Developing a KPI-Driven Data Strategy

Abstract

Amid increasing interest in data-driven decision-making, firms are inundated with an ever-expanding ecosystem of information technologies and analytical tools. While academic research has demonstrated a positive relationship between data use and business performance, many organizations still struggle to chart a practical path forward. This article argues that the journey to effective data strategy should begin not with technology, but with the identification of Key Performance Indicators (KPIs) tailored to the firm’s business model and industry. By centering data initiatives around KPIs, firms can prioritize the right data sources, analytic capabilities, and governance structures necessary to drive performance improvements.

We clarify commonly misunderstood terminology in analytics, explore the strategic alignment between business processes and data practices, and review internal vs. external data sources and types of analytics. We then introduce a four-step KPI-centered framework: (1) start with industry benchmarks, (2) consider operational and financial inputs, (3) identify and capture the right data, and (4) establish KPI ownership. To illustrate this approach, we present a detailed case study of a hypothetical SaaS company, “Under the Bridge Software,” highlighting how structured revenue analysis can yield actionable insights through a simple revenue bridge.

This article contributes to both academic and practitioner conversations by offering a clear, actionable framework for executives seeking to integrate analytics into business strategy. It closes with practical recommendations for low-cost analytics adoption and execution, including leveraging internal champions, open-source tools, and targeted consulting support.

Keywords: Decision Sciences, Business Strategy, Data, Management Practices, Business Performance, Business Analytics, Business Intelligence