As the business dynamics of the competitive marketplace accelerate and the velocity of information increases, more and more companies are seeking opportunities to compress and streamline management decision making. Actionable information is now the required mantra for superior performance. This has elevated the importance of key performance indicators (KPIs) and their ability to measure, predict and manage the business health of a company in real (or near real) time. Since the mid-'90s, KPIs have morphed from static siloed measures to dynamic real-time enterprise metrics. Statistical modeling and data mining under the guise of predictive analytics have become critical building blocks in setting the new KPI standard for leading indicators. This journey to KPI maturity and its salient features are illustrated in Figure 1.
During the first wave of KPI development - The BI Enterprise - KPIs focused on what had happened historically to individual product lines and strategic business units (SBUs). Some of the source data resided in separate data marts, while other data existed in legacy systems and Excel spreadmarts. If one was lucky, an OLAP data mart provided manual drill down, ad hoc query and navigation. Operational managers and business executives were data slaves rather than decision-makers. The ability to calculate, track and react to KPIs was severely limited by the data availability, infrastructure integration and software capabilities. Leading indicator KPIs existed in name only - they were retrospective, not perspective, and certainly not actionable.
The second wave of KPI development - The Business Performance Management (BPM) Enterprise - moved the focus from a siloed product/SBU mind-set to an enterprise perspective. Data from product lines and SBUs were integrated into enterprise data warehouses. Business performance management frameworks provided alignment between company strategies and business initiatives, while BPM software suites streamlined KPI tracking and management. The intensive initial focus on financial measures was expanded to incorporate the customer, process and learning perspectives. KPIs tend to be captured by basic metrics. More importantly, most of the KPIs still captured business metrics that measured either the past or current health of the company rather than predicting future directions. Collaborative decision making, employee empowerment and information democratization are key features of this wave.
At present, several leading-edge companies are now embarking on the third wave of KPI development - The Predictive Enterprise - and the bar has ratcheted up another notch. The focus has shifted to real-time KPI monitoring, causal analysis and predictive analytics. KPIs are now forecasted using mathematical models to predict future behavior based on current and historical data. The extensive portfolio of statistical techniques includes data mining, segmentation, clustering, regression modeling, market-basket analysis and decision trees. The real challenge in developing effective KPIs is deciding which statistical technique to mate with a specific business problem, as there are significant tradeoffs and assumptions associated with each.
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