Many organizations have adopted multi-cloud strategies to gain flexibility, avoid vendor lock-in, and access best-of-breed services. But the tradeoff for this distributed and complex landscape is a set of substantial data management challenges. Let’s take a look at some of the most pressing challenges and how to tackle them:
Data Management Challenges in Multi-Cloud Environments
As the pace of digital transformation accelerates, more organizations are implementing multi-cloud strategies to improve agility, reduce costs, and foster innovation. While the benefits of multi-cloud are clear, data management in a landscape comprising disparate cloud service providers presents an entirely different set of challenges. From data governance to security, here are some of the critical data management challenges facing enterprises working in a multi-cloud environment:
- Data Silos and No Unified Visibility
One of the first challenges is the multiplication of data silos everywhere. When data in one cloud is organized in one format, organized differently in another cloud, etc., it can quickly complicate your understanding of your overall data estate. This lack of unified visibility eliminates practical data analysis, reporting, and ultimately decision-making.
Impact: inconsistent data; delayed time to insight; inability to verify a truthful view of data accuracy and completeness in the organization.
- Data Governance and Compliance Complexity
Each cloud provider has a different set of services, APIs, and compliance certifications. Implementing consistent data governance policies (e.g., data retention, data lineage, data access) across multiple cloud providers will require considerable effort and coordination. For example, you will also need to establish governance models to ensure compliance with various industry regulations (e.g., GDPR, HIPAA, PCI DSS) and regional data residency requirements.
Impact: potential for non-compliance; risk of data breaches; inability to demonstrate compliance during audits.
- Data Security and Privacy Issues
The challenge of extending security protocols across multiple clouds with different security models and tools is great. Achieving the same level of encryption, access management, threat detection, and incident response across all clouds requires coordination. Additionally, data privacy issues become a concern, especially when data crosses borders between cloud regions.
Impact: Larger attack surface, increased possibility of data exfiltration, and public damage for organizations from security incidents.
- Data Integration and Interoperability Issues
This will often be a complex problem involving data collected from multiple sources that may reside in various clouds, on-premises systems, and edge devices. Even just integrating data that comes in different formats, using various APIs and integration patterns, will require substantial development effort and solid integration platforms. Ensuring data integration and interoperability is ultimately best for applications and services when they work across multiple clouds.
Impact: Inefficient data pipelines, delayed application development, and failure to realize the advantage of integrated data.
- Data Migration and Portability Constraints
Moving data between clouds in a multi-cloud environment is rarely a straightforward task. For example, moving terabytes or even petabytes of data today is a significant undertaking. Where possible, cloud data migration can become expensive and result in unnecessary downtime for the organization. This is compounded by factors such as the egress costs of moving data, bandwidth limitations, and variations in data formats across cloud providers, all of which can hinder seamless data migration. For many organizations around the world, true data portability—the ability to move accepted data and access it across clouds without significant architectural transitions to the data —feels unlikely, if not impossible.
Impacts: Vendor lock-in, limited ability to switch cloud vendors, and increased operational costs.
- Cost Management and Tuning
While the multi-cloud model is built to help lower your overall costs, managing typical costs related to storing and working with data provides challenges to the organization, because it complicates accounting for regular costs like egress costs for moving your data, cloud storage tiers, costs to process your cloud data, and network costs, if applicable. This will require regular attention and cost tuning by your company. Costs associated with clouds in the organization are often complex, and sometimes care is needed. If used inefficiently, you may encounter unexpected costs, which can quickly derail the multi-cloud cost potential.
Impacts: Over budget, inefficient use of resources, and difficulty tracking budget expenditures, among other cloud-related issues.
Methods for Addressing the Multicloud Data Management Problem
Tackling these issues necessitates a strategic and proactive approach:
Create a Unified Data Strategy: Develop specific data governance policies, data standards, and security frameworks that will be consistently applied across all clouds.
Build Data Management Platforms: Establish multi-cloud data management platforms that provide unified visibility, automatic data discovery, data cataloging, and central policy compliance.
Define a Secure Data Strategy: Adopt a “zero trust” security approach, consistent encryption standards, and cloud-agnostic security solutions for threat detections, analytics, and access.
Leverage Data Virtualization and APIs: Use a data virtualization layer to create a single view of distributed data, instead of moving data physically, and leverage APIs to help link data among cloud services.
Data Portability Plan: Design with portability in mind, use open standards and formats whenever possible, and evaluate cloud-agnostic data services and tools.
Manage and Optimize Costs: Develop a solid cost management plan and implement techniques and tools to track and optimize data costs across cloud providers.
Utilize Automation: Streamline and automate data lifecycle management, compliance monitoring and verification, and security activities to minimize manual effort and reduce the possibility of human error.
By identifying these data management challenges beforehand, organizations can successfully optimize their multi-cloud environments, extract maximum value from their data, and ensure their operations are secure and compliant.
