Trustworthy Analytics and Machine Learning with Data Virtualization (EMEA)

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Jona Boeddinghaus
Jona Boeddinghaus COO Gradient Zero
Chris Fitzpatrick
Chris Fitzpatrick Partner Sales Engineer Denodo

 2020/12/17

Advanced data science techniques, like machine learning, have proven an extremely useful tool to derive valuable insights from existing data. For these projects to be successful, data scientists, business analysts, and other personnel need access to data from across the enterprise. However, gaining access to all of the data in an integrated central repository has been a challenge, resulting in up to 80% of the project time being spent on data acquisition and preparation tasks.

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 during this webinar include:

  • How data virtualization addresses trustworthy analytics challenges in multiplatform, multicloud, and big data environments
  • 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

Gradient Zero and Denodo have partnered to combine state-of-the art professional services with the industry’s most advanced data virtualization platform to streamline data access in support of the most critical business needs.

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