هوش کسب و کار برای اندازه گیری عملکرد: تجزیه و تحلیل مبتنی بر مورد Business intelligence for performance measurement: A case based analysis

1. INTRODUCTION Measuring organizational performance, and using the information to drive organizational policy and functioning is at the core of management (Neely & Al Najjar, 2006). A well-designed system for measuring performance enables an organization to translate its strategy to operational goals (Neely, Gregory, & Platts, 2005; Grosswiele, Röglinger, & Friedl, 2013), and drive the behavior of employees to achieve the goals (Neely, Gregory, & Platts, 1995). Also, it enables a better management of resources by promoting transparency (Halachmi, 2002). The history of performance measurement can be traced back to the era of industrial revolution when it was used to monitor and manage performance of shop floor workers (Radnor & Barnes, 2007). Until 1970s, performance reports were largely paper-based. With the advent of IT, paper-based reports were replaced by decision support systems (DSS). DSS allowed faster and timely access to information, and allowed managers to observe interesting trends and patterns in data with ease. The next level of evolution was the development of executive information systems (EIS), which unlike DSS were specifically designed to address the decision needs of senior management (Watson & Frolick, 1993). The major component of EIS was an electronic dashboard, which displayed information relevant to senior executives. The dashboard allowed senior managers to view, synthesize and relate a large amount of information, not possible with standalone DSS. EIS remained a popular tool for performance measurement till late 1990s. With the growing amount of data, often in silos, it became necessary to integrate data from various sources to provide a ‘single version of truth’. Furthermore, a faster access to data and use of advanced analytical tools became important (Frolick & Ariyachandra, 2006). This led to the adoption of business intelligence (BI) systems to support performance measurement. With increase in competition and complexity of environment, and rapid technological development, the adoption of IT-based performance measurement systems (PMS) has spiked in recent years. This is evident from the increased spending on BI systems (which are primarily used for performance measurement activities), with the market for BI expected to reach more than US$ 50 billion by 2018 (ITEuropa, 2014). Implementation of a PMS is accompanied by unique managerial problems, solutions to which may help enhance the benefits obtained from the system. This paper focuses on two important questions: a) how should a firm implement a new PMS? and b) how do we qualitatively ascertain the critical success factors (CSF) for the implementation of a PMS?