July 11, 2019DATA VIRTUALIZATION VS. DATA WAREHOUSE
Cloud migration is a reality. The cost savings, increased agility, and economic benefits are just too high for organizations to ignore. Enterprises are increasingly migrating, lifting, shifting and re-engineering to seize the promised benefits of the cloud. According to Gartner, the shift to the cloud will drive $1.3T in IT spending by 2022.
The last stage of cloud migration – and one of the most complicated – is a process called cloud data transformation. Cloud data transformation makes all data from all sources in all formats readable and accessible. This process is crucial and can make or break the success of an organization’s cloud migration, because enterprises must be able to access, read, and analyze all of their data, no matter where it’s located or what format it’s in.
There are, however, seven key issues an organization must address in order for their cloud data transformation to succeed. They are:
- Data is in multiple formats: Cloud platforms may require restructuring of the enterprise’s data, forcing huge ETL and data translation projects.
- Vendor lock-in: Many vendors store data in proprietary formats, effectively chaining customers to one solution.
- Legacy systems: Some siloed or on-premises data is attached to older legacy systems that cannot be moved without re-engineering those systems.
- Compatibility issues: Interoperability with outside systems and BI tools is limited.
- Security: Security and entitlements are difficult to maintain when merging data from many silos that may have different users and configurations.
- Strategic blind spots: Limited strategic plans to realize business benefits beyond moving to the cloud, such as broadening user access or leveraging integrated data.
- Cost: Hidden cost implications of cloud transformation often exceed initially anticipated or budgeted numbers.
These challenges stem from most cloud solutions being technical solutions to technical problems. They solve niche challenges, such as query performance, data visualization, or expensive storage. These individual cloud solutions may reduce some costs and add some benefits, but they don’t adequately address the aforementioned challenges or serve the larger, holistic business objective: Accelerating increases in revenue with greater efficiency.
In order to ensure a cloud migration is successful and truly reaps the promised economic and operational benefits, enterprises need immediate access to clean, comprehensive data. They must empower users to support company-wide goals and derive better insights that increase profitability. Luckily, there is such a solution: Enter intelligent data virtualization (IDV).
Intelligent Data Virtualization Bridges the Gap Between Users and Data
IDV facilitate analytical interactions between data and the tools that consume data, allowing multiple data sources to be accessed securely and consistently by multiple software applications and/or BI tools. The ultimate goal is uniform and shared access to all data that is cost-effective, highly performant and secure—irrespective of where it is physically located.
No matter where the data is stored – in disparate silos across many different systems – IDV provides a complete view of all data from a single portal that is accessed by all enterprise applications and analysis tools. This creates a shared data intellect that enables all the different branches of a company to act cohesively, making insight-driven decisions for a shared purpose. Business users, analysts and data scientists will come from disparate backgrounds, and will have individual preferences for the BI tools they wish to use. Rather than struggling to bend all users to a single standard for BI software, IDV ensures that data will be accessible and queries will return consistent answers no matter which BI tool is being used.
Additionally, IDV integrates and conform with the data management, governance and security practices that the enterprise IT team meticulously built for legacy on-premises solutions. The ultimate benefits are dramatically reduced costs for running data infrastructures, improved query performance, and significantly deeper insights across the organization
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