Modernizing Data Governance: Enhance Quality & Control in Cloud

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Organizations need to prioritize modern data governance practices in the cloud to ensure data quality, seamless integration, better decision-making, cost optimization, and compliance with regulations.

In the modern digital era, data quality appears to be a critical factor for the success of organizations.

Due to the unprecedented growth of volumes of data across the systems and the wide adoption of cloud technology, new complexities and challenges in data governance and quality management have emerged. Some of those related to data include data sprawl, management, security and privacy, integration, governance, and many others.

As such, organizations need to embrace modern data governance practices in the cloud environment to stay on top of the game. By leveraging data-related transformation, they can enhance control over their data and maximize the potential of cloud assets.

Why Modernized Data Governance in the Cloud is the Need of the Hour?

As organizations increasingly turn to cloud technology for storing, processing, and analyzing data, modernizing data governance practices becomes a pressing priority.

Data migration to the cloud brings several unique challenges that traditional data governance approaches need help to address. Data sprawl, where data is stored in different cloud servers and platforms, makes it difficult to maintain centralized control over the data in the cloud.

Moreover, frequent infrastructure updates and deployment modifications increase complexities, data management, and governance-related issues.


To mitigate the challenges posed due to the inherent design of the cloud, companies must shift toward the data-centric management approach from the conventional infrastructure-centric approach.

The data-centric approach treats data as a valuable asset for the enterprise. As such, procedures related to responsible data management are emphasized.

As a result, organizations can better control and protect their data stored in the cloud through prioritized data management practices.

Specific Benefits of Cloud-Native Data Governance and Control

Adopting modern data governance practices in the cloud brings several opportunities for enterprises to regulate better access control over the data.

Improved Data Quality

To ensure the reliability and trustworthiness of data, cloud-native data management and governance enable organizations to adopt validation mechanisms, quality control processes, and cleansing procedures for data.

This includes establishing policies for data governance and introducing data stewardship roles and quality metrics specific to cloud data.

As a result, the data quality improves, and the trust of the stakeholders in the cloud-based data environments increases.

Seamless Data Integration and Interoperability

Modern data governance practices enable the seamless integration of data from different sources. The heterogeneous data from multiple sources can be made interoperable by introducing data integration standards, frameworks, and data-sharing regulations.

Better Decision-Making

Modern cloud-native data management services have made data discovery, cataloging, and metadata management easier for stakeholders.

As a result of the comprehensive views about the data, organizations may find it convenient to share data for productive collaborations, faster data insights, and better decision-making.

Cost Optimization

Cloud-native data management techniques ensure cost optimization and scalability. By exploiting cloud storage and processing resources, enterprises can scale their data infrastructure to the extent necessary and cut initial investments and operating costs.

In addition, another benefit of cloud-native data management is elasticity, which means adapting to changing business goals and data volumes.

Compliance with Data Protection Regulations

Another benefit of adopting modern data governance and control mechanisms is that they help organizations comply with the procedures and regulations about data.

For example, companies can conform to data protection regulations through data privacy policies and access control procedures and by establishing data classification into more concrete types.

As a result, several legal issues can be evaded.

Best Practices for Cloud-Native Data Governance

Organizations can follow the following practices for effective cloud-native data governance:

  • Developing a comprehensive data governance roadmap

A comprehensive data governance strategy and roadmap should be developed to address several data-related issues, such as data lifecycle management, data protection and integration, and data classification. The data governance strategy should be aligned with the organizational goals. The strategy must clearly define the goals and responsibilities of the associated roles, policies, and procedures for implementing cloud-native data governance.

  • Utilizing contemporary platforms and technology

To effectively manage cloud-native data, highly specialized platforms, such as Kubernetes, and virtualization technologies, such as containerization, should be employed to ensure scalability, flexibility, and agility in data management operations.

Since these platforms and technologies are cloud-native, organizations can benefit from the cloud’s inherent capabilities for efficient data processing and management by adopting them.

  • Implementing data security and privacy procedures

The security and privacy of data in the cloud are subjects of serious concern and are already receiving significant attention. However, ensuring foolproof security and privacy of cloud-native data requires consistent efforts.

In particular, privacy-by-design principles aid compliance with security and privacy procedures by cloud providers and organizations.

  • Facilitating teamwork for data management

Another strategy to ensure effective data governance is strengthening collaboration among various stakeholders and defining the roles and responsibilities for data management and stewardship tasks. The designated roles must ensure that the integrity of the data is preserved and the quality of the data is assured by adhering to the data governance policies.

Moreover, training the roles responsible for data stewardship helps perform data management operations effectively.

  • Implementing data cataloging

Organizations must adopt robust data cataloging solutions to improve asset discovery, knowledge, utilization, automated governance, and integration.

Moreover, the significance of metadata management increases manifolds due to the distributed nature of data and its diverse formats and sources. By employing metadata management approaches, organizations can automate critical data management tasks such as discovery, profiling, and lineage tracking, enabling efficient location and enhanced understanding of data assets.

This enables organizations to make informed decisions and have actionable insights into the data and corresponding operations.

Tools and Technologies for Data Governance

Several tools and technologies can be helpful for effective data governance and control in the cloud environment. Below, we discuss some of them.

Data lakes and warehouses: An appropriate and flexible way to store different data types where data can be stored in its most raw form.

Data lakes have become an integral part of modern data platforms, specifically catering to streaming and machine-learning applications. They support diverse data types, minimize processing expenses, and provide scalability. Data lakes offer the advantage of leveraging lakes for analytical purposes and gaining valuable data insights.

A few popular data lake vendors include Amazon Lake FormationGoogle Cloud Platform BigLakeDatabricks Delta Lake, and Snowflake.

Function as a Service (FaaS) or serverless computing: An efficient and scalable way to execute various functions or code snippets without the need to manage underlying servers.

In this case, the organizations do not need to manage or develop the underlying infrastructure. FaaS enables scalable data processing, validation, transformation, and quality checks.

Some of the most popular examples of FaaS are Azure Function and Google Cloud Functions.

Other technologies and tools effective for data governance in the cloud include:

  • Containerization and orchestration
  • Data governance platforms
  • Data integration and ETL tools
  • Analytics and machine learning services

Examples of Successful Enterprise-Level Data Governance Initiatives

Several large-scale enterprises have already taken initiatives to manage cloud-native data effectively.

Netflix, a leading streaming service, faced multiple issues managing vast volumes of data across various geographical regions and systems.

To manage the metadata for data cataloging, the company adopted cloud-native data processing and storage and leveraged solutions. The measures help improve the data quality, restructure data governance processes, and efficient data discovery.

Procter & Gamble (P&G) encountered synchronization issues between the Master Data Management (MDM) and metadata regarding operational reporting for global and regional users.

To ensure the data’s completeness, consistency, and quality, the data governance team employed Right Data (RDt) framework.

The list of entities addressing data governance issues includes many other enterprises. Cloud-based tools and frameworks have effectively addressed data discovery, sprawl, quality, and regulatory compliance challenges.

As a result, the benefits that organizations are getting include:

  • Better data accessibility
  • Improved data governance procedures
  • Easier and more efficient decision-making
  • Data-driven insights

The Bottom Line

Data management modernization is strategically significant for organizations trying to harness the power of data in the cloud. Organizations gain greater control over their data assets and enhance data quality and integrity through data-centric approaches and cloud-native solutions.

Effective cloud-based data management maximizes knowledge value, enables informed decisions, fosters innovation, and ensures compliance.


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Assad Abbas
Tenured Associate Professor
Assad Abbas
Tenured Associate Professor

Dr Assad Abbas received his PhD from North Dakota State University (NDSU), USA. He is a tenured Associate Professor in the Department of Computer Science at COMSATS University Islamabad (CUI), Islamabad campus, Pakistan. Dr. Abbas has been associated with COMSATS since 2004. His research interests are mainly but not limited to smart health, big data analytics, recommender systems, patent analytics and social network analysis. His research has been published in several prestigious journals, including IEEE Transactions on Cybernetics, IEEE Transactions on Cloud Computing, IEEE Transactions on Dependable and Secure Computing, IEEE Systems Journal, IEEE Journal of Biomedical and Health Informatics,…