Knowledge loss in software development
Manner, Veera (2023)
Manner, Veera
2023
Tietojohtamisen DI-ohjelma - Master's Programme in Information and Knowledge Management
Johtamisen ja talouden tiedekunta - Faculty of Management and Business
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Hyväksymispäivämäärä
2023-03-22
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202303213058
https://urn.fi/URN:NBN:fi:tuni-202303213058
Tiivistelmä
Knowledge loss is identified in knowledge management literature as an inability to capture new knowledge or retain existing knowledge. Knowledge loss implies losing strategic advantage in knowledge intensive organizations. As knowledge intensive organizations, health technology companies, specifically businesses producing quality data software and data analytics, are exposed to many risks of knowledge loss. Issues such as low productivity have been reported from other fields of business following from knowledge loss. It is to be examined what are the effects of knowledge loss on software and data development (SWDD) process.
The aim of this research was to investigate how the product development process of a quality data and health care software company is affected by unintentional knowledge loss. This was accomplished by exploring the knowledge loss incidents, the knowledge at risk, and the consequences of knowledge loss. Data collecting was done by an interview, a questionnaire and searching case company documentation. Both qualitative and quantitative methods were used in analyzing the empirical material. Furthermore, a knowledge loss risk model (KLRM) was created and applied to the SWDD process in the case company.
Knowledge loss incidents uncovered were related to documentation, expertise, master data, overall process, client specificities, integrations, specifications. The knowledge at risk was found to be technical expertise, technical product knowledge, general process knowledge, technical customer knowledge, business and management knowledge, and customer related knowledge. Knowledge loss consequences were inefficiency, faulty products, customer dissatisfaction, and cause-and-effect. KLRM provided with a complete set of steps to be integrated to the production process and to evaluate and mitigate risks of knowledge loss. As a conclusion, knowledge loss was considered to affect the SWDD process in several ways and be a factor in efficiency and quality issues.
This work contributes to the knowledge loss research from the viewpoint of software development process quality. In addition, the case company will benefit from the performed evaluation of knowledge loss in the SWDD process. As a practical implication, KLRM could be regularly applied to the SWDD process of the case company to continue identifying, assessing, and mitigating risks of knowledge loss. KLRM may be adapted to be applied to other software development processes as well.
The aim of this research was to investigate how the product development process of a quality data and health care software company is affected by unintentional knowledge loss. This was accomplished by exploring the knowledge loss incidents, the knowledge at risk, and the consequences of knowledge loss. Data collecting was done by an interview, a questionnaire and searching case company documentation. Both qualitative and quantitative methods were used in analyzing the empirical material. Furthermore, a knowledge loss risk model (KLRM) was created and applied to the SWDD process in the case company.
Knowledge loss incidents uncovered were related to documentation, expertise, master data, overall process, client specificities, integrations, specifications. The knowledge at risk was found to be technical expertise, technical product knowledge, general process knowledge, technical customer knowledge, business and management knowledge, and customer related knowledge. Knowledge loss consequences were inefficiency, faulty products, customer dissatisfaction, and cause-and-effect. KLRM provided with a complete set of steps to be integrated to the production process and to evaluate and mitigate risks of knowledge loss. As a conclusion, knowledge loss was considered to affect the SWDD process in several ways and be a factor in efficiency and quality issues.
This work contributes to the knowledge loss research from the viewpoint of software development process quality. In addition, the case company will benefit from the performed evaluation of knowledge loss in the SWDD process. As a practical implication, KLRM could be regularly applied to the SWDD process of the case company to continue identifying, assessing, and mitigating risks of knowledge loss. KLRM may be adapted to be applied to other software development processes as well.