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ERIC Number: ED566772
Record Type: Non-Journal
Publication Date: 2016
Pages: 288
Abstractor: As Provided
Reference Count: N/A
ISBN: 978-1-3394-7992-7
ISSN: N/A
A Quantitative Study of the Resultant Differences between Additive Practices and Reductive Practices in Data Requirements Gathering
Johnson, Gerald
ProQuest LLC, D.C.S. Dissertation, Colorado Technical University
With the increase in technology in all facets of our lives and work, there is an ever increasing set of expectations that people have regarding information availability, response time, and dependability. While expectations are affected by gender, age, experience, industry, and other factors, people have expectations of technology, and from technology products, and to expect it at purchase time. Yet the sub-industry of development of data related solutions (software development, data warehouse development, BI development, big data management, etc.) has a 40 year history of high failure rates. In some cases, problems persist because they are not addressed, but that is not the case with data (and the software that manipulates that data) oriented project failures. The problems have constantly and consistently been addressed through a continual series of new and revised methodologies, processes, and procedures. Because the problems have not been solved, it could be concluded then that whatever the root cause of the issues are, they have not yet been addressed. Additive practices (as employed within various methodologies) have shown a propensity to fail in the software and database development industries (and the experience level of IS employees can't be arbitrarily changed across the industry). It is time that the theory of Technological Frames of Reference (TFR) be taken into account and the potential of non-additive practices (that may account for lack of optimal skills) be explored. The field research and testing quantitatively analyzed the differences between additive and reductive data requirements gathering practices, using a universal set of requirements for a common set of data found in high percentages of data systems, to determine if reductive practices could be a potential impetus of improvement in data requirements gathering, and therefore a reduction in failure factors in the overall development process based on reducing sub-optimal data requirements gathering. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]
ProQuest LLC. 789 East Eisenhower Parkway, P.O. Box 1346, Ann Arbor, MI 48106. Tel: 800-521-0600; Web site: http://www.proquest.com/en-US/products/dissertations/individuals.shtml
Publication Type: Dissertations/Theses - Doctoral Dissertations
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A