Data governance is a complex process. Each organization needs to tailor its framework to specific business processes, goals, and objectives. A framework should be transparent, with participants being honest and forthcoming about constraints, challenges, and impacts of data governance decisions. It should also include accountability mechanisms establishing roles and responsibilities for cross-functional data management activities.
A data governance framework is the mix of policies, procedures, and people that ensure privacy and compliance across your data domains. It is important for organizations that deal with sensitive or confidential information, such as customer or employee data. A good framework also includes processes to clean your data, removing duplicate or inaccurate information. Automation is an important part of this process and should be included in your data governance framework. As your organization grows, it can take a lot of work to maintain quality control over the data that’s being collected by multiple teams. A well-designed data governance framework addresses this challenge by providing standards that can be applied to all stakeholders. While a central team provides guidance and support, a decentralized model empowers departments to establish data governance rules based on their business needs. This approach is often preferred by companies that have a decentralized decision-making culture. Data catalogs can help organizations implement this model by analyzing user behavior and spotting governance patterns with machine learning or AI.
Data governance also includes a focus on metadata or information about the data. This pillar ensures that metadata is properly classified and available to the appropriate users and includes activities like setting standards and leveraging tools that facilitate metadata management. Metadata is essential for putting data into context, revealing who, what, where, when, and why the information was created. It can help answer critical business questions like what data is important, how to combine data from different sources, and whether it’s safe to keep or must be discarded.
Automation can streamline metadata processes and should be part of data governance. It can consist of using tools that identify, catalog, and deliver data and automated processes for cleansing and translating raw, unstructured, and disparate data into a standard format. It enables organizations to build a unified metadata foundation that provides the intelligence required for data governance. It can also provide a framework for assessing, auditing, and reviewing data management policies and processes. In addition, it can help ensure that data is used as intended and meets organizational and regulatory requirements.
Data governance efforts must ensure the data used by employees and other teams is accurate, consistent, and reliable. Bad data can save time, reduce productivity, and increase costs. It can also tarnish customer satisfaction, damage brand reputation, and force organizations to pay heavy penalties for regulatory non-compliance. Data quality can be determined by accuracy, completeness, and timeliness. Accuracy measures whether data is correct and reliable, while completeness refers to whether all the data needed for a specific business function is available. Timeliness measures how up-to-date the data is at any given moment, which can directly impact its accuracy and viability. Organizations can create a framework to establish data standards and processes for each data domain. It’s important to communicate how these processes will benefit data end-users so they can focus on analysis and decision-making rather than worrying about compliance. It will help drive user buy-in and ensure a smooth rollout of the framework. For example, automating the process of applying data privacy rules can free up staff to focus on more high-value projects.
A unified data catalog profiles and documents every source, making all of your policies and rules available to users and allowing them to access trusted data. It helps to prevent the proliferation of siloed data and improves communications across your organization. The top-down model starts with a central team; then, delegated responsibilities are assigned to specific sections or departments responsible for a given data domain. It is popular with regulated industries, such as banks, insurance companies, and healthcare institutions with strict data governance requirements. This approach is more scalable than the centralized model and allows for easier scalability as your business grows. However, establishing control is still challenging because each team can enter data anytime, making it more difficult to ensure quality and compliance. Data cleansing removes inaccurate, corrupted, or duplicate data and can be automated or manual. It’s critical to your data governance strategy because it allows you to identify poor data and make changes. It also reduces the manual work you must do, which can increase efficiency and lower costs.
Good data governance makes it possible to know when accurate information enters your systems and where it is coming from. It leads to lower business efficiency and reduces stakeholders’ trust in your organization. Furthermore, it can also put you at risk for non-compliance with government and industry regulatory requirements like GDPR, CCPA, and HIPAA. It’s important to make sure your data governance strategy is scalable. It involves creating a system to identify data sources, catalog data, and deliver it to front-line employees. It’s also a good idea to include automation in your data governance framework, as it will improve the speed and accuracy of the process. Creating a culture of support for your new policies is also important. It can be done by reassuring your teams that data governance processes will keep their work and allow them to access the necessary data. The right tool can help you manage this change by integrating your existing processes and providing an intuitive user experience. It can also provide a roadmap for your data governance program and its evolution.