Business Intelligence Dashboard Examples

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The following business intelligence dashboard examples provide illustrations of what an organization can do to gain enhanced timely insight to their business. Through this business intelligence dashboard design, personnel at all levels of an organization can gain insight to appropriate actions or non-actions in their business. This business intelligence system can be used by not only business intelligence analysts but all personnel throughout an organization. This form of creating business intelligence reports can be invaluable to an organization.

Business Intelligence Dashboard Examples

Organizations benefit when performance metrics are presented in a dashboard or business intelligence report format that provides predictive metrics throughout an organization’s value chain. The following business intelligence dashboard examples transform various forms of traditional business intelligence reports into a predictive performance metric format. At the end of this blog, a system will be referenced for integrating these predictive metrics with a process improvement methodology so that the enterprise as a whole benefits from improvement efforts.

Several business intelligence dashboard examples that illustrate the transition of the following report types to predictive performance metrics are:

The above business intelligence dashboard examples provide information that addresses issues (from a high-level point of view) mentioned in the Wall Street Journal article ″The Data Companies Wish They Had″

I will next add to the above set of business intelligence dashboard examples, which enhance traditional reporting, to address specifically the topic of on-time delivery of a product or service.

Business Intelligence Dashboard for On-time Delivery

The following is an actual on-time delivery performance report-out that an organization gave me permission to share.

 

Executive traditional dashboard report-out for on-time Delivery
Executive traditional dashboard report-out for on-time Delivery

 

What business intelligence is gained from this form of reporting? What actions or not actions will the organization undertake? Have improvements occurred over time or are things getting worse? It is difficult to say.

This form of reporting is not providing information as how one would like to see a presentation in business intelligence reports. This form of reporting is not uncommon in businesses; this form of performance reporting does not really provide any business intelligent dashboard information. Furthermore, this form of information is often presented in a PowerPoint presentation where the data are not timely.

Another point is that the data from this form of reporting are historical and does not make a statement about the future. If a futuristic statement could be made from an enhanced business intelligence dashboard, then one could determine an appropriate action or non-action to undertake from this form of business intelligence reports.

A business intelligence dashboard reporting method to accomplish this objective is 30,000-foot-level business intelligence reports.

Predictive Business Intelligence Dashboard Reporting: The 30,000-foot-level Reporting System

The 30,000-foot-level predictive performance reporting system for business intelligence dashboard reporting is a process based tracking system that consistent of two steps:

  1. Determine if the process that produces the response is stable.
  2. If this process is stable, then provide a predictive response for what could be expected in the future if something were not done differently either positively or negatively in the process.

The following 30,000-foot-level report-out may initially appear complex but it is quite simple to understand once any initial intimidation is overcome. This form of 30,000-foot-level business intelligence reports provides much more business intelligence than the above traditional report-out.

 

Business Intelligence Dashboard Examples Series: On-time Delivery
Business Intelligence Dashboard Examples Series: On-time Delivery

 

Let’s examine the above 2-step process for this 30,000-foot-level form for business intelligence reports:

STEP ONE in Business Intelligence Dashboard Examples: Determine if the process that produces the response is stable.

  1. The top two charts are to assess process stability. For one thing, the reader should note that the response of the process was changed from an attribute response; i.e., did you meet the due date or not was previously report. The change was made to how late the delivery was, where a positive one would indicate one day late and a minus one would indicate one-day early. Much additional business intelligence can be gained from this form of dashboard business intelligence reports
  2. The top left chart assesses whether the process mean has shifted over time, while the upper right chart assesses whether the process standard deviation has changed over time.
  3. The red ″1″ in the upper left chart indicates a potential special cause situation while data within the upper and lower control limits (UCL and LCL) suggest that the process has stability for a certain period of time. Potential special cause conditions like this are candidates for detailed causal analysis of individual points that provided additional business intelligence.
  4. For this set of data, the chart in the upper left indicates that the process has had a couple shifts over time. From this example of our list of business intelligence dashboard examples, one has gained much more insight relative to how the process has performed over time that the traditional reporting format shown initially.
  5. The process in its current state has improved and is now considered stable from a 30,000-foot-level point of view.

STEP TWO in Business Intelligence Dashboard Examples: Provide a Predictive Statement, if Appropriate

  1. When a process is considered stable with this form of business intelligence dashboard reporting, a prediction statement can be made. Data from the recent stability region in this business intelligence reports format can be considered a random sample of the future.
  2. When data are considered continuous (as it is in this case), a probably plot is an excellent tool to determine a prediction statement. For the above business intelligence dashboard report-out, the probability plot is presented as a lower plot in the 3-set graphic.
  3. The x-axis of a probably plot describes the magnitude of the response, while the y-axis provides a percentage less than business intelligence quantification of how the process is performing.
  4. A probability plot can be used to provide business intelligence for many distributions. The selection of which distribution to use is dependent upon the physical situation. For this case, a normal distribution was used.
  5. Since there was no real specification for this particular process, the 30,000-foot-level performance assessment was reported as a median and 80% frequency of response magnitude.
  6. In 30,000-foot-level business intelligence dashboard reporting, process performance statements are automatically determined and presented in an easy-to-understand format at the bottom of the series of charts.
  7. The accuracy of a prediction statement is dependent upon how many points are used when making a prediction statement and how well the data fit the model used to make the estimate.
  8. Data is considered to fit a distribution model (normal in this case) if the data follow a straight line in the probability plot. In this case, the data do not fit a straight line; hence, the prediction statement is not accurate.
  9. A lack of fit like that shown in the above probability plot provides business intelligence guidance on what should be investigated. When a curvature like that shown in the above plot occurs, it indicates that multiple distributions are occurring within the process. For this case, certain product deliveries, for example, could be experiencing additional delays, which other types of product deliveries are not. This information can provide much guidance for the business intelligence analyst to determine what might be done to the overall process for the product-types that are experiencing the delays not occurring with other products.

Traditional Business Performance Reporting versus 30,000-foot-level Business Intelligence Dashboard Predictive Quantifications

Often organizations provide only monthly performance reporting to executives, like the report-out initially shown. Why is this done? If something changed during a month, the impact of both the past process and the new process are described in a monthly report-out value. This is not a business intelligence reports format.

The 30,000-foot-level predictive performance reporting for business intelligence dashboard addresses this issue and more, especially when the data are automatically updated.

Integration of Business Intelligence Dashboard Examples Report-outs

A business intelligence dashboard design which provides the above predictive performance metric assessments offers much value to an organization. This business intelligence system helps organization achieve timely business intelligence reports, where the appropriate action or non-action can be undertaken so that the enterprise as a whole benefits.

What is needed is a business intelligence system where business intelligence reports are initially designed by business intelligence analyst but are in a form that is readily available to users throughout the organizational chain of command through simply a click of the mouse.

Integrated Enterprise Excellence (IEE) is a business intelligence system for achievement of the above objectives. The Enterprise Performance Reporting System (EPRS) is business intelligence dashboard software that provides automatic update of the business intelligence dashboard.

Application of Business Intelligence Dashboard Examples and Additional Information

Additional 30,000-foot-level business intelligence dashboard reporting information can be found at:

Other conversions for dashboards to a performance reporting that is predictive are available in the article Transitioning Traditional Dashboards to Predictive Metric Reporting.

More information about the IEE business intelligence system for integrating predictive performance metrics with analytically/innovatively determined strategies, with undertaken improvement efforts that benefit the enterprise as a whole:

Addressing how 30,000-foot-level business intelligence dashboard reporting or IEE business intelligence system applies to specific situations, contact me at:

Looking forward to your comments about the “business intelligence dashboard examples” methodology described in this blog. Again, feel free to contact me if you would like to discuss any application possibilities.