Date of Conferral

2020

Degree

Doctor of Information Technology (D.I.T.)

School

Information Systems and Technology

Advisor

Jon W. McKeeby

Abstract

As a result of innovation and technological improvements, organizations are now capable of capturing and storing massive amounts of data from various sources and domains. This increase in the volume of data resulted in traditional tools used for processing, storing, and analyzing large amounts of data becoming increasingly inefficient. Grounded in the extended technology acceptance model, the purpose of this qualitative multiple case study was to explore the strategies data managers use to transition from traditional data warehousing technologies to big data technologies. The participants included data managers from 6 organizations (medium and large size) based in Munich, Germany, who transitioned from data warehousing technologies to big data technologies. Data collection included interviews with 10 data managers and a review of 15 organizational documents. Inductive coding was used to analyze the data. Four major themes identified included identify a business need or use case, identify data sources, executive support, and use a data lake. A key recommendation is that data managers can use these findings to implement best practices when transitioning to big data technologies, thereby improving successful big data transition implementations and adoption. An implication for positive social change from this study is that data managers and organizational leaders might use the research findings to improve products and services offered, leading to better products and or services offerings for consumers.

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