Root-cause analysis leverages a BPM system's access to external information to further explain the reasons behind a variance. Predictive analytics technologies delve into transactional and operational data outside of the performance management application, looking for patterns within that data which correlate to patterns in the organization's KPIs. For example, a leading-edge predictive BPM system might be able to connect shortfalls in the company's product-quality KPIs with shop floor accidents and absenteeism, problems revealed through the automatic analysis of the detailed information in the company's operational systems.

A predictive BPM system should also include automated context searches, which further help end users understand variances. These could involve searches of the company's supporting documentation, or unstructured data, for contextually relevant information. Just as Google might index a company's Web site, a predictive BPM system can search the corporate network for relevant information within unstructured files such as Word documents, spreadsheets, PowerPoint presentations, and e-mails. Most explanatory information within a company is stored in these types of documents -- in fact, according to Gartner, upwards of 80 percent -- and sophisticated BPM solutions can identify those that relate to a particular KPI variance and leverage that information to offer an in-depth explanation of the given variance.

BPM systems that incorporate predictive analytics technologies can also report on KPI risks and the probable impact of potential events. They can forecast performance variances and present probable reasons for the deviations, along with ratings of the likelihood that the variance will occur again in the future. Advanced statistical techniques anticipate changes in higher-level KPIs by analyzing base component metrics, serving as an early warning system for business managers.

As predictive BPM software matures, it will track not only the relationships of KPIs with other metrics, but also the connections between KPIs and business-process events. It will identify the process steps driving KPIs that are at risk and then notify managers about which ones need to be addressed to improve each metric. Even more important, the history of events, KPI values, and actions taken to influence them will accumulate within the performance management system. Eventually, when the BPM application identifies a KPI that it deems to be at risk, it will be able to present business managers with a description of the action taken in previous circumstances when the same KPI was at risk.

Predictive BPM software also can make recommendations for action based on the predicted variance and outcome. Some solutions have this recommendation logic programmed in, and suggestions for action are delivered to managers based on the nature of the variance. For example, suppose the metric for DSO comes in at an average of 90 days, versus an expected 75 days, for a certain business unit. At the same time that it automatically alerts the affected business manager to this variance from plan and identifies the root cause for the variance (perhaps a particular customer or category of customer is driving the variance), a predictive BPM solution could also recommend a course for action. It might suggest that the business manager follow a specific, predefined procedure for collecting monies owed. The advantage of this approach, versus training each employee on the company's recommended courses of action, is that the software ensures that every manager is on the same page; it automatically reinforces corporate policies and standard operating procedures.