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10-Step Guide for Data Governance in Cloud Computing

With an industry-wide 92% cloud adoption rate, well over half of organizations using multiple public clouds and enterprises planning to spend a third of their IT budgets on cloud tools in 2020, it’s clear that the future of enterprise analytics is in the cloud.

Cloud computing and analytics promises to enhance business agility and propel innovation by empowering organizations to leverage data in new ways. However, the rapid acceleration of data privacy and security regulations has made it challenging for enterprises to efficiently share, access and leverage data in the cloud while remaining compliant.

To meet this challenge, data architects and data engineers must adopt new cloud data governance solutions and strategies that enable effective data stewardship, support innovation by reducing time-to-data and automate compliance with data privacy regulations.

What is Cloud Data Governance?

Cloud data governance is the practice of regulating the availability, integrity, usage and security of data in cloud computing environments to achieve key business objectives. These objectives are likely to include:

  • Improving data privacy and security
  • Regulating and monitoring access to sensitive data
  • Optimizing operations and business decision-making with timely data analytics
  • Achieving and maintaining ongoing compliance with data privacy and security regulations
  • Guarding against data breaches and other cyber security threats

Cloud data governance takes on an additional dimension of complexity in multi-cloud or hybrid cloud computing environments, where data is found in multiple places and data governance protocols (authorizations, policies, metadata, etc.) are inconsistent amongst databases.

Data teams can navigate the challenges of cloud data governance with modern tools that empower data engineers and compliance teams to automate data governance, data access controls and privacy protection – all from a single software interface.

In this guide, you’ll discover 10 ways organizations can leverage data governance in cloud computing and analytics to achieve automated, legally compliant data governance in a cloud-first world.

10 Steps for Data Governance in Cloud Computing

1. Centralize Control of On-Prem and Cloud-Based Data

Organizations leveraging a hybrid cloud architecture — which is the majority in 2020 — may have data stored in on-premises data centers and multiple public or private clouds. Effective cloud data governance starts with centralizing data governance and establishing control over all data sets, regardless of where they’re situated in the network.

Centralizing data control provides massive efficiency benefits by reducing rework and empowering data engineers to apply data governance policies more consistently throughout their IT infrastructure. Implementing a single platform for data access governance also allows data engineers to grant data access requests and create policies that modify access requirements for all data sets from a single interface.

Managing data access from a centralized platform eliminates issues with conflicting authorizations, metadata or security policies that can lead to non-compliance. This enables data teams to move data amongst public cloud platforms without impacting data governance policies.

2. Save Time with Scalable Global Policies

As organizations expand their ability to capture and store data, manual methods of governing data become increasingly time-consuming and inefficient. Additionally, manual processes are more prone to human error and therefore introduce higher levels of risk.

Organizations that have adopted a centralized cloud-based data governance platform can save time and effort by implementing global policies that regulate the availability and usage of data throughout the network – not just within a single database or application. This significantly simplifies data teams’ process of maintaining consistent data governance policies across multi-cloud compute platforms.

3. Empower Data Consumers with Self-Service Access

Modern data catalogs compile all data into a single, searchable platform, making it easier for data consumers to explore, discover and analyze it. Self-service access means data consumers can access any available data set they have the right permissions for, instead of having to manually request access from each separate data owner.

While all data consumers have access to the same catalog, data architects and engineers can restrict access to specific data sets based on user permissions, ensuring protection of sensitive data.

4. Automate Discovery of Sensitive Data

Some data access governance platforms can automatically detect, classify and tag sensitive data across multiple platforms. Sensitive data discovery allows data teams to spend less time performing manual data classification and reduces the risk of errors associated with manual data entry. Once detected, sensitive data is automatically tagged to enable the appropriate access control policies.

5. Streamline Sensitive Data Certification Workflow

Even when sensitive data discovery is automated, data governance teams must be able to certify that it has been detected, classified and tagged appropriately. To meet these requirements, data architects and engineers should establish workflows for inspecting, reviewing and approving the results of automated discovery and tagging.

When data consumers need to access a data source or table, the authorization process should take seconds or minutes – not weeks or months, as is often the case when data owners, IT, security and other stakeholders get involved. A centralized data governance platform enables a simplified data request workflow, allowing data teams to promptly review access requests and rapidly connect consumers with the resources they need.

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6. Manage Role Explosion with ABAC

Data teams that depend on role-based access controls face increasing complexity as the number of roles in the organization grows, sometimes to the hundreds or thousands. This phenomenon, known as role explosion, makes it exponentially more complicated to accurately and uniformly apply data governance rules across the organization.

Attribute-based access controls (ABAC) can be used to dynamically apply data governance policies to each query based on user attributes like physical location, clearance level and purpose. This eliminates the need for data engineers to create new roles for each new data need and allows organizations to scale data access as their size and data sources grow.

Another instance in which ABAC streamlines compliant data access is in the case of data sovereignty, which is the idea that data falls within the regulatory jurisdiction of the country in which it is collected. This means organizations may be subject to the privacy and security laws of any country from which it collects citizens’ data – and currently there are over 100 countries with data sovereignty laws in place. It’s easy to see how quickly this can be a significant burden on data teams tasked with ensuring data adequately complies with these laws.

To manage these complex requirements, organizations can leverage cloud-based data governance and attribute-based access control to create location-based policies that regulate access based on the user’s location and the type of data.

7. Enable Fine-Grained Data Access Controls

Data architects and engineers with fine-grained data access controls can create policies that restrict access to specific rows, columns or cells within a table for unauthorized data consumers. Fine-grained data access controls allow organizations to remain compliant with data regulations and protect sensitive data that is contained in a table with other frequently used data or that must be accessed for a specific purpose.

In the past, data teams would have to make a copy of the file and remove or anonymize the sensitive data before allowing access – a time-consuming and tedious process. Now, cutting-edge dynamic data masking capabilities, like k-anonymization, randomized response and differential privacy automatically hide sensitive data from unauthorized users without copying or moving data.

8. Meet Compliance Requirements with Purpose-Based Access Controls

Regulations like the EU’s GDPR dictate that data must be collected for specific and legitimate purposes and that it cannot be used for anything other than those stated purposes. Under a purpose-based access control system, each data object is assigned a set of intended purposes, and access may only be granted if the data consumer specifies an access purpose that matches the intended purpose of the data. A predetermined and approved data access purpose helps enable regulatory compliance.

9. Monitor & Log Data Usage for Auditing Purposes

Cloud data governance policies must be audited on a regular basis to assess the effectiveness of the existing policies, identify any security risks or threats and enable ongoing compliance with regulatory requirements. To support the data audit trail, data architects and engineers should develop capabilities to monitor and log data usage.

Data-rich audit logs that include all data sources, who subscribes to each one, when they were accessed, what data was accessed and all queries performed enable data teams to share data usage details with compliance and legal teams, and are essential for proving compliance and troubleshooting issues when necessary.

10. Enforce Transparency with Automated Reporting

The combination of centralized data access and automated reporting ensures full transparency amongst data consumers, data architects, data engineers and compliance teams when it comes to understanding the who, what, when, why and how of data access. With auditing and reporting capabilities, data teams can quickly generate automated reports that reveal who is accessing data, why they are accessing it and how the data is being used.

Automate Cloud Data Governance with Immuta

For data engineers and architects managing data pipelines and usage in a cloud-first world, Immuta is the modern data access and control solution that securely streamlines data governance in cloud computing. By automating data access and privacy controls, Immuta accelerates data delivery, simplifies data administration, reduces risk and unlocks more data-driven insights and results. Immuta’s ability to automate data access across multi-cloud compute platforms eliminates the need for data teams to copy or move data, identify and manually mask sensitive information and manage user roles.

With Immuta, you can safely discover, access and analyze more cloud data – even the most sensitive data – without having to adopt new processes or tools. Data teams use Immuta to take the risk-prone, manual responsibilities out of cloud-based data governance, enabling organizations to maximize the value of their data more quickly and securely.

Are you ready to modernize your cloud data governance with Immuta?

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