Agile Project Management Based on Data Analysis for Information Management Systems

Advances in Design, Simulation and Manufacturing IV: Proceedings of the 4th International Conference on Design, Simulation, Manufacturing: The Innovation Exchange (DSMIE-2021). Lecture Notes in Mechanical Engineering 2021
Bohdan Haidabrus, Jānis Grabis, S Protsenko

Nowadays, many projects and product managers, industry, and portfolio leads understand that data from the project or portfolio can be valuable for increasing their activities. There are many different types of project and portfolio lifecycle processes of managers daily duties: pre-sales and sales, mobilization, delivery, and closure phases. Definitely, in research, we focus on the processes, staffing, governance, and reporting activities. The day-by-day tasks are quite regulated and clearly described using templates and techniques as a company standard. Our literature review shows that Data Science methods can increase the level of project management and project success in several business problems. This study gives new opportunities to improve project management evaluation and results for managers, industry, and delivery leads. The proposed approach allows doing a project, portfolio management, and agile development more accurately, considering best practices and project performance data. Moreover, our results can provide more efficient benefits for different internal and external stakeholders.

Keywords
Agile development | Data analysis | Data science | Lean | Machine learning | Project management | Safe | Scrum
DOI
10.1007/978-3-030-77719-7_18
Hyperlink
https://link.springer.com/chapter/10.1007%2F978-3-030-77719-7_18

Haidabrus, B., Grabis, J., Protsenko, S. Agile Project Management Based on Data Analysis for Information Management Systems. In: Advances in Design, Simulation and Manufacturing IV: Proceedings of the 4th International Conference on Design, Simulation, Manufacturing: The Innovation Exchange (DSMIE-2021). Lecture Notes in Mechanical Engineering, Ukraine, Lviv, 6-11 June, 2022. Cham: Springer Nature Switzerland AG, 2021, pp.174-182. ISBN 978-3-030-77718-0. e-ISBN 978-3-030-77719-7. ISSN 2195-4356. e-ISSN 2195-4364. Available from: doi:10.1007/978-3-030-77719-7_18

Publication language
English (en)