How does an organization decide to implement analytics-based performance management system methodologies? To answer this we can learn a lesson from the Malcolm Gladwell, a social scientist and author of best-selling book The Tipping Point, who describes how changes in mindset and perception can attain a critical mass and then quickly create an entirely different position of opinion. Let’s apply Gladwell’s thinking to the question of whether the widespread adoption of analytics-based performance management is near its tipping point or whether we will only know this in retrospect after it has happened.
Gladwell observed that to determine whether something is approaching the verge of its tipping point, such as an event or catalyst, it should cause people to reframe an issue. For example, just-in-time production control reframed manufacturing operations from classical batch-and-queue economic order quantity (EOQ) thinking to the method based on customer demand-pull product throughput acceleration. So, is analytics-based performance management reframing issues and nearing its tipping point? To answer this, we should first acknowledge that business analytics and performance management methodologies are not something new that everyone has to learn, but rather it is the assemblage and integration of existing quantitative techniques and methodologies that most managers are already familiar with. Collectively, these methodologies manage the execution of an organization’s strategy.
Multiple tipping points of analytics-based performance management components
Since analytics-based performance management is comprised of multiple methodologies, all interdependent and interacting, what is profound is that we’re now actually experiencing multiple and concurrent sub-tipping points all at once. Ultimately their collective weight is resulting in an overall tipping point for adopting performance management. These tipping points are:
Synergy from the integrating analytics-based performance management components
It is not a coincidence that each of the four tipping points above mentioned interdependencies amongst them. Transaction-based information systems, like enterprise resource planning (ERP) systems, although good for their designed purposes, do not display the relevant information to apply business analytics to and that are required for decision analysis and ultimately for decision making. Transactional systems may provide some of the raw source data, but it is only through transforming that raw data into decision-based information that the potential ROI trapped in that raw data can be unleashed and realized financially. This in part explains the growing demand for analytics-based performance management systems as value-multipliers.