MEASURE FACTORY

• US 20180181897A1
• Filed: 12/28/2017
• Published: 06/28/2018
• Est. Priority Date: 06/05/2012
• Status: Active Grant

• Current Assignee: Dimensional Insight Incorporated
• Original Assignee: Dimensional Insight Incorporated
• Inventors: Clark, James, Powers, Frederick A., Jablonski, Daniel J., Gorman, Matthew J., Dealy, George M.
• Granted Patent: US 10,839,327 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/00 Information retrieval; Database structures therefor; File system structures therefor
• CPC Class Codes: G06F 16/26 Visual data mining; Browsing structured data
• CPC Class Codes: G06F 3/048 Interaction techniques based on graphical user interfaces [GUI]
• CPC Class Codes: G06F 3/0484 for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/06316 Sequencing of tasks or work
• CPC Class Codes: G06Q 10/067 Enterprise or organisation modelling

Abstract

A measure factory for generating analytic measures includes data sets representing business activities arranged as columnar arrays with each column being associated with a distinct source rule that applies to the column when it is used as a data source. The measure factory includes factory rules that govern which operations on available data sources may be executed under what conditions in the measure factory, such as by taking into account the source rules and other applicable factory rules. A factory rule execution hierarchy governs the execution of ready factory rules that lack dependency on other factory rules before executing ready factory rules that have dependency on other factory rules. A script generation facility generates a script to process the plurality of factory rules according to the factory rule execution hierarchy.

Claims

1. A method comprising:

detecting changes in business activity-specific performance measures by automated computation of measures by applying source rules and factory rules to business-specific data sets in a series of automated operations under control of a processor in a measures factory, the operations automatically generated from;

the factory rules, wherein a portion of the factory rules are described as relationships of data set-specific source rules and relationships of other factory rules;

the source rules;

the business-specific data sets arranged as a columnar array, wherein each column is associated with a distinct source rule; and

a factory rule execution hierarchy that executes ready factory rules without dependency on other factory rules before executing ready factory rules with dependency on other factory rules.

2. The method of claim 1, wherein computed measures are compared to normalized measures of the same measure type to identify departures from normal.

3. The method of claim 1, wherein normalized measures are established based on historical averages.

4. The method of claim 1, wherein normalized measures are established based on a past time period.

5. The method of claim 1, wherein the automated detection comprises detection of at least one variance-impacting dimension of a plurality of dimensions of data in the business-specific data sets that contributes to a measure of business performance of business activities that are represented at least in part by the data.

6. The method of claim 1, wherein detection further comprises:

determining data that contributes to the measure of business performance;

comparing differences between at least one of summaries of the determined data and elements of the determined data for a plurality of varying, time-specific measures;

ranking at least a plurality of the differences from largest to smallest of the plurality of the differences; and

presenting descriptive data for the top ranked differences to a user in an electronic user interface of a computer.

7. The method of claim 6, wherein the user interface facilitates selecting the plurality of varying measures.

8. The method of claim 7, wherein the varying measures are time-period specific.

9. The method of claim 1 further comprising automatically ranking by degree of impact, business activities that impact a change in business performance detectable from a comparison of a plurality of distinct time period-specific measures of business performance, the measures generated by processing with the measures factory data representing the business activities for the plurality of time periods.

10. The method of claim 9, wherein a ready for calculation factory rule is applied before other factory rules.

11. The method of claim 9, wherein a ready flag rule is applied after all ready for calculation rules have been applied to a given data set.

12. The method of claim 9, wherein the hierarchy of factory rule execution indicates an order of factory rule execution.

13. The method of claim 9, wherein the order of factory rule execution requires ready for calculation, flag, and lookup rules to execute before ready link rules which execute before ready plugin rules.

14. The method of claim 9, wherein factory rules that apply only to data within a specific data set are executed independently of factory rules that apply to data within other data sets.

15. The method of claim 1 further comprising:

projecting a difference of a business performance measure based on analysis of differences over time of data elements that, when processed through the measure factory, produce measures of the business performance.

16. The method of claim 1 further comprising, suggesting a business activity as a source of a variance between two business-centric performance measures of a business, the measures generated by processing data representing activities of the business with the measures factory.

17. The method of claim 1, further comprising configuring a business operational dashboard that automatically presents contributors to notable variances of a measure of business-performance, the contributors determined from sources of measures as defined by a set of factory rules of the measure factory, the contributors tagged with a variance-impact confidence factor, the dashboard automatically configured based on a determined role of a user of the dashboard.

18. The method of claim 17, wherein the contributors are further filtered based on the determined role of the user so that sources of data for contributors associated with the determined role of the user are represented in the dashboard by descriptive information about role-specific business activities that correspond to the sources of data for the filtered contributors.