August 24, 2021 | 9:00 a.m. - 9:30 a.m. CDT (UTC-05:00)

A metadata, simply put, is data about data. Essentially, these are meticulously catalogued information across various domains that are categorized based on keyword and subject. The practice of metadata management is responsible for upholding a system of records of information kept in check in each enterprise data lake. Despite having well defined, globally recognized data management standards, there still exists serious gaps when it comes to ensuring high levels of metadata quality.

The traditional method used to ensure metadata quality is based primarily on business rules, and this comes with a lot of challenges and limitations. These business based KPIs cannot see the hidden patterns and insights present in large historical metadata that are scattered across the oil and gas domain. Moreover, data managers are still required to spend a significant amount of time tracking metadata quality issues and fixing them manually. In addition to that, the conventional methods do not allow the self-continuous learning of the system and thus limits the scalability of the solution.

In this webinar, we showcase an automated metadata quality management system that self improves over a period. This approach can bring in significant improvements and efficiencies in the analysis of historical oil and gas metadata using a statistical approach combined with a business-based model.

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Presented by:

Shreshth Srivastav

Shreshth Srivastav

Senior Data Scientist