Data governance and a proper data governance framework is a set of policies and practices that ensures data quality, accountability, and safety throughout an organization. Its goal is managing data to keep it usable, understandable, and in accordance with relevant legislation. Data is managed for the sake of secure data storage, data security, data integrity, and data access. A data governance plan is the key framework for creating a governance function that meets organizational needs in terms of functionality and risk management. It should be a top concern for any data governance office or department.
Customer data protection is a rising issue and a spotlight for the EU’s GDPR, California’s CCPA, and New York’s Part 500. Compliance with these regulations is an obligation that also brings organizational risk. Having a highly effective data governance plan can help you achieve compliance in relevant territories while also achieving better ROI for your DataOps. To ensure that your organization achieves its data compliance goals, we’ll look at how to build an effective data governance plan.
1. Identify existing data
The first step of any data governance plan is identifying and cataloging all existing data assets. During this step, the metadata records for existing data should also be brought up to current data governance standards in terms of details and relevant compliance requirements. This includes permissions and sources so that future data audits will be conducted under the correct terms.
2. Clarify data classification and inventory
To ensure consistent and easily accessible data across all functions, not just data operations, there needs to be a set of clear policies on how data is classified and stored. This also involves developing a consistent protocol for metadata tagging within the data catalog, which will enable data scientists to identify and collate required datasets for downstream users with ease. This also prevents the growth of data swamps.
Better classification and cataloging enable a faster pipeline of data from edge collection to processing to delivering actionable insights to decision-makers. A more agile DataOps function delivers greater ROI in terms of improved data usage and timely insight delivery.
3. Establish a governance model
Every organization will have their own data governance processes and governance model that is adapted to their own size, needs, and functions and business outcomes. A specific governance model provides clarity to current and future data users about how data is to be used, providing a dynamic approach to governance that improves data flows throughout the organization, thereby contributing to better business decisions.
A governance model is often either a centralized model where one core group sets the rules for data usage throughout the organization, or a federated model where different groups, mostly separate departments, set their own rules. Often a data governance program will take the first approach to preserve and maintain data integrity and proper data modeling.
4. Identify risk
Built into any data governance plan should be an assessment of the risks carried by the data function. This should include complete information on compliance procedures around data collection and usage, as well as an assessment of the risks inherent in data sharing and usage of data across the organization. A data governance plan should outline the expectations for data administrators to minimize risk connected to data along with reporting procedures and contingency plans in the case of a data breach.
5. Clarify roles and responsibilities
A central part of a data governance plan is assigning responsibilities for certain actions and functions. Compliance mistakes and data breaches can often occur at the interface between where two people believed their responsibilities ended, creating organizational risk. Different titles, whether they are administrators, data scientists, analysts, or other data processors should be aware of their responsibilities and duties of care when it comes to their role in data lifecycle management.
6. Delineate access
A large part of ensuring compliance with data safety regulations is controlling who has access to what data and what they can do with it. This is of special concern in situations with widespread data sharing between departments, data stewards, or data collaboration with outside parties. Data collaboration is essential but the right access needs to be in place so you can securely share data and collaborate with key stakeholders, partners, and customers—all while maintaining strict permissions and compliance.
Data administrators need to have as much access control as possible over the organization’s data assets to maintain compliance and minimize risk. A data governance plan should outline how this will be achieved across all the organization’s assets, especially in fluid use cases, and what powers data administrators have.
7. Value measurement and reporting
The value of a data function to an organization can often be under-represented, as it is always a “supporting” factor rather than a clearly determined input or end-product. This should be clarified in a data governance plan, setting out the objectives for the organization’s data function to ensure it is delivering a good ROI.
Reporting year-on-year changes should also be outlined so that decision-makers have a clear insight into the performance of the function and what investments or actions need to be made.
Achieve compliance and collaboration with Intertrust
A data governance plan is designed to ensure that an organization’s data function is meeting its business goals of providing timely and effective insight while also minimizing risk in terms of regulatory compliance. It allows for greater collaboration and accountability—with higher data quality that’s protected against today’s risks and threats. One of the best ways a data governance team can create a data governance plan that is both implemented and followed continuously is through deploying data tools such as a virtualized data platform.
Intertrust Platform allows highly efficient automatic metadata tagging and cataloging to ensure data collection and storage never gets to data swamp proportions. It also lets data administrators preserve data lineage and enforce strict access controls on all datasets, down to row and column level, with all processing taking place in secure execution environments, further removing the possibility for data leaks. Additionally, data never has to be physically moved or copied. Instead, it is accessed in virtual form wherever it is stored.