Key Performance Indicators and Data Mining

The terms KPI and data mining are often used to discuss the benefits of BPM and the ways in which BPM drives business decisions. Knowing what those terms mean, however, does not alone guarantee business success. Instead, organizations should identify appropriate ways to apply KPI and data mining, in order to determine the metrics required for making the right strategic decisions.

KPIs are defined as the critical metrics set by an organization to reflect its financial or nonfinancial success. They help organizations identify and monitor factors that are quantifiable, measurable, and important to the organization's overall success. Although KPIs can help drive business decisions, they are only beneficial if they are set properly and reach the right people at the right time. For example, with traditional BI online analytical processing (OLAP) cubes, sales data can be reflected multidimensionally with rolling sales data over a three-year period. This, however, pales in comparison to dashboard functionality, which allows a sales manager to see up-to-date sales figures in real time, and to compare them against predefined metrics. The sales manager can then drill down on the data to access and analyze operational data, in order to determine a plan of action.

KPIs vary, depending on the function of an organization. A nonprofit company may want to measure the ratio of graduates to overall participation for a specific volunteer training course, in order to identify the success of a program. However, a sales-oriented corporation may want to set metrics to identify the amount of revenue generated by return customers. To increase sales, a KPI measuring customer satisfaction and repeat sales might be implemented. For a financial institution, it may be important to set KPIs to identify potential risk management issues, such as meeting regulatory requirements or minimizing the potential credit risks of clients. This differs from a manufacturing organization that needs to monitor parts delivered from suppliers, or from a government body that wants to measure and improve employee performance. Setting the right KPI and providing that information to the right people can make the difference between implementing a successful BPM tool and a total failure.

Data mining, also called knowledge discovery in databases (KDD), uncovers data patterns within databases. It is used as a tool to discover patterns among large amounts of data. Data mining allows organizations to identify why things happen, and helps them make connections between seemingly unrelated items. For example, if an organization wants to increase sales, identifying customer buying patterns with intuitive software saves time, and allows decision makers to focus on developing strategies based on those patterns (as opposed to spending their time identifying what those patterns are). A credit card company may want to identify buying patterns and spending habits of customers, and an organization in the pharmaceuticals industry may choose to create a KPI to improve their manufacturing process and inventory control.

Data mining can also be used to find patterns among multiple tables within relational databases. This is advantageous because data centralization (and having one view of corporate data) enables organizations to set the appropriate metrics. Data mining also allows them to measure these metrics more easily, find patterns which enable proactive corporate planning, and target customers based on pattern recognition.

Before identifying more ways in which BPM can benefit an organization, it is important to identify the user interface components, to show how organizations are using performance management software

This article was taken from Technology Evaluation Center