Earlybird Develops Automotive Data Case Study For Notre Dame
SOUTH BEND, INDIANA – Earlybird announced Friday the contribution of an original business case study for use in undergraduate coursework at the University of Notre Dame in Indiana.
The case, which describes a business strategy problem at the fictional Callahan Auto Group, is inspired by Earlybird’s extensive experience serving automotive retailers over the last five years. The written narrative, and accompanying hands-on exercise instructions, are supported by a highly realistic artificial dataset designed to exemplify the real-life challenges of using live data, as set forth in the case. Earlybird hopes this interactive learning project will help students and future business leaders gain valuable exposure to the common strategic pitfalls of working with less-than-perfect data, such as that extracted from legacy software systems and databases.
Earlybird CEO Andrew Parnell, himself a 2009 Notre Dame graduate (IT Management), shared the news:
Practical exposure to these sorts of systems is the best way to learn relevant data engineering skills, such as pipeline development and basic report building using Python, SQL, and popular cloud platform tools. Without giving too much away, we wanted to demonstrate how data, especially the sort you often see extracted from legacy systems in the field, can be unexpectedly tough to wrangle. Our team had a lot of fun putting this together.
Though not a formal partnership or supplier relationship, the contribution is a continuation of Earlybird’s work supporting academic business programs. In previous years, partners have volunteered as startup mentors at Purdue University’s student business incubator, The Anvil, and participated in featured programming from the IDEA Center, Notre Dame’s innovation and commercialization office.
Earlybird works with business clients across a range of different industries, but auto dealerships have long been an area of particular expertise. Systems built using Highbeam, Earlybird’s proprietary framework for the data engineering needs of automotive retailers, currently handle the data of over 90 dealership stores across North America.
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