Top 6 Data Governance Principles

December 15, 2023

Data is everywhere around us and it has become the companies’ most important resource. It helps them get valuable insights into their clients’ behavior, measure their current performance, and foresee what the future may hold for them.

Thus, organizations invest heavily in various systems and tools that help them unleash the power hidden in their data. They try to build the perfect data warehouse to store their data and choose the best Business Intelligence tool to analyze it.

A well-design data governance program provides the right ownership and accountability model to get to the root cause and resolution of data issues.

John Owen

However, for companies to use their data effectively, they need to ensure both data quality and security, and the key to doing that is data governance.

Data governance establishes the processes, rules, and responsibilities that ensure the quality and security of the data used across all business units and allows businesses to exert control over the management of data assets.

Unlike data management, which is mostly viewed as an administrative process involving acquiring, validating, storing, protecting, and processing required data, data governance is a strategic process.

It goes beyond IT and includes stakeholders from across the organization to ensure the safety, reliability, and accuracy of all data. Therefore, to help you build a successful data governance strategy, we are going to walk you through the top six principles of data governance you need to follow.

The 6 principles of data governance

1. Accountability

Accountability requires that companies implement programs that foster compliance with data protection principles, and describe how those programs provide the required protections for individuals. These types of programs allow organizations to create a solid approach to data protection and ensure compliance with the data governance requirements.

data accountability

The accountability principle is based on fair information practices. It is a model where the organization and the individual share the responsibility to protect information by implementing transparent processes and programs. Accountability benefits the individual because it requires organizations to remain responsible for the protection and management of data. It also benefits the organization because it allows for greater flexibility to adapt its data governance practices to new market demands and technologies.

2. Data quality standards

Data quality is responsible for accurate, complete, timely, and consistent data, with all requirements and business rules. This principle ensures that your data isn’t contaminated or invalid. Data cleaning, standardization, and enrichment are all parts of the data quality principle, maintaining high data quality within the organization.

data quality

Unfortunately, many organizations think it’s a one-off process. Data quality is a principle that needs to be continuously reviewed and improved. This ensures your legacy data is in order and any changes in the data flow and collection are compliant with the data governance principles, which brings me to the next point.

3. Monitoring and control

Every organization has its own data quality metrics and cycles, and that is normal, Data monitoring and control ensure that your data governance principles are working and your data is secure and up-to-date at all times.

data control data governance

It is best when data monitoring processes have automated performance reports that would alert you when there is an anomaly. Data monitoring and control not only monitors the quality and completeness of data, but it also provides insights into who is using the data and what they are doing with it. At any point in time, your organization needs to be able to monitor the data and act on any event – data monitoring and control allow that.

4. Rules and regulations

Aligning your data governance strategy with the rules and regulations of your region is an integral part of keeping your data secure and your clients happy. While it can get confusing, having a clear list of rules and regulations that everyone in the company knows and follows will save a lot of trouble. These rules typically include data usage, access and availability, integrations, and security.

rules and regulations in data governance

5. Transparency

Not much to discuss here. The more data transparency there is in an organization, the faster the work gets done. Having limitations on data based on seniority, etc. is a good thing, but it shouldn’t go too far. Transparent data practices have only proven to be a productivity booster. It also helps team morale when no one thinks that you try to hide something from them. We proved this theory in a case study on sales bonus schemes.

data-driven bonus calculations for data transparency and performance improvement

6. Data stewardship

Data stewardship, as mentioned in TechTarget, is the management and oversight of an organization’s data assets to help provide business users with high-quality data that is easily accessible in a consistent manner. It is all about the tactical implementation of the data governance practices. Data stewardship is ensuring that everything you do with data is compliant with rules and regulations.