Overcoming data silos for effective multi-cloud monitoring

It’s increasingly common for enterprises to use at least two different cloud providers. And while managing a multi-cloud environment can be complex, benefits like enhanced resilience, flexibility, and cost optimization, are too significant to pass up. But complexity isn’t the only drawback of multi-cloud.

Another issue that often arises in multi-cloud environments is siloed data. Data silos are a frequent problem in large enterprises and sometimes they arise due to organizational factors. Individuals and teams don’t always share knowledge effectively across the organization, impeding monitoring and visibility. But within a multi-cloud environment, data silos can easily form for technical reasons—with similar negative effects.

In this article, we’ll explore strategies to avoid data silos in multi-cloud environments, so that you can improve visibility and monitoring practices while benefiting from using several cloud providers.

The challenge of data silos in multi-cloud environments

Data silos emerge naturally in multi-cloud setups, where each cloud provider has unique storage and management systems. It’s easy for silos to emerge when an organization lacks a unified data management plan that would require adherence from teams in different departments. Learn more about multi-cloud strategies and how they can address these challenges.

Instead, data silos limit visibility across environments, making it challenging for IT teams to gain a comprehensive understanding of the whole infrastructure. Moreover, they inhibit interoperability and integration, complicate tracking performance metrics, and increase the risk of inefficiencies and service disruptions.

Fragmented data also makes security management more complex. It becomes more difficult to implement consistent security measures across disparate cloud platforms—and as a result, it’s more difficult to avoid breaches and data leaks.

Fig. 1: Graphic illustrating the disparate, isolated nature of different cloud services and their management systems

The importance of unified monitoring in multi-cloud environments

Unified monitoring is a crucial strategy for combating data silos within multi-cloud environments. You can gain a holistic view of your entire cloud ecosystem by integrating data from disparate cloud platforms into a centralized monitoring system. This cohesive approach enables comprehensive oversight of cloud resources, applications, and services.

Unified monitoring provides several benefits to multi-cloud management:

• It enhances operational efficiency by eliminating the need to navigate through multiple monitoring tools and interfaces.
• A consolidated view supports informed decision-making, as stakeholders can access real-time insights into the performance and health of the entire multi-cloud infrastructure.
• Unified monitoring empowers organizations to proactively address issues across the cloud ecosystem, facilitating early detection of anomalies or security threats that might otherwise go unnoticed within data silos. This proactive approach minimizes downtime, mitigates risks, and strengthens resilience as cloud environments evolve.

Strategies for breaking down data silos

There are a number of strategies that can help organizations dismantle data silos within multi-cloud environments:

• Cross-platform monitoring tools — Multi-cloud monitoring tools that aggregate data from diverse cloud environments into a unified dashboard are essential investments. They provide a centralized view of performance metrics, allowing for proactive, simultaneous monitoring and troubleshooting across all cloud platforms.
• Cloud-agnostic data management practices — Cloud-agnostic data management practices ensure compatibility and interoperability across different cloud services. By standardizing data formats and storage methodologies, organizations can minimize data silos and facilitate seamless data exchange between cloud platforms.
• Fostering collaboration among IT teams — Encouraging collaboration and knowledge sharing among IT teams is vital for overcoming the organizational barriers that can contribute to data silos. By promoting cross-functional communication and collaboration, organizations can break down silos and foster a culture of transparency and shared responsibility for monitoring and managing multi-cloud environments.
• Role of APIs and integration tools — APIs and integration tools are instrumental in facilitating data flow between disparate cloud services and platforms. You can use APIs for data integration and synchronization to enhance monitoring and visibility capabilities and gain real-time insights into the performance and health of the entire multi-cloud infrastructure. There are numerous integration tools, like Apache NiFi, MuleSoft Anypoint, and Zapier, that streamline the process of connecting different cloud services. These tools also reduce complexity and minimize the risk of data silos.

Leveraging automation for efficient multi-cloud monitoring

Automation of key processes is a crucial strategy for handling the complexity of multi-cloud environments. Let’s look at how automation can help you avoid the risks of data silos.

Automated anomaly detection

Automated monitoring solutions can employ machine learning (ML) algorithms to detect anomalies in real-time across multi-cloud environments. For instance, anomaly detection algorithms can identify unusual spikes in network traffic or deviations from normal performance patterns, alerting IT teams to issues before they escalate. Anomalies are identified promptly and consistently across the entire multi-cloud environment, mitigating the risk of data silos caused by disparate monitoring practices.

Consider a financial services company, which might employ automated monitoring tools to detect unusual spikes in transaction volumes or latency across their cloud-based payment processing systems. By analyzing historical data and comparing it to real-time metrics, these tools can identify anomalies, such as unexpected increases in transaction processing times, triggering alerts for immediate investigation and resolution by IT teams.

Dynamic resource scaling

Automation can facilitate dynamic resource scaling according to predefined thresholds. An automated scaling mechanism can automatically provision additional resources, such as virtual machines or additional storage capacity, across multiple cloud platforms in response to increased demand for a particular application or service.

Conversely, during periods of low usage, excess resources can be automatically de-provisioned to optimize cost efficiency. This ensures efficient resource use across the entire multi-cloud infrastructure, preventing silos from forming around specific cloud services or platforms.

For example, an e-commerce platform experiences a surge in website traffic during holiday sales events. Automated monitoring systems detect the increased load and trigger provisioning additional virtual servers across different cloud providers to handle the spike in user requests. Once the traffic returns to normal levels, the system automatically scales down resources to minimize expenses, ensuring efficient resource use without manual intervention.

Predictive maintenance

Automated predictive maintenance systems leverage data from diverse sources, including multiple cloud platforms, to identify potential issues before they escalate. Analyzing data holistically, rather than in isolation within individual cloud environments, these systems break down data silos and give a comprehensive view of equipment health and performance.

This approach helps ensure critical systems remain operational across all cloud providers and reduces the risk of silos caused by fragmented maintenance practices.

For example, in manufacturing, predictive maintenance through automation is critical for ensuring uninterrupted production processes. A manufacturing company may deploy IoT sensors across its production facilities to collect real-time data on equipment performance. Using ML algorithms, you can detect anomalies in equipment behavior, such as abnormal vibrations or temperature fluctuations, early. Predictive maintenance systems then generate alerts, prompting maintenance teams to perform pre-emptive repairs or replacements.

Best practices for effective multi-cloud monitoring

There are several best practices you should follow to ensure effective multi-cloud monitoring and reduce the risks of data silos:

• Auditing cloud resources — Your team should regularly audit cloud resources to ensure they align with business objectives. Audits also help you identify areas for optimization or consolidation.
• Standardized monitoring metrics — This standardization facilitates consistent performance evaluation across diverse cloud environments, promoting transparency and simplifying analysis.
• Comprehensive security coverage — With robust access controls and encryption protocols, comprehensive security coverage is essential for safeguarding data integrity and compliance across multiple clouds. Furthermore, you should prioritize flexible and scalable monitoring solutions capable of accommodating the dynamic nature of multi-cloud deployments.
• Adopting a unified monitoring tool — Most crucially, you need to adopt a unified monitoring tool that uses automation to help you follow all of these best practices.
• Leveraging cloud-native observability insights — Studies like the Site24x7 ISG Cloud Native Observability Study 2024 can offer valuable insights into the latest trends, tools, and best practices for achieving effective monitoring in multi-cloud environments.

Conclusion

Advanced monitoring solutions are essential to achieve comprehensive visibility, security, and operational efficiency within multi-cloud environments.

As you navigate the complexities of diverse cloud environments, Site24x7 offers invaluable cloud monitoring capabilities. With the ability to provide real-time insights, Site24x7 empowers businesses to optimize their cloud infrastructure.

To experience the benefits, try a free 30-day trial of Site24x7 today.