Service Maturity Model for Data Quality Engine in OP
Goyal, Ankur (2023)
2023
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2023112731958
https://urn.fi/URN:NBN:fi:amk-2023112731958
Tiivistelmä
In Today’s financial world, having accurate and reliable data is crucial for an organisation like the case company of this thesis, OP. Organizations rely on data to make important business decisions and provide service to their customers. To improve its Data Quality, the case company developed a product called Data Quality Engine (DQE) with the idea of monitoring the quality of data across the organisation. This thesis aims at assessing the capabilities of DQE and provide guidelines for its improvement.
The primary objective of this thesis is to create a framework aligned to DQE needs and use it to evaluate DQE’s current maturity level. To achieve this goal, an extensive analysis of the current state of DQE was conducted to identify the strengths and weaknesses of this tool. The theoretical framework focused on exploring the available knowledge and industry best practice - standard maturity models, which were then used as inspiration when developing the case company´s DQE tool in the subsequent steps.
The outcome of the thesis is a service maturity framework focused on DQE’s capability to provide “DQ monitoring as a Service” and included three dimensions: Data Quality, Service Design and Service Management. Each dimension is further broken down into specific metrics for evaluation, providing valuable insight into the effectiveness and efficiency of DQE.
The primary objective of this thesis is to create a framework aligned to DQE needs and use it to evaluate DQE’s current maturity level. To achieve this goal, an extensive analysis of the current state of DQE was conducted to identify the strengths and weaknesses of this tool. The theoretical framework focused on exploring the available knowledge and industry best practice - standard maturity models, which were then used as inspiration when developing the case company´s DQE tool in the subsequent steps.
The outcome of the thesis is a service maturity framework focused on DQE’s capability to provide “DQ monitoring as a Service” and included three dimensions: Data Quality, Service Design and Service Management. Each dimension is further broken down into specific metrics for evaluation, providing valuable insight into the effectiveness and efficiency of DQE.