? ›

    Lack of focus on the right areas.

    In many cases, data strategy and governance protocols fail because the governance committee members focus more on their day-to-day tasks rather than the defining and monitoring of company-specific metrics, rules, and KPIs.

    What are data governance gaps? ›

    In our recent on demand webinar, “Designing Storage Architectures for Data Privacy, Compliance and Governance,” we refer to the gap between the expectations of the regulators and data center reality as the Data Governance Gap.

    What is data governance interview questions? ›

    'Data Governance and you' questions

    How did you come to be in the Data Governance arena? Give me your Data Governance elevator pitch. How do you measure the success of Data Governance initiatives? Give me an example of how you went about implementing governance before and what you would do differently this time.

    What is data governance risk and compliance? ›

    Governance, Risk, and Compliance (GRC) is a structured way to align IT with business goals while managing risks and meeting all industry and government regulations. It includes tools and processes to unify an organization's governance and risk management with its technological innovation and adoption.

    What is risk management in data governance? ›

    Data governance supports enterprise risk management activities by identifying risks, developing policies and controls, executing those controls and having a framework in place for ongoing monitoring.

    What are data controls? ›

    Data control is management oversight of information policies for an organization's information. Unlike data quality, which focuses on fixing problems, data control is observing and reporting on how processes are working and managing issues.

    What is data governance implementation? ›

    A data governance strategy provides a framework that connects people to processes and technology. It assigns responsibilities, and makes specific folks accountable for specific data domains. . It creates the standards, processes, and documentation structures for how the organization will collect and manage data.

    Why does MDM fail? ›

    In fact many of the problems seem to be tied to the typical reasons any IT project can fail: Underestimating the work. Not enough resources. Trying to do too much at once (including scope creep)

    Which of the following is a benefit of data governance for cloud DW Lakes? ›

    Benefits of data lake governance

    Effective data governance enables organizations to improve data quality and maximize the use of data for business decision-making, which can lead to operational improvements, stronger business strategies and better financial performance.

    What is data governance in Salesforce? ›

    Salesforce data governance is a combination of methods and standards that are designed to maintain a quality pool of data. A proper data governance strategy will provide a series of benefits including simplifying planning, heightening accountability, informing future improvements, and more.

    What is big data and explain its characteristics? ›

    Big data is a collection of data from many different sources and is often describe by five characteristics: volume, value, variety, velocity, and veracity. Volume: the size and amounts of big data that companies manage and analyze.

    What is meant by the term Siloed data? ›

    A data silo is a collection of data held by one group that is not easily or fully accessible by other groups in the same organization. Finance, administration, HR, marketing teams, and other departments need different information to do their work.


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