Data Governance: What Is It and Why Is It Important?

What is data governance?

Data governance is a system for managing a company’s data throughout its lifecycle. It refers to set principles and processes that determine who can do what with information, under what circumstances, using specific methods, and when. Data governance also helps establish accountabilities and decision rights.

Due to industry- and company-specific needs and requirements, every company will have its own data governance framework. However, the shared goal of data governance is to guarantee that data is secure, high quality, and relevant to the business’s goals.

Proper data governance allows your teams to benefit from streamlined data processes with ensured compliance and accessibility, even as your company collects more data.

Data governance at the macro level vs. the micro level

It’s important to distinguish between the two definitions of data governance, which differ on the macro and micro levels.

Data governance is a political concept on the macro level. It refers to data regulation between countries, also known as international data governance.

On the micro level, data governance is a data management concept, as previously discussed. Data governance on the micro level focuses on corporate principles and rules that companies should follow to comply with regulations and ensure data remains secure.

Data governance vs. data management

Although they may sound similar, data governance is part of the larger data management process.

Data governance details and documents the corporate data policies and procedures that ensure your company’s data can be leveraged for current and future use. On the other hand, data management works to put these policies into action. In other words, data governance acts as the blueprint, while data management is the actual execution.

Components of data governance

Good data governance empowers your organization’s teams to make trustworthy, better-informed business decisions. It optimizes your data analysis by focusing on the pillars of data governance. Five of the most important components of data governance include the following.

1. Data quality

Data quality refers to how complete, up-to-date, and accurate your data is based on how it is collected, analyzed, and processed. Data governance is often measured mainly by how compliance-oriented the data is. However, it’s just as important to consider the overall data quality.

Your data governance framework should incorporate the best ways to identify and fix or remove low-quality data. Data quality is an integral part of your data governance strategy to keep a competitive edge in today’s fast-evolving data landscape.

2. Data privacy

Do your data assets contain sensitive information? Are you following industry- and law-based regulatory requirements to determine how your data is collected, processed, handled, and stored?

Most companies handle some form of customer personal data, such as medical records, financial information, and email or physical addresses. With how sensitive the information is, data privacy and regulatory compliance are crucial cornerstones of a comprehensive data governance program.

An up-to-date data governance framework will ensure that all data compliance laws and regulations are in place before data is even collected. This allows your company to track the data it captures from source to accessibility to use.

3. Data security

Similar to data privacy, data security is a compliance necessity within data governance processes. Some ways to guard data against security breaches include:

• Encryption
• Multi-factor authentication
• Activity monitoring

Malware is a huge threat to the sensitive data housed by your company’s data warehouse. If your business suffers from digital security threats, any potential leak can hurt or destroy your company’s future success.

Your data security should track:

• The source of your data
• Where it’s housed
• Use of data
• Disposal of unnecessary or inaccurate data

These details and more should be a part of your data governance framework, especially as your company’s data assets grow.

4. Data management

Proper data management is key in the data governance framework. It ensures appropriate measures are in place to handle data when it is useful and serves a purpose, or when it is no longer necessary and can be archived or deleted. With data governance being an ongoing management process that covers the entire data lifecycle, keeping up with data changes is critical.

Use top-of-the-line data software tools to monitor and analyze data effectively, efficiently, and securely. Using a data management platform lets your company maintain the quality of your data without wasting time and additional resources. These tools can help managers set guidelines and accountability measures for all data users, helping improve data governance.

4. Data management

Data stewardship is another pillar of data governance crucial for framework implementation. Stewardship ensures organizations use and manage data assets correctly. Data stewards coordinate and confirm the data governance implementation of policies and procedures is working. They are also responsible for defining data and establishing quality standards.

Why is data governance important?

If your data governance plan hits every component successfully, your business can organize critical data so it’s always accessible to those who need it. This can benefit your company enormously, almost guaranteeing:

• Higher data quality: Established data governance will lead to more effective data, as handling and use are standardized to reduce errors and clutter.
• Better decision-making and business planning: Clearly defined data governance protocols empower employees to feel confident in their company’s data quality.
• Faster and improved compliance: Your company will be able to profit from decreased costs and resources in other areas of data management, including data security and privacy.

Data is your strongest company asset, and data governance will ensure it won’t become a liability in the long run. As an ongoing process, data governance is an investment in the maturation of your data over time. Implementing a comprehensive and consistently improving data governance framework is in every company’s best interest, across any industry.

Who is responsible for data governance within an organization?

Every employee should uphold data governance at every level of your organization. It will be used by multiple departments and stakeholders, regardless of whether they are end users, IT staff, or business executives.

However, some have more primary governance responsibilities than others:

Data Governance Manager

A data governance manager is responsible for data use and integrity across the business. This person sets the data governance framework and ensures it’s implemented across various systems and business processes. A Chief Data Officer (CDO) often acts as a data governance manager.

Responsibilities include:

• Collaborating with the steering committee and data stewards to standardize data approaches between business units.
• Giving final approval to data glossaries and definitions.
• Ensuring data quality.
• Administration of master data management (MDM).
• Sharing and training colleagues on data governance frameworks.
• Providing feedback to the steering committee.

The CDO or data governance manager usually helps create the steering committee and acts as the leader.

Cross-functional steering committee

The steering committee is in charge of facilitating data governance decisions. This committee comprises a mix of executive-level leaders, managers, and end users within your company. An ideal steering committee is a small group that represents all your company’s stakeholders. After all, the committee needs to be able to analyze information and make final decisions efficiently.

For data governance, the steering committee is responsible for informing general strategy, as well as maintaining deadlines and achieving previously set goals.

Data steward

Data stewards and system administrators are responsible for the day-to-day management and implementation of data governance processes within the business systems they manage. These team members are subject matter experts (SMEs) on the systems they manage. They also know the best practices for data management and how data flows between their system(s) and other business systems. This allows the data steward to work effectively with the CDO or data governance manager to give insight into the data and advise on how to achieve new data goals.

How to implement data governance

Every organization should have its own unique data governance framework to fit individual needs and meet data goals. How can your company implement its own data governance plan?

First, your CDO or Data Governance Manager needs to establish company-specific goals and determine how you measure your progress. This will help them build a basic setup for your policies, leading your company to the technology and teams required to effectively carry out data governance processes.

Once your company has done this, it can identify the Data Stewards and Steering Committee members of your company’s data assets and business units. Collaborate with these employees and involve them in the ongoing evolution of your data governance framework whenever possible.

As the data governance framework is finalized, document and internally distribute the policies to all employees working with data within your company. Implement training and education resources to ensure all data rules are followed to comply with standards and regulations.

Challenges of data governance

Implementation of data governance processes and programs is challenging for most organizations—especially as they evolve and consistently change with new data policies. Data Stewards or system admins must be involved in Steering Committee meetings and updates to resolve any difficulties in practice.

Other challenges your company may face with the data governance framework include:

Organizational

While it may seem like data governance is a clear-cut necessity for data quality, it can be hard to get organizational support if each team doesn’t understand why it applies to them.

To educate each person who interacts with data in your company, it’s essential that your data governance framework details exactly how its policies improve the employee experience.

For example, data governance improves data quality. Outline how these policies will help to address data errors, such as data duplication, which can waste important time, money and resources for your sales and marketing teams. For C-suite executives, document the correlation between high-quality data maintained by good data governance and successful business decisions.

Organizational leaders should engage with different team managers to discuss how certain data governance policies or issues affect their specific work. Doing so helps promote policy clarity.

Acceptance and communication

A detailed and well-documented data governance framework will only guarantee success if it is widely communicated to data users and accepted by all.

Ensure every steering committee meeting is well-documented so that all relevant stakeholders can be kept up to date on data governance plans, changes, and achievements. Transparent decision-making and change management are best practices for data governance.

Meanwhile, data stewards and system admins should be properly prepared to implement all data governance policies. They are often the go-between for the steering committee and the daily users of your data and business systems. Organizations can achieve company-wide acceptance of data governance processes by effectively communicating plans.

Budgets and stakeholders

Getting your company’s data governance initiative approved by stakeholders at the start can often be a struggle, especially when they need help understanding the significance or impact of data governance.

The solution is to demonstrate its overall business value. A good basis for this is to give real-life examples of company issues that arose from bad data quality, which grew from little to no data governance practices. Another method is showing how specific data governance program policies can help achieve company goals.

Once implemented, your organization should set up quantifiable ways to measure data quality improvements due to data governance. Similarly, share materials that show the correlation between data quality and governance, and successful business strategies and results. Showing how data quality and governance benefit each other and overall business strategies can help stakeholders grasp the importance of data governance.

Standardizing and flexibility

Companies collect massive amounts of data daily, often from different sources and in various forms. Data systems must be standardized and filtered to achieve successful business analytics and governance. However, it can be overwhelming if you don’t have the proper data management tools and software.

Beyond standardization, your company must also be flexible to adjust to changing needs and requirements. Open communication and the right data management tools can help your company uphold and implement successful data governance practices.

Validity DemandTools® can help ensure you have clean data that supports your data governance efforts. DemandTools is the go-to solution for your data quality and cleaning processes. It provides easy-to-use functions like deduplication, mass modification, standardizing, importing, and duplicate prevention to help your team manage data.

Ensure you have the tools to help implement a data governance strategy.

Data governance is a vital part of your data management process. Take advantage of data management tools from Validity to help you implement and meet your company’s data governance standards and policies.

Learn more about our tools and how they can support your data governance strategy by contacting us to speak with a representative today.