TDWI Checklist: Six Critical Capabilities of a Logical Data Fabric

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David Loshin
David Loshin President of Knowledge Integrity
Ravi Shankar
Ravi Shankar SVP and Chief Marketing Officer, Denodo Denodo

 2020/06/04

As organizations opt for cloud computing platforms and migrate their data and applications to a hybrid cloud environment, they are perplexed about how to handle the transition of multiple legacy systems and applications, acquired over the years, to support business-critical analytical workloads. Without a good strategy and solution in place for the movement to the hybrid data environment, there is an increased risk of added complexity for business users in finding, accessing, and using enterprise data, which can impact their efficacy in day-to-day business operations and even cause business discontinuity.

One way to manage data across this distributed environment and use it for analytics is to consider a modern approach—a logical data fabric—to knit disparate data and bring it together in an intelligent fashion. A logical data fabric, grounded in data virtualization techniques, can play an important role in supporting the need to access, manage, and analyze data across disparate platforms for traditional reporting and BI—as well as modern use cases such as machine learning and artificial intelligence, integrated analytics for automated decision making, and analysis combining traditional data at rest with real-time streaming data sources.

This webinar discusses important capabilities of the logical data fabric to deal with modern data management and analytics efforts, such as:

  • Data analyst enablement across the emerging hybrid and multicloud data landscape
  • Techniques for seamless real-time data integration across multicloud platforms
  • Augmented intelligence to facilitate data awareness and seamless accessibility
  • Boosting performance by reducing data latency delays

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