Practical Steps for Cloud Data Migration
Strategy 1 – Establish data platform independence
The majority of data infrastructure teams (62%) indicate the management of their data warehouse solution is either difficult or very difficult [Data Warehouse Trends Report 2018 from Panoply]. Instead of managing data platforms, manage how the company accesses the data, with an emphasis on abstracting the data’s source location.
62% of data infrastructure teams say it is difficult or very difficult to manage their data warehouse. [Data Warehouse Trends Report 2018 from Panoply]
Enterprise IT teams need a data platform-agnostic approach that enables them to avoid vendor lock-in and future-proof data architecture against technology shifts and data migration challenges. Intelligent intelligent data virtualization connects data stores and cloud platforms with live connections or SDKs to make data location agnostic. This allows companies to get the most out of their existing Enterprise Data Warehouse (EDW) and gradually migrate to cloud platforms which are cheaper, higher-performing, and easier for IT teams to manage.
Strategy 2 – Centralize access to data
Moving data to the cloud will provide only minimal benefits if the data remains untapped in multiple locations or in a multitude of proprietary formats. An effective cloud migration approach must level the playing field for data, presenting all of the various databases on the same level, whether on-premises or in the cloud, making them equally discoverable and usable by business users.
Provide users with a centralized location to locate, discover, and work with the data of the EDW. The platform interface make all the enterprise’s data discoverable and facilitate building queries and connecting to Business Intelligence (BI) tools with intelligent and automated business logic and security support.
Strategy 3 – Transform data with business logicM
Cloud data migration should seek to alleviate the differences in formats and structures surrounding operational data. To do so, enterprises must extend their business logic across data in on-premises and cloud platforms. Use metadata to guide how fields from different data sources and with different granularities should be combined, to prevent misinterpretation of joined data. Virtualizing data in this way will provide a single source of truth that can be shared with anyone in the organization.
The same strategy applies to BI tools. Create an environment that is agnostic to BI tools and data platforms, returning the same results no matter what tool and what database were used. Establish common definitions of business metrics to which all BI tools and databases will adhere. This means understanding the different query dialects of BI tools and the different formats of databases, facilitating translation between them, and creating a live connection that eliminates data set discrepancies.
Strategy 4 – Ensure continuous and unified hybrid cloud security, governance and compliance management
Intelligent data virtualization preserves the security policies of individual databases, orchestrates seamless merging of policies when users are working with multiple databases that may have different security policies, and applies global security policies across all data. By checking the source database for security policies, intelligent data virtualization preserves security and privacy information all the way to the user by tracking the data’s lineage and the user’s identity. The user’s identity is also preserved and tracked, even when using shared data connections from a connection pool.
Visibility, agility and security for your data as you migrate, on your own terms
Enterprises face many technical hurdles to clear in order to succeed at cloud data migration. Selecting the right migration strategy will support efforts to promote visibility and agility in data analysis, free the enterprise of potentially costly security and data governance risks, and enable cloud data migration on a schedule that works across the organization.