Improve your company's data governance program by following these 8 essential data governance best practices:
Start with people
Ensure the data in your warehouse is usable
Get relevant stakeholders to document their data use
Guarantee data security at all costs
Enforce semantic and relationship standards between data tables
Set up automated data tests and alerts
Keep governance centralized
Read on to learn what data governance best practices are, how you can implement each one into your day-to-day work, and why they are beneficial for your data governance framework.
Data governance best practices ensure that accurate and consistent data is available to your organization. It also enables easy compliance with regulations to reduce the risks associated with poor data management, such as violation penalties and data breaches. Implementing governance strategies gives data teams clarity into the kind of data they have, where to find that data, who owns it, and how to access, modify, and share it.
There are no fixed rules when it comes to data governance best practices, but several tried-and-tested procedures have helped organizations effectively scale their data governance framework.
Let’s have a look at the top eight data governance best practices and dig deeper into how they can improve your organization’s governance efforts.
Data governance is a blend of people, processes, and technology. Yet, its efficiency relies heavily on the people involved in implementing it. You can help your teams use data in a way that aligns with the organization’s overall goals and objectives and that supports the business and its decision-making. This is possible when you have the right people involved in your data governance process.
Building an effective data governance team involves assigning specific roles and responsibilities. These include leads, data owners, data stewards, engineers, analysts, and administrators who will ensure coordinated implementation of data quality, security, and compliance. Selecting data governance team members is all about giving representation to the different ways various departments use data. You will need to appoint data governance advocates from different departments and task them with fostering a collaborative culture that encourages their teams to follow governance processes, meet compliance standards, and cultivate data literacy.
Moreover, as your organization scales and decisions become less centralized, a governance team formed of individuals from different departments and levels has multiple benefits. It can help ensure that the data governance strategy is well-suited to the business’s specific needs and effectively addresses the challenges that all teams are facing. For example, a clean interface for direct requests to the data engineering team and requiring approval for more sensitive requests may be useful when your finance team implements its own analytics.
Setting clear governance goals that highlight the importance of every process and technology involved is essential to ensuring your data governance strategies involve the right people. Goals should be based on sound data governance principles, such as data security, accountability, integrity, transparency, and quality, grounded in legislative guidelines such as the EU GDPR.
The people in your organization need to understand the purpose of a data governance program and how it aligns with the company’s wider goals. This will make them more likely to be invested in the process and work together to achieve data governance objectives.
Collaboration is a critical component of good data governance. Organizations can enhance communication, increase productivity, and improve decision-making by fostering cross-team and cross-functional collaboration.
One of the most important aspects of collaboration in data governance is bridging the gap between business and data teams. Data teams manage and use data aligned with the organization’s data goals and policies. In contrast, business teams are responsible for using data to support decision-making and drive business outcomes. These teams must work together to achieve the same overall organizational objectives.
Cross-team collaboration between data teams, like data analysis and data engineering teams, allows team members to share resources, knowledge, and expertise and empower one another to solve problems and achieve their objectives more efficiently.
It also brings the different functional areas together earlier in the process, resulting in the integration of multiple perspectives and skills. This involves data teams and business stakeholders like marketing, finance, and sales teams working together to comprehend business requirements, design decision-making strategies, and, ultimately, provide data-driven insights that make everybody’s work more efficient and effective.
You can use a variety of communication and data discovery, management, and data sharing tools to facilitate collaboration. Some of these facilitators include a data catalog, asset ownership, and version control.
A data catalog is a centralized repository of information about an organization’s data assets (i.e., metadata), providing table, column, and metric definitions. For data governance purposes, it can also provide the location/lineage, format, and usage of all data assets. This helps teams understand how data is being used across the organization, allowing them to identify areas for improvement.
Asset ownership defines clear individual and team roles for owning, managing and controlling particular data assets. It enables collaboration by ensuring accountability over data assets, making communication easier when questions about specific models or dashboards arise.
A version control system (powered by Git) enables teams to work together asynchronously by keeping track of all changes made across a set of files or code.
The goal of your data governance efforts is to make data usable. Usable data is characterized by its reliability, trackability, security, and cleanliness. If data in your warehouse fails to meet these usability characteristics, your data team will fail to deliver high-quality data. This will affect decisions and business outcomes.
Ensuring that the data in your warehouse is usable involves implementing a comprehensive set of data governance practices and procedures, including data quality control, data security, and data management, which help to ensure that data is accurate, consistent, and secure.
Data quality control involves implementing processes and procedures to ensure that data is entered correctly and is free of errors. It can involve using data tests, setting up alerts, and correcting errors to validate the data you enter into your systems.
Data security includes processes like encrypting sensitive data, implementing access controls, and regularly monitoring for and responding to security threats and breaches.
Data management is responsible for managing data consumption. It creates clear and consistent naming conventions for data, establishes data lineage, and creates data dictionaries and other documentation to help you understand how your data is used and where it came from.
The ease with which data is available and understandable to relevant stakeholders reflects the efficiency of your data governance frameworks. This aspect of data governance is based on the principle of data transparency.
One key aspect of data transparency is getting relevant stakeholders to document their data use. This includes identifying the different groups of stakeholders that consume data, such as data analysts, business users, and data scientists, and understanding their specific data needs and requirements. Data documentation results in the most effective data use and enables stakeholders to easily find and understand the data they need.
To promote data transparency and ensure that stakeholders document their use of data, organizations can take several steps:
Educate stakeholders: Start by organizing meetings with each stakeholder to get an overview of their data requirements.
Create a space for feedback: Preferably in a tool used by both the stakeholders and the data engineering team, create a place where stakeholders can post updates on their data usage and needs, starting a conversation between the teams.
Protecting sensitive organizational data from unauthorized access, misuse, or theft is a crucial aspect of data governance. Data security helps organizations prevent data breaches, protect sensitive information, and maintain compliance with security standards.
Guaranteeing data security as a data governance practice involves implementing a combination of security measures that ensure data protection at all times and promptly identify and deal with potential data threats. Some of these data security methods include:
Role-based access control: This approach controls access to data based on an individual’s role within the organization. By limiting access to only those who require it (the principle of least privilege), role-based access helps to minimize the risk of unauthorized access or misuse of data.
Data lineage: This is the ability to trace the flow of data from its origin to its final destination. By maintaining a clear record of data lineage, organizations can ensure that data is being used for its intended purpose and quickly identify any potential security breaches. This can be achieved using data lineage tools that automate the process of tracking data movement and provide real-time visibility into data usage.
Data encryption: Encryption to secure data both at rest and in transit.
Auditing: Regular security audits and vulnerability assessments to identify and address potential weaknesses.
Security compliance: Compliance with industry-specific regulations.
System updates: Regularly updating and patching systems to protect against known vulnerabilities.
Security culture: Training and awareness programs for employees to ensure they understand the importance of data security and know how to handle sensitive information safely.
Your organization must establish and enforce semantic and relationship standards between data tables to ensure your data teams deliver accurate, consistent, and reliable data. This practice is crucial for achieving and maintaining data integrity and quality.
Semantic and relationship standards should be enforced with data contracts. These are a set of standards that define the structure and relationships between data tables. The contracts specify the data type allowed in each column, the data format, and the relationships between tables, such as which tables must be linked together and which data type can be stored in each table. Enforcing data contracts enables organizations to enter valid and consistent data into tables and identify and correct any errors or inconsistencies.
Referential integrity refers to the set of rules within data contracts that ensure relationships between data tables are maintained, record relationships are valid, and that data is consistent across tables. For example, you can use it to enforce that a record cannot be deleted if other records are still referencing it.
You can then use data modeling tools to design the database schema based on the data contracts and referential integrity rules. These rules and constraints can be applied automatically when data is stored or retrieved from the database. Additionally, data lineage and data governance tools can help you track data flow and usage across the organization, ensure that data contracts and referential integrity are being adhered to, and detect any data quality issues.
Implementing the data quality principle of data governance requires a combination of quality management and automated processes like data tests and alerts. This paves the way for efficient data monitoring and real-time anomaly detection, allowing data teams to promptly identify and address any quality issues.
Using data monitoring and observability tools to automate tests and alerts can help you assess the effectiveness of your data quality, lineage, usage, and overall performance. Data tests and alerts can also be a part of your organization’s data quality checks. For example, you might use a test to validate data against a contract or to check referential integrity between tables.
A centralized data governance stack refers to a set of unified data governance and management tools and processes used across an organization. It includes data quality, security, lineage, and monitoring, as well as other methods and technologies related to data governance.
One of the main benefits of a centralized data governance stack is that it provides a holistic view of all your data across different departments and systems. A centralized data view allows your governance team to identify and address quality issues, security risks, and compliance concerns.
A centralized data governance stack enables governance policies and procedures across your organization to be consistently enforced. This ensures that every individual handles data in the same way, minimizing errors and inconsistencies.
Moreover, a centralized data governance stack empowers you to automate many data governance activities, such as data monitoring, quality checks, and lineage tracking. You can save valuable time and resources as a result and ensure that your organization consistently implements your data governance strategy.
Implementing these data governance best practices can lead to valuable contributions to the health of your data products and the productivity of data teams. For instance, starting with the people and building a good data governance team will enable you to consider each stakeholder’s concern and increase buy-in and adoption of the governance plan.
There are other benefits, too. Prioritizing collaboration helps break down data silos, and ensuring there is usable data in the warehouse is crucial for data accuracy, completeness, and consistency.
Getting relevant stakeholders to document their data consumption ensures appropriate data usage in compliance with any applicable regulations. And guaranteeing data security at all costs ensures protection from unauthorized access or breaches.
As for data integrity, enforcing semantic and relationship standards between data tables ensures consistent and accessible data, while automated data tests and alerts help detect any errors or inconsistencies. Finally, keeping governance centralized helps to ensure the effectiveness of data management and governance processes.
These best practices directly impact data engineers, analysts, and the overall performance of your business. Data engineers and analysts benefit by having access to accurate, consistent, and usable data, which helps to improve the quality and speed of their work. This can help businesses make impactful decisions and improve their overall performance in turn.
Managing and protecting an organization’s data assets and the flow of organizational data is a complex task. However, you can break it down into simplified data governance measures to accomplish as day-to-day practices.
You can set up an efficient data governance framework. All that you’re missing is a tool that can help you implement data governance best practices in a centralized way. . . . Enter Y42.
Y42 can empower you to improve how your organization works with data by automating and simplifying the implementation of data governance practices. Get in touch with the data experts at Y42 to get started.