Cloud Data Governance: How to Solve Its Core Challenges

Updated June 21st, 2024

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The cloud continues to grow as a platform for application deployment, with usage set to expand 22.6% by 2026. However, the cloud’s ease of use and accessibility also pose a challenge in regulating and monitoring access to a growing volume of data.

In this article, we’ll discuss what cloud data governance is, what makes it challenging, and how best to approach cloud data governance for your enterprise.

Table of Contents #

  1. What is cloud data governance?
  2. Why you need cloud data governance
  3. The challenges with cloud data governance
  4. Solutions for cloud data governance
  5. Best practices for implementing cloud data governance
  6. Conclusion
  7. Related reads

What is cloud data governance? #

Cloud data governance specifies the rights and accountabilities for data maintained in cloud computing systems, such as Amazon Web Services (AWS), Google Cloud Platform (GCP), and Azure. It consists of policies, procedures, and tools that enterprises use to ensure data quality, secure access to data, monitor data usage, and ensure compliance with local laws and industry regulations.

Cloud data governance encompasses all of the traditional facets of data governance, including:

  • Gathering and enriching metadata to add context, enable data discoverability, and provide accountability
  • Ensuring data quality and trustworthiness
  • Measuring the business value of data
  • Implementing data security controls consistently across teams, departments, and cloud providers
  • Monitoring access to data and raising alerts when the system detects anomalous behavior
  • Creating policies for data retention, archiving, and purging to ensure organizational compliance with data retention and erasure requests

Why you need cloud data governance #

One of the cloud’s benefits is that it makes it easier for teams to launch new products. Instead of requesting and provisioning new hardware, they can use technology such as Infrastructure as Code (IaC) to easily set up and tear down new infrastructure in a cloud computing environment.

However, this ease of use also complicates governance. Without the proper policies, procedures, and tools in place, teams may implement - or, worse, not implement - their own unique governance systems.

At best, that can result in inconsistent data quality. At worst, it can lead to costly regulatory fines or security breaches that damage your company’s reputation.

Cloud data governance balances the bottom-up, decentralized benefits of cloud computing with a consistent and comprehensive approach to ensuring a high level of data quality, security, and compliance. A consistent approach to cloud data governance brings multiple benefits, including:

  • Improved data quality across the organization
  • Increased trust in data, which leads to increased utilization
  • Easier discoverability of data
  • Improved data interoperability through data formatting and quality standards
  • Reduction in data breaches and security leaks
  • Easier compliance and auditing
  • Reduction in redundant cloud costs due to centralized tooling

The challenges with cloud data governance #

Data governance is challenging in and of itself. Applying it to the cloud adds several new complexities, including:

  • The emergence of “shadow IT”
  • Data silos
  • Cloud data governance at scale
  • Organizational resistance
  • Lack of a common language
  • Data governance as an afterthought

The emergence of “shadow IT” #

Most enterprise teams own their own pool of cloud provider accounts—sometimes across multiple cloud providers—that they can leverage to deploy apps to different environments. As we alluded to earlier, this can lead to teams using their own tools or even creating their own separate tech stacks.

The result is the growth of “shadow IT” - data and infrastructure with no centralized management or oversight. This can lead to multiple issues, including:

  • Teams using unapproved tools or tools with known security flaws
  • Redundant infrastructure
  • Runaway cloud computing costs

Data silos #

A related problem to shadow IT is the emergence of data silos. These are outposts of data in cloud accounts used by one or a handful of teams that no other team can discover or utilize.

Data silos make it hard for employees to find and extract business value from data. Their existence also forces employees to waste time hunting down the data they need to start a new project. According to one report, corporate employees spend 3.6 hours every day looking for information.

Since their existence is unknown and unmonitored, data silos also increase the risk of data governance issues. These include - but aren’t limited to - poor quality data, lack of data ownership, security breaches, and unauthorized access to sensitive data, such as Personally Identifiable Information (PII).

Data silos can happen anywhere. However, they’re especially prone to emerge in the cloud due to the lower barriers to entry that cloud computing provides.

Cloud data governance at scale #

Another challenge with cloud data governance is implementing it at cloud scale. An enterprise that supports cloud computing will likely own hundreds, if not thousands, of accounts across many teams.

Additionally, data producers themselves may feel overwhelmed by the effort required to achieve cloud data governance at scale. The time and resources involved in cleaning, securing, tagging, and monitoring ever-growing volumes of data may seem impossible, especially for smaller teams.

Ensuring governance means establishing a consistent approach to provisioning, registering, operating, securing, tagging, and monitoring activity across all of an organization’s accounts. This requires an investment in time, personnel, and technology to implement successfully.

Organizational resistance #

Implementing a new approach to data governance is hard in and of itself. Cloud data governance may create additional resistance. Teams may perceive governance as “red tape” that forces them to sacrifice speed and control.

Lack of a common language #

Different roles in an organization—data producers, consumers, stewards—may discuss data using different terms and concepts. For example, data producers may refer to data pipelines or Python code. Meanwhile, a data consumer may be more focused on SQL and data visualization.

This lack of a common approach to discussing data makes it harder for teams to communicate, collaborate, and build on one another’s successes.

Data governance as an afterthought #

Without a defined standard and toolset to support cloud data governance, many teams are apt to view it as overhead. In the rush to get new products to market, they may push data governance work off to a later date. This continues until a high-profile data breach or expensive compliance fine forces everyone into emergency mode.

Solutions for cloud data governance #

Given this, how do you implement cloud data governance effectively? Here are a few principles to follow:

  • Develop a scalable plan for data governance
  • Create a common language
  • Fit governance to people, not people to governance
  • Embed data governance throughout your processes

Develop a scalable plan for data governance #

To scale cloud operations, companies need to trust teams to own their own data. At the same time, the company needs a way to track, audit, and validate that teams are complying with local laws and industry best practices.

To implement scalable cloud data governance, all stakeholders must come together to create a plan that gives teams the tools and best practices they need to clean, classify, and report on the state of their data. This will involve creating processes and toolsets that make it easier for teams to create and publish new data products without unnecessary overhead.

Create a common language #

One way to address the lack of a common language around data is to create a common model for representing data. For many organizations, that means representing data as data products - a published unit of data with contextualizing metadata that’s easy to find, consume, and version.

Another element of creating a common language is using metadata to standardize the way everyone represents and talks about data. Metadata provides a consistent representation describing what a piece of data is, who owns it, where it comes from, and who can access it.

Metadata can reduce or eliminate questions about a data set’s purpose or how its values were derived. This helps ensure teams use the same terms, concepts, and values consistently across the organization.

Fit governance to people, not people to governance #

Organizational resistance can crop up when a team feels a data governance solution is being imposed from the top down. That’s especially true in the cloud, where different teams may have different work styles, deployment cadences, data and visualization tools, etc.

The cloud has empowered more teams to “go their own way” if they feel a company-wide program imposes too much red tape. That, in turn, encourages the growth of data silos and shadow IT.

A cloud data governance program can be centralized, decentralized, or federated. Whatever approach you take, a successful program should adapt to the way people already work - not force them to fit a mold.

Embed data governance throughout cloud operations #

It’s easy for data governance to become an afterthought if it’s not a part of the daily flow of working with data. A successful cloud data governance program should make it easy for teams to implement every aspect of data governance, including:

  • Gathering and enriching metadata data
  • Creating and enforcing data access policies and data roles
  • Tagging and classifying data
  • Propagating data tags automatically across your data estate
  • Viewing data usage and access reports

Best practices for implementing cloud data governance #

Here are a few concrete ways you can put the above principles into practice:

Support centralized tooling. One way to ensure cloud data governance fits to people (instead of vice versa) is by creating tools that teams can utilize as part of their own workflows. This typically includes tools to create data products, register new data sources and data products with a data catalog, tag and classify data, write and deploy data governance policies, etc.

Cloud data governance tooling makes it easy for teams to integrate data governance best practices into their existing processes. It also helps lower people’s natural resistance to change by providing ready-to-use solutions. Finally, it reduces the proliferation of shadow IT by providing a common toolset everyone in the organization can leverage.

Utilize automation. The volume of data is increasing year over year. No company can properly secure their data through manual effort alone.

Automated data governance processes can help by replacing error-prone and time-consuming manual data governance tasks with sustainable and reproducible processes. Examples of automated data governance include automatically enforced access control, system-generated data lineage to trace data provenance, and propagation of data tags based on lineage relationships.

Adapt current and emerging technology. New technology, such as Artificial Intelligence, can help teams perform common cloud data governance tasks in less time. For example, AI tools can offer metadata suggestions based on historical data or help data governance policy creators build effective policies more easily.

Raise awareness. A successful data governance program doesn’t implement itself. Look for opportunities to educate data producers, data consumers, and business users about your cloud data governance program - e.g., via on-demand training, brown bags, workshops, etc. Make sure you dedicate resources to work closely with new teams to help them understand how to create new data products that are secure and compliant by default.

Conclusion #

A successful cloud data governance system needs to be flexible enough to adapt, not just to the changing nature of cloud computing, but to the way different teams and organizations work with their data. By taking a flexible, embedded approach to governance and utilizing the right technology, you can empower teams to build effective data governance into their cloud projects from the ground up.

Atlan provides data governance tooling using state-of-the-art technology so that you can easily embed governance throughout your entire data lifecycle. Book a demo to see what Atlan can do for your organization’s governance systems today.

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