At a time when vast amounts of highly sensitive data are being collected and used, and as data breaches become increasingly common and costly, being able to manage who has access to your organization’s data is more important than ever. However, the volume of data, number of users, speed of the cloud, and stringency of regulations makes doing so exceedingly complex. That’s where data access governance comes in.
At a high level, data access governance refers to the policies and procedures that organizations follow to manage how their data gets accessed. Below, we take a closer look at the principles of data access governance, how it differs from data management, and some of the most common data governance challenges. We’ll also cover what you should be looking for when considering a potential data access governance solution.
The Principles of Data Access Governance
Data access governance is the term used to describe the people, processes, and systems associated with efficiently collecting, storing, securing, and facilitating access to data. That includes the infrastructure and tooling that allows organizations to identify, control, and protect sensitive data while ensuring that it remains private. A key part of data access governance is data access control — the ability to restrict access to data based on a set of robust policies designed to keep personally identifiable information (PII) and other confidential data from falling into the wrong hands.
Data access governance is important because it allows data teams to leverage their resources to their full potential. Fundamentally, there are five main principles associated with it that are important to be aware of:
- Transparency and auditability. As an organization, it’s essential to be transparent about what data you’re collecting and why you’re collecting it. As part of that work, take care to ensure that your data is indeed appropriate for the intended purpose and that you’re able to demonstrate as much, should you ever be called upon to do so.
- Data quality. The quality of your data can vary wildly depending on factors like whether or not it’s accurate, consistent, complete, reliable, or up-to-date. Understanding data quality levels is critical for identifying potential weaknesses or inconsistencies that could compromise the outcomes of whatever you’re trying to achieve.
- Accountability and stewardship. It’s also important to ensure that everyone in your organization acts with a sense of ownership over their data. This includes serving as data stewards who are committed to protecting the integrity of the data assets they work with or have access to.
- Standardization of definitions and processes. Strong data access governance programs standardize data definitions and data processes used across the organization. Doing so helps make governing data access more efficient and streamlined by helping to ensure that everyone is on the same page.
- Collaboration. A final aspect of data access governance to keep in mind is collaboration. It’s important to ensure that different teams within your organization can work together to decide how best to deal with data across the business.
Collectively, these principles should help to dictate how your organization approaches data access governance.
Data Access Governance vs. Data Management
Though they sound similar, data access governance and data management are not the same. Data management refers to the systems and processes put in place to organize and maintain data so it can be supervised and used efficiently. Data access governance, by contrast, is a subset of data management that refers to the framework and controls put in place to determine who has access to what data. For that reason, data access governance is focused on answering questions like who owns specific data, who can access it, and what measures are in place to protect it?
Both data management and data access governance are key components of an organization’s data strategy – managing data is the first step in making use of it, and governance helps provide visibility into that usage, so as to mitigate threats and noncompliance. Together, they streamline data workflows and accessibility for greater efficiency and productivity.
What Are the Most Common Data Governance Challenges?
Data governance is necessary, but it’s not always straightforward. Organizations typically face an array of challenges when they set out to implement a data governance framework. Some of the most common issues include:
Unscalable Access Controls
Controlling who can access what data is one of the biggest issues in data governance. Legacy solutions like role-based access control (RBAC), where users are assigned specific permissions based on their role in the organization, have long been the default solution. Those permissions typically dictate what data they can access, for how long, and what they can do with it. The challenge with such solutions is that they tend to be unwieldy and aren’t able to scale effectively as data proliferates, more people need access to it, and ever more regulatory requirements come into play.
Attribute-based access control (ABAC) is an alternative solution that many organizations increasingly favor. It allows access based on attributes about the user, such as their job title or seniority level; the resource that person is trying to access, like the file type or level of sensitivity; the environment, such as the time of day or location of access; and the purpose for accessing the data. As such, it gives administrators a greater level of control over who can and cannot access specific data.
Lack of Ownership
Many organizations don’t have defined roles for policy management and enforcement, and haven’t prioritized putting them in place. Instead, decentralized approaches to data use often mean that no one actually owns data governance. A study of data professionals found that nearly a third of data owners play a role in both policy management leadership and execution – in other words, the planning and the doing. This indicates that data governance and policy enforcement frequently lack a clear chain of command.
A better solution would be for data platform owners to work with their CISO and security team, governance, risk, and compliance stakeholders, and data engineers on developing a more collaborative approach to management. Ensuring all parties are on the same page improves the chances of a data governance program meeting its intended goals.
Complex Data Architectures and Siloed Data
With organizations increasingly under pressure to deliver greater speed and agility, many are running their applications and workloads in multiple cloud environments in an effort to keep up with demand. At the same time, the growing use of decentralized data mesh architectures is leading to an explosion of data access policies.
While data mesh allows for more efficient data use across lines of business, managing policies is exceedingly complex when enforcement is widely distributed. This often leads to inconsistent or duplicative policy implementation, which can hinder the efficiency that makes data mesh appealing in the first place.
Constrained Resources
Finally, implementing effective data access governance frameworks requires data engineering and technological resources. At a time when budgets are tightening and data engineering challenges are rampant, ensuring that you have those resources at your disposal is an issue itself. According to the 2023 State of Data Engineering Survey, 41% of data and IT teams don’t have enough people to manage their data, and 39% feel burnt out by their data access management responsibilities, to the point that they would consider switching jobs. Finding ways to streamline data access governance is necessary to reduce the burden on these key resources.
Being aware of data governance challenges like these is important so that you can find the best solution to help you overcome them and meet your business objectives.
What to Look for in Data Access Governance Solutions
When looking for a data access governance tool, you’ll want to find one capable of solving all of the issues cited above. Specifically, that tool should:
- Use automation to reduce manual processes, thus easing resource constraints. This is particularly important when it comes to sensitive data discovery and policy enforcement.
- Separate policies from individual platforms and enable distributed stewardship to avoid data silos and enable scalability.
- Offer an attribute-based approach to ensure access control implementation is able to easily scale and evolve with the business.
- Provide plain language policy authoring to help with collaboration and ownership.
Ultimately, you’ll want to find a tool that allows you to understand all of the data across your organization with easy discovery and accessibility, control access to that data at the most granular level, and monitor and audit all actions against your data so that you can understand who is accessing what, when, and why. Threat detection capabilities also allow you to proactively identify and remediate data risks, before they can grow out of control.
Next Steps with Data Access Governance
Data access governance is an important part of any company’s overall data security posture. Being aware of the key principles of, and challenges associated with, data access governance allows you to find the best solution to help ensure that you always know who is accessing your data, when, and for what reason. Ultimately, armed with that information, you will be in a better position to keep your data secure while ensuring you remain compliant with the latest regulatory requirements.
Take the next step in the data governance process. Find out how to create a data governance framework.