Predictive Business Intelligence Strategy Examples

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The following predictive business intelligence strategy examples illustrate the value of transitioning traditional dashboard reporting to a predictive measurement methodology. The described business intelligence tool format for dashboard reporting provides the business intelligence architecture for performance reporting so that, for example, healthcare business intelligence can be provided at a click of the mouse.

Predictive Business Intelligence Strategy Examples

The following examples will illustrate the benefits of using a business intelligence services methodology for predictive performance dashboards. With the describe business intelligence analytics approach, focus is given to the creation and use of predictive dashboards. Business intelligence analyst and others can then systematically identify business intelligence projects that have whole enterprise benefits.

This integration objective for a business intelligence solution can be accomplished when the described predictive performance dashboard reporting system is organizationally integrated through the later described Integrated Enterprise Excellence (IEE) value chain.

This article will next:

  1. Present an actual dashboard (used with permission) that is representative of what other organizations might be using; i.e., the first of several predictive business intelligence strategy examples referenced in this article.
  2. Transition this dashboard reporting to a predictive performance metric dashboard as a business intelligence solution.
  3. Provide links to additional predictive business intelligence strategy examples where dashboards are converted to predictive performance metric reporting.
  4. Describe how an enterprise that uses the described predictive performance reporting throughout its organization can gain up-to-date business intelligence and performance measures at a ″click of the mouse.″
  5. Describe how business intelligence reports can be created so that the business intelligence analyst and others can determine what should be done to run the business by evaluating their predictive performance metrics. In addition intelligence analysts, management, and others timely determine where improvement efforts should focus so that the enterprise as a whole benefits.

Traditional Business Dashboard: Illustration 1

An organization reported defects per million units (DPMU) produced. In this relationship, the referenced ″unit″ was a fixed length of manufactured product for their various types of products produced. This key performance indicator (KPI) metric was currently being reported out with other related metrics in a spreadsheet.

In the spreadsheet, the DPMU report-out was:

Performance Measurement System Traditional Report Example
Traditional Metric Reporting

Questions one might ask about the interpretation of this chart are:

  • What actions or non-actions should be made?
  • Did the organization’s metric improve over time? If so, when did the improvement occur?

These important questions are not addressed with this common form of reporting; i.e., is not a business intelligence solution. What will next be suggested is an alternative dashboard reporting format, which provides business intelligence reports information, which provides answers to these questions and more.

Predictive Business Intelligence Strategy Examples: Illustration 1 – Creation of a Predictive Performance Metric

With the previous form of dashboard reporting, emphasis is given to evaluation of monthly numbers against goals. However, a resulting metric at any given time is the result of a process that is creating the metric. That is Y, the output of a process, is a function of the inputs to a process and the process itself (i.e., x´s). This could then be stated as Y=f(x). From this relationship we understand that if the Y of a process is not desirable, than the inputs or the process needs to be enhanced.

If a Y monthly response is an undesirable magnitude and a special cause issue, one could expend effort to determine the cause and then fix this problem so that the special-cause-event does not reoccur; however, if the response is a result of normal up-and-down process variability of the process, then the process needs fundamental enhancement for the monthly deviation from the objective not to reoccur. This form of thinking is the first step to creating a business intelligence solution for the situation.

One of the issues with traditional dashboard formats is that often focuses are given meeting the annual numbers for each time interval; e.g., monthly. The problem with this type of thinking is that the approach does not address, for example, the reality that processes don’t typically magically change from December 31 to January 1 of the next year. Because of this, evaluating a process yearly can be deceiving or lead to unhealthy behaviors. A 30,000-foot-level performance reporting methodology that offers predictive performance measures that addresses this issue. This form of dashboard reporting is providing a form of self service business intelligence.

A 30,000-foot-level predictive performance metric for the DPMU dashboard shown previously is:

 

Predictive Business Intelligence Strategy Examples -- Number 1
Predictive Business Intelligence Strategy Examples — Number 1

 

The 30,000-foot-level format may appear similar to statistical process control (SPC) charting; however, there are some fundamental differences. SPC charting is used to control processes; however, 30,000-foot-level reporting is not. The 30,000-foot-level form of reporting provides a high level view of how the process is performing. There are fundamental technical differences between the two reporting approaches, as described in the article, 30,000-foot-level reporting; e.g., for technical reasons x-bar and R charts, p-charts, and u-charts are not used in 30,000-foot-level reporting.

From this business intelligence strategy form of reporting for this set of data, one notes that there are no trends relative to the upper and lower control limits (UCL and LCL), which are determined from the amount of between-subgroup data variability. Another way of stating this is that from this chart we note that no improvements were demonstrated in the response level of the process. Also, the process has a recent region of stability; hence it is predictable. With 30,000-foot-level predictive performance level reporting, a prediction statement is outputted at the bottom of the graph in terms that everyone can easily understand; i.e., one aspect of this business intelligence solution approach to dashboard creation. .

Using the 30,000-foot-level predictive performance metric reporting format though out an organization would provide readily available business intelligence reports at a click of the mouse. This alternative 30,000-foot-level performance charting methodology shows how the common practice of setting monthly goals and tracking performance against these goals (i.e., the original chart in this article) does not often lead to efforts that have any long-lasting positive benefits.

If one has common-cause variability like that shown, an organization needs to improve the process.

In addition, goal setting of a process response for a response like this one should in the original graph would be better served if it addresses the process average, as opposed to a magnitude for individual months. Validation that an improvement was made to the process would be that the 30,000-foot-level control chart transitions to a new, improved level of performance.

Predictive Business Intelligence Strategy Examples: Creation of Predictive Performance Metrics from a Table of Numbers, Red-Yellow-Green Scorecards, Time-series-plot, etc.

The following predictive business intelligence strategy examples provide more illustrations of the transformation of traditional dashboards to a more business intelligence reporting format:

Business Intelligence Platform that uses Predictive Performance Reporting

The above predictive performance reporting examples illustrate, for a variety of situations, an enhanced 30,000-foot-level performance measurement dashboard tracking methodology. The next question to address is how a business intelligence platform can be created to convey up to date information about these metrics.

Rather than use spreadsheets or some other means to convey the level of process performance, organizations benefit when they apply a generic approach for performance reporting structured around what they do and how they measure what they do. The Integrated Enterprise Excellence (IEE) value chain fulfills this need. An IEE value chain integrates predictive performance metrics with the processes that create those metrics; i.e., another aspect of this business intelligence solution.

In the following IEE value chain graphic, an organization would click on the rectangular boxes and on the oblong boxes for 30,000-foot-level metrics.

 

Integrated Enterprise Excellence (IEE) Value Chain: Reproduced from IEE Volume II
Integrated Enterprise Excellence (IEE) Value Chain: Reproduced from IEE Volume II

 

The 30,000-foot-level metrics in this IEE value chain could automatically be updated through the Enterprise Performance Reporting System (EPRS) software so that the reported metrics are up to date. These up to date metrics can then become actionable or non-actionable (or viewed as just information) in a timely fashion to the appropriate person throughout the hierarchy of the organizational chart.

Business Intelligence Platform Management, Goal Setting and Business Intelligence Projects that have Whole-Enterprise Benefits

Management and others can utilize the IEE value chain for addressing everyday management and create performance management goals that benefit the enterprise as a whole. This can be accomplished using the IEE 9-step business management system.

 

Integrated Enterprise Excellence (IEE) 9-step Business Intelligence Strategy system. Reproduced from IEE Volume II
Integrated Enterprise Excellence (IEE) 9-step Business Intelligence Strategy system. Reproduced from IEE Volume II

 

In step 2 of this 9-step IEE system the value chain can be used to provide timely information to process owners and executives as well. Through other steps in this process, targeted goals for metric improvement needs, which benefit the enterprise as a whole, can be created so that whole-enterprise improvement opportunities are generated; e.g., creation of business intelligence projects.

Business Intelligence Platform Management Tools List and Implementation

For more information about various aspects of the described business intelligence management system and its business intelligence strategy, see:

Additional dashboard conversions to predictive performance reporting illustrations are available in the article Transitioning Traditional Dashboards to Predictive 30,000-foot-level Metric Reporting Examples.

Applying the Concepts in the Predictive Business Intelligence Strategy Examples

For those in organizations that can relate to the described Predictive Business Intelligence Strategy Examples and would like to investigate how these concepts can be applied to their organization, contact me so that we can discuss your situation. I will then freely share with you my thoughts.

My contact information is:

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