Developing Item Policy to Enhance Proprietary Item Data Quality
Salmi, Juho (2020)
2020
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2020060115907
https://urn.fi/URN:NBN:fi:amk-2020060115907
Tiivistelmä
The objective of this study was to research issues in data quality in the context of proprietary items. Proprietary items have historically had little limitation of how the data should be displayed and what the minimum requirement is for data quality.
The large variation in data quality creates waste in the later stages in the products’ lifecycle by creating unnecessary workload, confusion and increased response times which could have been avoided if the data had been in good shape. Good quality data, especially in the engineering context, significantly reduces the risk of supplying incompatible or wrong items to customer, whose location may not always be the most convenient possible.
This study is based on conducting a current state analysis concentrated on systematic issues rather than humane issues. This approach justified itself as there were no systematic rules of data creation, which rendered local differences in working processes insignificant.
The current State Analysis revealed issues in the current item data, where the fluctuation in data quality was clearly visible. It also revealed the difference between applied processes regarding item creation, modifying and release. In relation to the Current State Analysis, the Conceptual Framework provided some essential and relevant findings for building the proposal. The Conceptual Framework of this thesis was created on three key topics to solve the issues found.
The outcome of this study is a global item policy for one product line which allows the implementation of this policy for multiple product lines later.
The large variation in data quality creates waste in the later stages in the products’ lifecycle by creating unnecessary workload, confusion and increased response times which could have been avoided if the data had been in good shape. Good quality data, especially in the engineering context, significantly reduces the risk of supplying incompatible or wrong items to customer, whose location may not always be the most convenient possible.
This study is based on conducting a current state analysis concentrated on systematic issues rather than humane issues. This approach justified itself as there were no systematic rules of data creation, which rendered local differences in working processes insignificant.
The current State Analysis revealed issues in the current item data, where the fluctuation in data quality was clearly visible. It also revealed the difference between applied processes regarding item creation, modifying and release. In relation to the Current State Analysis, the Conceptual Framework provided some essential and relevant findings for building the proposal. The Conceptual Framework of this thesis was created on three key topics to solve the issues found.
The outcome of this study is a global item policy for one product line which allows the implementation of this policy for multiple product lines later.