What is Data Access Management?

What is Data Access Management?

Virtually all modern organizations prioritize the collection and storage of insight-fueling data. As these groups strive towards more data-driven initiatives, they are collecting increasingly large amounts of data. In turn, this data needs to be accessible to a growing number of data users in order to maximize the impact on the organization’s goals. As data sources and users increase, the relationship between resource and user must be maintained.

Like any resource, data must be functionally managed in order to be useful to an organization’s goals. The processes involved with efficiently collecting, storing, securing, and facilitating access to data is known as data management. By effectively maintaining these processes, data teams can ensure that their resources are being leveraged to their fullest potential.

Why is Data Access Management Important?

Consider the various components of the modern data stack. To create an environment suitable for deriving insights from data, an organization needs to compile an array of (largely cloud-based) tools that operate in concert with each other. This likely includes platforms that facilitate data access control, governance, storage, computing, analysis, ELT/ETL, data visualization, business intelligence (BI), and a range of other important functions. And these tools need to successfully function together to accommodate more users accessing more data.

As companies start to return to physical office spaces, think about the many different aspects of office management. Offices need to provide both opportunities for collaboration and individual workspaces for employees. Conference rooms have to be reserved appropriately, office security must be maintained as people come and go, wifi has to be accessible and dependable, and (most importantly) the kitchen should be stocked with drinks and snacks to fuel the team.

Managing each of these functions serves its own distinct purpose, but the utility of the office as a whole depends on the cohesive and consistent success of the overall management framework. In the same way, the modern data stack needs proper management to align its many moving parts and produce desired results. Without maintaining these functions through consistent oversight, the system could easily fall apart. Designing and implementing a data management framework enhances an organization’s data stack and streamlines the success of its data users.

What to Look for in an Access Management Solution

Requiring the oversight of a range of tools, platforms, and capabilities, there’s not necessarily a single conventional approach to data access management that will fit every organization. There are, however, certain standards that should be considered for any data management plan. By building access management frameworks around these standards, data teams can ensure wide and repeatable success.

Ultimately, your approach to data access management should allow you to:

  1. Understand all data across your company with easy discovery and accessibility.
  2. Control access to data at the most granular level: each piece of data to each user. That control must be dynamic in nature, allowing Data Owners to make data policies more or less restrictive on the fly, without retagging data.
  3. Monitor and audit all actions by users against your data. Understand who is accessing what, when, and why.
  4. Avoid placing an excess load on production systems that rely on your data.
  5. Provide a “single sign-on” abstraction layer so data consumers can access data easily and in ways that are familiar to them.
  6. Create a lab-to-factory workflow that allows experimentation to happen in sandboxes, and in turn promote successful experiments to production without upfront data migration and security tasks.
  7. React. Instead of copying/consolidating all your data up front, let your data consumers’ actions drive the consolidation of your data over time.

Who Controls Data Access Management?

Access management is not the job of just one stakeholder within an organization. Given its wide-ranging nature and the amount of processes it covers, managing data is fully a team effort. It spans both those who control and use data resources, requiring collaboration to achieve success.

While many people are involved in this process, they fall into three core categories:

  • Data Owners: Those who own the sensitive data in an ecosystem. Data Owners recognize the power of data, so their concerns and goals must not be minimized simply for the benefit of data science objectives. They have production systems to maintain, auditing requirements to meet, and security restrictions to obey.
  • Compliance Officers: If these law- and regulation-focused officers make a single misstep, they could potentially go to jail. They must follow all data compliance laws and regulations, and ensure organizational data is only seen by those with the proper authority or training. They must also follow data retention and auditing guidelines. The Compliance Officer’s concerns cannot be ignored, and they should understand their organization’s production systems, as well as its security and auditing capabilities.
  • Data Consumers: This is the most diverse group involved in access management. As any end consumer of the data, this category may include Data Scientists, Business Analysts, Chief Data Officers, Sales or Marketing consultants, and more. These users require access to relevant data fast in order to do their jobs–as long as it is done compliantly.

Each of these roles interacts with the data in its own unique way. By collaborating with one another through an effective access management framework, however, these stakeholders can ensure that resources are utilized productively and securely across an organization.

Common Data Access Management Challenges

Access management can often be an afterthought. Even when considered up front, there are many data management challenges to overcome. What makes this measure so difficult?

Institutional data governance and data access control are at the core of the access management problem. Managing disparate data sets within a single enterprise can be complex and challenging, let alone managing external data sharing and analysis. Even if internal stakeholders agree to collaborate, it may take a significant investment to develop a common means of governing different types of data across the enterprise.

A lack of unification across data sources can also make access management quite difficult. If data sources are copied, stored in team- or department-specific silos, or fragmented across a data ecosystem, it becomes very hard to control this information. Sharing data between these sources is arduous, and ensuring compliant data use across all possible locations can be nearly impossible. Data access management should be consistent throughout a data stack, and this can only be achieved when data policies are applied universally.

Lastly, access uncertainty and poor visibility can complicate the management process. Data Consumers will often go directly to the Data Owner to request access to the information they control. The Data Owner will either provide direct access or export the data in bulk to the requesting user. Unfortunately, this process does not provide a true representation of the data, since that “screenshot” will be missing new data the second it leaves the owner. The requester will also likely have the burden of making sense of the information without help from the Data Owner. Even if the Data Owner can help the Consumer on an as-needed basis, doing so for every following Consumer places a huge burden on the Data Owner, and is simply not scalable.

Enabling Effective Data Access Management

To address these challenges and create a powerful access management standard, the modern enterprise needs the support of a dynamic data access platform. This type of platform will provide all data users (Owners, Compliance Officers, and Consumers) with access to data in a simple, secure, scalable, and auditable manner.

Immuta provides a universal, platform-agnostic framework to tackle these challenges and serve as the required access and governance layer in a data ecosystem. This allows Data Owners to supply data to users securely, facilitated by fine-grained and dynamic attribute-based access controls. By discovering, securing, and monitoring your sensitive data, Immuta enables data management and policy enforcement across cloud ecosystems to ensure no information is left out or compromised.

For a deeper understanding of how Immuta strengthens effective data access management and enables successful governance, risk, and compliance frameworks, download our GRC Playbook here.

How to Design and Implement a Governance, Risk, and Compliance Framework for Enterprise Data Analytics

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