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In simple terms, cloud data governance is a set of processes and policies that determine how an organization’s data is managed in the cloud throughout its lifecycle.

It might not be the coolest tech topic around, but it’s an exceedingly important one. It helps ensure that a company’s cloud-based data is available, usable, accurate, compliant, and secure, among other requirements. It’s a critical data management practice for any organization that relies on cloud infrastructure or otherwise consumes cloud services. 

That’s because data now commonly travels well beyond the corporate network perimeter. Moreover, enterprise data increasingly moves across multiple environments, as is the case in hybrid cloud and multi-cloud strategies. This trend continues to expand with the emergence of edge computing and other distributed architectures. 

Industry Insight

“Cloud data governance represents a strategic methodology of data management aimed at optimizing the use of data in the cloud to align with business goals, adhere to regulatory standards, and encourage innovation,” says Kausik Chaudhuri, CIO of Lemongrass. “It entails the development and application of various standards, procedures, and policies critical for the effective management of data stored in the cloud.”

In this article, we’ll take a deeper look at cloud data governance: what it is, how and why companies use it, best practices, and more.

Cloud Data Governance Defined

Scott Wheeler, Cloud Practice Lead at Asperitas Consulting, shares another definition of cloud data governance to help us build on the above, including the fundamentals an organization needs to do it right.

Cloud data governance includes the management of data availability, usability, integrity, and security. Effective cloud data governance requires a combination of technology solutions, organizational policies, and human oversight.

Scott Wheeler, Cloud Practice Lead, Asperitas consulting

Like other modern IT disciplines, effective data governance requires a well-functioning relationship between people, processes, and technologies so that an organization’s data management policies are appropriately implemented and adhered to. It’s also best to view cloud data governance as an ongoing and evolving practice – not a one-off project or checkbox.

“It's a continuous process that evolves as new technologies emerge and regulatory requirements change,” Wheeler says.

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Why is Data Governance Important?

Data governance is, in general, a vital practice. Without it, it becomes more difficult – if not virtually impossible – for an organization and its people to derive value from its data. Governance is a must-have practice as part of a broader data management strategy. It is crucial to ensure an organization’s data is available, accurate, comprehensible, compliant, secure, and perhaps most of all – trusted. 

Without proper governance, organizations expose themselves to far more significant risks. Data is more likely to be incomplete or inaccurate – or simply inaccessible or unusable – greatly limiting its ability to produce business value. Untrusted data could lead to worse outcomes in the form of slow or poor business decisions, broken processes, and more. The risks of data breaches and compliance issues also increase.

Cloud data governance, then, is the application of these principles to data in cloud environments outside of the on-premises data center or network. In a sense, the widespread adoption of cloud computing – as well as the growth of technologies such as IoT, edge computing, and other inherently distributed paradigms – makes governance even more critical since many enterprises have less direct control over their data assets than in the past.

Cloud also changes the calculus for security and compliance since the threat surface changes and expands as data increasingly travels beyond the traditional corporate perimeter.

With the increase and complexity of enterprise data, due in part to cloud computing, having a solid governance framework is paramount.

kausik Chaudhuri

What Cloud Data Governance is Used For

Chaudhuri adds that cloud data governance is fundamentally used to ensure data accuracy, data accessibility, data quality, and data protection. But what does it mean in business terms?

“Such aspects are critical for making well-informed business decisions, preserving operational efficiency, and ensuring data integrity,” Chaudhuri says. “Cloud data governance also plays an essential role in protecting sensitive data from unauthorized access and threats of cyber attacks.”

To look at it another way, consider how different departments and roles might rely on proper data governance – whether they realize it at the time or not:

  • C-level executives need precise and up-to-date information – in real-time or close to it – to make important strategic decisions for the business.
  • Sales and marketing teams need a comprehensive view of their customer data to understand demand and deliver exceptional consumer experiences.
  • Procurement and supply chain management teams depend on accurate data for maintaining and optimizing inventory levels and reducing production expenses. 
  • Compliance officers must ensure data management aligns with both internal policies and external regulations. 
  • Security teams need complete visibility and understanding of an organization’s data to manage risks and prevent incidents effectively – you can’t protect what you can’t see or don’t know.

Believe it or not, that’s just a quick list of examples – in so many businesses and industries, data has become that gold-standard currency. It drives digital transformation, day-to-day decisions, operational efficiencies, and more.

From an IT perspective, Wheeler from Asperitas Consulting notes that organizations also use cloud data governance for a range of requirements that enable all of those business uses we just covered.

Industry Insight

In addition to security and compliance, Wheeler points to data integration tools and interoperability (across multiple applications, systems, clouds, etc.), lifecycle management (including knowing when data is no longer needed), quality management, metadata management, and auditing and reporting (both for internal uses and external compliance.) “All contribute to increased efficiency in strategy and daily operations.”

Best Practices for Cloud Data Governance

“Effective cloud data management hinges on a set of key practices that ensure secure and efficient handling of data,” Chaudhuri says. “This involves setting up clear policies and standards for data handling.”

The details may differ from organization to organization – a healthcare organization will necessarily align its policies and standards with HIPAA, for example, whereas a manufacturing business would not.

But there are some best practices that any cloud data governance program should generally take into account, including:

  • Develop a Comprehensive Data Management Strategy: “Define clear goals and objectives for your data in the cloud, including how it will be used, who will have access, and how it will be protected,” Wheeler says. “This strategy should align with the overall business objectives and IT strategy.”
  • Implement Robust Data Governance: “Establish strong data governance policies that dictate how data is handled, who is responsible for it, and the processes for data quality, privacy, security, and compliance,” Wheeler advises. “Regularly review and update these policies to adapt to changing regulations and business needs.”
  • Prioritize Data Privacy and Security: “Security and privacy measures form another cornerstone, requiring the implementation of protection measures like encryption, secure access controls, and regular security audits,” Chaudhuri says.

This facet is also where organizations need to ensure their policies and processes are aligned with any regulatory compliance standards, such as HIPAA, CCPA, or GDPR.

  • Ensure Data Quality and Reliability: Wheeler notes that robust data governance initiatives must include the adoption of processes and tools for data cleansing, validation, and deduplication – those are prerequisites for deriving value from data analytics and insights. 

“High-quality data is critical for accurate analytics and decision-making,” Wheeler says.

  • Adopt Scalable and Flexible Data Storage: Cloud data governance programs must also cover how and where data gets stored in the cloud. Wheeler recommends choosing cloud storage solutions that can scale with your business needs and support different types of data, such as structured vs. unstructured, as well as different formats. “Flexibility in storage allows for cost-effective and efficient data management.”
  • Backup and Disaster Recovery: Business continuity is another key principle behind data governance strategy: “Implement robust backup and disaster recovery plans to ensure data availability and business continuity,” Wheeler says. “Regularly test these plans to ensure they work as intended.”
  • Utilize Automation and AI: As enterprise datasets continue to grow in size and complexity, human personnel will need help simplifying the operational burden while also ensuring sensitive information and other requirements remain protected. Automation, machine learning, and AI are becoming powerful levers for data governance as a result.

“Utilize automation for routine data management tasks and AI for more complex processes like data classification and anomaly detection,” Wheeler says. “This can improve efficiency and reduce the risk of human error.”

  • Monitor and Manage Data Lifecycle: “Implement lifecycle management policies to handle data from creation to retention to deletion,” Wheeler says. “This includes archiving data that is no longer actively used but needs to be retained for regulatory or historical reasons.” Paired with data storage policies, data lifecycle management is also vital in terms of cost management – for example, not paying to store data that is no longer needed.
  • Use Data Analytics Effectively: Finally, data governance can be seen as a means to multiple ends – not the least of which is turning information into insights that can drive better business decisions. AI is becoming intertwined with analytics as well, especially in areas like machine learning and predictive or next-best-action insights.

“Utilize cloud-based analytics tools to gain insights from your data,” Wheeler says. “Ensure that these tools are integrated with your data management system for seamless access and analysis.”

What Tools Are Used for Cloud Data Governance?

There is a growing list of data governance tools that organizations can use to help implement, manage, and optimize their data governance strategy. Their capabilities can vary but typically cover a range of governance needs, such as data discovery, data lineage, metadata management, data cataloging, automation, APIs, and more.

The Bottom Line

Cloud data governance is a must for today’s complex enterprise IT environments. It’s a foundation for generating business value from an organization’s growing data stores while enhancing security and compliance. Share how your organization manages its data in the cloud in the comments below, and join The CTO Club newsletter for more industry news and discussions.

By Kevin Casey

Kevin Casey is an award-winning technology and business writer with deep expertise in digital media. He covers all things IT, with a particular interest in cloud computing, software development, security, careers, leadership, and culture. Kevin's stories have been mentioned in The New York Times, The Wall Street Journal, CIO Journal, and other publications. His on ageism in the tech industry, "Are You Too Old For IT?," won an Azbee Award from the American Society of Business Publication Editors (ASBPE), and he's a former Community Choice honoree in the Small Business Influencer Awards. In the corporate world, he's worked for startups and Fortune 500 firms – as well as with their partners and customers – to develop content driven by business goals and customer needs. He can turn almost any subject matter into stories that connect with their intended audience, and has done so for companies like Red Hat, Verizon, New Relic, Puppet Labs, Intuit, American Express, HPE, Dell, and others. Kevin teaches writing at Duke University, where he is a Lecturing Fellow in the nationally recognized Thompson Writing Program.