Modern Cloud Data Management

What Is Cloud Data Management?

Cloud data management involves the implementation of cloud data management platforms and tools, as well as policies and procedures that give organizations control over their business data. This applies to both cloud environments and hybrid setups, where data is stored or sourced from a combination of on-premises and cloud applications.

The ever-growing list of cloud applications and tools adopted by enterprises is leading to an exponential increase in data—whether structured, unstructured, or semi-structured. Since this data is a critical asset for modern enterprises, managing it has become a strategic imperative. This is especially true as the number of data users grows, the volume and types of data expand, and business processes evolve.

Read more: Cloud Computing: All Things That You To Know

How Does Cloud Data Management Differ from Traditional Data Management?

Organizations are increasingly recognizing the value of migrating workloads to the cloud, leveraging improved agility to optimize new products and services while reducing CapEx and OpEx. As enterprise businesses continue to shift IT operations and applications to the cloud, the need for data management tools and platforms that are more cloud-centric becomes vital.

Traditional data management tools perform well for on-premises workloads but often struggle with cloud-based environments. Cloud-centric data management tools are developed as cloud-native, meaning they are designed to operate within the elastic and distributed nature required by modern cloud computing platforms. Key tenets of cloud data management platforms (also known as cloud data lakehouse management platforms) include:

• Support for data across various cloud ecosystems (multi-cloud).
• API-driven architecture and delivery as microservices.
• Use of modern constructs like containers and serverless technology for faster, scalable deployment.
• Simple installation and setup.
• Ease of management, with automatic upgrades and patch management.
• Pricing based on service utilization.

Read more: Why Should Startups Need to Cloud Managed Services?

Key Tools and Capabilities for Data Management in the Cloud

As organizations plan or revise their data architectures in response to changing business requirements and processes, cloud data management should be a top consideration. Here are five important capabilities to consider when developing your cloud strategy:

Cloud Integration

The cloud can drive innovation, uncover efficiencies, and help redefine business processes. However, these benefits are only realized when your cloud infrastructure enables you to integrate, synchronize, and relate all data, applications, and processes—whether on-premises or across any part of your multi-cloud environment.

Businesses may seek to design, run, and automate processes that span applications. They might need real-time integration using orchestration, APIs, and messaging, or running extract, transform, load (ETL) batch integration jobs for their analytics platforms (cloud data warehouses and data lakes) to keep application data synchronized.

For these needs, organizations require intelligent data and application integration, API management tools, and a broad set of connectivity capabilities—all of which are core components of a modern integration platform as a service (iPaaS).

Cloud Data Quality and Governance

As companies place data at the core of their business processes, the most successful organizations recognize the importance of high-quality, trusted data in their digital transformation efforts. According to a recent survey by McKinsey & Company, “Companies that empower employees to consistently use data as a basis for their decision-making are nearly twice as likely as others to report reaching their data and analytics objectives.” Additionally, data regulations have become increasingly complex and dynamic.

To advance their initiatives, organizations must ensure that employees across the enterprise can easily locate, access, understand, and use data. Enabling business and IT users to quickly derive value from trusted, clean, high-quality data through automated, cloud-based data quality and governance processes is essential.

Cloud Data Privacy and Security

As organizations migrate workloads to the cloud, they gain agility to optimize new products and services and reduce CapEx/OpEx, staying ahead of their competition. However, in public cloud and hybrid environments, data is more exposed to risks of abuse and attacks beyond traditional firewalls. Protecting data, managing safe access, and enforcing compliance and appropriate use policies are crucial to reduce the risk of security breaches and corporate abuse.

Traditional system-level data protection, which remains on-premises, is inadequate for today’s expanded data sharing and migrated applications. The accelerated growth of new data types deemed sensitive and personal, along with surging data volumes, creates a “perfect storm” for data protection challenges. Customer backlash for data breaches often targets both unprepared organizations and criminal actors. Compliance is not only about avoiding fines and penalties but also about maintaining long-term customer loyalty.

But there is an upside—privacy assurance helps democratize safe data use, accelerate and unblock cloud workload migration, and deliver innovative products and services that build on customer trust. Integrated cloud data privacy and protection tools can help you automate the discovery and classification of sensitive data, map identities for clear ownership and support data access rules, operationalize privacy policies, model and analyze data risk exposure across data stores and locations, and orchestrate data protection.

An integrated approach based on metadata-driven intelligence and automation can facilitate quick action—such as responses to the SARS outbreak—providing data use transparency, data masking for the protection of personal information, and monitoring the effectiveness of controls in place for audit reporting.

Read more: The Importance of DevOps in Cloud Security Management

Cloud Master Data Management

With all the data being generated across business lines, you need a complete, 360-degree view of any domain and any relationship in the cloud. Furthermore, there is a push for intelligent data stewardship and improved search and visualization of data, as well as improved verification and enrichment—the goal being to attain a “golden record,” which provides access to the purest, most validated, and most complete picture of the individual records in your domain.

Cloud master data management capabilities synchronize the most critical data across various systems in your organization, enabling AI and analytics teams to derive deep insights from that data to power your business.

Cloud Metadata Management/Data Cataloging

Companies are transforming their businesses to drive innovation, improve customer experience, lower costs, and enhance operational efficiencies. Regardless of the business drivers, all of these transformations depend on good, trusted data. However, as the data landscape becomes more complex, data is diverse and distributed across many departments, applications, data warehouses, and data lakes (some on-premises, others in the cloud), making it challenging to know exactly what data you have and where.

A comprehensive data cataloging solution uses machine learning-based data discovery to scan and catalog data assets across the enterprise. It provides analysts, data scientists, and IT users with powerful semantic search, detailed data lineage, profiling statistics, data quality scorecards, holistic relationship views, automated data curation, crowd-sourced data curation, and much more.

By enabling comprehensive data discovery across the enterprise, intelligent data catalogs allow organizations to maximize the value of their data assets. Leveraging a combination of technical, business, operational, and usage metadata, intelligent data catalogs also help build the metadata foundation to support cloud modernization, data governance, and other business priorities.

Enhanced Intelligence Through AI

With the geometric pace at which enterprise data is growing, data processing now requires the aid of artificial intelligence (AI). Comprehensive cloud data management platforms provide key AI capabilities that enable the automatic discovery and cataloging of data across various systems such as ERP, CRM, and more. These capabilities include the automatic discovery of relationships between customer data, matching insights to specific individuals, automation of data integration and quality tasks, intelligent policy management and enforcement, and much more.

The Value and Benefits of Cloud Data Management

As modern enterprises evolve and increasingly adopt cloud solutions, having the right tools and processes for managing data becomes essential.

Cloud data management encompasses the entire data lifecycle, from creation to retirement, and involves the controlled transition of data through each stage. It helps mitigate risks and costs associated with regulatory noncompliance, legal issues, and security breaches. Moreover, it ensures that accurate data is accessible when and where needed, reducing ambiguity and preventing miscommunication.

Here are some core benefits of implementing an effective cloud data management strategy and utilizing appropriate tools:

• Enhanced analytics through better integration and data ingestion.
• Improved data security and governance.
• Higher data quality, eliminating the “garbage in, garbage out” issue.
• Faster data discovery and superior metadata management.
• Optimized record maintenance and management across systems, achieving the “golden record.”

Example Use Case: Modernization With a Cloud Data Warehouse

A key initiative for digital modernization is the adoption of cloud data warehouse (CDW) systems to improve analytics capabilities. CDWs offer numerous advantages over traditional data warehouses, including greater scalability, flexibility, agility, faster time to value, and improved performance.

The right cloud data management tools can facilitate and expedite the migration of workloads from existing on-premises data warehouses to the cloud or the establishment of a new cloud data warehouse. Key steps in optimizing a CDW include:

1. Discovering the Right Data: Identify and migrate relevant data to the new CDW. For example, determine if data in other cloud applications like Salesforce or data in spreadsheets on OneDrive is useful for the CDW.
2. Integrating Data Across Sources: Data in the CDW will come from various sources, each with its own data models and formats. Potential data sources include applications hosted on public clouds, such as software as a service (SaaS) apps.
3. Ensuring Data Quality: High-quality data is crucial for generating valuable insights. Data quality should be a fundamental requirement for any successful analytics project.

Without a comprehensive set of cloud data management tools to support these processes, many CDW projects may encounter challenges in achieving seamless operation.

Conclusion

An effective cloud data management strategy is crucial for modern enterprises, particularly as they accelerate their adoption of cloud infrastructure, applications, and services. Whether migrating and synchronizing data across systems, securing critical organizational and customer data, ensuring high-quality data, or uncovering deep insights into data lineage, defining data requirements and potential solutions is the first step.

A platform solution can provide a significant advantage by consolidating all key capabilities under a unified umbrella, leveraging a common data model.