Top 10 Data Governance Best Practices 

It is important to start with a small, achievable data governance project and then scale up as needed. This could be a project to improve the quality of a specific dataset, to implement data governance policies and procedures for a specific business process, or to establish data stewardship roles and responsibilities for a specific department.

Establishing a small, achievable project means you can ensure that the project is successful and that it can be sustained over time. This lowers the risk involved in implementing the governance project as you’re less likely to encounter unexpected problems with a small project.

Data governance is a cross-functional initiative that requires support from all levels of the organisation, especially from senior management. It is important to get executive buy-in early on in the process and to communicate the benefits of data governance to all stakeholders.

Data governance can require significant changes to the way that data is managed within an organisation. When senior management is supportive of a data governance project, it is easier to get buy-in from other stakeholders, such as business users, IT staff, and legal counsel.

The foundation of any successful data governance program is a clear understanding of its goals and objectives. Before diving into the technical aspects of data governance, you need to establish what the organisation aims to achieve. This could include improving data quality, ensuring compliance with data regulations, enhancing data security, or facilitating better decision-making. 

What do you want to achieve with data governance? Do you want to improve data quality? Reduce compliance risk? Increase the value of your data assets.? Once you know your goals, you can develop a data governance plan that will help you to achieve them.

Once the goals and objectives are defined, it's essential to establish a robust data governance framework. This framework provides a structure for making decisions about data and for ensuring that data is managed in a consistent and effective manner. It often includes the appointment of data stewards and data custodians who are responsible for data quality and security, as well as the creation of data governance committees to oversee the program's implementation.

Building a solid governance framework improves data quality by ensuring that data is collected, stored, and accessed in a consistent and accurate manner. It also reduces compliance risk by ensuring that data is handled and protected in accordance with all applicable regulations and improves data accessibility by ensuring data is properly organised and documented. 

Not all data is created equal. Some data is more important to the organisation than others, and you need to identify which data is most critical to your business to ensure this data is prioritised in your data governance efforts. 

Identifying your critical data is crucial – not only because it can allow you be make better informed decisions, but also because critical data assets are more likely to be targeted by cybercriminals and other malicious actors. By identifying your critical data assets, you can take extra steps to protect them from malicious actors and keep your business secure. 

Once you’ve established which data is most critical to your business, you need to classify the rest of your data. Data classification is the process of assigning data to different categories based on its sensitivity and importance. This can help to improve data governance efficiency by making it easier to identify and manage data. For example, you can use data classification to identify all of the data that is subject to a particular regulation or policy, and implement additional measures to secure that data. 

Many regulations actually require organisations to classify their data and implement appropriate security measures for each classification level. For example, the Payment Card Industry Data Security Standard (PCI DSS) requires organisations to classify their customer credit card data as sensitive data and implement additional security measures to protect it.

Data stewards are an important part of any data governance strategy because they help to ensure that data is handled and protected in a consistent and effective manner. These stewards are responsible for overseeing the implementation of data governance policies and procedures. They also play a key role in ensuring that data is managed effectively and efficiently.

By establishing data stewardship roles and responsibilities, you can make sure that your data is managed in a way that meets your business goals and compliance obligations. Data stewards can also help to improve data quality by implementing and enforcing data quality controls. For example, data stewards can develop and implement data validation rules to ensure that data is accurate and complete.

Speaking of data quality, it’s important to establish clear data quality controls early on in a government strategy to ensure that data is accurate, complete and consistent. Implementing tools for data validation, data cleansing, and data monitoring will help you ensure that your data is reliable and reduces costs caused by errors and inaccurate data.

Read our Top 10 for the Best Data Quality Tools

Data quality controls are especially important for businesses highly dependent on customer relationships and loyalty. They can help to improve customer satisfaction by ensuring that customers receive accurate and timely information. For example, if an organization's customer data is accurate, it will be able to provide customers with accurate quotes, order status updates, and support. This can lead to a more positive customer experience and increased customer loyalty.

If your data is not protected, your entire governance framework can collapse in an instant. Data breaches can be detrimental to business operations, potentially destroying a company’s reputation and leaving it with hefty fines and financial losses. By implementing data security measures, organisations can reduce the risk of data breaches and protect themselves against the associated consequences. 

Many regulations require organisations to implement data security measures to protect the data they collect and store. For example, the General Data Protection Regulation (GDPR) requires organizations to implement appropriate technical and organizational measures to protect the personal data of their customers at all times. By investing in data security, organisations can demonstrate to regulators and their customers and partners that they are committed to protecting their data.

Data governance is an ongoing process. It is important to monitor the effectiveness of your data governance program and to make adjustments as needed. By constantly monitoring and improving your data governance program, you can ensure that your data is accurate, complete, consistent, and timely.

There are a number of data governance tools available that can help you monitor and improve your data governance program. These tools can help you to track data quality, identify data breaches, and manage data compliance. You should also stay up-to-date on the latest data governance trends and best practices. Data governance is a continuous process, and staying up to date will allow you to keep up with new challenges and barriers to making the most of your data. Don't think of data governance as a one-time project.