Integrating a Data Governance program into an organization not only means adding some software, but more dramatically, it means changing the workplace culture. Data Governance broadly describes the policies and procedures used to collect, organize, and manage data. It supports improved data analytics, which, in turn, promotes better operations management and better decision-making. Data Governance also assists in preventing data inconsistencies or errors.

Some organizations have never developed consistent Data Governance policies. These same organizations may be supporting multiple databases, each with different levels of data security and differing priorities. The lack of oversight and uniform security increases the chances of data breaches and loss.

Ensuring organizations meet regulatory obligations consistently is also a critical aspect of Data Governance. This feature is necessary to avoid paying fines and to minimize vulnerabilities. Mukesh Deshpande, a Data Governance lead and partner at PwC India, stated:

“Regulation is just one hurdle businesses face, but their path to Data Governance is much more complex than merely maintaining compliance. With the help of Data Governance frameworks and platforms, we’re able to realize our proven strategies for data trust and business use, which can only come to fruition once an organization understands the data it has, where it came from, how it is being consumed, and whether it can truly be trusted by respective stakeholders.”

Understanding the Data Governance Tools

Data Governance tools help to streamline the work culture changes by automating different aspects of the Governance program. The software uses modern tools and visualization processes to manage the uses, storage, availability, and security of data. While the software is not absolutely essential, it does help significantly in supporting the changes in employee behavior, and in responding to some tasks automatically. Some of the processes that become automated are:

• Metadata Management: “Metadata” describes small amounts of data that are meant to supply reference information about other data. It provides small bits of information, such as title, format, file type, origin, etc., from reference purposes. This “reference information” provides a search label of sorts, which can be picked up by search engines. (Similar to the card catalogs that disappeared from libraries in the mid-1990s.)

• Business Intelligence, Data Warehousing, and Analytics: Many organizations have combined business intelligence, data warehousing, and analytics to create a Data Management system. Adding Data Governance to this management system helps to optimize the analytical process. Additionally, Data Governance promotes communication and collaboration across the organization.

• Regulatory Compliance: If your market is worldwide, regulatory compliance is especially important. You do not want to get hit with fines from various governments, and Data Governance software can keep this from happening. There are several global regulations designed to protect people’s privacy. They range from Brazil’s Lei Geral de Proteção de Dados (LGPD) to the European Union’s General Data Protection Regulation (GDPR) law to California’s Consumer Privacy Act (CCPA), and others. Data Governance can keep you from breaking their laws.

• Data Security: Data Governance software automatically enforces policies and procedures that promote data security and compliance. Data Governance helps to protect against data breaches. It can also help to ensure data is stored and classified according to its sensitivity.

• Data Integration: Advanced and predictive analytics work best when there is a seamless integration of information coming from a broad range of sources, formats, and applications. By establishing standards for the data’s use (common data definitions, best practices), Data Governance can accelerate the process of data integration.

Changing the Workplace Culture

Data Governance is really more about changing the way people handle and think about data than the technology used for automated processes. These automated features, however, help to reinforce the Data Governance framework, and the staff’s behavior.

Creating a board of Data Governance Board or Steering Committee is a good first step when integrating a Data Governance program and framework. There are many examples of data frameworks.

The Data Governance framework is a set of rules, processes, and policies, typically created by the board of Data Governance. The framework is also used to describe the program’s—not the business’, but the Data Governance program’s—goals, mission statement, and KPIs. The framework should also support the business’s mission statement and goals. An organization’s governance framework should be printed out, and circulated to all staff and management, so everyone understands changes taking place.

The Data Governance framework should also include management policies around the database’s operations. Typically, these frameworks should establish policies on data protection, database environments, service delivery, performance levels, and licensing.

Saul Judah, a Gartner analyst, has listed seven basic concepts needed to successfully govern data and analytics applications. They are:

• A focus on business values and the organization’s goals
• An agreement on who is responsible for data and who makes decisions
• A model emphasizing data curation and data lineage for Data Governance
• Decision-making that is transparent and includes ethical principles
• Core governance components include data security and risk management
• Provide ongoing training, with monitoring and feedback on its effectiveness
• Transforming the workplace into collaborative culture, using Data Governance to encourage broad participation

The board should also create a job description and approve the hiring of a data steward.

The Data Steward

The data steward makes day-to-day decisions based on the policies created by the board of data governors. They don’t make policy; they make decisions based on policy. Data stewards should report to the board and provide feedback on the effectiveness of those policies. (Depending on circumstances, they might be on the board of data governors. At the very least, they should report once a month to the board.)

The data steward acts as a resource to support the user community, while policing data use and making sure various data policies are followed.

As a resource, the data steward is the “go to” person. They know how the data is gathered, maintained, and interpreted. In the role of data police, they make sure the data policies and standards are followed consistently.

Creating an Ineffective Data Governance Program

Sadly, most Data Governance programs today are ineffective. Sometimes it’s the result of Data Governance software that doesn’t integrate well with the other software. For example, a C-suite may not recognize the value-creation potential offered by Data Governance software. Because there is no baseline guidance from the software, all that’s left are policies and guidance that don’t describe how software actually works, but how it’s supposed to work. The workplace culture shift never takes place, and the software doesn’t work properly.

Another option for creating an ineffective Data Governance program is to rely only on the software. With this scenario, the board of data governors is never created, and no policies and guidance are created, or the board is thrown together somewhat thoughtlessly, and the policies are weak or confusing.

It should be pointed out both options are excellent ways to waste time and money.

Steps to Improve the Chances of a Successful Data Governance Program

A good Data Governance program should align with existing business strategies. To create the alignment, a vision of the business’s future can be remarkably helpful. This may involve creating a five-year business plan, or something as simple as the goal of selling 100,000 products the first year.

Create a chart that clearly defines the data domains of the company. This process should include determining which individuals or teams are responsible for data being generated by the business. It should show all of the staff, and describe their roles concerning Data Governance.

Adopt a standard language (shop talk) when talking about data in the workplace. Require the staff learn the new vocabulary, and use it (metadata, business intelligence, regulatory compliance, data analytics, etc.). Management has to set an example by also using the new terminology.

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