Govern Relevant Key Performance Indicators For Business Alignment While Developing Data Products

Abstract

In today’s world of data driven business landscape, data product development has become pivotal for all organizations for effectively generating insights and taking timely data driven decisions. Aligning business objectives and expectations versus the data product development is a challenge for any organization. The fields of Data Operations (DataOps) and Machine Learning Operations (MLOps) have paved paths for the accelerated delivery of data products by unlocking the potential of the data that is present in the organization data stores. While developments in the areas of DataOps and MLOps are providing new horizons to explore, simultaneously bringing more challenges to organizations. Organizations are increasingly relying on data powered data products for strategic decision making, the effective governance of Key Performance Indicators (KPIs) become pivotal, requiring attention towards measurable methods that can seamlessly align with the business objectives.
This paper presents the challenges that are being observed in the areas of DataOps and MLOps and the need for a measuring system that can measure the maturity of these systems. The research will enhance the understanding of governing KPIs to mature and align the data products towards business maturity. With a measurable framework, organizations will be empowered to make decisions in an agile manner, thereby accelerating the developments of the data products that aid in decision making.
This study employed mixed method approach, it begins with a comprehensive literature review for understanding the need and significance of the KPIs that are aligned with data product development to achieve business goals. Subsequently, qualitative interviews were conducted with industry experts to understand the challenges in the current practices and also to select the KPIs that fit to the real-world problem statements.
The findings from the study revealed a multi-faceted problem where the organizations are facing diverse challenges in effectively utilizing the data insights through the data products. Throughout the data product life cycle, there are challenges at every stage and self-healing process are to be established to make the processes reliable and effective. This study in overall gives an insight into the complexities of data product development and offers actionable insights to optimize their data operations.