What is Cloud Data Management? Strategies for Data Operations

Companies generate and consume vast amounts of data—petabytes of cloud usage metrics, thousands of daily software commits, terabytes of security logs, and continuous infrastructure performance data. As organizations increasingly adopt cloud computing and migrate their data to cloud platforms like DigitalOcean, strong cloud data management practices are non-negotiable.

But navigating the world of cloud storage can be overwhelming with the multitude of options and technical considerations at play. A number of data management challenges exist for organization; one report found that 39% of respondents find it difficult to keep pace with changing technology and tools in the space. Meanwhile, 22% of respondents lack clear data governance policies and processes.

There are also the practical realities: companies must weigh whether their workloads require high-performance SSD storage or more economical standard storage options. IT teams need to consider data access patterns—determining which applications need low-latency connections and which can tolerate occasional delays. Questions around geographic distribution often arise, as hosting data across multiple regions improves availability but introduces synchronization complexities.

Effective cloud data management is not merely about storing data in the cloud; it’s a comprehensive approach that combines data governance, data integration, data security, and data quality assurance. Read on to learn about effective cloud data management strategies to help to reduce complexity and prevent resource sprawl.

What is cloud data management?

Cloud data management is the processes, policies, and technologies employed to store, organize, secure, and analyze data within cloud environments. In this process, cloud platforms and services streamline data management processes, eliminate cloud sprawl, and enable organizations to derive more value from their cloud data assets.

In practice, cloud data management might include:

• Establishing data retention policies that automatically archive or delete data after defined time periods

• Implementing access control frameworks that restrict database and storage permissions by role

• Creating cross-region replication rules to ensure business continuity during outages

• Defining automated backup schedules with clear recovery point objectives

Cloud data management vs cloud governance

Cloud data management is about the practical implementation of how organizations store, organize, access, and analyze their data in cloud environments. It focuses on operational tasks like resources allocation, performance monitoring, and maintaining cloud services. Cloud data management handles the day-to-day operations such as managing data integrity, controlling access permissions, and overseeing data growth across cloud platforms. It’s concerned with the technical execution of data processes—ensuring data is properly structured, secured, backed up, and available when needed.

In contrast, cloud governance provides the overarching framework within which data management operates. It establishes the rules, policies, and systems that companies adopt for controlling and monitoring their cloud services. Cloud governance manages risk and enables smooth operations by defining policies, procedures, and controls to ensure cloud compliance and cost management. It determines what should be done with data rather than how to do it, setting the guardrails and standards that management must follow.

Think of cloud governance as the architecture blueprint of a building, while cloud data management is the actual construction and maintenance of that structure. In an effective organization, these concepts work in tandem—governance creates the specifications and requirements, while management handles the execution and daily operations that bring those plans to reality.

How to develop a comprehensive cloud data management strategy

Crafting a practical cloud data management strategy helps your organization to use the full potential of your data assets while operating in the cloud environment. This strategy should align with your OKRs and address data security and compliance concerns.

1. Create a data governance framework

Develop a clear data governance framework that outlines policies, processes, and roles for managing data throughout its lifecycle, ensuring data quality, security, and compliance. This might involve developing standardized procedures for data classification, implementing regular security training for all employees, and establishing protocols for emergency data breaches.

Establish a data governance committee or council to oversee and enforce these policies, ensuring optimal cloud application management across the organization. This lean team might include your startup’s technical co-founder or CTO, the lead developer responsible for cloud infrastructure, a product manager who understands customer data needs, and potentially an external advisor with compliance expertise who can provide guidance on an as-needed basis.

2. Design a logical cloud data architecture

Design a scalable and flexible cloud data architecture that integrates various data sources, cloud platforms, and developer tools, enabling data integration and accessibility.

Use cloud-native technologies such as serverless architecture and managed services offered by cloud providers to construct a scalable, flexible, fault-tolerant data infrastructure that can adapt to changing workload demands and ensure high availability.

3. Develop processes for data migration and integration

Define processes for migrating data from on-premises systems to the cloud, as well as integrating data from different cloud platforms and third-party sources. Many startups face cloud migration challenges when moving data to the cloud, from managing data volume to navigating complex security requirements and technical dependencies. These obstacles can be addressed by selecting migration tools that support real-time data ingestion, transformation, and synchronization across hybrid and multi-cloud environments.

4. Choose the right data storage and processing tools

Evaluate and select cloud storage solutions and data processing tools based on your organization’s needs and workloads. For instance, when comparing block storage vs object storage, consider block storage for high-performance requirements such as databases or transaction-heavy applications, and object storage for scalable, cost-effective solutions ideal for storing vast amounts of unstructured data like multimedia files or backups.

Implement cloud-based data processing frameworks to efficiently handle large-scale data processing and analytics workloads. These frameworks, like Apache Spark, enable distributed computing across multiple machines, allowing your team to process massive datasets and run complex analytics with simplified code that automatically executes in parallel across your infrastructure.

5. Implement data security and compliance

Implement efficient security measures, such as encryption, access controls, and audit logging, to protect sensitive data and ensure compliance with relevant data regulations (e.g., GDPR, HIPAA, PCI DSS).

Consider cloud service providers like DigitalOcean, which has six robust security pillars and adheres to popular security control frameworks, including SOC 2, SOC 3, and GDPR.

6. Use data analytics and reporting tools

Use cloud-based analytics and business intelligence tools to gain insights from your data, enabling data-driven decision-making and reporting. Consider cloud-native analytics platforms and services offering scalability, performance, and advanced machine-learning capabilities for data analysis.

For example, integrating a platform like Google BigQuery can provide real-time analytics and insights, allowing your organization to analyze petabytes of data with ease while scaling automatically according to demand.

7. Put data lifecycle management in place

Define clear lifecycle policies that specify how long different types of data should be kept in the cloud before being archived or deleted. For instance, establish data backup, archiving, and retention processes to ensure data availability, recoverability, and adherence to retention policies. For instance, implement nightly backups to multiple geographic locations to safeguard against data loss from regional outages, and utilize automated archiving solutions to move older data to cost-effective storage systems while keeping them accessible for future audits. Automate data lifecycle management tasks using cloud storage lifecycle policies and cloud-based backup and archiving solutions.

8. Draft a cloud vendor management strategy

Develop a strategy for managing relationships with cloud service providers, including vendor selection, service-level agreements (SLAs), and contract negotiations. Regularly review and evaluate cloud vendor performance, security measures, and compliance certifications to ensure alignment with your organization’s requirements. This might include establishing a formal process for conducting annual performance reviews against SLAs, focusing on cloud metrics such as latency, uptime, and incident response times, to ensure the provider meets agreed-upon standards.

5 cloud data security best practices

Securing your data in the cloud requires a thoughtful approach that addresses both security and compliance while maintaining usability. Here are five practices every organization should implement:

1. Data classification and risk assessment

Organizations should implement a data classification framework to categorize data based on sensitivity levels and follow best practices to secure cloud data. Assessing potential risks associated with each category enables your business to implement appropriate security controls and data handling procedures based on the data’s risk profile.

2. Access controls and authentication

Implementing strong access controls, authentication mechanisms, and cloud audits helps to protect sensitive data in the cloud. Two-factor authentication and the principle of least privilege ensure that only authorized users can access and manage data based on their roles and responsibilities.

3. Data encryption

Encrypting data both at rest (stored in the cloud) and in transit (during transmission) supports safeguarding sensitive information from unauthorized access or interception. Organizations should implement industry-standard encryption algorithms and key management processes to protect their data.

4. Audit trails and monitoring

Maintaining comprehensive audit trails and monitoring data access, usage, and security events can help detect potential threats, unauthorized access attempts, or anomalies that may indicate data breaches or misuse. Regularly monitor and analyze audit logs to proactively identify and respond to security incidents.

5. Shadow IT discovery and management

Regular scans across your network can identify unauthorized cloud applications and services that employees have adopted without IT approval. Clear policies must balance productivity needs with security requirements, offering teams approved alternatives to common shadow IT tools. Cloud access security broker (CASB) technology provides essential monitoring of data flowing to unauthorized services, preventing sensitive information from exposure through misconfigured personal accounts.

6. Third-party risk management

Assess and manage risks associated with third-party vendors, cloud service providers, or partners who may have access to your data. This includes conducting thorough due diligence, implementing solid contractual agreements, and regularly monitoring third-party security practices and compliance.

Cloud data management FAQ

What is cloud data management?

Cloud data management involves storing, organizing, securing, and analyzing data using cloud-based infrastructure rather than on-premises systems. It provides the framework for handling data throughout its lifecycle in cloud environments, from creation and storage to processing and archiving.

How does cloud data management differ from traditional data management?

Traditional data management focuses on on-premises storage with fixed capacity and higher upfront costs, while cloud data management offers pay-as-you-go pricing and virtually unlimited scalability. Cloud solutions also provide built-in redundancy, disaster recovery options, and the ability to access data from anywhere with an internet connection.

What security considerations should I keep in mind for cloud data?

Data encryption (both at rest and in transit), strong access controls, and regular security audits are all important parts of a strong cloud security strategy. Additionally, understanding your cloud provider’s shared responsibility model helps clarify which security aspects you manage versus what the provider handles.

How can I ensure regulatory compliance with my cloud data?

A comprehensive data governance framework that includes classification, retention policies, and regional storage controls is key for meeting compliance requirements. Regular compliance audits and working with cloud providers that offer industry-specific certifications can reduce your regulatory risk.

What tools do I need for effective cloud data management?

Core tools should include cloud storage services, managed database solutions (like PostgreSQL or MySQ options), and security platforms for access control and data protection. Additional tools for data integration, analytics, backup/recovery, and monitoring will create a complete ecosystem that maintains data integrity across your entire cloud environment.

Resources

• State of Data Ops: Unleashing the Power of Data

Elevate your data strategy with DigitalOcean’s cloud solutions

DigitalOcean’s strong focus on simplicity, performance, and affordability helps organizations streamline their data management processes and ensure data security, compliance, and accessibility.

• DigitalOcean Spaces, an object storage service, provides a secure and durable solution for storing and managing unstructured data, such as backups, media files, and large datasets. With built-in features like versioning, lifecycle policies, and cross-region replication, organizations can efficiently manage their data lifecycle and ensure data protection and availability.

• Additionally, DigitalOcean Volumes offer network-based bock storage solutions, providing features like snapshots, automatic backups, and high-performance storage options to store and manage structured data efficiently.

• DigitalOcean offers Managed Databases, a fully managed database service that supports popular databases like PostgreSQL, MySQL, MongoDB, Kafka, and Valkey for structured data management. This service simplifies database administration tasks, such as provisioning, scaling, and backups, allowing organizations to focus on their applications rather than database maintenance.

• DigitalOcean’s cloud data management solutions allow businesses to pay for only the resources they consume and scale horizontally or vertically as their data evolves, This flexibility enables organizations to optimize their cloud ROI and avoid over-provisioning or underutilizing resources. Moreover, DigitalOcean facilitates powerful data integration and analytics.

Whether you’re a small startup or a large enterprise, DigitalOcean’s products provide the tools and services you need to manage your data effectively, drive innovation, and optimize cloud costs.

Sign up to see how DigitalOcean’s infrastructure can help you build hassle-free data governance and management for your business.