KPI by proxy
M.J.W. van den Hoek (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Sebastian Proksch – Mentor (TU Delft - Software Engineering)
Arie Van van Deursen – Graduation committee member (TU Delft - Software Technology)
Elvan Kula – Graduation committee member (TU Delft - Software Engineering)
Luciano C. Cavalcante Siebert – Graduation committee member (TU Delft - Interactive Intelligence)
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Abstract
Metrics are widely used in the software engineering industry and can serve as Key Performance Indicators (KPIs), which are used by management to make informed decisions and understand the performance of the organisation. Many companies measure themselves against industry-standard metrics, in addition to their own set of metrics. This thesis aims to investigate the relationship between these industry-standard metrics and the metrics that are additionally collected. Instead of focusing on the performance of a single organisation, the DORA report focuses on the comparison of organisations. It measures the Software Delivery and Operational (SDO) performance of organisations by four key and industry-standard metrics representing two aspects, stability and throughput, of a software product. The use of one metric as a proxy for another metric or a thematic group of metrics is a common phenomenon. However, there rarely is evidence supporting the assumption that the proxy reflects the intended metric or represents the full thematic group. This thesis performs a single case study within ING, a large and highly digital bank in the Netherlands. It investigates the KPIs that are collected by the bank and analyses the relationships between those KPIs and the four metrics from the DORA report. It establishes a list of 27 KPIs that are in use by ING and shows that there are no correlations between the DORA metrics as collected within the bank and that these metrics show very little correlation with the metrics that ING collects additionally. Furthermore, it is established that nearly all metrics contain a bias at the organisational level and that these biases have a significant impact on the correlation between metrics.