US20050080662A1

US20050080662A1 - Decision HUB business intelligence collaboration

Info

Publication number: US20050080662A1
Application number: US10/970,627
Authority: US
Inventors: Sunil Srivastava
Original assignee: Oracle International Corp
Current assignee: Oracle International Corp
Priority date: 2003-10-10
Filing date: 2004-10-20
Publication date: 2005-04-14
Also published as: JP2005116131A; US20050117440A1; JP4345428B2; US6975546B2

Abstract

An analysis of a business process is received from an analytics application and stored at a decision hub is provided. The analysis may include a problem and a possible solution. The business hub then identifies an entity responsible for the process and the analysis information is sent to the entity. The entity may review the analysis information and determine a decision based on the analysis. For example, the decision approves a change that can be implemented in the process based on the problem and solution received. Information for the decision made is then received and stored at a decision hub. The change in the process may be implemented in an operational application. Result information resulting from the change in the process is then received at the decision hub. The analysis information, decision information, and result information may thus be stored by the decision hub.

Description

BACKGROUND OF THE INVENTION

The present invention generally relates to collaborating between applications and more specifically to techniques for collaborating between an analytics application and an operational application.
Typically, a business process is implemented on an operational application. Analytics applications are used to provide reports on business processes based on information received from operational applications. The analytics applications analyze data generated from the operational applications to generate the reports.
The reports generated include a wealth of data that is analyzed by a user. The user is typically trained in interpreting the reports generated by the analytics application. After analyzing the reports, the user makes a decision on any changes that should be made to the business process.
The above process includes many disadvantages. For example, the user interpreting the reports may not be the best person to make decisions on any changes for the business process. For example, there may be many business owners that are responsible for the business process. Typically, these business owners are not involved in the interpretation of reports for the analytics application. Accordingly, the changes made to the business process and the operational application may not be the most informed decisions. Also, once the change is made there may not be any documentation or tracking of the decision made and how it affects the business process because of the separation of the user who interprets the reports and the business owners who make changes to the business process.

BRIEF SUMMARY OF THE INVENTION

The present invention generally relates to techniques for collaborating between analytics applications and operational applications.
In one embodiment, an analysis of a business process is received from an analytics application and stored at a decision hub. The analysis may include a problem and a possible solution. The business hub then identifies an entity responsible for the process and the analysis information is sent to the entity. The entity may review the analysis information and determine a decision based on the analysis. For example, the decision approves a change that can be implemented in the process based on the problem and solution received. Information for the decision made is then received and stored at a decision hub.
The change in the process may be implemented in an operational application. Result information resulting from the change in the process is then received at the decision hub. The analysis information, decision information, and result information may thus be stored by the decision hub.
Using the stored information, the decision hub may validate whether the change made in the business process worked. Also, the decision hub may provide prediction analysis using the stored information. For example, patterns similar to patterns found in other business processes may be detected in result information from the operational application. Changes similar to those changes previously made may then be made based on prior decisions stored by the decision hub.
In one embodiment, a method for collaborating between a first application and a second application is provided. The method comprises: receiving an analysis of a business process from the first application; identifying a decision entity responsible for making decisions for the business process; sending analysis information for the analysis of the process to the decision entity; receiving decision information detailing a change to be implemented in the business process on the second application; receiving result information resulting from the change in the business process on the second application; and storing the analysis information, decision information, and result information.
In another embodiment, a system for managing business processes is provided. The system comprises: a first application configured to generate an analysis of a business process; a decision hub configured to determine a decision entity responsible for making decisions for the business process, the decision entity receiving analysis information from the analysis of the business process and making a decision for the business process, wherein the decision hub receives decision information for the business process from the decision entity, the decision information detailing a change to be implemented in the business process; and a second application configured to implement the change in the business process and configured to generate result information resulting from the change in the business process on the second application, wherein the decision hub is configured to receive and store the analysis information, decision information, and result information.
A further understanding of the nature and the advantages of the inventions disclosed herein may be realized by reference of the remaining portions of the specification and the attached drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts a system for collaborating between an analytics application, a decision entity, and an operational application.
FIG. 2 depicts a simplified flowchart of a method for collaborating among the analytics application, decision entity, and operational application according to one embodiment of the present invention.
FIG. 3 depicts a simplified flowchart of a method for implementing a change in a business process according to one embodiment of the invention.
FIG. 4 depicts a simplified flowchart of a method for validating a decision according to one embodiment of the present invention.
FIG. 5 depicts a simplified flowchart of a method for providing predictive analysis according to one embodiment of the present invention.
FIG. 6 is a simplified block diagram of a computer system according to an embodiment of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 depicts a system 100 for collaborating between an analytics application 104, decision entity 106, and an operational application 108. As shown, system 100 includes a decision hub 102, analytics application 104, decision entity 106, and operational application 108.
Analytics application 104 may be any application configured to analyze data. For example, information from operational application 108 may be reviewed by analytics application 104. Analytics application 104 may be configured to generate reports based on the information from operational application 108.
In one embodiment, analytics application 104 may be any application configured to analyze information generated for a business process. An example of an analytics application includes any business intelligence applications. A business process includes any process implemented by decision entity 106 regarding a business decision. For example, the business process may be a marketing program. Also, a business process may be any offers made to customers. For example, a five percent discount given to a segment of customer's may be a business process. Additional examples of business processes include the compensation models offered to sales representatives for various product lines and the allocation of resources in a supply chain.
Analytics application 104 is configured to generate a hypothesis based on its analysis of information from operational application 108. The hypothesis may include a problem and solution. The problem may include information that indicates a possible problem in a business process being implemented by operational application 108. The solution may suggest changes to the business process that may be made in order to rectify the problem. For example, a problem may be that customer turnover is high for a segment of customers. Analytics application 104 may report the problem and then suggest a solution of offering certain programs to reduce turnover, such as having a call center call the customer's and offer them a discount for future purchases.
Decision entity 106 may be any entity that may make a decision for the business process. For example, decision entity 106 may be a business owner of the business process. The business owner may be a group of users that are in charge of the business process in a corporation. For example, decision entity 106 may be a person who is the head of marketing.
Decision entity 106 may also be multiple groups that collaborate to make a decision. For example, a first group may be in charge of approving a change to the business process and a second group may be in charge of determining pricing information for the change. Decision entity 106 may also be an automated process configured to make decisions for the business process.
Decision entity 106 is configured to receive the analytic information from analytics application 104 and to generate decision information based on the analytics information. For example, decision entity 106 may determine if a change in the business process should be made based on the problem and solution received from analytics application 104. The solution may include a change to be made for a business process. Decision entity 106 may approve the solution or make changes to the solution.
Operational application 108 may be any application configured to implement the business process. For example, operational applications 108 may include customer relation management (CRM) applications, enterprise resource planning applications, or any other business management applications, etc. Result information based on the performance of the business process is generated. Operational application 108 is then configured to send the result information to decision hub 102.
Operational application 108 may be any application configured to effect the change in the process. Result information based on the performance of the change in the business process is generated. Operational application 108 is then configured to send the result information to decision hub 102.
Decision hub 102 is configured to provide collaboration among analytics application 104, decision entity 106, and operational application 108. Decision hub 102 receives the analytics information from analytics application 104, decision information from decision entity 106, and result information from operational application 108. Decision hub 102 may store the analytics information, decision information, and result information.
Decision hub 102 may also provide collaboration for various users or groups that communicate in the process. For example, a decision may require input from various users in decision entity 106. Decision hub 102 can coordinate the communication and record the results of any decisions. Decision hub 102 may also contact a subsequent user that should be contacted after a first user makes a decision.
Also, decision hub 102 may validate that a problem/solution generated by analytics application 104 is acceptable. For example, because decision hub 102 is configured to receive result information from operational application 108 and has stored the analysis information from analytics application 104, decision hub 102 may review the result information to gauge the performance of the decision. If any changes were made to application 104 by decision entity 106, the decision information may be used to determine if those changes were successful.
Decision hub 102 may also provide predictive analysis. For example, based on result information received from operational application 108, decision hub 102 may detect patterns in the result information for business processes. If the patterns are similar to other patterns that have resulted in problems detected by analytics application 104, decision hub 102 may suggest possible changes based on prior analysis information. For example, if a first business process is generating result information where a solution was proposed for a second business process, the solution may be suggested for the first business process. Decision hub 102 may review the result information when the change was made to the second business process. If the change was validated as being successful, then the same change may be generated or suggested to a decision entity 106 who is the owner of the first business process. Decision entity 106 may then cause the change to be implemented to application 108.
Accordingly, decision hub 102 may proactively monitor result information from application 108. If similar problems result, actions may be generated that business entity 106 may implement. Thus, decision hub 102 may quickly react to result information that has resulted in past problems. Accordingly, analytics application 104 may not need to be run in order to determine these problems. This may be more efficient than having analytics application 104 analyze the result information.
Consequently Decision hub 102 is able to measure the returns generated by various business analysts for the enterprise and provide accountability for the decisions and actions taken. Decision Hub 102 may also be able to provide a return on investment (ROI) measure of analytics application 104, and the specific analytic reports that are most effective.
FIG. 2 depicts a simplified flowchart 200 of a method for collaborating among analytics application 104, decision entity 106, and operational application 108 according to one embodiment of the present invention. In step 202, analytics information is received. For example, a business analyst may analyze a report generated by analytics application 104. The analyst may determine problems and possible solutions based on the reports. Also, in another embodiment, analytics application 104 may automatically determine problems and possible solutions based on the reports.
In step 204, problems/solution hypothesis are modeled. In modeling a problem/solution hypothesis, A user identifies current and historical patterns received from analytics application 104 and the impact to the business performance. The user is able to decide on specific actions within operational application 106 as a solution, or leave it open for other groups to decide.
In step 206, decision hub 102 determines support for the problem/solution hypothesis. In one embodiment, decision hub 102 determines any stored information that may be relevant to the hypothesis and may be useful for analyzing the hypothesis. For example, if the problem involves a segment of customers, decision hub 102 may look up any previous actions that were taken for the customers. For example, previous changes to business processes for the segment of customers and the results of the changes may have been stored by decision hub 102. This information may help a business process owner in evaluating the hypothesis. For example, what changes were most successful may be used again.
In step 208, decision hub 102 determines a business process owner for the process being analyzed. For example, if a certain problem is found in a report, a business process related to the problem is identified and one or more of the owners are determined.
In step 209, the process owner is notified by decision hub 102. For example, emails and/or any other notifications may be sent to a decision entity 106 that may be able to implement a change in the business process.
In step 210, decision hub 102 receives decision information from decision entity 106. For example, the decision information may indicate an approval of the solution for the problem. Also, the decision information may indicate changes to the solution that should be implemented.
In step 212, decision hub 102 stores the decision information. The decision information may be stored and correlated with other information for the business process. For example, the decision information may be associated with the stored analytics information for the business process.
In step 214, changes may be made to operational application 108. For example, decision hub 102 may automatically cause the changes to be made to operational application 108. Also, certain operational departments that can implement the changes may be contacted and these departments may make the changes. For example, human resources (HR) and inventory may be contacted with the changes to be made. HR and inventory may subsequently make the changes. Inventory may be an organization in charge of the inventory and production of certain products. The inventory organization may order a build up of inventory based on a new campaign that may be run because of the change. HR may be in charge of hiring new people to run the campaign.
In step 216, result information is received based on the changed process. The result information is then stored by decision hub 102. The result information may be correlated with other information for the business process, such as the analytics information and decision information. Accordingly, analytics information, decision information, and result information for a business process is stored by decision hub 102.
FIG. 3 depicts a simplified flowchart 300 of a method for implementing a change in a business process according to one embodiment of the invention. In step 302, a problem/solution hypothesis is generated. In one embodiment, the solution may be generated automatically by analytics application 104. In another embodiment, a user may analyze reports generated by analytics application 104 to generate a problem/solution hypothesis.
In step 304, the problem/solution hypothesis may be validated and refined by a decision entity 106. For example, a business owner of the process may be contacted and sent the problem/solution hypothesis. The business owner may then validate or refine the solution.
In step 306, additional information may be needed in order to validate or refine the problem/solution hypothesis. In this case, the process reiterates to step 302, where additional information is requested and received from analytics application 104.
In step 308, the decision is supported. In one embodiment, decision hub 102 determines any stored information that may be relevant to prior decisions that may be useful for making a decision on the hypothesis. For example, if the problem involves a segment of customer's, decision hub 102 may look up any previous actions that were taken for the customers. For example, previous changes to business processes for the segment of customer's and the results of the changes may have been stored by decision hub 102. This information may help a business process owner in evaluating the hypothesis and making decisions.
In step 310, the decision is approved and decision information describing the decision is sent to decision hub 102. The decision information may indicate the changes to be made to a business process on operational application 108.
In step 312, a changed business process is created. For example, an engineering group, marketing group, and any other group may be notified by decision hub 102 to implement the change. Engineering may create a build of materials (BOM) using an approval ID. The build of materials is an action creating products in anticipation of the change. The approval ID may be used to track the action when stored in decision hub 102.
Using the stored information, validation and predictive analysis may be performed by decision hub 102. FIG. 4 depicts a simplified flowchart 400 of a method for validating a decision according to one embodiment of the present invention. In step 402, result information for a changed business process is received at business hub 102. The result information may be generated by operational application 108.
In step 404, decision hub 102 determines stored analytics information and decision information for the business process. The stored information may have been associated with the business process and then retrieved.
In step 406, the analytics information, decision information, and/or result information is analyzed to validate the decision to change the business process. For example, the analytics information indicates the problem/solution determined and the decision information indicates the change that was made. Also, decision hub 102 may determine whether the problem has been rectified or alleviated by the change that was made. This determination may be made automatically by decision hub 102. For example, if certain goals are set by the change and are reached, then the decision may be validated. In one example, the goal may be to limit customer turnover to less than 5% for a group of customers. If turnover for a certain time period is less than 5%, then the decision may be validated. In another embodiment, the information may be outputted to a user, who can decide if the change should be validated. If decision hub 102 determines the change improved the problem in the business process, the decision to change the business process may be validated.
In step 408, the validation for the change may be stored by decision hub 102. This validation may be used in predictive analysis to determine if similar changes to other business processes should be made. Also, the validation information may be sent to decision entity 106 so the business process owners know that the change is successful.
FIG. 5 depicts a simplified flowchart 500 of a method for providing predictive analysis according to one embodiment of the present invention. In step 502, decision hub 102 uses the result information to identify any patterns in result information. For example, a pattern may be found in result information that has been received for a first business process that resulted in a problem/solution hypothesis being generated. If similar result information is determined in a second business process, the problem/solution for the first business process may be applicable.
Decision hub 102 may then suggest changes based on the prior problems/solutions that were generated. Also, if decision hub 102 had validated the prior problems/solutions, decision hub 102 can determine that these solutions may be acceptable.
In step 504, decision hub 102 determines the business process owner for the second business process. The business process owner may then be alerted. For example, correspondence indicates the problem/solution may be sent to a decision entity 106.
In step 506, decision entity 106 may review the problem/solution and determine a change to implement. For example, business entity 106 may approve the solution or provide changes to the suggested solution. Decision information indicating a decision made by business entity 106 is then received at decision hub 102.
In step 508, a change to the business process is implemented in operational application 108. For example, the process described in FIG. 3 is performed.
In step 510, result information for the change in the process is received at decision hub 102. Accordingly, decision hub 102 may track the result of the change made to the business process.
Thus, a process in which decision hub 102 may proactively monitor result information is provided. Instead of waiting for analytics application 104 to detect a problem, decision hub 102 may use stored analytics information, decision information, and/or result information to suggest a solution to a possible problem detected. Accordingly, a step of analyzing reports from analytics application 104 may be avoided using decision hub 102. Also, because decision hub 102 stores validation information for problems/solutions, decision hub 102 may provide validated problem/solution recommendations based on prior successful solutions that have been implemented.
Embodiments of the present invention provide many advantages. For example, collaboration among analytics application 104, decision entity 106, and operational application 108 is formalized. Thus, ad hoc communications among users of analytics application 104, business decision entity 106, and operational application 108 may be avoided. Further, changes to operational application 108 may be automatically implemented using coordination provided by decision hub 102.
Results of any changes in business processes may be tracked and the changes validated by decision hub 102. Further, using the stored information for business processes, patterns or problems may be detected and possible solutions suggested before a problem becomes serious.
Accordingly, the effectiveness of decisions may be monitored by decision hub 102. Also, patterns from effective decisions may be determined and similar changes may be implemented in later decisions. Further, because decision hub 102 coordinates the change in the business process, greater consistency in the decision making process is provided. For example, the same business entity business owners may be contacted to make the changes to the business process. Further, decision hub 102 may provide regulatory and standards compliance by insuring that established protocols are followed in the decision making process.
FIG. 6 is a simplified block diagram of a computer system 600 according to an embodiment of the present invention. Embodiments of the present invention may be implemented using computer system 600. As shown in FIG. 6, computer system 600 includes at least one processor 602, which communicates with a number of peripheral devices via a bus subsystem 604. These peripheral devices may include a storage subsystem 606, comprising a memory subsystem 608 and a file storage subsystem 610, user interface input devices 612, user interface output devices 614, and a network interface subsystem 616. The input and output devices allow user interaction with computer system 600. A user may be a human user, a device, a process, another computer, or the like. Network interface subsystem 616 provides an interface to other computer systems and communication networks.
Bus subsystem 604 provides a mechanism for letting the various components and subsystems of computer system 600 communicate with each other as intended. The various subsystems and components of computer system 600 need not be at the same physical location but may be distributed at various locations within a network. Although bus subsystem 604 is shown schematically as a single bus, alternative embodiments of the bus subsystem may utilize multiple busses.
User interface input devices 612 may include a remote control, a keyboard, pointing devices, a mouse, trackball, touchpad, a graphics tablet, a scanner, a barcode scanner, a touchscreen incorporated into the display, audio input devices such as voice recognition systems, microphones, and other types of input devices. In general, use of the term “input device” is intended to include all possible types of devices and ways to input information using computer system 600.
User interface output devices 614 may include a display subsystem, a printer, a fax machine, or non-visual displays such as audio output devices. The display subsystem may be a cathode ray tube (CRT), a flat-panel device such as a liquid crystal display (LCD), a projection device, or the like. The display subsystem may also provide non-visual display such as via audio output devices. In general, use of the term “output device” is intended to include all possible types of devices and ways to output information from computer system 600.
Storage subsystem 606 may be configured to store the basic programming and data constructs that provide the functionality of the computer system and of the present invention. For example, according to an embodiment of the present invention, software modules implementing the functionality of the present invention may be stored in storage subsystem 606. These software modules may be executed by processor(s) 602. In a distributed environment, the software modules may be stored on a plurality of computer systems and executed by processors of the plurality of computer systems. Storage subsystem 606 may also provide a repository for storing various databases that may be used by the present invention. Storage subsystem 606 may comprise memory subsystem 608 and file storage subsystem 610.
Memory subsystem 608 may include a number of memories including a main random access memory (RAM) 618 for storage of instructions and data during program execution and a read only memory (ROM) 620 in which fixed instructions are stored. File storage subsystem 610 provides persistent (non-volatile) storage for program and data files, and may include a hard disk drive, a floppy disk drive along with associated removable media, a Compact Disk Read Only Memory (CD-ROM) drive, an optical drive, removable media cartridges, and other like storage media. One or more of the drives may be located at remote locations on other connected computers.
Computer system 600 itself can be of varying types including a personal computer, a portable computer, a workstation, a computer terminal, a network computer, a mainframe, a kiosk, a personal digital assistant (PDA), a communication device such as a cell phone, or any other data processing system. Server computers generally have more storage and processing capacity then client systems. Due to the ever-changing nature of computers and networks, the description of computer system 600 depicted in FIG. 6 is intended only as a specific example for purposes of illustrating the preferred embodiment of the computer system. Many other configurations of a computer system are possible having more or fewer components than the computer system depicted in FIG. 6.
The present invention can be implemented in the form of control logic in software or hardware or a combination of both. The control logic may be store in an information storage medium as a plurality of instructions adapted to direct an information processing device to perform a set of steps disclosed in embodiment of the present invention. Based on the disclosure and teachings provided herein, a person of ordinary skill in the art will appreciate other ways and/or methods to implement the present invention.
The above description is illustrative but not restrictive. Many variations of the invention will become apparent to those skilled in the art upon review of the disclosure. The scope of the invention should, therefore, be determined not with reference to the above description, but instead should be determined with reference to the pending claims along with their full scope or equivalents.

Claims

1. A method for collaborating between a first application and a second application, the method comprising:

receiving an analysis of a business process from the first application;

identifying a decision entity responsible for making decisions for the business process;

sending analysis information for the analysis of the process to the decision entity;

receiving decision information detailing a change to be implemented in the business process on the second application;

receiving result information resulting from the change in the business process on the second application; and

storing the analysis information, decision information, and result information.

2. The method of claim 1, further comprising causing a change in the second application to be made for the business process based on the decision information.

3. The method of claim 1, wherein causing the change comprising sending the analysis information and/or decision information to a change entity configured to implement the change in the second application.

4. The method of claim 1, further comprising automatically implementing the change in the second application based on the decision information.

5. The method of claim 1, further comprising using the stored analysis information, decision information, and/or result information to analyze a second business process.

6. The method of claim 5, further comprising determining if the change implemented for the business process is applicable for the second business process.

7. The method of claim 6, further comprising sending the decision information to a second decision entity for the second business process, wherein the second business entity can implement a change indicated by the decision information.

8. The method of claim 7, further comprising automatically causing the change indicated by the decision information to be implemented for the second business process on the second application.

9. The method of claim 1, further comprising validating the change in the business process using the result information.

10. The method of claim 9, wherein validating comprises using the analysis information and/or decision information to determine if the change is successful.

11. The method of claim 1, further comprising monitoring performance information received for the change in the business process.

12. The method of claim 11, further comprising determining second analysis information based on the performance information.

13. The method of claim 12, further comprising determining a second change to implement based on the second analysis information.

14. The method of claim 13, further comprising sending information for the second change to implement to the decision entity.

15. The method of claim 13, further comprising causing the change to be implemented for the business process on the second application.

16. The method of claim 1, wherein the decision entity comprises a business owner for the process.

17. The method of claim 1, wherein the decision entity comprises one or more groups, the method further comprising collaborating among the one or more groups to receive the decision information.

18. The method of claim 1, wherein the first application comprises an analytics application.

19. The method of claim 1, wherein the second application comprises an operational application.

20. The method of claim 1, further comprising sending the decision information for the problem and solution to a second entity configured to implement the change.

21. A system for managing business processes, the system comprising:

a first application configured to generate an analysis of a business process;

a decision hub configured to determine a decision entity responsible for making decisions for the business process, the decision entity receiving analysis information from the analysis of the business process and making a decision for the business process, wherein the decision hub receives decision information for the business process from the decision entity, the decision information detailing a change to be implemented in the business process; and

a second application configured to implement the change in the business process and configured to generate result information resulting from the change in the business process on the second application,

wherein the decision hub is configured to receive and store the analysis information, decision information, and result information.

22. The system of claim 21, wherein the decision entity comprises a business owner of the business process.

23. The system of claim 22, wherein the business owner comprises one or more users capable of authorizing changes to the business process, wherein the decision hub collaborates among the one or more users to receive the decision information.

24. The system of claim 21, wherein the decision hub coordinates communications among the first application, decision entity, and operational entity.

25. The system of claim 24, wherein the decision hub complies with regulatory rules for the communications.

26. The system of claim 21, wherein the decision hub is configured to automatically cause the change to be implemented in the second application based on the decision information.

27. The system of claim 21, wherein the decision hub is configured to use the stored analysis information, decision information, and/or result information to analyze a second business process.

28. The system of claim 21, wherein the decision hub is configured to validate the change in the business process using the result information.

29. The system of claim 21, wherein the decision hub is configured to provide predictive analysis for second result information for a second business process from the second application.

30. The system of claim 21, wherein the second application comprises an operational application.

31. The system of claim 21, wherein the second application comprises an operational application.

32. The system of claim 21, further comprising sending the decision information for the problem and solution to a second entity configured to effect the change.

33. An information storage medium having a plurality of instructions adapted to direct an information processing device to perform a set of steps for collaborating between a first application and a second application, the steps comprising:

receiving an analysis of a business process from the first application;

storing the analysis information, decision information, and result information.

34. The information storage medium of claim 33, further comprising causing a change in the second application to be made for the business process based on the decision information.

35. The information storage medium of claim 33, wherein causing the change comprising sending the analysis information and/or decision information to a change entity configured to implement the change in the second application.

36. The information storage medium of claim 33, further comprising automatically implementing the change in the second application based on the decision information.

37. The information storage medium of claim 33, further comprising using the stored analysis information, decision information, and/or result information to analyze a second business process.

38. The information storage medium of claim 37, further comprising determining if the change implemented for the business process is applicable for the second business process.

39. The information storage medium of claim 38, further comprising sending the decision information to a second decision entity for the second business process, wherein the second business entity can implement a change indicated by the decision information.

40. The information storage medium of claim 39, further comprising automatically causing the change indicated by the decision information to be implemented for the second business process on the second application.

41. The information storage medium of claim 33, further comprising validating the change in the business process using the result information.