By 2025, experts anticipate that there will be around 175 zettabytes of data in the world, up from only 44 zettabytes in 2022. While those numbers are incomprehensibly large (a zettabyte is the equivalent of a trillion gigabytes), they help demonstrate that there’s an enormous and rapidly growing amount of data in the world. For modern businesses, having robust data governance frameworks in place to help wrangle all of that data is imperative.
Data governance refers to the policies and procedures that organizations create to manage the accuracy and integrity of their data. This is distinct from data management, which is how organizations choose to enact those policies and procedures. As more people access more data across more systems, organizations need to know what data they have, where it’s stored, and how it’s being utilized. Having a data governance framework is essential to ensure that data is consistent, trustworthy, and being used appropriately while also remaining compliant with regulatory requirements.
In this article, we’ll take a closer look at the importance of data governance frameworks, why organizations need them, and best practices for building them.
What Are Data Governance Frameworks?
A complete data governance framework sets forth the rules of engagement that an organization must follow with regard to its data. That includes articulating:
- The policies and procedures that dictate how an organization will collect, store, protect, and analyze its data.
- Who has access to the data and what their individual roles and responsibilities are.
- How the data will be structured, what its intended use will be, and what technology will be used to manage it.
- Which compliance requirements, laws, and regulations will apply to the data.
- Standards for everything from maintaining data quality and security to promoting data collaboration and literacy.
Simply put, a data governance framework should outline the steps an organization needs to take to ensure that its data remains accessible, trustworthy, secure, compliant, and confidential.
Why Are Data Governance Frameworks Important?
The more data and users that an organization has, the more variation there will be regarding how, when, and why data is accessed. Having an effective data governance framework is essential because it allows organizations to manage all of these variables while remaining compliant with strict regulations such as GDPR and HIPAA. Ultimately, data governance and compliance operate hand-in-hand, mutually reinforcing one another.
Beyond ensuring compliance, having a data governance framework is important because it creates streamlined and efficient data workflows while maintaining the quality and accuracy of that data. This brings about a number of important benefits, like enabling faster data access and greater scalability, reducing risk, fostering enhanced collaboration, and allowing organizations to derive greater value from their data to make decisions that drive better business outcomes.
Additionally, when organizations have a strong data governance framework, they gain a single source of truth for all their data. That drives accuracy and efficiency by ensuring more effective alignment across teams.
How to Build a Data Governance Framework
Building a data governance framework starts with an understanding of the internal and external factors that influence your data use. These factors can include federal or international laws, contractual agreements, internal regulations, and beyond. Organizations should then identify and assess any potential risks, hazards, or adverse outcomes that should be avoided in their data access and use.
Armed with those insights, data teams can decide which controls to put in place to govern data use. This is an important step because it gives organizations more agency over how risk is treated within the context of its data initiatives. Among the controls that teams can choose to implement include:
- Preventive controls proactively define who can access what data, at what time, and for what purpose. Implementing dynamic access controls helps prevent unauthorized access and use before they can happen.
- Directive controls involve making data analysts aware of risks and obligations inherent in data processing, closely supervising data use, and documenting key controls to achieve enterprise-wide understanding.
- Detective controls monitor data access and usage to ensure that it’s being used compliantly while limiting any negative impacts when it’s not. Pseudonymization, dynamic data masking, and differential privacy are all examples of detective controls.
- Corrective controls serve to identify issues and help restore them to acceptable or normal states and draw attention to system and process failures so that they can be remediated upstream to prevent future occurrences.
Finally, it’s important to determine how the effectiveness of a data governance framework will be monitored and reviewed. This includes periodically revisiting and updating the framework as necessary to accommodate new use cases, regulatory requirements, and user needs.
Tips for Creating Effective Data Governance Frameworks
When building your organization’s data governance framework, it’s important to keep several tips in mind. First, it’s a good idea to measure your organization’s overall data maturity to help you better understand your data capabilities and identify vulnerabilities, while also pinpointing where employees require further training. Measuring your current position relative to a privacy maturity assessment framework will allow you to score elements of your business across organizational design and governance, operations, and culture, giving you a baseline understanding of where things stand.
Next, be sure to align all of the relevant stakeholders from across the business, including data engineers and architects, data owners, IT staff, analysts, your chief data officer, any compliance or privacy officers, and internal auditors. Ensuring their alignment around a single, feasible initial use case will be imperative to the success of your framework.
Finally, identify a platform that helps streamline data governance framework implementation, both from the top down and the bottom up. With Immuta, governance frameworks benefit from automated sensitive data discovery and classification; flexible, attribute- and purpose-based access controls; and policies that are written in plain language so anyone can manage and understand them. Immuta also enables dynamic policy enforcement that’s separated from compute platforms and can be consistently enforced across them, as well as on-demand auditing and reporting so you can see who is accessing data, when, and for what purpose.
To see how Immuta’s policy authoring can streamline your governance framework, try our self-guided walkthrough demos with Databricks and Snowflake!
