Predictive Analytics: BPM Drives the Dynamic Organization
The promise of building a dynamic organization has been as elusive as the promise of actionable information on every manager's desktop. The truth is that few companies are capable of getting the right information to decision-makers. And few software companies in the business performance management (BPM) space have been able to help them do so; instead, most of these solutions rely on "rear-view mirror" reporting and analysis. However, BPM systems that include predictive analytics help create dynamic and truly predictable enterprises.
Article Tools
Resource Center
Access white papers, product demos, and presentations from companies whose reputations have been built on helping BPM practitioners get the most from initiatives.
- BPM 101: Selecting a Business Performance Management Vendor" -- new white paper from BPM Partners
- "The Finance Challenge of Aligning the Business With Strategic Goals," a podcast featuring Palladium Group's Phillip Peck
- Ventana Research white paper "Decision-Making and Performance: Improving Essential Business Analytics and Technologies"
- “XBRL at a Glance,” white paper from XBRL US
advertisement
A decade ago, business intelligence (BI) software provided a select group of business users with stand-alone data query, reporting, and analysis capabilities. But its focus on historical data handicapped the software's ability to improve business decision-making in the present. In addition, traditional BI applications isolated performance data in the hands of the organization's financial analysts. Thus, they were inherently flawed in their ability to collect, aggregate, and validate data on an enterprise scale; only power users had the access, time, and expertise to make them work.
The first generation of BPM software, viewed by many as the evolution or strategic application of traditional business intelligence tools and technologies, advanced organizations toward a truer sense of performance intelligence on two fronts. First, it brought analytics to the masses; the BPM vision included in its scope everyone from the CEO to the line-of-business manager. And second, BPM vendors emphasized the need for real-time information, stressing the limitations of viewing performance information only in the past tense. BPM became effective at centralizing data and automating the routine yet time-consuming tasks around data collection, validation, and manipulation. This ensured not only that information was accurate, but also that it was available quickly companywide.
BPM Gets Predictive
Although the move from BI to BPM represented a strong shift in the right direction, it failed to give decision-makers everything they needed in order to understand and effectively forecast their financial and operational performance. This is why a number of BPM vendors have begun talking about the potential of "predictive analytics."
Whereas traditional BI tools forced users to spend too much time on data collection, aggregation, and the like, BPM systems are sometimes accused of providing too much data. The age-old problem of analysis paralysis can hamstring users. The idea of predictive analytics, when applied to BPM, is to help users overcome information overload by accelerating the analysis process. An automated "agent" can dynamically alert decision-makers to problems and opportunities.
Like standard BPM solutions, predictive BPM tools address both historical and real-time concerns, but they add to the mix the ability for business managers to be proactive. They look to the future, while accounting for the present, and automate time-consuming processes including data collection, aggregation, analysis, and the identification of variances. They can also suggest courses of action to the business decision-makers. Exhibit 1, below, shows how the scope of BPM systems can expand to include predictive analytics.
A majority of BPM applications offer drilldown capabilities for analyzing performance variances and their underlying causes, but the analysis is typically manual, time-consuming, reactive, and cumbersome. Valuable time can elapse between the variance reporting (what happened), variance analysis (why it happened), and final determination of the appropriate action for proceeding. Conversely, predictive BPM software eliminates this time delay by automating the discovery process. It offers dashboards in which variances are reported automatically. And for variances falling outside acceptable limits, predictive BPM can provide a root-cause analysis and an explanation of the context in which the variance occurred. For example, if performance on a key business metric such as days sales outstanding (DSO) falls outside of specified parameters, predictive BPM can automatically alert decision-makers and provide them with the reasons, root causes, and context behind the variance.
Predictive BPM in Context
How does it work? Using OutlookSoft's solution as an example, the software automatically scans its data sets for the reasons behind successes and failures, based on the data hierarchies that are set up during implementation. The reasons typically reflect the component parts of the business, such as shifts in product sales, underperforming geographies, rising raw material costs, or other factors. If corporate net income comes in below expectations, the predictive analytics segment of a BPM application may scan the numbers of all the company's business units to discover which fell furthest below budget. In this way, predictive analytics is able to automate the drilldown process that is usually a manual function for BPM software users.
