Knowledge Discovery in Databases for enhanced Software License Management : An activity-based Clustering model
Nyström, Alex (2023)
Nyström, Alex
2023
Julkaisu on tekijänoikeussäännösten alainen. Teosta voi lukea ja tulostaa henkilökohtaista käyttöä varten. Käyttö kaupallisiin tarkoituksiin on kielletty.
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2023052748871
https://urn.fi/URN:NBN:fi-fe2023052748871
Tiivistelmä
In the contemporary business landscape, software has become the primary work tools for many employees. However, a lack of effective software management can result in numerous challenges, including escalated costs, compliance risks, poor understanding of employees' IT needs, as well as cybersecurity vulnerabilities. This thesis delves into the realm of Software Asset Management (SAM), specifically focusing on leveraging the power of data to enhance software license management. The research presents a novel clustering-based knowledge discovery model, designed to uncover actionable insights that could potentially refine software license management practices. By employing user activity data, this model categorizes software users into distinct groups according to their usage patterns. This method of categorizing users provides an in-depth understanding of how various employee segments engage with software, thereby facilitating more in-depth analysis of software use. Applying this model to real-world business data allowed for testing its efficacy. The implementation of the model exposed several inefficiencies in the subject company's current license management practices. These insights led to recommendations for improving the company's software license management. Additionally, the application of the model offered the company an in-depth understanding of the behaviours and IT requirements of various employee segments, paving the way for enhanced operational efficiency and more strategic allocation of software resources.
Kokoelmat
- 512 Liiketaloustiede [433]