Melee of Data Catalog to MLOps Mechanisms: DataOps for Actionable Analytics

Presented by

Pragyansmita Nayak, Ph.D., Chief Data Scientist, Hitachi Vantara Federal

About this talk

The melee of raw data in the various forms of structured, unstructured and semi-structured data has its own unique challenges. Extracting the hidden nuggets of information to meet the vision and aid the strategic and tactical goals of an organization is a resonating objective of every stakeholder today. This requires a number of systematic, both synchronous and asynchronous processes and techniques to rhythmically accomplish the objectives in a transparent manner. Data driven process enablement and the resulting effective decision making starts from scratch - identification of the most relevant and related data assets, posing the business problem, determining the analytics components. The solution should ideally be as reusable as possible in order to aid related problems resolution down the road; aiding the knowledge growth, process automation and the effective business and data interplay. The overarching goal needs to be to complete this unique jigsaw puzzle specific to every individual organization; fitting as comfortably and seamlessly as one wants their hands to fit in a pair of gloves. This talk will focus on how the DataOps mechanisms such as data catalogs, data lineage and, data fusion leading to near-transparent self-service analytics and MLOps enable and enhance advanced actionable analytics outcomes. About the speaker: Pragyansmita Nayak is Chief Data Scientist at Hitachi Vantara Federal (HVF), a wholly owned subsidiary of Hitachi Vantara. She has over 20+ years of experience in software development and data science-related research and development. She holds a Ph.D. in Computational Sciences and Informatics from George Mason University (GMU) and Bachelor's degree in Computer Science from Birla Institute of Technology and Science (BITS), Pilani, India. Her Ph.D. thesis focused on the application of Machine Learning techniques such as Bayesian Networks for redshift estimation.

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