Data Governance 101

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data governance

Data Governance (DG), a formalized practice that connects different components – such as roles, processes, communications, metrics, and tools – increases data’s value. By harmonizing these fundamental elements, governance ensures that the right data flows efficiently to the right resources at the right time. In this Data Governance 101 article, we will look at the definition of Data Governance and the components of a Data Governance program, as well as the skills, tools, and best practices needed for success.

While Data Governance forms an essential part of Data Management, it does not encompass all Data Management activities. Instead, governance focuses specifically on structuring policies and procedures around data.

Organizations prioritize good governance programs to get better data quality and business intelligence, as well as to protect their information and comply with international regulations, such as the General Data Protection Regulation (GDPR).

Other lifecycle management and foundational activities also fall under Data Governance’s umbrella, captured in the Data Management Association (DAMA)’s Data Management Body of Knowledge (DMBOKv2) evolved wheel (pictured).

What Is a Data Governance Program?

A Data Governance program coherently coordinates people, technology, and activities throughout the enterprise. It represents a corporate journey, starting with a shared desire toward improving data creation, transformation, and usage to meeting data goals and key performance indicators (KPIs) set out by the program.

DMBoKv2 evolved wheel
Image Credit: DAMA International

To start, a governance program needs the right people to fund and create the program, including a Data Governance lead. Typically, this person or people comes from senior management and initiates the governance program. 

In parallel, Data Governance leadership starts developing and implementing a Data Governance framework, a collection of processes, rules, and responsibilities to formally structure a governance program. Think of this framework as scaffolding to construct a governance program, helping its practices evolve. Typically, this governance system takes a high-level view in the beginning and firms up details, over time, through iteration and seeing what works.

Next, the Data Governance lead establishes roles and responsibilities. These include:

  • Data Governance Committee: A core group of representatives across an organization interprets and decides how to execute the Data Governance framework. Also, this team may suggest updates to the existing framework to adapt to new contexts. Typically, critical data managers and stakeholders make up this group.
  • Data Stewards: Data stewards work with data daily on the ground. They act as department representatives in the governance program. Data stewards ensure their divisions implement governance decisions and bring any questions or problems back to the governance program. In addition, they train their co-workers on governance program initiatives and communicate helpful solutions.
  • Everyone: Each employee participates in the Data Governance program outside of committee and stewardship activities. Everyone in the company takes responsibility for actively becoming data-literate by understanding how the firm organizes its data and stays compliant with regulations. Consequently, employees need to participate in training offered by the governance program and work according to best practices.

Throughout its lifespan, a Data Governance program primarily focuses on changing the workplace culture and how employees handle and think about data. Additionally, a governance program uses tools and technologies to implement its directives. However, governance program members must align on technical decisions for the success of any governance software platform.

What Are Data Governance Skills?

For Data Governance to work, everyone needs some basic skills. These capabilities include organizational data literacy, communication, understanding and interpreting data, and recognizing when data needs better data quality. Fundamentally, anyone needs skills in collaborating with others across the organization, such as co-workers, managers, and customers.

Those that take on a more prominent role in a Data Governance program need additional capabilities, as explained below:

  • Data StewardData stewards need to know how to give feedback to their teammates so they can work using Data Governance initiatives. Furthermore, data stewards need skills to communicate and train others on governance tasks. A sense of humor, good listening skills, and a strong ego help too, as governance discussions and decisions can get heated.
  • Data Governance Lead: A Data Governance lead needs people skills to market Data Governance initiatives and to empathize with other staff members. Such a person needs to communicate and train others about governance decisions. Also, the lead must have clear decision-making ability and problem-solve any emerging issues. A Data Governance lead needs to function in a quick, changeable environment as conversations and activities evolve.
  • Data Governance Owner: A responsible executive owns the purse strings and has accountability for the Data Governance program. Such a person must have a clear data strategy, a passion for data, a good sense of what data investments pay off, and outstanding social skills to evangelize the governance program and handle politics around people and data.

What Is a Data Governance Tool?

Data Governance tools enable alignment with a governance program and support its management and foundational activities through automation. Automated governance tools support the following:

  • Data Security and Privacy: These tools monitor legal validation and enforcement for regulatory compliance and access settings. Furthermore, such software can set information security controls, run helpful audits and reports on potential security risks, and classify data according to its sensitivity.
  • Data Architecture: Data Architecture tools identify and address operational efficiencies and simplify data integration with other products.
  • Data Modeling: Data Modeling tools create diagrams of database structures. Also, they assist professionals with standardizing data assets and understanding data flow within the data fabric, a distributed data platform.
  • Data Design: Data catalogs make up an essential automated data design tool. Data catalogs describe the available data assets in the company and where to find them.
  • Data Quality: Data Quality tools profile, clean, monitor, validate, transform, and standardize data. Those relating to Data Governance tend to work at an enterprise level.
  • Metadata Management: Metadata Management tools automate data classification tasks so that people and systems can find and access what they need for work activities.

What Are Data Governance Challenges?

The biggest challenges for Data Governance center around alignment and culture. Organizations tend to grow their data organically with “more departments undertaking more digital initiatives of their own.” As a result, each corporate group governs and manages data projects, uniquely overlapping in some approaches while tackling others differently.

Furthermore, when senior managers want everyone to be on the same page using the Data Governance framework, they find this integration difficult. Some people want to do data the way they always have and resist change

Some indications of Data Governance difficulties include:

  • Verbally agreeing, in meetings, to implement newer data processes while continuing to do the same data activities
  • Withdrawing engagement in a Data Governance program regarding key data issues 
  • Hearing more frequent, prevalent, and incorrect stereotypes about the Data Governance program. These comments manifest informally, such as “Oh, that is IT’s Data Quality project” or “That Data Governance program will eventually replace X application and its engineers.”

Often, Data Governance starts strong with enthusiasm and commitment but lacks clear communication and drive to implement critical policies. For example, in a Capital One survey, 82% of participants reported confusing governance policies as a top difficulty.

Also, employees may perceive that their feedback for the enterprise Data Governance program has not received attention or value. Whether these viewpoints are valid, they contribute negatively to governance success.

What Are Data Governance Best Practices?

Establishing a single executive owner, as suggested by Ryan Doupe – VP and chief data officer at American Fidelity – underlies the most important and best Data Governance practice. This person needs to have the following characteristics:

  • Accountability for the overall Data Governance program
  • The budget authority and ability to make decisions
  • A passion for getting data to flow better while complying with data security and privacy needs
  • A broad strategic view of data movement in the organization
  • Grit to persevere past major roadblocks

Other best practices, according to Ryan Doupe, include:

  • Identify a solid lead for the Data Governance program and make that the person’s primary role
  • Determine an objective for the governance program
  • Set a structure for the governance program
  • Assess the organization’s Data Management Maturity – The CMMI Institute’s Data Management Maturity Model provides a solid choice
  • Create a roadmap to improve underdeveloped governance capabilities and areas that the Data Governance Committee would like to address
  • Create a formal Data Governance Committee Charter, clarifying roles, responsibilities, and objectives, including their scope
  • Include data stewards and employees that create and work with the data directly, and support their activities to control data
  • Set up resources, like a Data Quality tool, that support governance initiatives
  • Communicate the impact of a successful governance program consistently

Data Governance 101

Ready to learn more? Here are some additional Data Governance 101 resources discussing trends, challenges, and key components for business success.

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