Big Data Analytics for Today’s Distributed, Dynamic and Diverse Data

Big Data is only valuable when you can collect it, analyze it, and use it to derive advantageous business insights. However, the enormous volume of data and its distributed, dynamic and diverse formats and use cases makes analytics overwhelming.

Challenges for turning lots of data into Big Data analytics include the proliferation of data platforms, distributed data, lack of data engineering resources, the inherent security risks associated with hybrid cloud environments, and the increasing demands of the business.

The good news?  Enterprises can “consumerize” data more easily today, making it readily accessible and agile for use throughout the organization, with the help of an adaptive analytics fabric.

This paper outlines how this new approach to using enterprise data includes:

  • Leveraging autonomous data engineering to save money, time, and resources
  • Managing cloud cost predictability to realize the anticipated cost savings of moving to the cloud
  • Automatically enforcing governance, compliance and security while making data accessible
  • Enabling data discoverability and self-service analytics in distributed data environments – resulting in a shared data intellect

It’s never been easier or more affordable to achieve the game-changing benefits of Big Data analytics.

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