Maintaining Reporting Capabilities During System Migrations : Challenges and Strategies for SaaS Transitions
Sinh, Thu (2024)
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2024120933935
https://urn.fi/URN:NBN:fi:amk-2024120933935
Tiivistelmä
This thesis investigates the intricate challenges of maintaining reporting quality during a transition
from an in-house developed recruitment system to a Software as a Service (SaaS) platform within
a prominent staffing and employment services company in the Nordics. Driven by the need for
increased efficiency and scalability, this transition necessitates a comprehensive re-evaluation of
existing reporting metrics and the development of new data extraction processes. The study delves
into a complex data environment comprising over 400 database tables, posing significant hurdles
in identifying, mapping, and migrating critical data elements necessary for business intelligence
and decision-making.
This research employs a mixed-methods approach, combining theoretical analysis of the staffing
industry and data analytics ecosystems with empirical investigation of the specific database in
question. Qualitative methods, including in-depth database exploration, collaboration with
developers, and stakeholder consultations, are utilized to identify minimum reporting requirements
and potential development areas.
A key finding of this study is the critical need to redefine reporting processes and metrics to ensure
business continuity during and after the technological transition. The inherent limitations of
migrating a complex data structure to a new platform, coupled with the time constraints imposed
by the urgency of system deployment, often lead to compromises. In this specific case study, the
immediate need for operational continuity necessitated prioritizing essential functionalities over
complete reporting capabilities. However, through rigorous analysis and documentation of the
existing system, this thesis provides a roadmap for future database development, enabling the
organization to progressively achieve its comprehensive reporting objectives. This research
contributes valuable insights into the complexities of data migration in the context of SaaS adoption,
offering practical guidance for organizations navigating similar transitions
from an in-house developed recruitment system to a Software as a Service (SaaS) platform within
a prominent staffing and employment services company in the Nordics. Driven by the need for
increased efficiency and scalability, this transition necessitates a comprehensive re-evaluation of
existing reporting metrics and the development of new data extraction processes. The study delves
into a complex data environment comprising over 400 database tables, posing significant hurdles
in identifying, mapping, and migrating critical data elements necessary for business intelligence
and decision-making.
This research employs a mixed-methods approach, combining theoretical analysis of the staffing
industry and data analytics ecosystems with empirical investigation of the specific database in
question. Qualitative methods, including in-depth database exploration, collaboration with
developers, and stakeholder consultations, are utilized to identify minimum reporting requirements
and potential development areas.
A key finding of this study is the critical need to redefine reporting processes and metrics to ensure
business continuity during and after the technological transition. The inherent limitations of
migrating a complex data structure to a new platform, coupled with the time constraints imposed
by the urgency of system deployment, often lead to compromises. In this specific case study, the
immediate need for operational continuity necessitated prioritizing essential functionalities over
complete reporting capabilities. However, through rigorous analysis and documentation of the
existing system, this thesis provides a roadmap for future database development, enabling the
organization to progressively achieve its comprehensive reporting objectives. This research
contributes valuable insights into the complexities of data migration in the context of SaaS adoption,
offering practical guidance for organizations navigating similar transitions