Achieving High-Value Analytics with Data Virtualization

Join this TDWI Webinar to learn how you can apply data virtualization to analytics projects and workloads. You will hear use case examples, best practices, and insights into technology trends.

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David Stodder
David Stodder Senior Director of Research for BI TDWI

Analytics projects are critical to business success, and as a result, they are growing in size, number, complexity, and perhaps most important, in their data requirements. TDWI finds that data scientists, business analysts, and other personnel need to view and access data that resides in multiple sources, both on premises and in the cloud, to draw insights from data relationships and discover important patterns and trends.

Data lakes or enterprise data warehouses work for some projects, but for many others it is faster, more efficient, and more cost-effective to query the data where it resides rather than move the data to another system. Data virtualization enables many organizations today to gain data insights from multiple, distributed data sources without the time-consuming processes of data extraction and loading. As analytics become more diverse, ranging from descriptive to predictive, prescriptive, operational, and more, data virtualization can support this range and enable users to realize value sooner.

Topics to be covered include:

  • How data virtualization addresses advanced BI and analytics challenges in multiplatform, multicloud, and big data environments
  • How data virtualization supports a spectrum of analytics, including descriptive, predictive, prescriptive, and operational
  • The role of data catalogs in data virtualization for improving collaboration, governance, and efficiency of analytics
  • How analytics workloads and development lifecycles can benefit from data virtualization
  • Performance best practices for data virtualization

What's Next?

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