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Data Science: Influences on the Expected Outcome of Data Science

Riley, Anna Charlotta LU and Briskog, Sanna LU (2020) INFM10 20201
Department of Informatics
Abstract
By using data to generate insights, Data Science has increased in popularity and has become a substantial part of many companies. However, Data Science efforts often result in failure, leading to companies not having the expected outcome of their Data Science investments. There seems to be knowledge lacking to why, therefore, this thesis aims to describe what may influence the expected outcome of Data Science with the research question: What influences companies’ expected outcome of Data Science? To conduct this research, a qualitative method was chosen where six interviewees from various industries working with Data Science were interviewed. Conclusively, there are both negative and positive influences on companies’ expected outcome of... (More)
By using data to generate insights, Data Science has increased in popularity and has become a substantial part of many companies. However, Data Science efforts often result in failure, leading to companies not having the expected outcome of their Data Science investments. There seems to be knowledge lacking to why, therefore, this thesis aims to describe what may influence the expected outcome of Data Science with the research question: What influences companies’ expected outcome of Data Science? To conduct this research, a qualitative method was chosen where six interviewees from various industries working with Data Science were interviewed. Conclusively, there are both negative and positive influences on companies’ expected outcome of Data Science. The negative influences identified mainly concern data quality, ethics, knowledge and organizational support. The positive influences are related to creating value from data used in Data Science, the capabilities of companies to adapt, seeking help externally to solve problems and having clear goals, clear problem formulation and a clear division of responsibilities. However, since Data Science is a broad concept with many areas of application, the expected outcome of Data Science is subjective, and depends on the organizational context and maturity. (Less)
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author
Riley, Anna Charlotta LU and Briskog, Sanna LU
supervisor
organization
course
INFM10 20201
year
type
H1 - Master's Degree (One Year)
subject
keywords
Data Science, Data Science Challenges, Data Science Success
report number
INF20-053
language
English
id
9017004
date added to LUP
2020-06-26 17:07:34
date last changed
2020-06-26 17:07:34
@misc{9017004,
  abstract     = {{By using data to generate insights, Data Science has increased in popularity and has become a substantial part of many companies. However, Data Science efforts often result in failure, leading to companies not having the expected outcome of their Data Science investments. There seems to be knowledge lacking to why, therefore, this thesis aims to describe what may influence the expected outcome of Data Science with the research question: What influences companies’ expected outcome of Data Science? To conduct this research, a qualitative method was chosen where six interviewees from various industries working with Data Science were interviewed. Conclusively, there are both negative and positive influences on companies’ expected outcome of Data Science. The negative influences identified mainly concern data quality, ethics, knowledge and organizational support. The positive influences are related to creating value from data used in Data Science, the capabilities of companies to adapt, seeking help externally to solve problems and having clear goals, clear problem formulation and a clear division of responsibilities. However, since Data Science is a broad concept with many areas of application, the expected outcome of Data Science is subjective, and depends on the organizational context and maturity.}},
  author       = {{Riley, Anna Charlotta and Briskog, Sanna}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Data Science: Influences on the Expected Outcome of Data Science}},
  year         = {{2020}},
}