Data Governance Best Practices
Organizations receive and create an immense amount of data each day. As such, it’s important that you have effective data governance in place.
Data governance is managing the availability, integrity, and security of information within an organization. However, it’s more than just putting up measures for data quality and compliance. After all, there’s data governance, and then there’s good data governance.
What Does Good Data Governance Mean?
Good data governance means managing and overseeing your data assets to make sure they are used effectively, efficiently, and responsibly. It involves setting policies and establishing processes to handle information properly throughout its lifecycle.
Aside from making sure that data is accurate, accessible, and secure, good data governance should also guarantee that the assets support your company’s objectives. Moreover, compliance is also a key aspect. Your master data must follow all relevant laws and regulations regarding data privacy and security. As such, you should employ the best practices to take all these factors into account.
Data Governance vs Data Management
Data governance and data management are closely related, which is why many people use them interchangeably. However, they are two distinct concepts that work together to promote data quality and effectiveness within your organization.
Data Governance
Data governance refers to the overarching strategy and policies that describe how your organization should handle data. You can view it as setting the rules of the game through the framework, which includes:
Defining policies and standards: Establishing how data should be used, accessed, and protected.
Assigning roles and responsibilities: Identifying who is accountable for various aspects of data, such as data quality, privacy, and security.
Prioritizing compliance: Making sure that data practices adhere to legal, ethical, and industry standards.
Managing risks: Identifying and mitigating risks associated with data, including breaches and misuse.
Data Management
Data management describes how you execute the strategies and policies set out in your data governance framework. It involves the day-to-day operations and technologies used to handle data. Its key aspects include:
Storing and organizing data: How and where data is stored, archived, and organized.
Checking data quality: Making sure that data is accurate, complete, and reliable.
Integrating and processing data: Combining data from different sources and converting it into a usable format.
Keeping data secure: Implementing technical measures to protect data from unauthorized access and breaches.
How Data Governance and Data Management can work together
While distinct, data governance and data management work together to support your business goals. Proper data governance sets the stage for effective data management. Without clear governance policies, data management efforts can be inconsistent, inefficient, or even non-compliant with legal and ethical standards. Conversely, you need proper data management practices to implement the vision and policies you set in your data governance framework.
The Importance of Good Data Governance
Data governance is more than a set of practices that you do for the sake of managing data. It’s a critical component of modern business strategy. Proactively controlling your data assets through effective data governance will yield the following benefits to your organization:
Uphold data quality
With good data governance practices, you can make sure that the information you collect on your platform is accurate, consistent, and reliable each time. As a result, you and everyone in your organization can make informed decisions and avoid costly errors.
Secure your data
Data governance helps protect sensitive information from unauthorized access and potential breaches in your network. These security protocols and controls can help you mitigate risks and maintain the confidentiality of your data.
Easily comply with regulations
Adhering to laws like the General Data Protection Regulation (GDPR) and relevant consumer privacy acts is important. Non-compliance can lead to severe penalties and damage to your organization’s reputation.
With effective data governance, you can put up live measures and processes so that your data handling practices comply with these regulations.
Increase productivity and save resources
Governance can also help you streamline data management processes. For instance, you can establish clear guidelines and workflows for data collection, storage, and analysis, which can improve efficiency and productivity.
Allow for data-driven decisions
Proper data governance promotes accurate and reliable data at your fingertips. With this, you can confidently make data-driven decisions based on facts and insights rather than relying on intuition and guesswork.
Foster trust among stakeholders
Making a commitment to handle data responsibly and implementing strong governance practices can increase trust in your organization from key stakeholders. When customers see that you prioritize data accuracy, security, and compliance, they’re more likely to continue doing business with you and want to work with your organization.
Top Data Governance Best Practices
Now that you know the importance of good data governance, let’s explore some best practices that you can implement in your company:
Assess your current data governance level
Before you can implement the right practices, you must evaluate the current maturity level of your data governance processes. Knowing where your organization stands will help you find the areas that need extra focus and guide your decisions and efforts effectively.
Create a clear data governance framework
Build a comprehensive and functional framework that outlines your data governance program’s objectives, policies, and standards. The framework should be tailored to your organization’s specific needs and goals.
Different organizations will have different frameworks but each should examine these three core areas:
All assets
Your data governance framework should encompass all data assets, from customers’ personal data to financial records. It should clearly define the types of data that are within scope and establish guidelines for how each type of data should be governed.
For example, your framework can state that handling personally identifiable information (PII) is subject to stricter security measures and access controls compared to non-sensitive data. Include guidelines for data classification, retention, and destruction as well.
All users
The next pillar is that all users should be involved in upholding the best data governance practices. While previously it may have been acceptable for only a few users to be data literate and practice proper governance, it’s now become important to engage everyone in the organization from top to bottom. This includes executives, partners, managers, data stewards, and even end users.
Although there still should be a core team that needs to continue overseeing and managing data governance, involving all users makes sure that everyone understands the importance of data governance and follows the established guidelines.
Providing training and education on data governance principles and procedures is an important step in the rollout and implementation of your framework. Make sure everyone understands their roles and responsibilities in maintaining data quality, security, and compliance regardless of their job titles and descriptions.
All processes
The final pillar of the data governance framework is that all processes should uphold data governance best practices. Structure your workflows to continuously incorporate data governance principles and procedures, from data collection, data storage, data analysis, and data sharing processes.
Establish data quality checks at every stage of the data lifecycle for accuracy and consistency. Additionally, review and update your policies and procedures regularly to adapt them to your organization’s needs and external regulations.
Define roles and responsibilities
Once you have the framework, another best practice is to identify the specific roles and responsibilities of data owners, stewards, and users within your organization. As mentioned, everyone in the company should know and follow data governance best practices, but there should still be people in the following key roles:
Data owner
The data owner is the individual or department responsible for the overall management and governance of specific data assets. For instance, it would make the most sense for the accounting team to be the data owner for financial records. They must make sure the data is accurate, up-to-date, and meets the organization’s governance standards.
Data steward
The data steward is responsible for the day-to-day management of specific data assets. They make sure that the data is properly classified, stored, and protected. They also work to improve data quality and resolve any data-related policy issues or inconsistencies.
Often, data stewards must have technical skills, such as data analysis and database management, to carry out their responsibilities effectively. Other tasks include data cleansing, data integration, and data governance enforcement.
Data user
Data users are individuals or teams that produce, consume, and rely on data for their work. They may include analysts, marketers, salespeople, and other business professionals.
In line with the second pillar of the governance program, data users must follow the established guidelines for data handling and uphold data accuracy and security. They should also use data responsibly in their decision-making processes to continuously improve business outcomes.
Prioritize data quality
If you want to achieve accurate analysis and decision-making, you must monitor and enhance data quality. You can do this by setting data quality benchmarks.
Next, audit your data warehouses and data lakes regularly and establish validation processes to cross-check if the information in your system meets the required standards. Then, perform data cleansing to remove duplicate or irrelevant data.
Keep data secure and private
Develop comprehensive security and privacy policies to protect sensitive data and comply with legal and regulatory requirements. A good policy to start with is to implement strong access controls so that only authorized individuals have access to sensitive data. Encrypting data at rest and in transit is another critical step to protect it from unauthorized access.
Aside from auditing and monitoring for data quality, you should also check for potential vulnerabilities or security breaches. You can put up firewalls, intrusion detection systems, and antivirus software to safeguard your data from external threats.
Promote a pro-data governance culture
Not everyone in the organization will immediately understand and learn the importance of managing data properly. As such, you must continuously promote a workplace that prioritizes it.
It involves raising awareness about data governance and its benefits. Moreover, it’s about fostering a mindset of responsibility and accountability when it comes to data management.
Encourage open communication and collaboration among different teams and departments to share knowledge and best practices. As a data owner or steward, you should also promptly announce updates and changes to data governance policies and procedures so everyone can read them right away.
A great way to promote a team that prioritizes data governance is to recognize and reward those who strive to uphold the framework. You can do this through performance evaluations, incentives, or even public recognition.
Maximize tools for efficiency
Leverage tools to streamline processes and automate tasks in your data governance framework. There’s also software you can buy that provides insights into data quality and compliance and recent AI technologies that make the process even more efficient.
Examples of such resources are:
Data governance platforms
Data governance platforms provide a centralized hub for managing and monitoring data governance activities. They allow you to define and enforce data policies, track data lineage, and conduct data quality assessments. Collibra and Microsoft Purview are two of the leading data governance platforms in the market.
Data catalog tools
These can help you create a comprehensive inventory of your data assets. Alation, Collibra, and Atlan are popular data catalogs that provide metadata management capabilities. This way, users can easily discover and understand the data available to them through each label attached.
Data integration tools
These tools enable you to integrate and consolidate data from various sources. They help keep data consistent and accurate by providing data transformation features. A product like Informatica PowerCenter can be your partner in data integration tasks.
Data cleansing software
Data cleansing software helps to identify and correct errors, inconsistencies, and inaccuracies in your data. It can automate the process of cleaning and standardizing data, ensuring its quality and reliability. Examples of data cleansing software include Trifacta and Informatica.
Data lineage tools
These tools allow you to trace the origin and movement of data throughout its lifecycle. Tools like Manta and Octopai provide visibility into how data is transformed and used, which is crucial for auditing.
Data encryption tools
To keep your assets secure, use data encryption tools that encrypt data at rest and in transit. Tools such as BitLocker and VeraCrypt protect sensitive information from unauthorized access and help you comply with privacy regulations.
Data visualization software
Data visualization software, such as Klipfolio, allows you to create interactive and visually appealing representations of your data. These tools can help you gain insights and communicate data findings effectively through dashboards.
Review and optimize your data governance practices
Keep monitoring and evaluating your data governance practices to keep them aligned with your business goals. Identify areas for improvement by asking for feedback from data users and stakeholders to understand their needs and challenges.
Based on these insights, you can then optimize your framework and update policies and procedures. You may also find that it’s time to invest in new tools or resources to support your data governance efforts.
Things to Avoid in the Data Governance Process
Let’s take a look at the things you shouldn’t be doing when you’re developing your data governance process:
Overlooking the human element
When talking about anything related to data, technology is often the first thing that comes to mind. While leveraging tools and investing in the best digital systems can greatly support your framework, the human element of data governance is equally crucial.
Leadership, collaboration, and culture can significantly impact the success of your data governance process. That’s why you should also spend an equal amount of effort on nurturing a culture of data governance within your organization through consistent communication, training, and education.
Ignoring data privacy and security
Information has always been a valuable asset and organizations collect and store vast amounts of data. Being lenient with data privacy and security can lead to severe consequences, including data breaches, legal troubles, and damage to your organization’s reputation.
We can never stress this enough: put up comprehensive security measures to keep your data safe. Make sure to check all aspects of your data framework to spot potential holes in the system.
It can be tempting to think that data breaches only happen to large corporations. After all, what use would cybercriminals have with data from a small business, right? Well, 43% of cyberattacks in 2022 were on small businesses. If you leave even a little bit of vulnerability in your data governance process, you could become a target.
Lacking focus on clear communication
Communication within an organization can make or break your business. It can also lead to the success or failure of your data governance process.
First, if you don’t teach your team the concept of data governance, they won’t understand its importance or how to contribute to it. Second, effective communication is necessary when implementing changes or updates to the data governance framework. With it, you can boost awareness and understanding of each user’s role and responsibility within the framework.
Lastly, regular communication helps you foster a workplace that encourages transparency and accountability. When you keep everyone informed about your data governance goals, progress, and outcomes, you can get them working towards the same objectives.
Failing to align with business objectives
The overarching purpose of data governance is to keep data accurate, consistent, accessible, and secure so that you can make informed decisions and drive business success. However, if your data governance framework doesn’t align with your business objectives, it will never yield the desired outcome.
To avoid this, you must take the time to understand the direction you want your enterprise to take. Are you looking to increase efficiency or expand into new markets? Once you know these things, you can then develop a game plan on how to handle your data in a way that supports your company’s growth.
Conclusion
Following these data governance best practices requires continuous effort, adaptation, and commitment. More than just collecting and handling data, you should set up a framework that makes your data governance process more efficient and effective.
This way, you can guarantee the accuracy, integrity, and security of your data. At the same time, you can leverage its potential to bring your business to the top of the market.
FAQs
Who leads data governance?
In most companies, data governance is led by a dedicated team or individual, often referred to as the Chief Information Officer. They’re responsible for developing and implementing the data governance framework. They also coordinate with various departments and data owners, IT teams, and business leaders to align the data governance program with the organization’s overall objectives.
What is the core of data governance practice?
The central idea of data governance is that information should be managed properly so that it can be used as a valuable resource. With accurate data, everyone in the organization can make data-driven decisions and take actions that drive business success.
What happens if there is no data governance?
Without proper governance, you run the risk of the data becoming siloed, which means that different departments or individuals have their own separate data sets that are not integrated with each other. This can lead to inconsistencies and duplication. Data could also be mishandled, compromised, or, worse, lost.
How can small businesses benefit from data governance?
Small businesses also generate and manage information. Implementing data governance best practices can help them use their assets more effectively. Good data governance can improve data quality and accuracy, which are crucial for the growth and sustainability of small businesses. Even with limited resources, small businesses can develop and implement a basic data governance framework tailored to their specific needs.
Can data governance help a company with risk management?
Data governance plays a critical role in risk management by establishing practices that help identify, assess, and mitigate risks associated with your business information, such as data breaches, data loss, unauthorized access, and non-compliance with legal and ethical standards.
With strong governance practices, you can proactively manage and mitigate these risks for the security and integrity of your data. This, in turn, helps protect your company’s reputation, customer trust, and overall business success.