Enterprise Data Management

Understand structured transactional data and known questions along with unknown, less-organized questions enabled by raw/external datasets in the data lakes. Topics include data strategy and data governance, relational databases/SQL, data integration, master data management, and big data technologies.

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Created by: Vijay Khatri

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Course Description

High-quality information is the key to successful management of businesses. Despite the large quantity of data that is collected by organizations, managers struggle to obtain information that helps them make decisions. While operational processing systems help capture, store, and manipulate data to support day-to-day operations of organizations, reconciled systems -- sometimes referred to as data warehouses or business intelligence (BI) systems -- support the analysis of data, thus, enabling decision making. With the advent of big data systems, organizations have turned to enterprise data management frameworks to manage and gain insights from the vast amount of data collected. While storage costs themselves are relatively affordable, the bigger challenge has been finding an appropriate mechanism to manage the data as many technologies (e.g., relational databases, data warehouses) have limitations on the amount of data that can be stored. This course focuses on realizing the business advantage and business potential of operational, reconciled, and big data systems as well as data assets in supporting enterprise data management strategies and enterprise data analytics.
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Advanced Level

Time: 12 Weeks - 8-12 hours per week

Instructor Details

Vijay Khatri

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Vijay Khatri is a Professor in and the Chairperson of the Operations and Decision Technologies Department at the Kelley School of Business. He is the Arthur M. Weimer Faculty Fellow and the Co-Director of the Kelley Institute for Business Analytics. He holds a B.E. from Malaviya National Institute of Technology, a management degree from the University of Bombay, and a Ph.D. from the University of Arizona. His research centers on issues related to data semantics, technology adoption, and data governance. He has published articles in journals such as IEEE Transactions on Knowledge and Data Engineering, MIS Quarterly, IEEE Transactions on Software Engineering, Annals of Mathematics and Artificial Intelligence, Information Systems Research, and Communications of the ACM. For his research with an undergraduate student, he was a recipient of 2011 Provost's Award for Undergraduate Research and Creative Activity. He teaches predictive analytics/data mining and machine learning in the online and in-residence graduate programs. He has been a recipient of over a dozen teaching awards including Kelley's MBA Teaching Excellence Award, IU Trustees' Teaching Award, Kelley's Innovative Teaching Award, Sauvain Teaching Award, and Outstanding MSIS Faculty Award. Previously, he has worked with Infosys Technologies and IBM Consulting Group.