Gartner Research

4 Key Considerations to Differentiate Your Data Virtualization Product

Published: 22 January 2019

ID: G00376238

Analyst(s): Sharat Menon , Ehtisham Zaidi


Data virtualization vendors need to capitalize on the surge in use-case requirements of accessing, integrating and analyzing data in place. To do so, technology product managers must develop comprehensive tools with capabilities that link to the top business drivers for adoption of these use cases.

Table Of Contents
  • Key Challenges


  • Driver 1: Flexibility in Data Management Architecture
    • Heterogeneity in Data Sources
    • Heterogeneity in Consuming Applications
    • Heterogeneity in Personas
    • Heterogeneity in Deployment
    • Heterogeneity in Embedded Capabilities
  • Driver 2: Facilitating a Bimodal Strategy
    • The Ability to Capture, Store and Analyze Active Metadata
    • Incorporating Machine Learning/Artificial Intelligence to Automate Parts of the Operationalization Process
  • Inhibitor 1: Performance Issues When Dealing With Increasing Data Volumes
  • Inhibitor 2: Questionable Reliability and Resilience
    • Final Points

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