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Proactive Operational Business Performance Management

Proactive analytics and unstructured data are the key components of an operational business performance management solution.

This article originally appeared on the BeyeNETWORK.

Over the last twenty years, we have seen steady growth in the use and popularity of business performance management (BPM) solutions. If you look at business performance management use by functional area, you will find that it has been deployed in the approximate ratio of 60% financial, 30% marketing and 10% operational. Thus, if you have been using business performance management to analyze data, it is likely that you have been working with financial or marketing data.

Perhaps it is time to realize that traditional BPM systems have not “cut it” when it comes to operational business performance management. With exponential growth in computing power delivering voluminous amounts of near real-time structured and unstructured operational data, there is clearly a void in the ability of traditional BPM solutions to address operational use. BPM vendors have been listening to the market and now recognize the market opportunity. If you are looking for a BPM solution that addresses operational business performance management, don’t be fooled by the often clever product repositioning statements of some vendors. What should you look for if your organization is beginning to investigate operational business performance management? Two areas that operational business performance management should address are proactive analytics and unstructured data. 

Proactive Analytics
We are clearly entering the next phase of business performance management use which could aptly be called “proactive business performance management.” Initially, BPM systems ran in batch mode. They were soon replaced by interactive BPM systems which have been the norm during the last decade.  Proactive business performance management facilitates operational analytics by giving organizations the ability to “automate” what was the interactive OLAP slice-and-dice discovery process offered by traditional BPM solutions. This automated discovery process is a “must have” feature for operational business performance management analysis for two simple reasons: once analysts have asked all the questions they know to ask of their data, there is simply too much data and too much complexity for any team of analysts to know where to look next. To expect that a business person can “discover” a valuable insight hidden in their data by stumbling across it is not an effective way to become proactive. With the proliferation of data, users need to be guided to emerging issues they had previously never considered.  

As proactive business performance management delivers previously hidden insights to business domain experts, they can determine the relevance and importance of the newly uncovered information. The traditional approach is comparable to trying to find a needle in a haystack without even knowing which haystack may contain the needle. While the traditional approach of creating predefined triggers that automatically send e-mail notifications is useful to monitor and manage known issues, it delivers no value when it comes to uncovering unknown issues. This is the difference between being reactive rather than proactive.

Unstructured Data
Traditional BPM solutions facilitate performance management by offering solutions that help organizations make sense out of structured data (i.e., numbers). Essentially, they turn structured data into readily accessible information. We all know that numbers are only one form of data. Let’s not forget unstructured data: text, voice and pictures.

Operational business performance management data is often unstructured in nature. Readily available sources of free-form text (i.e., Web sites and call centers) exist and should be included in operational BPM solutions. They can offer better insight into a greater number of real-world operational business problems such as early warning analysis in manufacturing quality, warranty analysis, compliance analysis and call-center operations.

The analysis of unstructured data has been the target of specialty vendors’ products including text mining and voice recognition systems. These products are typically used by analysts and usually require special training and education. They tend to concentrate only on the unstructured form of data for which they were designed and do not bring together both unstructured and structured data for a richer analytic environment. They usually require analysts to take the results from text mining and use a different solution to further explore their structured data with the insights they have gained from text mining. Not only are these solutions difficult to use, but they also inhibit speed-of-thought analysis by stovepiping the data into text and structured data as if there was no obvious relationship between the two types of data. Traditional solutions ignore the reality that the richest picture of what is happening can only be seen when text and structured data are considered together.  

One alternative approach is offered by PolyVista, Inc. PolyVista has developed a patent-pending approach for organizing both structured and textual data. The solution employs a dimensional hierarchy using Microsoft’s Analysis Services to facilitate building cubes containing dimensions that incorporate textual hierarchies. The result is the ability to seamlessly incorporate unstructured textual analysis in combination with structured data. Not only is the process of building a textual dimension novel, but also the benefits of performing analytics against such cubes are simply astounding. 

By incorporating readily available sources of free-form text and building cubes that contain dimensions representing textual key words, operational business performance management users are empowered to obtain results faster and with more meaning. By performing drill-through analysis, BPM users can read the text associated with the results of interest. This approach yields more meaningful results, and it is likely to offer insights impossible to uncover with traditional BPM solutions. Using proactive analytics against these cubes results in an operational business performance management process that is both fruitful and practical.

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