Creative Commons

Key performance indicators (KPIs) are frequently used by businesses to control their operations. Despite the abundance of metrics, it can be challenging for businesses to pinpoint the ones that are most important to their success. This is an issue for the industry because trivial KPIs can result in management insights that are incorrect. In today's data-driven world, organizations are constantly seeking ways to improve their performance and stay ahead of the competition. Key Performance Indicators (KPIs) are essential tools that help businesses measure their progress towards achieving their goals. However, with the vast amount of data available, it can be challenging to identify the most relevant KPIs and make informed decisions based on them. This is where machine learning comes in. By applying machine learning algorithms to KPI data, organizations can develop a more data-driven strategy that is tailored to their specific needs. This thesis aims to explore the potential of machine learning in KPI analysis and provide insights into how it can be used to improve business performance. The strategy involves collecting and analyzing historical data to identify patterns and trends in KPIs, and using this information to develop predictive models that can forecast future KPI performance. This dissertation also discusses the importance of selecting appropriate KPIs as well as the need for ongoing monitoring and refinement of the data strategy. The proposed approach has the potential to improve financial decision-making by providing more accurate and timely insights into KPI performance.