Multi-Cloud Success Data Management Essentials


Now that data is being generated everywhere and stored in siloed locations, businesses need a holistic view across multi-cloud data stores.

As companies have transitioned to the cloud, many realize there are benefits to diversifying their cloud environments between the multiple cloud service providers. One way to simplify this process is by leveraging a multi-cloud data management platform to streamline migration, optimize costs, and provide a unified view across multiple cloud and on-premises environments. In this article, we’ll cover why you should consider a multi-cloud approach and how a multi-cloud data management platform provides the essentials needed to reduce the complexities of a decentralized data ecosystem.

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Why multi-cloud?

The first question that many ask about multi-cloud is why it’s needed if they are already happily using a single provider? As cloud service provider offerings have matured over time, we’ve seen that different providers excel at different use cases or integrations with other tools in your technology stack. You may also have teams within your organization that need a specific vendor for their project or use case. For example, your IT organization may be using a cloud service provider such as AWS, but the marketing team uses a data management platform (DMP) based on Azure. The marketing team creates their cloud environment for their customer relationship management (CRM) use case, resulting in a data silo with a lack of visibility or control for the centralized IT team. These types of fit-for-purpose cloud environments may continue to arise within your organization, creating a need for data management and governance that spans across these siloed environments.

The next reason, and often the most common, many companies are moving to multi-cloud is to optimize cloud costs and reduce risk. By leveraging multiple cloud vendors, you avoid vendor lock-in and select vendors that are more efficient at a particular task or provide the cheapest resources to get the job done. Companies can spin up a cluster with the appropriate cloud vendor and quickly execute at the most affordable cost possible. Finally, many adopt multi-cloud to reduce risk. As data is diversified across providers, risks of disruption of service for mission-critical or customer-facing applications is reduced.

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Multi-Cloud data management platform essentials

Now that data is being generated everywhere and stored in siloed locations, you need a holistic view across data stores like public cloud, cloud data warehouses, and on-premises environments. Multi-cloud data management platforms can provide this unified view of your decentralized data ecosystem. Below are essential data management capabilities to look for in a multi-cloud platform to help you achieve success.

  • Connectivity and management across systems: The first essential capability is the ability to connect, catalog, and ingest from a multitude of data sources and systems. Whether you bring in third party data into a data lake, connect to an existing relational database, or migrate data to a cloud data warehouse, having a control plane that sits across these systems allows you to have complete visibility and control. With this layer of abstraction, you can standardize and automate data pipelines and push down processing to the appropriate cloud service provider, reducing costs and time to analytics.
  • Standardized governance: When data is siloed or sprawled across an organization, data governance becomes problematic. With a unified view or control plane set up across your environments, you can create governance policies and actions that standardize governance across the various systems. A common use case for our customers is the need to build golden customer records from disparate source systems. With a unified multi-cloud data management platform, you are able to connect to data in the various source systems, then merge and link customer data, even in the absence of unique identifiers, by applying a combination of deterministic rules and probabilistic machine learning algorithms. Data profiling and classification can automatically flag and obfuscate sensitive customer data, creating high-quality and reliable golden customer records that are also compliant with consumer protection regulations. In addition to data quality, mastering, and obfuscating sensitive data, a unified data governance platform also needs to include data lineage and role-based access controls.
  • Self-service data access: With the proper governance in place, you are able to truly empower data democratization by providing self-service data access to your data consumers through a governed data marketplace. Traditionally, data consumers would request data sets from IT. Increasing requests results in increased wait time, provoking the risk of data being stale and no longer usable for its intended use case. With a self-service data marketplace, data consumers can search for relevant, trusted data through an e-commerce-like experience before preparing and provisioning the data with an analytics tool or sandbox environment, increasing the time to analytics while reducing the burden on IT.
  • Optimization through unified DataOps: The next multi-cloud essential is leveraging DataOps as your data management approach. DataOps combines operations management with the agile methodologies of DevOps and views the data journey from source to consumer as a supply chain where various steps may be streamlined or automated to improve efficiency. The key to this success is to have consistent feedback on supply chain performance. A modern multi-cloud DataOps platform allows you to generate insights on data usage, costs, platform performance and begin making decisions around what should be processed where and how to improve the overall performance of your data supply chain to reduce costs, improve efficiency and improve the outcomes of your AI/ML initiatives.


There are many advantages to adopting a multi-cloud data architecture. Multi-cloud has become a reality for many organizations today as fit-for-use implementations have popped up across their organizations. With the right data management platform in place, you’ll have a unified view and control over your siloed, decentralized ecosystem. By leveraging DataOps as your data management methodology, you’ll begin automating and improving your data supply chain to improve efficiency and reduce time to analytics.

Ben Sharma

About Ben Sharma

Ben Sharma is the Co-founder and Chief Product Officer of Zaloni. He is a published author, and holds two patents for his innovative Big Data, Enterprise Infrastructure, and Analytics solutions. His impressive range of knowledge across data and business software disciplines has led him to leadership roles at leading companies like Fujitsu and NetApp before Zaloni.

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