Data Access Management Basics & Implementation Strategy

Data Access Management Basics & Implementation Strategy

Regulating access to data is one of the most important aspects of data governance. As a result, data access management has evolved into an independent initiative that requires an autonomous strategy, budget, and implementation schedule.

In many ways, data access management, or data access governance, can be considered the most significant outcome of a data governance initiative because it covers so many crucial areas. These include maintaining data security, protecting PII, providing access to critical data assets, managing permissions, and more.

Read on to learn what data access management is, why it’s important, how to implement it, and the best practices for developing and maintaining a data access management solution in your organization.

What does Data Access Management mean?

Data access management is a process undertaken by organizations that determines who has access to which data assets. It enables companies to secure confidential information, define ownership of data, and implement managed access controls that enable users to achieve data-driven innovation.

The process governs who can access what data, when they can access it, and how they can access it. It also lays out the models, methods, and technologies required to achieve this result.

Without well-managed access, users can't retrieve the information they require, collaborating on data assets is virtually impossible, data owners don't take responsibility for the data in their care and the risk of non-compliance is high.

In terms of data governance, data access management is crucial. Without well-managed access, users can’t retrieve the information they require, collaborating on data assets is virtually impossible, data owners don’t take responsibility for the data in their care, and the risk of non-compliance is high.

Why access management is a crucial part of your overall data strategy

The best way to explain the importance of data access management is through an industry example. In this scenario, a firm wishes to build an analytics solution with a potential revenue stream for a customer. The firm assembles a team, and the customer's business data is requested.

However, it's impossible to get direct access to the data. It takes a massive effort and lots of questions to various departments, before finally, after three months, the scrubbed data is presented. This is one example of what happens when data access is restricted, but there are many more.

Many people might perceive data governance as primarily a means to support data access management. It isn't, but although there are numerous other areas of data governance, data access management is one of the most important.

When users manage data access correctly, they are provided with the tools and knowledge to find the data they need to work more efficiently. A tool could take the form of a data management platform. At the same time, users could distribute knowledge via a series of policies that govern how business users should access data in an organization.

Good data access management ensures data is available across departments and even with external entities too. For modern businesses, the key to growth in data-driven innovation is collaboration.

Data access management is also crucial for defining data ownership. Once teams identify data owners, they must establish specific rules that govern how the data they are responsible for is managed and maintained. Fail to do this, and there's a risk that companies could lose data.

Data access management is also crucial for defining data ownership. Once teams identify data owners, they must establish specific rules that govern how the data they are responsible for is managed and maintained. Fail to do this, and there's a risk that companies could lose data.

Most importantly, data access management enables business owners to safeguard PII and other confidential or sensitive data. It achieves this by only allowing verified users access to specific data sets. When these measures are not in place, there is a real risk of exposing data, and companies could face huge penalties for non-compliance.

Challenges faced by organizations to do data access management

In many cases, users' department heads can't grant them access to departmental data. This scenario exists because, no-one can identify who is responsible for accessing the data, and both automation and workflows are non-existent.

Instead, users need to go through IT teams. There is often a huge backlog, and users must wait weeks or even months for a response.

When there are no workflows in place, there is no structure. So, even if your manager can confirm your identity and that access is required, you still need someone to grant access to PII if the data you request contains it. There is no automation, something a data governance tool like OvalEdge can provide.

When clearly defined policies are in place, it's easy to determine who a user is, and the access permissions. For example, everyone can access public data, PII data needs info sec approval, and confidential data needs HR approval. This ease of access is a core reason why identifying data owners is so important. It enables business users, even if these business users are data stewards, to grant access to information independently from IT.

Steps to implement successful data access management

There are a series of critical steps to implementing a data access management initiative. We’ve laid them out here in order.

Steps to implement successful data access management

1. Discover and Classify Sensitive Data

The first step in a data access management strategy is to identify where all of your data is stored. Once you discover it, you need to take further steps to tag, classify, and score it in relation to its sensitivity.

When data is correctly classified, you can focus your efforts on securing your most sensitive data assets. This will enable you to pinpoint resources and efforts more efficiently.

Once you know where all your data is stored and what it is, you can delete any redundant data assets. The final step in this initial process is to conduct a risk assessment.

2. Assign Access Controls

Utilizing the risk assessment you completed in the first stage of your data access management program, you can create access controls for individual business users. However, instead of granting access on a user-by-user basis, it's better to allocate permissions based on defined roles and responsibilities and classification.

When clearly defined policies are in place, it's easy to determine who a user is, and the access permissions.

This approach is far easier to administer and requires less of an investment. Access groups will likely be split into categories such as administrators or managers. The key is not to grant access to any user unless they expressly need it.

3. Analyze User Behavior

The next step is to establish a series of analysis techniques to monitor user behavior. This analysis should be an ongoing process.

The aim is to analyze how business users change, duplicate, create, or remove sensitive data from your company's systems. This analysis will enable you to determine whether the user had the authority to make the modifications they did and whether any changes need to be reversed.

When you commit to continuous monitoring of your users, you can quickly identify and act on any potential threats to the security of your data.

4. Review the Compliance Requirements

Compliance is a key outcome of successful data governance. And, as we mentioned earlier in this article, is directly related to data access management. The first thing you need to do is identify which regulations are applicable to your organization.

By the time you complete all of the steps we outline above, it's unlikely that you will be in violation of any compliance regulations. However, for many regulatory bodies, you'll still need to prove this by completing a compliance certificate.

Best practices for data access management

Create an inventory: You must create an inventory of everyone that has access to data in your organization. This will enable you to keep track of data usage and record any changes to data access.

Identify data ownership and location: A fundamental part of good data access management is to identify where your data is stored and who is responsible for it. When you don’t identify data owners, data can be mistreated or even lost. You should also have a system of rewards in place for data ownership responsibilities to incentivize data owners.

Create a security group: One of the best ways to maintain data access security in your organization is to create a series of security groups. These groups can oversee data access and must include representatives from each company department that deals with data.

Conduct regular audits: Unless you regularly audit the activities of data users and owners, you won’t be able to track the success of your strategy.

Create a certification program: If you install an access certification program, you can ensure that your users are working with data correctly and with a desired degree of literacy.

A full-fledged data governance tool adds convenience

Data access management is far more effective and less labor-intensive when company’s use a data governance tool to aid them. A data governance tool like OvalEdge, enables users to streamline the implementation process.

For example, when it comes to assigning user controls, this process can be automated, so that users are blocked from accessing content that they aren’t permitted to view. In another example, a data governance tool makes it effortlessly simple to find data sources and catalog them in a single location.

Conclusion

Without a sufficient data access management strategy, your data governance initiative will fail. Data access is more than just protecting sensitive data. Without access management in place, you can’t expect your users to derive the potential value from the data at their disposal.

However, you can’t dive straight in with a data management initiative. Just like a full-fledged data governance plan, it has to be measured and carried out methodically.

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