Table of Contents
- Data Virtualization Primer
- Report Methodology
- Decision Criteria Analysis
- Evaluation Metrics
- Key Criteria: Impact Analysis
- Analyst’s Take
- About Andrew Brust
The diversity of the data ecosystem within modern enterprises is one of the most difficult aspects of managing data today. Companies struggle with more data than ever before, at high velocities, across a range of use cases involving artificial intelligence (AI) and machine learning (ML), mobile and internet of things (IoT) endpoints, and cloud and on-premises deployments. Consolidating these resources for comprehensive insight across data types and geographic environments with traditional methods results in silos and brittle data pipelines.
Data virtualization (DV) technologies deliver a modern means of integrating data logically that drastically reduces or obsolesces data movement. These technologies provide unified queries across sources, a single view of consolidated data assets, and consumption by any application or end-user analytics tool of choice. Implicit in these capabilities are mechanisms for managing data, harmonizing their semantics, and blending together different data types.
This GigaOm Key Criteria report details the criteria and evaluation metrics for selecting an effective DV platform. This research provides a solid foundation for assessing the ways data virtualization can meet firms’ present and future needs. The companion GigaOm Radar report identifies vendors and products that excel in those criteria and metrics. Together, these reports provide an overview of the category and its underlying technology, identify leading DV offerings, and help decision-makers evaluate these platforms so they can make a more informed investment decision.
Our research led us to a number of useful conclusions:
- Data virtualization can significantly influence firms’ data strategy and overall data management.
- There are a variety of approaches for implementing virtualization, making the term somewhat ambiguous.
- All forms of virtualization provide direct data access to a range of different sources; the distinctions are in how well they do so and which approach they use.
- A semantic layer that makes data friendly to business users, in terms they understand, is a critical part of the more comprehensive virtualization platforms.
- Users are most concerned about time to value, ease of use, and the fundamentals of data management and data governance (like metadata management and data discovery).
- The ability to readily exchange data in the service of end-user collaboration has become valuable to virtualization users.
In addition, we found that prominent data virtualization solutions generally provided features that involve:
- Security and data governance
- Basic data access
- Federation capabilities
- Virtual data access
- An abstraction layer for logically connecting to sources
- Publication capabilities for end-user tools
- Collaboration capabilities
- Data integration
- Data caching
- Data management mechanisms
- Query acceleration
How to Read this Report
This GigaOm report is one of a series of documents that helps IT organizations assess competing solutions in the context of well-defined features and criteria. For a fuller understanding, consider reviewing the following reports:
Key Criteria report: A detailed market sector analysis that assesses the impact that key product features and criteria have on top-line solution characteristics—such as scalability, performance, and TCO—that drive purchase decisions.
GigaOm Radar report: A forward-looking analysis that plots the relative value and progression of vendor solutions along multiple axes based on strategy and execution. The Radar report includes a breakdown of each vendor’s offering in the sector.
Solution Profile: An in-depth vendor analysis that builds on the framework developed in the Key Criteria and Radar reports to assess a company’s engagement within a technology sector. This analysis includes forward-looking guidance around both strategy and product.