Estimate the Results of Configuration Changes in an Enterprise

Abstract

Techniques are described that can estimate the results of (multiple) configuration changes in an enterprise. Enterprise information about a designated enterprise is collected. The enterprise information is combined with anonymized information received from multiple enterprises to provide combined information. The actual impact of a configuration change in at least one enterprise (e.g., with respect to a first subset of machines therein) and / or the predicted impact of (multiple) configuration changes in at least one enterprise (e.g., with respect to a second subset of machines therein) is determined. An estimate of the net financial result of implementing (multiple) configuration changes in the designated enterprise (e.g., with respect to the second subset of machines) is generated at least in part based on the actual impact and / or the predicted impact.

Description

Background Art

[0001] Information technology (IT) professionals typically perform actions (also referred to as IT actions) to facilitate the systems management of an organization's enterprise. One functional component of systems management is configuration management, which involves implementing configuration changes in the enterprise, such as to maintain the integrity of the enterprise. The actions performed by IT professionals are typically driven by providing the hardware and software that end users of the enterprise need in a manner that complies with the organization's security and compliance standards. However, many IT actions (or inactions), such as those performed for deploying software, hardware, and security and compliance settings, have hidden costs. Summary of the Invention

[0002] Various methods are described herein, particularly for estimating the results of (multiple) configuration changes in an enterprise. For example, the estimated results can be indicated (e.g., communicated) via an enterprise management tool used by an administrator to perform enterprise systems management. Systems management generally refers to the enterprise-wide management of distributed systems (e.g., computer systems). Some example tasks that can be performed via systems management include, but are not limited to, anti-manipulation management, anti-virus and anti-malware management, security management, storage management, capacity monitoring, server availability monitoring and metrics, monitoring of user activities, network capacity and utilization monitoring, hardware inventory, and software inventory and installation. Systems management often includes various functional components, including but not limited to data center infrastructure management, help desk management, network management, security information and event management, and configuration management. Configuration management typically systematically handles changes in a system to maintain the integrity of the system. Such changes can be implemented for beneficial purposes, including but not limited to: modifying the capabilities of the system; improving the performance, reliability, and / or maintainability of the system; extending the lifespan of the system; reducing the costs, risks, and / or liabilities of the system; and correcting the (multiple) defects of the system.

[0003] In an example method, enterprise information is collected. The enterprise information can include configuration information, ticket information, and / or performance information. The configuration information indicates configuration changes made to a specified enterprise. The ticket information indicates the volume of support tickets received regarding the configuration changes. The performance information indicates the performance of machines in the specified enterprise in response to the configuration changes. The enterprise information is combined with anonymous information received from multiple enterprises to provide combined information. The anonymous information can include anonymous configuration information, anonymous ticket information, and / or anonymous performance information. The anonymous configuration information indicates configuration changes made to an enterprise. The anonymous ticket information indicates the volume of support tickets received regarding the configuration changes made to an enterprise. The anonymous performance information indicates the performance of machines in an enterprise in response to the configuration changes made to the enterprise.

[0004] In a first aspect of the method, the predicted impact of (a) configuration change(s) on an enterprise is inferred by analyzing combined information using an adaptive algorithm. The predicted impact includes a predicted change in the performance of machines in the enterprise due to (a) configuration change(s) and a predicted change in the volume of support tickets received in the enterprise due to (a) configuration change(s). An estimate of the net financial result of implementing (a) configuration change(s) in a specified enterprise is generated based at least in part on the predicted impact of (a) configuration change(s) on the enterprise.

[0005] In a second aspect of the method, the actual impact of (a) configuration change(s) in at least one enterprise is determined. The actual impact includes an actual change in the performance of machines in at least one enterprise due to (a) configuration change(s) and an actual change in the volume of support tickets received regarding (a) configuration change(s) in at least one enterprise. An estimate of the net financial result of implementing (a) configuration change(s) in a specified enterprise is generated based at least in part on the actual impact of (a) configuration change(s) in at least one enterprise.

[0006] In a third aspect of the method, the actual impact of (a) configuration change(s) on a first subset of machines in a specified enterprise is determined. The actual impact includes an actual change in the performance of the first subset of machines in the specified enterprise due to (a) configuration change(s) and an actual change in the volume of support tickets received in response to (a) configuration change(s) regarding the first subset of machines in the specified enterprise. The predicted impact of (a) configuration change(s) on a second subset of machines in the specified enterprise is inferred by analyzing combined information using an adaptive algorithm. The predicted impact includes a predicted change in the performance of the second subset of machines in the specified enterprise due to (a) configuration change(s) and a predicted change in the volume of support tickets received in response to (a) configuration change(s) regarding the second subset of machines in the specified enterprise. An estimate of the net financial result of implementing (a) configuration change(s) regarding the second subset of machines in the specified enterprise is generated based at least in part on the actual impact of (a) configuration change(s) regarding the second subset of machines in the specified enterprise and further based at least in part on the predicted impact of (a) configuration change(s) regarding the second subset of machines in the specified enterprise.

[0007] This Summary is provided to introduce a series of concepts that will be further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter. Additionally, it should be noted that the invention is not limited to the specific embodiments described in the Detailed Description and / or other parts of this document. These embodiments are presented herein for illustrative purposes only. Based on the teachings contained herein, additional embodiments will be apparent to those skilled in the relevant art(s). BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The accompanying drawings incorporated herein and forming a part of the specification illustrate embodiments of the invention and, together with the specification, further serve to explain the principles involved and enable one of ordinary skill in the relevant art(s) to make and use the disclosed technology.

[0009] Figure 1 is a block diagram of a result estimation system according to an embodiment.

[0010] Figure 2 is according to an embodiment Figure 1 a block diagram of an example implementation of the result estimation system shown in

[0011] Figure 3 illustrates an example value report according to an embodiment.

[0012] Figures 4 - 6 depicts a flowchart of an example method for estimating the result of (a) configuration change(s) in an enterprise according to an embodiment.

[0013] Figure 7 is a block diagram of an example computing system according to an embodiment.

[0014] Figure 8 depicts an example computer in which embodiments may be implemented.

[0015] The features and advantages of the disclosed technology will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings, in which like reference numerals throughout identify corresponding elements. In the drawings, like reference numerals generally indicate identical, functionally similar, and / or structurally similar elements. The drawing in which an element first appears is indicated by the leftmost digit(s) in the corresponding reference numeral.

[0016] Specific implementation

[0017] I. Introduction

[0018] The following detailed description refers to the accompanying drawings that illustrate exemplary embodiments of the invention. However, the scope of the invention is not limited to these embodiments, but is defined by the appended claims. Thus, embodiments other than those shown in the drawings, such as modified versions of the illustrated embodiments, may still be covered by the invention.

[0019] References to "an embodiment", "embodiment", "example embodiment", etc. in the specification indicate that the described embodiments may include particular features, structures, or characteristics, but each embodiment may not necessarily include the particular features, structures, or characteristics. Furthermore, these phrases do not necessarily refer to the same embodiment. Moreover, when a particular feature, structure, or characteristic is described in connection with an embodiment, whether or not explicitly described, those skilled in the relevant art will know to implement such feature, structure, or characteristic in connection with other embodiments.

[0020] Descriptors such as "first", "second", "third", etc. are used to refer to some of the elements discussed herein. Such descriptors are used to facilitate the discussion of example embodiments and do not indicate a required order of the elements being referred to, unless a positive statement is made herein that such an order is required.

[0021] II. Example Embodiments

[0022] The example embodiments described herein are capable of estimating the results of (multiple) configuration changes in an enterprise. Examples of configuration changes include, but are not limited to, deployment of software (e.g., updates to software), changes to hardware, changes to (multiple) security settings, and changes to (multiple) compliance settings. For example, the estimated results can be indicated (e.g., communicated) via an enterprise management tool used by IT professionals (e.g., administrators) to perform enterprise system management. System management generally refers to the enterprise-wide management of distributed systems (e.g., computer systems). Some example tasks that can be performed via system management include, but are not limited to, anti-manipulation management, anti-virus and anti-malware management, security management, storage management, capacity monitoring, server availability monitoring and metrics, monitoring of user activities, network capacity and utilization monitoring, hardware inventory, and software inventory and installation. System management typically includes various functional components, including but not limited to data center infrastructure management, help desk management, network management, security information and event management, and configuration management. Configuration management generally systematically handles changes in a system to maintain the integrity of the system. Such changes can be implemented for beneficial purposes, including but not limited to: modifying the capabilities of the system; improving the performance, reliability, and / or maintainability of the system; extending the life of the system; reducing the costs, risks, and / or liabilities of the system; and correcting (multiple) defects in the system.

[0023] Compared with conventional system management techniques, the example techniques described herein have various benefits. For example, the example techniques can be able to estimate the results of (multiple) configuration changes in an enterprise. The estimation of the results can include an estimation of the net financial results of implementing (multiple) configuration changes in the enterprise. The net financial results can take into account the initial costs associated with implementing (multiple) configuration changes (e.g., the cost of reduced end-user productivity during implementation) and the reduction in ongoing costs associated with the enterprise (e.g., the financial benefits of increased end-user productivity after implementation is complete). The net financial results can be based on any of a variety of factors, including but not limited to the predicted impact of (multiple) configuration changes on one or more enterprises, the actual impact of (multiple) configuration changes on one or more enterprises, or a combination thereof. The example techniques can provide IT professionals with insights into traditional hidden costs associated with implementing (multiple) configuration changes in an enterprise. Examples of such traditional hidden costs include but are not limited to costs associated with reduced productivity of end-users in the enterprise (e.g., resulting therefrom) and costs associated with an increase in the volume of support tickets received from end-users in the enterprise (e.g., per unit of time). By providing visibility into the total cost of implementing (multiple) configuration changes in an enterprise, the example techniques can lead to more effective planning and decision-making regarding the system management of the enterprise.

[0024] The example techniques can reduce the amount of time and / or resources (e.g., processor cycles, memory, network bandwidth) used to manage the system. The example techniques can reduce the costs associated with managing the system. For example, by generating an estimate of the net financial results of implementing (multiple) configuration changes, the implementation of (multiple) configuration changes with a net financial result greater than a threshold amount can be not performed, or one or more of the (multiple) configuration changes can be modified to reduce the net financial result. The example techniques can increase the efficiency of a computing system that performs system management operations for an enterprise. For example, if the estimated net financial cost of implementation exceeds a cost threshold, the computing system can be configured to not implement the configuration change, or the computing system can be configured to modify one or more of the configuration changes to reduce the financial cost of implementation to below the cost threshold.

[0025] In addition, by generating an estimate of the net financial results of implementing (multiple) configuration changes, IT professionals do not have to spend time manually performing calculations to estimate the net financial results. Reducing the time IT professionals spend managing the system reduces the costs associated with IT professionals. The example techniques can increase the efficiency of IT professionals. For example, by generating an estimate of the net financial results of implementing (multiple) configuration changes, the example techniques can reduce the number of steps performed by IT professionals, the amount of effort spent by IT professionals, and / or the time spent by IT professionals overseeing enterprise system management.

[0026] Example techniques can improve the efficiency of end users. For example, the implementation of (multiple) configuration changes in an enterprise can result in significant downtime for end users. By generating an estimate of the net financial result of implementing the (multiple) configuration changes, mitigation measures can be taken to reduce the downtime of end users. For example, the implementation of (multiple) configuration changes that consume more than a threshold amount of time can be delayed until the end user is not interacting with the computing device that will be affected by the (multiple) configuration changes, one or more of the (multiple) configuration changes can be selectively excluded from the implementation, or one or more of the (multiple) configuration changes can be modified to reduce the downtime of end users. By reducing the downtime of end users due to implementation, a portion of the implementation cost attributed to end users can be reduced.

[0027] Figure 1 FIG. is a block diagram of an example result estimation system 100 according to an embodiment. In general, the result estimation system 100 operates to perform system configuration operations with respect to enterprises 114A - 114M (e.g., customer environments). As Figure 1 shown, the result estimation system 100 includes (multiple) system management servers 102 and enterprises 114A - 114M. Communication between the (multiple) system management servers 102 and the enterprises 114A - 114M is performed over a network 108 using well-known network communication protocols. The network 108 can be a wide area network (e.g., the Internet), a local area network (LAN), another type of network, or a combination thereof.

[0028] Each of the enterprises 114A - 114M includes a management system 104, a network 110, and user devices 106. For example, the first enterprise 114A includes a first management system 104A, a first network 110A, and a first user device 106A. The second enterprise 114B includes a second management system 104B, a second network 110B, and a second user device 106B. The Mth management system 104M includes an Mth management system 104M, an Mth network 110M, and an Mth user device 106M. In each of the enterprises 114A - 114M, communication between the management system 104 and the user devices 106 is performed over a network 110 using well-known network communication protocols. The network 110 can be a wide area network (e.g., the Internet), a local area network (LAN), another type of network, or a combination thereof.

[0029] In each of enterprises 114A - 114M, user device 106 is a processing system capable of communicating with management system 104. An example of a processing system is a system including at least one processor capable of manipulating data according to an instruction set. For example, the processing system can be a computer, a personal digital assistant, etc. User device 106 is configured to provide a request to a server (not shown) for information stored on the server (or otherwise accessible via the server). For example, a user can initiate a request to execute a computer program (e.g., an application) using a client (e.g., a web browser or other type of client) deployed on a user-owned or otherwise accessible user device. According to some example embodiments, user device 106 is capable of accessing a domain (e.g., a website) hosted by a server such that user device 106 can access information available through the domain. For example, such a domain can include web pages, which can be provided as hypertext markup language (HTML) documents and objects (e.g., files) linked therein.

[0030] Each of user devices 106 can include any client-enabled system or device, including but not limited to desktop computers, laptop computers, tablet computers, wearable computers such as smartwatches or head-mounted computers, personal digital assistants, cellular phones, Internet of Things (IoT) devices, etc. It will be recognized that any one or more of user devices 106 can communicate with any one or more servers.

[0031] User device 106 is also configured to automatically or in response to a query from management system 104 provide information to management system 104. For example, such information can relate to the hardware and / or software configuration of user device 106 (e.g., its updates), the workload of user device 106, resource utilization on the user device, etc.

[0032] Management system 104 is a processing system capable of communicating with user device 106. Management system 104 is configured to perform operations that facilitate system management of enterprise 114 in response to instructions received from an IT administrator of enterprise 114. For example, management system 104 can provide commands to (one or more) system management servers 102 indicating configuration changes to be implemented by (one or more) system management servers 102 to perform system management of enterprise 114. In another example, management system 104 can provide a request for information to user device 106. The IT administrator can make decisions regarding system management of user device 106 and / or network 110 at least in part based on information received from user device 106. It will be recognized that at least some (e.g., all) of the information can be collected by (one or more) system management servers 102 rather than by management system 104.

[0033] (Multiple) system management servers 102 are processing systems capable of communicating with enterprises 114A - 114M. For example, (multiple) system management servers 102 may be capable of communicating with management systems 104 and / or user devices 106 in each enterprise 114. (Multiple) system management servers 102 include result estimation logic 112. The result estimation logic 112 is configured to provide information to and / or collect information from management servers 104 and / or user devices 106 in each enterprise 114 for performing system management of the enterprise 114. For example, the result estimation logic 112 may push information to the user device 106 or provide information in response to a request received from the user device 106. The request may be user-generated or generated without user intervention. For example, a policy applied to the user device may be completed without an explicit user request. According to this example, the policy is applied in the background even if the user is not logged in to the user device. Further according to this example, the user device (e.g., an agent thereon) may poll the server for the policy based on a schedule (e.g., once per hour) or based on an event (e.g., device wake-up, user unlock, etc.). Further according to this example, the server may push the policy to the user device (e.g., an agent thereon) via an open HTTP endpoint.

[0034] According to an example embodiment described herein, the result estimation logic 112 collects enterprise information. The enterprise information may include configuration information, ticket information, and / or performance information. The configuration information indicates configuration changes made to a specified enterprise (e.g., any one of enterprises 114A - 114M). The ticket information indicates the volume of support tickets received regarding the configuration changes. The performance information indicates the performance of machines in the specified enterprise (e.g., the first user device 106A in enterprise 114A, the second user device 106B in enterprise 114B, …, or the Mth user device 106M in enterprise 114M) in response to the configuration changes. For example, the performance information may indicate the startup time, login time, application startup time, crash rate, network latency, and / or resource consumption (e.g., CPU, disk, or battery consumption) of any one or more machines in the specified enterprise. The result estimation logic 112 combines the enterprise information with anonymized information received from multiple enterprises (e.g., any two or more of enterprises 114A - 114M) to provide combined information. The multiple enterprises may or may not include the specified enterprise. The anonymized information may include anonymized configuration information, anonymized ticket information, and / or anonymized performance information. The anonymized configuration information indicates configuration changes made to an enterprise. The anonymized ticket information indicates the volume of support tickets received regarding the configuration changes made to an enterprise. The anonymized performance information indicates the performance of machines in an enterprise in response to the configuration changes made to the enterprise. The anonymized information may be averaged across the enterprises, but the example embodiment is not limited in this regard. For example, such averaged information may be used as a baseline for comparing the enterprise information. In another example, previously collected enterprise information regarding the specified enterprise may be used as a baseline for comparing the enterprise information.

[0035] In a first example implementation, the result estimation logic 112 infers the predicted impact of the (multiple) configuration changes on the enterprise by analyzing the combined information using an adaptive algorithm. The predicted impact includes the predicted changes in the performance of machines in the enterprise due to the (multiple) configuration changes and the predicted changes in the volume of support tickets received in the enterprise due to the (multiple) configuration changes. In an example scenario, the (multiple) configuration changes may include the deployment of a security feature, and the predicted impact may indicate that the deployment of the security feature may reduce startup performance (e.g., increase startup time), negatively impact login attempts due to multi - factor authorization prompts (e.g., increase login time), and / or increase the volume of support tickets in the enterprise. The result estimation logic 112 generates an estimate of the net financial result of implementing the (multiple) configuration changes in the specified enterprise based at least in part on the predicted impact of the (multiple) configuration changes on the enterprise.

[0036] In a second example implementation, the result estimation logic 112 determines the actual impact of the (multiple) configuration changes in at least one enterprise (e.g., at least one of enterprises 114A - 114M). The actual impact includes the actual changes to the performance of the machines in at least one enterprise due to the (multiple) configuration changes and the actual changes to the volume of support tickets received regarding the (multiple) configuration changes in at least one enterprise. In an example scenario, the actual impact may indicate that the (multiple) configuration changes have caused the application to stop working (e.g., unable to execute) and / or an increase in the volume of support tickets in at least one enterprise. The result estimation logic 112 generates an estimate of the net financial result of implementing the (multiple) configuration changes in a specified enterprise based at least in part on the actual impact of the (multiple) configuration changes in at least one enterprise. In an example scenario, by translating the actual impact of the (multiple) configuration changes into end - user time interruptions and then into actual costs based at least in part on the full - load cost per hour of the end - user and the estimated cost of processing support tickets (e.g., historical costs), the estimation logic 112 can generate an estimate of the net financial result.

[0037] In a third example implementation, the result estimation logic 112 determines the actual impact of the (multiple) configuration changes on a first subset of machines in a specified enterprise. The actual impact includes the actual changes to the performance of the first subset of machines in the specified enterprise due to the (multiple) configuration changes and the actual changes to the volume of support tickets received in response to the (multiple) configuration changes regarding the first subset of machines in the specified enterprise. The result estimation logic 112 infers the predicted impact of the (multiple) configuration changes on a second subset of machines in the specified enterprise by analyzing the combined information using an adaptive algorithm. The predicted impact includes the predicted changes to the performance of the second subset of machines in the specified enterprise due to the (multiple) configuration changes and the (multiple) predicted changes to the volume of support tickets received in response to the (multiple) configuration changes regarding the second subset of machines in the specified enterprise. The result estimation logic 112 generates an estimate of the net financial result of implementing the (multiple) configuration changes on the second subset of machines in the specified enterprise based at least in part on the actual impact of the (multiple) configuration changes on the first subset of machines in the specified enterprise and further based at least in part on the predicted impact of the (multiple) configuration changes on the second subset of machines in the specified enterprise. Thus, the third example implementation enables predicting the incremental financial impact of extending the (multiple) configuration changes from pilot machines (e.g., the first subset of machines) to production machines (e.g., the second subset of machines) in a specified enterprise.

[0038] In the first, second, and third example implementations above, the net financial result can be a net positive number (e.g., net financial gain) or a net negative number (e.g., net financial loss), depending on whether the combination of various financial factors on which the net financial result is based is positive or negative, respectively.

[0039] It will be appreciated that at least some of the above functionality may be implemented by the management system 104 rather than by the result estimation logic 112. For example, if an administrator does not want to provide information to the (one or more) system management servers 102 and instead wishes to only receive information from the (one or more) system management servers 102, the management system 104 may gather enterprise information about a specified enterprise. The management system 104 may combine the enterprise information with anonymized information to provide combined information. The management system 104 may determine the actual impact of the (one or more) configuration changes and / or infer the predicted impact of the (one or more) configuration changes. The management system 104 may generate an estimate of the net financial result based at least in part on the actual impact and / or predicted impact.

[0040] The result estimation logic 112 may be implemented in various ways to estimate the result of the (one or more) configuration changes in an enterprise, including being implemented in hardware, software, firmware, or any combination thereof. For example, at least a portion of the result estimation logic 112 may be implemented as computer program code configured to execute in one or more processors. In another example, at least a portion of the result estimation logic 112 may be implemented as hardware logic / circuitry. For example, at least a portion of the result estimation logic 112 may be implemented in a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), an application specific standard product (ASSP), a system on a chip (SoC), a complex programmable logic device (CPLD), etc. Each SoC may include an integrated circuit chip that includes one or more of the following: a processor (e.g., a microcontroller, a microprocessor, a digital signal processor (DSP), etc.), a memory, one or more communication interfaces, and / or other circuitry and / or embedded firmware for performing its functions.

[0041] For illustrative purposes and not by way of limitation, the result estimation logic 112 is shown as being incorporated in the (one or more) system management servers 102. It will be appreciated that the result estimation logic 112 (or any portion thereof) may be incorporated in any one or more of the management system 104 and / or the user devices 106. For example, the client side of the result estimation logic 112 may be incorporated in one or more of the management system 104 and / or the user devices 106, and the server side of the result estimation logic 112 may be incorporated in the (one or more) system management servers 102.

[0042] Figure 2 is a block diagram of a result estimation system 200 according to an embodiment, the result estimation system 200 being Figure 1An example implementation of the results estimation system 100 shown in. The results estimation system 200 can be configured to measure key device metrics and changes in an enterprise and compare those metrics and changes to company and global baselines to gain insights such as the net financial results of implementing changes in the enterprise. As Figure 2 shown, the results estimation system 200 includes system management server(s) 202 and customer environments 214A - 214C. For example, the communication between system management server(s) 202 and customer environments 214 can be performed over a network using well-known network communication protocols. System management server(s) 202 includes results estimation logic 212. The results estimation logic 212 is configured to estimate the results of configuration changes in customer environments 214A - 214M. The results estimation logic 212 includes an administrative portal 244, an enterprise repository 216, estimation logic 218, a global data repository 220, an event processor 222, and an event collector 224. The event collector 224 collects event information, including configuration information 236, support tickets 240, and performance information 242. For example, the event collector 224 collects configuration information 236 from management systems in respective customer environments 214A - 214M. According to this example, the event collector 240 collects support tickets 240 and performance information 242 from desktop clients in customer environments 214A - 214M. The configuration information 236 and performance information 242 are the same as the configuration information and performance information described with reference to Figure 1 the results estimation system 100 above. Each support ticket indicates a technical problem encountered by an end user related to the end user's desktop client.

[0043] The event processor 222 processes event information received from the event collector 224. For example, the event processor 222 generates ticket information at least in part based on the support ticket 240. The ticket information indicates the volume of support tickets 240 received regarding the configuration change indicated by the configuration information 236. The event processor 222 can cross-reference the support ticket 240 with the configuration change at least in part based on the (multiple) time instances when the configuration change is implemented, the (multiple) time instances when the support ticket 240 is received, aspects of the enterprise that will be affected by the configuration change, and / or aspects of the enterprise indicated by the support ticket 240 that are experiencing problems. By cross-referencing the support ticket 240 with the configuration change, the event processor 222 can determine the likelihood that each support ticket 240 is related to the configuration change. The event processor 222 can use the likelihood to determine the volume of support tickets 240 received regarding the configuration change. The event processor 222 can identify enterprise information 210 for each of the enterprises 214A - 214M. The enterprise information 210 for each enterprise includes enterprise-specific information for that enterprise (e.g., what it consists of). For example, the event processor 222 can filter the event information to identify the portion of the event information related to each enterprise. The event processor 222 can designate the portion of the event information related to each enterprise as the enterprise information 210 for that enterprise. The event processor 222 can generate anonymized information 208 by aggregating event information from the various enterprises 214A - 214M and removing any information that can be used to identify the individual parts from which customer environment 214A the event information was received.

[0044] The enterprise data repository 216 stores enterprise information 210. The global data repository 220 stores anonymized information 208. The estimation logic 218 generates result estimates 234A - 234M for respective customer environments 214A - 214M. The result estimate for a customer environment includes an estimate of the net financial result of implementing (one or more) configuration changes in the customer environment, based at least in part on an analysis of the enterprise information 210 and anonymized information 208 of the enterprise. The estimation logic 218 can generate each result estimate 234A - 234M based at least in part on the actual impact of the (one or more) configuration changes on device metrics (e.g., device downtime, user switching devices, user interaction time, user productivity) and / or the predicted impact of the (one or more) configuration changes in one or more of the customer environments 214A - 214M. For example, the actual impact can be determined based at least in part on support tickets 240 and performance information 242 received from one or more of the customer environments 214A - 214M in response to the implementation of the (one or more) configuration changes. Since the actual impact may be based on data from fewer than all of the enterprises 234A - 234M (e.g., a single enterprise), the set of variables used to determine the actual impact can be narrowed. For example, instead of using variables corresponding to relatively broad categories, multiple variables corresponding to sub - categories of relatively broad categories can be used.

[0045] In an example embodiment, the estimation logic 218 uses data from the customer environments 214A - 214M to train a machine - learning model to predict changes in device metrics and support ticket volume due to the (one or more) configuration changes. In another example embodiment, the estimation logic 218 includes a custom rules engine that uses data from the customer environments 214A - 214M to predict changes in device metrics and support ticket volume due to the (one or more) configuration changes. According to these embodiments, the data from the customer environments 214A - 214M can include enterprise information and / or the anonymized information described above with reference to Figure 1 The estimation logic 218 can be able to state that "X% of <devices|users> with <common characteristic> will see a Y% regression in <metric> due to <configuration change>". The set of all possible configuration changes can be very broad, which can result in a large amount of data, and the scope is limited to a manageable subset of predictive variables (characteristics and configuration changes) based on data analysis. The estimation logic 218 can look for metric regressions across all devices and end - users, trace back to a set of root causes, and then use clustering algorithms to identify common attributes of the affected devices and end - users.

[0046] The estimation logic 218 can calculate the net financial result of the (multiple) configuration changes in the following manner: convert the impact of the device metric changes (e.g., actual impact and / or predicted impact) into end-user time interruptions, determine the full-load cost per hour for each end-user, determine the estimated cost of processing each support ticket, and sum (a) the product of the end-user time interruptions and the full-load cost per hour for each end-user and (b) the estimated time of processing the support tickets resulting from the implementation of the (multiple) configuration changes and the estimated cost of processing each support ticket. The full-load cost per hour for each end-user can be equal to the total compensation provided to the multiple end-users for working over a period of time (e.g., the monetary value including pay and benefits) divided by the cumulative number of hours worked by the multiple end-users over a period of time. Converting the impact into end-user time interruptions may involve determining which parts of the operations performed to implement the (multiple) configuration changes occur when the end-users are using their devices and which parts occur when the end-users are not using their devices. For example, an update installed at night may have no impact on the end-users, but an update installed during working hours may delay the end-users' productivity by 10 minutes or more.

[0047] The management portal 244 is an interface through which an administrator in the customer environment can communicate with the result estimation logic 212. The management portal 244 is configured to provide the result estimations 234A - 234M to the corresponding customer environments 214A - 214M.

[0048] Each of the customer environments 214A - 214M includes a management system and a desktop client. For example, the customer environment 214A is shown as including a management system 204 and desktop clients 206A - 206C. The management system 204 can operate in a similar manner to any one of the management systems 104A - 104M described above with reference to Figure 1 The desktop clients 206A - 206C can operate in a similar manner to those described above with reference to Figure 1operate in a similar manner to any of the described user devices 106A - 106M. For non - limiting illustrative purposes, the desktop client 206 is shown as including an event log 226, a registry 228, an extension 230, and a performance monitoring agent 232. The event log 226 includes information related to events that occur with respect to the desktop client 206A. For example, the event log 226 can include a timestamp indicating the time instance at which a corresponding event occurred and descriptive information describing the event. The registry 228 can include a database of configuration settings in the operating system (OS) of the desktop client 206A. The extension 230 extends the operating system driver model. For example, the extension 230 can provide an OS interface through which instrumented components can provide information and notifications. The performance monitoring agent 232 views the event log 226, the configuration settings in the registry 228, and the information and notifications received through the OS interface provided by the extension 230, and generates performance information 242 based on them.

[0049] It will be appreciated that the result estimation system 200 may not include Figure 2 one or more of the components shown in Figure 2 In addition, the result estimation system 200 may include components in addition to or instead of

[0050] Figure 3 Illustrated is an example value report 300 according to an embodiment. The value report 300 indicates the amount of money that an organization has saved (or could save) by optimizing performance metrics (e.g., startup time and login time) relative to a previously measured baseline. The value report 300 includes a baseline menu 302, a filter menu 304, an hourly cost field 306, a currency menu 308, and a virtual creation button 310. The baseline menu 302 enables an administrator of an enterprise to select a date on which to capture a snapshot of metrics associated with the enterprise to establish a baseline for the enterprise. The baseline menu 302 is configured to provide a plurality of dates from which to select a date. For non - limiting illustrative purposes, the date is specified as June 19th. The filter menu 304 enables the administrator to filter events that occur in the enterprise in order to determine the net financial result of implementing (a) configuration change(s) in the enterprise. For non - limiting illustrative purposes, the administrator does not select a filter. The hourly cost field 306 enables the administrator to specify the hourly cost associated with each information worker (IW). The currency menu 308 enables the administrator to specify the currency associated with the hourly cost specified in the hourly cost field 306. For non - limiting illustrative purposes, the administrator specifies an hourly cost of $100.00 in the hourly cost field 306 and selects US dollars (USD) in the currency menu 308. The virtual creation button 310 enables the administrator to initiate the creation of the net financial result of implementing (a) configuration change(s) in the enterprise.

[0051] The value report 300 shows the results of the administrator's selection of the virtual creation button 310. Specifically, the value report 300 states that the implementation of the (multiple) configuration changes will result in monthly savings of $28,000, including a first part associated with the reduction in startup time due to the implementation of the (multiple) configuration changes and a second part associated with the reduction in login time due to the implementation of the (multiple) configuration changes. The value report 300 indicates that the implementation of the (multiple) configuration changes reduced the startup time by 21 hours, which corresponds to a monthly cost savings of $10,238. The monthly cost savings due to the reduction in startup time is based on the analysis of 38,700 restarts corresponding to 17,234 devices this month. The value report 300 further indicates that the implementation of the (multiple) configuration changes reduced the login time by 38 hours, which corresponds to a monthly cost savings of $17,762. The monthly cost savings due to the reduction in login time is based on the analysis of 38,700 logins corresponding to 17,234 devices this month. The value report 300 further indicates that the implementation of the (multiple) configuration changes fixed 154 issues, corresponding to 17,234 devices and 5 scripts. The administrator's selection of the "Print" user interface element causes the value report 300 to be printed. The administrator's selection of the "Export" user interface element causes the value report 300 to be exported (e.g., saved in a file at a location specified by the administrator). For example, the administrator can share the value report 300 with the organization's Chief Information Officer (CIO) to demonstrate the net amount of money saved (or to be saved) by implementing the (multiple) configuration changes.

[0052] Figures 4 - 6 Flowcharts 400, 500, and 600 depict an example method for estimating the results of (multiple) configuration changes in an enterprise. For example, flowcharts 400, 500, and 600 can be executed by Figure 1 one of the (multiple) system management servers 102, one of the management systems 104A - 104M shown, or a combination thereof. For illustrative purposes, flowcharts 400, 500, and 600 are described in relation to Figure 7 the computing system 700 shown, which can be an example implementation of one of the (multiple) system management servers 102, one of the management systems 104A - 104M, or a combination thereof. As Figure 7As shown in FIG. 700, the computing system 700 includes result estimation logic 712. The result estimation logic 712 includes combinational logic 702, determination logic 704, and estimation logic 706. The determination logic 704 includes actual impact logic 714 and predicted impact logic 716. The predicted impact logic 716 includes an adaptive algorithm 722. The estimation logic 706 includes timing logic 718 and generation logic 720. Based on the discussion of flowcharts 400, 500, and 600, further structural and operational embodiments will be apparent to those of ordinary skill in the relevant art(s).

[0053] As Figure 4 shown, the method of flowchart 400 begins at step 402. At step 402, enterprise information about a designated enterprise is collected. The enterprise information includes (a) configuration information indicating configuration changes made to the designated enterprise, (b) ticket information related to the volume of support tickets received regarding the configuration changes, and (c) performance information regarding the performance of machines in the designated enterprise in response to the configuration changes. In an example implementation, the combinational logic 702 collects enterprise information 710, which includes configuration information, ticket information, and performance information.

[0054] The performance of any one or more machines can include machine startup time (e.g., a phase of a multi-phase implementation of a (multiple) configuration change); login time (e.g., via a phase); (multiple) application startup time (e.g., the duration between the time instance when an application starts and the time instance when a user begins to interact with the application); the total duration consumed by the CPU, memory, disk input / output (I / O), and / or network I / O, which is greater than or equal to a consumption threshold (e.g., 75% of the total consumption capacity); machine utilization (e.g., the number of hours each machine is on and a user is interacting with the machine); application utilization; battery utilization; disk utilization; network latency and timeout rate (e.g., via an application and / or a Uniform Resource Identifier (URI) endpoint); mean time between failures of kernel crashes (BSOD); mean time between failures of abnormal shutdowns; predicted battery life as the mean time between consumptions; a subset of applications that consume battery at a rate exceeding a threshold rate when a user interacts with them; predicted life of a hard disk drive; and / or the frequency of hangs and crashes of drivers and / or applications.

[0055] The performance of any one or more machines can include the amount of downtime that one or more machines experience at least in part due to the implementation of (multiple) configuration changes. The downtime of a machine corresponds to the downtime of the end users using the machine. The downtime of an end user corresponds to a reduction in the productivity of the end user. The downtime of a machine can include the time when the machine is offline (e.g., not fully started) and / or the time when the machine is in a state where the end user cannot interact with the machine. For example, when an end user logs in to a machine, the time period between the time instance when the end user enters the end user credentials and the time instance when the end user can start an application on the machine is an example of the time when the machine is in a state where the end user cannot interact with the machine.

[0056] At step 404, enterprise information is combined with anonymized information received from multiple enterprises to provide combined information. The anonymized information includes (a) anonymized configuration information indicating configuration changes made to an enterprise, (b) anonymized ticket information related to the volume of support tickets received regarding the configuration changes made to an enterprise, and (c) anonymized performance information regarding the performance of machines in an enterprise in response to configuration changes made to the enterprise. In an example implementation, the combination logic 702 combines the enterprise information 710 with the anonymized information 708 received from multiple enterprises to provide combined information 732.

[0057] At step 406, the combined information is analyzed using an adaptive algorithm to infer the predicted impact of (multiple) configuration changes on the enterprise. The predicted impact includes the predicted changes in the performance of machines in the enterprise due to (multiple) configuration changes and the predicted changes in the volume of support tickets received in the enterprise due to (multiple) configuration changes. For example, the predicted impact of (multiple) configuration changes on an enterprise can be inferred at least in part based on the actual impact of (multiple) configuration changes on one or more enterprises. In another example, the predicted impact of (multiple) configuration changes on an enterprise can be inferred at least in part based on the actual impact of one or more other configuration changes on one or more enterprises. For example, a correlation can be made between one or more other configuration changes, the actual impact of one or more other configuration changes, and (multiple) configuration changes to infer the predicted impact. In an example implementation, the predicted impact logic 716 analyzes the combined information 732 using an adaptive algorithm 722 to infer the predicted impact of (multiple) configuration changes on the enterprise. According to this implementation, the predicted impact logic 716 generates a predicted impact indicator 726 in response to inferring the predicted impact. The predicted impact indicator 726 indicates (e.g., specifies) the predicted impact.

[0058] In an example embodiment, the inferred prediction impact at step 406 is at least partially based on how the (multiple) configuration changes are implemented. For example, the determination can be at least partially based on the amount of time it will take to implement the (multiple) configuration changes in a specified enterprise. According to this example, implementing the (multiple) configuration changes in a relatively short amount of time may have a greater impact than implementing the (multiple) configuration changes in a relatively large amount of time. For example, on average, rolling out an update quickly may have a greater impact than rolling out the update more slowly.

[0059] At step 408, an estimate of the net financial result of implementing the (multiple) configuration changes in a specified enterprise is generated at least partially based on the predicted impact of the (multiple) configuration changes on the enterprise. In an example implementation, the estimation logic 706 generates a result estimate 736 at least partially based on the predicted impact indicated by the predicted impact indicator 726. The result estimate 736 includes an estimate of the net financial result of implementing the (multiple) configuration changes within the specified enterprise.

[0060] In an example embodiment, generating an estimate of the net financial result at step 408 includes: determining, at least in part based on the predicted impact, the cumulative amount of time that the productivity of end users in a specified enterprise is statistically likely to change due to the (multiple) configuration changes. For example, the time logic 718 can determine the cumulative amount of time. According to this example, the time logic 718 can generate a time indicator 734 to indicate the cumulative amount of time. According to this embodiment, generating an estimate of the net financial result at step 408 is at least in part based on the sum of a first part and a second part. The first part is equal to the product of the cumulative amount of time and the cost per unit time for each end user in the specified enterprise. The second part is equal to the estimated cost of resolving support tickets related to the (multiple) configuration changes in the specified enterprise. For example, the generation logic 720 can generate a result estimate 736 at least in part based on the sum of the first part and the second part. According to this example, the generation logic 720 can generate the result estimate 736 in response to receiving the time indicator 734, the end user cost information 728, and the ticket cost information 730. The end user cost information 728 indicates the cost per unit time for each end user in the specified enterprise. The ticket cost information 730 indicates the estimated cost of resolving support tickets related to the (multiple) configuration changes in the specified enterprise. In one aspect, the estimated cost of resolving support tickets can be at least in part based on a predetermined fixed estimated cost associated with each support ticket. The fixed estimated cost can be any suitable cost, such as $100, $150, $185, etc. According to this aspect, the estimated cost of resolving support tickets related to the (multiple) configuration changes can be calculated by multiplying the fixed estimated cost by the number of support tickets. For example, if the fixed estimated cost is $100 and 23 support tickets are attributable to the implementation of the (multiple) configuration changes, the estimated cost of resolving support tickets is $100 * 23 = $2300.

[0061] In an example implementation of the present embodiment, the cumulative time amount includes a first time amount and a second time amount. According to this implementation, the first portion is equal to the sum of (a) the product of the first time amount and the first common cost per unit time for each end user in a first subset of end users in a specified enterprise and (b) the product of the second time amount and the second common cost per unit time for each end user in a second subset of end users in the specified enterprise. Further according to this implementation, the first common cost per unit time for each end user in the first subset of end users and the second common cost per unit time for each end user in the second subset of end users are different. For each subset of end users, the different common costs per unit time for each end user can be referred to as "different labor costs". For example, the first subset of end users may reside in a first geographical region, and the second subset of end users may reside in a second geographical region different from the first geographical region. According to this example, the first geographical region may be China, and the second geographical region may be Europe. In another example, the first subset of end users may belong to a first level (e.g., pay grade) in the hierarchy of an organization, and the second subset of end users may belong to a second level in the hierarchy, where the second level is different from the first level. According to this example, the first level may correspond to a management team, and the second level may correspond to a manager. It should be noted that if the product of the first time amount and the first common cost per unit time for each end user in the first subset of end users is greater than the product of the second time amount and the second common cost per unit time for each end user in the second subset of end users, it can be determined that (multiple) configuration changes are implemented before the machines in the first subset of end users with respect to the machines in the second subset of end users, and vice versa.

[0062] In another example embodiment, the estimation of the net financial result at step 408 takes into account the estimated initial costs associated with implementing the (multiple) configuration changes during the implementation of the (multiple) configuration changes and further takes into account the estimated financial benefits predicted to result from the completion of the implementation. The estimated initial costs are due to, during the implementation of the (multiple) configuration changes, a predicted decrease in the performance of the machines in the specified enterprise and / or a predicted increase in the volume of support tickets received regarding the (multiple) configuration changes in the specified enterprise. The estimated financial benefits are due to, after the completion of the implementation of the (multiple) configuration changes, a predicted increase in the performance of the machines in the specified enterprise and / or a predicted decrease in the volume of support tickets received in the specified enterprise. For example, the estimated financial benefits may correspond to a payback period, at which the estimated initial costs are offset by a reduction in the ongoing costs associated with the machines after the completion of the implementation of the (multiple) configuration changes. According to this example, on a daily basis after the completion of the implementation of the (multiple) configuration changes, the end users may be more productive, may receive fewer support tickets, the end users' machines may experience less downtime, the machines may be more efficient, the machines may perform operations faster, etc. If the estimated initial costs are greater than the estimated financial benefits predicted to result, the net financial result may be referred to as a net financial loss. If the estimated initial costs are less than the estimated financial benefits expected to result, the net financial result may be referred to as a net financial benefit.

[0063] In yet another example embodiment, the enterprise information further includes inventory information that indicates the hardware and / or software associated with the machines in the specified enterprise. According to this embodiment, the anonymized information further includes anonymized inventory information that indicates the hardware and / or software associated with the machines in multiple enterprises. The inventory information (e.g., anonymized inventory information) may indicate any suitable information about any one or more machines. Examples of inventory information include, but are not limited to, the manufacturer and / or hardware model associated with one or more machines; the vintage of such hardware; the memory, central processing unit (CPU), hard disk drive, network, and / or (multiple) additional peripheral devices associated with one or more machines; the version and / or patch level of the operating system (OS) deployed on one or more machines; the applications installed on one or more machines; the boot configuration of one or more machines; the version of the basic input / output system (BIOS) deployed on one or more machines; the inventory and / or (multiple) versions of the (multiple) drivers installed on one or more machines; and the settings and / or policies associated with one or more machines.

[0064] In yet another example embodiment, the performance information indicates the amount of time that end users of machines in a specified enterprise are unable to interact with the machines in response to (a) configuration change(s) (e.g., in response to the implementation of (a) configuration change(s) or due to the implementation of (a) configuration change(s)). For example, end users may be unable to interact with the machines during machine downtime. According to this example, end users may be unable to interact with the machines during machine startup and / or during end user login to the machines.

[0065] In another example embodiment, the performance information indicates that end users switch from using a first machine to using a second machine due to (a) configuration change(s) made to a specified enterprise. For example, end users may have a common end user identity across multiple machines of the end users. A cloud service (e.g., its proxy) or an application deployed on each end user's machine can detect when the user is using the machine. For example, the cloud service or the application can detect that the end user is actively using the cloud service to perform operations (e.g., checking the end user's email, replying to emails, interacting with social media accounts, etc.). Thus, it can be determined that the end user switches from using the first machine to using the second machine based at least in part on a first signal from the cloud service or an application deployed on the first machine and a second signal from the cloud service or an application deployed on the second machine, where the first signal indicates that the end user uses the first machine at a first time instance before the implementation of (a) configuration change(s), and the second signal indicates that the end user uses the second machine at a second time instance after the implementation of (a) configuration change(s) is completed. In an example scenario, it can be determined that although the downtime of the end user's (a) machine(s) has not changed, the implementation of (a) configuration change(s) has caused the end user to switch from using the first machine to using the second machine. According to this scenario, it can be known that the end user's productivity has decreased by X% when using the second machine compared to using the first machine. Thus, the end user's switch from using the first machine to using the second machine can indicate that the estimated initial cost of implementing (a) configuration change(s) is relatively high.

[0066] In yet another example embodiment, the performance information indicates the amount of time that end users interact (e.g., engage) with a specified application on one or more machines in a specified enterprise. For example, the amount of time that end users interact with the machines can be detected by an application (e.g., a productivity application) deployed on the machines or a cloud service proxy deployed on the machines. For example, an option to allow the cloud service proxy to monitor the end user's interaction with the machines can be presented to an administrator of the specified enterprise and / or the end user. According to this example, the cloud service proxy can detect the amount of time that end users interact with the machines based at least in part on the selection by the administrator of the specified enterprise and / or the end user to allow the cloud service proxy to monitor the end user's interaction with the machines.

[0067] In yet another example embodiment, the performance information indicates the productivity of an end user, which is determined by applications accessed by the end user on one or more machines in a specified enterprise.

[0068] In another example embodiment, the performance information indicates when the end user interacts with a machine in a specified enterprise. For example, the performance information may indicate the (multiple) times when the end user interacts with the machine, the time range within which the end user may statistically interact with the machine, the day of the week when the end user interacts with the machine, the day of the week when the end user may statistically interact with the machine, and so on. For example, the impact of implementing (multiple) configuration changes at night on the end user and the machine may be significantly the same as the impact of implementing (multiple) configuration changes during the day. In another example, the impact of implementing (multiple) configuration changes on the weekend on the end user and the machine may be significantly the same as the impact of implementing (multiple) configuration changes during a weekday. Thus, knowing when the end user interacts with the machine can help determine the impact that the implementation of (multiple) configuration changes may have on the end user and the machine, which may lead to a more accurate estimate of the net financial result of implementing (multiple) configuration changes in a specified enterprise.

[0069] In some example embodiments, one or more of steps 402, 404, 406, and / or 408 of flowchart 400 may not be performed. Additionally, steps other than or in place of steps 402, 404, 406, and / or 408 may be performed. For example, in an example embodiment, the predicted impact of (multiple) configuration changes on an enterprise includes a predicted decrease in the productivity of end users in the enterprise. According to this embodiment, the method of flowchart 400 further includes providing a recommendation to modify at least one aspect of the implementation of (multiple) configuration changes in a specified enterprise to at least partially compensate for the predicted decrease in the productivity of end users in the specified enterprise, which is at least partially based on the predicted decrease in the productivity of end users in the enterprise. For example, providing a recommendation may include providing a recommendation to change the time and / or date of implementing (multiple) configuration changes to at least partially compensate for the predicted decrease in the productivity of end users in the specified enterprise. For example, the recommendation may indicate that if the deadline for implementing (multiple) configuration changes is changed from Friday afternoon to Saturday afternoon, the estimated initial cost of implementing (multiple) configuration changes may be reduced by X% or $Y because more implementation can be performed during the end user's off-work hours. In an example implementation, the estimation logic 706 provides a recommendation to modify at least one aspect of the implementation of (multiple) configuration changes.

[0070] In another example embodiment, the method of flowchart 400 further includes determining the actual impact of the (one or more) configuration changes in at least one enterprise. The actual impact can include actual changes to the performance of one or more machines in at least one enterprise and actual changes to the volume of support tickets received regarding the (one or more) configuration changes in at least one enterprise. For example, the ticket information and performance information regarding at least one enterprise before implementing the (one or more) configuration changes can be compared with the ticket information and performance information regarding at least one enterprise after implementing the (one or more) configuration changes to determine the actual impact of the (one or more) configuration changes in at least one enterprise. The ticket information and performance information regarding at least one enterprise can be anonymized, but the scope of the example embodiments is not limited in this regard. In an example implementation, the actual impact logic 714 determines the actual impact of the (one or more) configuration changes in at least one enterprise. For example, the actual impact logic 714 can generate an actual impact indicator 724 that indicates the actual impact of the (one or more) configuration changes in at least one enterprise. According to this embodiment, the estimation of the net financial result at step 408 is at least partially based on the predicted impact of the (one or more) configuration changes in the enterprise and further at least partially based on the actual impact of the (one or more) configuration changes in at least one enterprise. For example, the estimation logic 706 can generate a result estimate 736 at least partially based on the predicted impact indicated by the predicted impact indicator 726 and further at least partially based on the actual impact indicated by the actual impact indicator 724. The result estimate 736 includes an estimate of the net financial result of implementing the (one or more) configuration changes in the specified enterprise.

[0071] In yet another example embodiment, steps 406 and 408 of flowchart 400 can be replaced with Figure 5 one or more of the steps shown in flowchart 500 of Figure 5 As shown, the method of flowchart 500 begins at step 502. In step 502, the actual impact of the (one or more) configuration changes in at least one enterprise is determined. For example, the actual impact can be determined at least partially based on combined information. For example, the actual impact can be determined at least partially based on enterprise information and / or anonymized information. The actual impact includes actual changes to the performance of machines in at least one enterprise due to the (one or more) configuration changes and actual changes to the volume of support tickets received regarding the (one or more) configuration changes in at least one enterprise. In an example implementation, the actual impact logic 714 determines the actual impact of the (one or more) configuration changes in at least one enterprise. For example, the actual impact logic 714 can determine the actual impact at least partially based on combined information 732. According to this implementation, the actual impact logic 714 generates an actual impact indicator 724 in response to determining the actual impact. The actual impact indicator 724 indicates (e.g., specifies) the actual impact.

[0072] At step 504, an estimate of the net financial result of implementing the configuration change(s) in the specified enterprise is generated, at least in part, based on the actual impact of the configuration change(s) in at least one enterprise. In an example implementation, the estimation logic 706 generates a result estimate 736, at least in part, based on the actual impact indicated by the actual impact indicator 724. The result estimate 736 includes an estimate of the net financial result of implementing the configuration change(s) in the specified enterprise.

[0073] Any of the embodiments described above with respect to steps 406 and / or 408 of flowchart 400 also apply to steps 502 and / or 504 of flowchart 500, but references to predicted impact and predicted change may be replaced with references to actual impact and actual change. For example, in an example embodiment, generating an estimate of the net financial result at step 504 includes: determining, at least in part, based on the actual impact, the cumulative amount of time that the productivity of end users in the specified enterprise is statistically likely to change due to the configuration change(s). For example, the time logic 718 may determine the cumulative amount of time. According to this example, the time logic 718 may generate a time indicator 734 to indicate the cumulative amount of time. According to this embodiment, generating an estimate of the net financial result at step 504 is at least in part based on the sum of a first part and a second part. The first part is equal to the product of the cumulative amount of time and the cost per unit time for each end user in the specified enterprise. The second part is equal to the estimated cost of resolving support tickets related to the configuration change(s) in the specified enterprise. For example, the generation logic 720 may generate the result estimate 736, at least in part, based on the sum of the first part and the second part.

[0074] In an example implementation of this embodiment, the cumulative amount of time includes a first amount of time and a second amount of time. According to this implementation, the first part is equal to the sum of (a) the product of the first amount of time and the first common cost per unit time for each end user in the first subset of end users in the specified enterprise and (b) the product of the second amount of time and the second common cost per unit time for each end user in the second subset of end users in the specified enterprise. Further according to this implementation, the first common cost per unit time for each end user in the first subset of end users and the second common cost per unit time for each end user in the second subset of end users are different.

[0075] In another example embodiment, the actual impact of the configuration change(s) in at least one enterprise includes an actual decrease in the productivity of end users in at least one enterprise. According to this embodiment, the method of flowchart 500 further includes providing a recommendation to modify at least one aspect of the implementation of the configuration change(s) in a specified enterprise to at least partially compensate for a predicted decrease in the productivity of end users in the specified enterprise, which is at least partially based on the actual decrease in the productivity of end users in at least one enterprise. For example, providing a recommendation can include providing a recommendation to change the time and / or date of implementing the configuration change(s) to at least partially compensate for a predicted decrease in the productivity of end users in the specified enterprise, which is at least partially based on the actual decrease in the productivity of end users in at least one enterprise. In an example implementation, the estimation logic 706 provides a recommendation to modify at least one aspect of the implementation of the configuration change(s).

[0076] In yet another example embodiment, steps 406 and 408 of flowchart 400 can be replaced with Figure 6 one or more steps shown in flowchart 600 of Figure 6 As shown, the method of flowchart 600 begins at step 602. In step 602, the actual impact of the configuration change(s) on a first subset of machines in a specified enterprise (e.g., on which) is determined. The actual impact includes an actual change in the performance of the first subset of machines in the specified enterprise due to the configuration change(s) and an actual change in the volume of support tickets received in response to the configuration change(s) on the first subset of machines in the specified enterprise. In an example implementation, the actual impact logic 714 determines the actual impact of the configuration change(s) on a first subset of machines in a specified enterprise. For example, the actual impact logic 714 can determine the actual impact at least partially based on the combined information 732. According to this implementation, the actual impact logic 714 generates an actual impact indicator 724 in response to determining the actual impact. The actual impact indicator 724 indicates (e.g., specifies) the actual impact.

[0077] At step 604, the predicted impact of the (multiple) configuration changes on a second subset of machines in a specified enterprise is inferred by analyzing the combined information using an adaptive algorithm. The predicted impact includes the predicted changes in the performance of the second subset of machines in the specified enterprise due to the (multiple) configuration changes and the predicted changes in the volume of support tickets received in response to the (multiple) configuration changes on the second subset of machines in the specified enterprise. The second subset of machines is different from the first subset of machines. In an example implementation, the predicted impact logic 716 infers the predicted impact of the (multiple) configuration changes on a second subset of machines in a specified enterprise by analyzing the combined information 732 using the adaptive algorithm 722. In this implementation, the predicted impact logic 716 generates a predicted impact indicator 726 in response to inferring the predicted impact. The predicted impact indicator 726 indicates (e.g., specifies) the predicted impact.

[0078] It will be appreciated that the actual impact of the (multiple) configuration changes can be determined at step 602 with respect to the first subset of machines rather than the second subset of machines. It will further be appreciated that the predicted impact of the (multiple) configuration changes can be inferred at step 604 with respect to the second subset of machines rather than the first subset of machines.

[0079] In an example embodiment, the implementation of the (multiple) configuration changes with respect to the first subset of machines is a pilot implementation of the (multiple) configuration changes. According to this embodiment, the implementation of the (multiple) configuration changes with respect to the second subset of machines is a production implementation of the (multiple) configuration changes.

[0080] At step 606, an estimate of the net financial result of implementing the (multiple) configuration changes with respect to the second subset of machines in the specified enterprise is generated, at least in part based on the actual impact of the (multiple) configuration changes on the first subset of machines in the specified enterprise and further at least in part based on the predicted impact of the (multiple) configuration changes on the second subset of machines in the specified enterprise. In an example implementation, the estimate logic 706 generates a result estimate 736 at least in part based on the actual impact indicated by the actual impact indicator 724 and further at least in part based on the predicted impact indicated by the predicted impact indicator 726. The result estimate 736 includes an estimate of the net financial result of implementing the (multiple) configuration changes with respect to the second subset of machines in the specified enterprise.

[0081] In an example embodiment, generating an estimate of the net financial result at step 606 includes determining, at least in part based on the actual impact and further at least in part based on the predicted impact, the cumulative amount of time that the productivity of the end users of a second subset of machines in a specified enterprise is likely to change statistically due to the (multiple) configuration changes. For example, the time logic 718 can determine the cumulative amount of time. According to this example, the time logic 718 can generate a time indicator 734 to indicate the cumulative amount of time. According to this embodiment, generating an estimate of the net financial result at step 606 is at least in part based on the sum of a first part and a second part. The first part is equal to the product of the cumulative amount of time and the cost per unit time per end user of the machines in the second subset of machines in the specified enterprise. The second part is equal to the estimated cost of resolving support tickets related to the (multiple) configuration changes regarding the second subset of machines in the specified enterprise. For example, the generation logic 720 can generate a result estimate 736 at least in part based on the sum of the first part and the second part. According to this example, the generation logic 720 can generate the result estimate 736 in response to receiving the time indicator 734, the end user cost information 728, and the ticket cost information 730. The end user cost information 728 indicates the cost per unit time per end user of the machines in the second subset of machines in the specified enterprise. The ticket cost information 730 indicates the estimated cost of resolving support tickets related to the (multiple) configuration changes regarding the second subset of machines in the specified enterprise.

[0082] In an example implementation of this embodiment, the cumulative amount of time includes a first amount of time and a second amount of time. According to this implementation, the first part is equal to the sum of (a) the product of the first amount of time and the first common cost per unit time per end user in a first subset of end users of the machines in the second subset of machines in the specified enterprise and (b) the product of the second amount of time and the second common cost per unit time per end user in a second subset of end users of the machines in the second subset of machines in the specified enterprise. Further according to this implementation, the first common cost per unit time per end user in the first subset of end users and the second common cost per unit time per end user in the second subset of end users are different.

[0083] In another example embodiment, the estimation of the net financial result generated at step 606 takes into account the estimated initial costs associated with implementing the (multiple) configuration changes during the implementation of the (multiple) configuration changes, and further takes into account the estimated financial benefits predicted to result from the completion of the implementation. The estimated initial costs are due to: during the implementation of the (multiple) configuration changes, a predicted decrease in the performance of a second subset of machines in the designated enterprise and / or a predicted increase in the volume of support tickets received regarding the (multiple) configuration changes to the second subset of machines in the designated enterprise. The estimated financial benefits are due to: after the completion of the implementation of the (multiple) configuration changes, a predicted increase in the performance of a second subset of machines in the designated enterprise and / or a predicted decrease in the volume of support tickets received in the designated enterprise.

[0084] In yet another example embodiment, the enterprise information further includes inventory information that indicates the hardware and / or software associated with the machines in the designated enterprise. According to this embodiment, the anonymized information further includes anonymized inventory information that indicates the hardware and / or software associated with the machines in multiple enterprises.

[0085] In yet another example embodiment, the performance information indicates the amount of time that end users of the machines in the designated enterprise are unable to interact with the machines in response to the (multiple) configuration changes.

[0086] In another example embodiment, the performance information indicates that end users switch from using a first machine to using a second machine due to the (multiple) configuration changes to the designated enterprise.

[0087] In yet another example embodiment, the performance information indicates the amount of time that end users interact with a designated application on one or more machines in the designated enterprise.

[0088] In yet another example embodiment, the performance information indicates the productivity of the end user, which is determined by the applications accessed by the end user on one or more machines in the designated enterprise.

[0089] In another example embodiment, the performance information indicates when end users interact with the machines in the designated enterprise.

[0090] In yet another example embodiment, the predicted impact of the (multiple) configuration changes on a second subset of machines in the designated enterprise includes a predicted decrease in the productivity of the end users of the second subset of machines in the designated enterprise. According to this embodiment, the method of flowchart 600 further includes providing a recommendation to modify at least one aspect of the implementation of the (multiple) configuration changes with respect to the second subset of machines in the designated enterprise to at least partially compensate for the predicted decrease in the productivity of the end users of the second subset of machines in the designated enterprise. In an example implementation, the estimation logic 706 provides a recommendation to modify at least one aspect of the implementation of the (multiple) configuration changes.

[0091] Although the operations of some of the disclosed methods are described in a particular order for convenience of presentation, it should be understood that such description includes rearrangements, unless the particular language set forth herein requires a particular order. For example, operations described in sequence may in some cases be rearranged or performed concurrently. In addition, for simplicity, the figures may not show the various ways in which the disclosed methods can be used in conjunction with other methods.

[0092] It will be appreciated that computing system 700 may not include one or more of combinatorial logic 702, determination logic 704, estimation logic 706, result estimation logic 712, actual impact logic 714, predicted impact logic 716, adaptive algorithm 722, timing logic 718, and / or generation logic 720. Additionally, computing system 700 may include components other than or instead of combinatorial logic 702, determination logic 704, estimation logic 706, result estimation logic 712, actual impact logic 714, predicted impact logic 716, adaptive algorithm 722, timing logic 718, and / or generation logic 720.

[0093] Any one or more of result estimation logic 112, result estimation logic 212, estimation logic 218, event processor 222, event collector 224, performance monitoring agent 232, management portal 244, combinatorial logic 702, determination logic 704, estimation logic 706, result estimation logic 712, actual impact logic 714, predicted impact logic 716, adaptive algorithm 722, timing logic 718, generation logic 720, flowchart 400, flowchart 500, and / or flowchart 600 may be implemented in hardware, software, firmware, or any combination thereof.

[0094] For example, any one or more of result estimation logic 112, result estimation logic 212, estimation logic 218, event processor 222, event collector 224, performance monitoring agent 232, management portal 244, combinatorial logic 702, determination logic 704, estimation logic 706, result estimation logic 712, actual impact logic 714, predicted impact logic 716, adaptive algorithm 722, timing logic 718, generation logic 720, flowchart 400, flowchart 500, and / or flowchart 600 may be at least partially implemented as computer program code configured to execute on one or more processors.

[0095] In another example, any one or more of result estimation logic 112, result estimation logic 212, estimation logic 218, event processor 222, event collector 224, performance monitoring agent 232, management portal 244, combinational logic 702, determination logic 704, estimation logic 706, result estimation logic 712, actual impact logic 714, predictive impact logic 716, adaptive algorithm 722, timing logic 718, generation logic 720, flowchart 400, flowchart 500, and / or flowchart 600 may be implemented at least in part as hardware logic / circuit. Such hardware logic / circuit may include one or more hardware logic components. Examples of hardware logic components include, but are not limited to, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system on a chip (SoC), complex programmable logic devices (CPLDs), etc. For example, an SoC may include an integrated circuit chip that includes one or more of the following: a processor (e.g., a microcontroller, a microprocessor, a digital signal processor (DSP), etc.), a memory, one or more communication interfaces, and / or other circuitry and / or embedded firmware for performing its functions.

[0096] III. Further Discussion of Some Example Embodiments

[0097] An example system includes a memory and one or more processors coupled to the memory. The one or more processors are configured to: collect enterprise information including (a) configuration information indicating configuration changes made to a specified enterprise, (b) ticket information related to the volume of support tickets received regarding the configuration changes, and (c) performance information regarding the performance of machines in the specified enterprise in response to the configuration changes. Combine the enterprise information with anonymized information received from multiple enterprises to provide combined information. The anonymized information includes (a) anonymized configuration information indicating configuration changes made to multiple enterprises, (b) anonymized ticket information related to the volume of support tickets received regarding the configuration changes made to multiple enterprises, and (c) anonymized performance information regarding the performance of machines in multiple enterprises in response to the configuration changes made to multiple enterprises. Analyze the combined information using an adaptive algorithm to infer the predicted impact of one or more configuration changes on multiple enterprises. The predicted impact includes predicted changes in the performance of machines in multiple enterprises due to one or more configuration changes and predicted changes in the volume of support tickets received in multiple enterprises due to one or more configuration changes. Generate an estimate of the net financial outcome of implementing one or more configuration changes in the specified enterprise based at least in part on the predicted impact of the one or more configuration changes on multiple enterprises.

[0098] In a first aspect of the example system, one or more processors are configured to: determine a cumulative amount of time during which the productivity of an end user in a specified enterprise is statistically likely to change due to one or more configuration changes, at least in part based on a predicted impact. According to the first aspect, an estimate of the net financial result of implementing one or more configuration changes in the specified enterprise is generated, at least in part based on the sum of a first part and a second part. The first part is equal to the product of the cumulative amount of time and the cost per unit time per end user in the specified enterprise. The second part is equal to the estimated cost of resolving support tickets related to one or more configuration changes in the specified enterprise.

[0099] In an implementation of the first aspect of the example system, the cumulative amount of time includes a first amount of time and a second amount of time. According to this implementation, the first part is equal to the sum of (a) the product of the first amount of time and a first common cost per unit time per end user in a first subset of end users in the specified enterprise and (b) the product of the second amount of time and a second common cost per unit time per end user in a second subset of end users in the specified enterprise. Further according to this implementation, the first common cost per unit time per end user in the first subset of end users and the second common cost per unit time per end user in the second subset of end users are different.

[0100] In a second aspect of the example system, one or more processors are configured to: generate an estimate of the net financial result of implementing one or more configuration changes in the specified enterprise by considering an estimated initial cost associated with implementing the one or more configuration changes during the implementation period and by considering an estimated financial benefit predicted to result from the completion of the implementation. According to the second aspect, the estimated initial cost is at least in part based on at least one of the following: a predicted decrease in the performance of machines in the specified enterprise during the implementation of the one or more configuration changes or a predicted increase in the volume of support tickets received regarding the one or more configuration changes in the specified enterprise. Further according to the second aspect, the estimated financial benefit is at least in part based on at least one of the following: a predicted increase in the performance of machines in the specified enterprise after the completion of the implementation of the one or more configuration changes or a predicted decrease in the volume of support tickets received in the specified enterprise. The second aspect of the example system may be implemented in combination with the first aspect of the example system, but the example embodiments are not limited in this regard.

[0101] In a third aspect of the example system, the enterprise information further includes inventory information that indicates at least one of hardware or software associated with machines in the specified enterprise. According to the third aspect, the anonymized information further includes anonymized inventory information that indicates at least one of hardware or software associated with machines in multiple enterprises. The third aspect of the example system may be implemented in combination with the first and / or second aspects of the example system, but the example embodiments are not limited in this regard.

[0102] In a fourth aspect of the example system, the performance information indicates the amount of time that end users of machines in a specified enterprise are unable to interact with the machines in response to a configuration change. The fourth aspect of the example system may be implemented in conjunction with the first, second, and / or third aspects of the example system, but the example embodiments are not limited in this regard.

[0103] In a fifth aspect of the example system, the performance information indicates that end users switch from using a first machine to using a second machine due to a configuration change made to a specified enterprise. The fifth aspect of the example system may be implemented in conjunction with the first, second, third, and / or fourth aspects of the example system, but the example embodiments are not limited in this regard.

[0104] In a sixth aspect of the example system, the performance information indicates the amount of time that end users interact with a specified application on one or more machines in a specified enterprise. The sixth aspect of the example system may be implemented in conjunction with the first, second, third, fourth, and / or fifth aspects of the example system, but the example embodiments are not limited in this regard.

[0105] In a seventh aspect of the example system, the performance information indicates the productivity of end users, which is determined by the applications accessed by the end users on one or more machines in a specified enterprise. The seventh aspect of the example system may be implemented in conjunction with the first, second, third, fourth, fifth, and / or sixth aspects of the example system, but the example embodiments are not limited in this regard.

[0106] In an eighth aspect of the example system, the performance information indicates when end users interact with machines in a specified enterprise. The eighth aspect of the example system may be implemented in conjunction with the first, second, third, fourth, fifth, sixth, and / or seventh aspects of the example system, but the example embodiments are not limited in this regard.

[0107] In a ninth aspect of the example system, the predicted impact of one or more configuration changes across multiple enterprises includes a predicted decrease in the productivity of end users across multiple enterprises. According to the ninth aspect, one or more processors are further configured to: provide a recommendation to modify at least one aspect of the implementation of one or more configuration changes in a specified enterprise to at least partially compensate for the predicted decrease in the productivity of end users in the specified enterprise, which is at least partially based on the predicted decrease in the productivity of end users across multiple enterprises. The ninth aspect of the example system may be implemented in conjunction with the first, second, third, fourth, fifth, sixth, seventh, and / or eighth aspects of the example system, but the example embodiments are not limited in this regard.

[0108] In a tenth aspect of the example system, one or more processors are configured to determine the actual impact of one or more configuration changes in at least one enterprise. The actual impact includes actual changes to the performance of one or more machines in at least one enterprise and actual changes to the volume of support tickets received regarding one or more configuration changes in at least one enterprise. According to the tenth aspect, an estimate of the net financial result of implementing one or more configuration changes in a specified enterprise is generated based at least in part on the predicted impact of the one or more configuration changes across multiple enterprises and further based at least in part on the actual impact of the one or more configuration changes in at least one enterprise. The tenth aspect of the example system may be implemented in conjunction with the first, second, third, fourth, fifth, sixth, seventh, eighth, and / or ninth aspects of the example system, but example embodiments are not limited in this regard.

[0109] In an eleventh aspect of the example system, one or more processors are configured to infer a predicted impact based at least in part on the amount of time over which the implementation of one or more configuration changes will be performed in a specified enterprise. The eleventh aspect of the example system may be implemented in conjunction with the first, second, third, fourth, fifth, sixth, seventh, eighth, ninth, and / or tenth aspects of the example system, but example embodiments are not limited in this regard.

[0110] In the example method, enterprise information is collected, which includes (a) configuration information indicating configuration changes made to a specified enterprise, (b) ticket information related to the volume of support tickets received regarding the configuration changes, and (c) performance information regarding the performance of machines in the specified enterprise in response to the configuration changes. The enterprise information is combined with anonymized information received from multiple enterprises to provide combined information. The anonymized information includes (a) anonymized configuration information indicating configuration changes made to multiple enterprises, (b) anonymized ticket information related to the volume of support tickets received regarding the configuration changes made to multiple enterprises, and (c) anonymized performance information regarding the performance of machines in multiple enterprises in response to the configuration changes made to multiple enterprises. The actual impact of one or more configuration changes in at least one enterprise is determined based at least in part on the combined information. The actual impact includes actual changes to the performance of machines in at least one enterprise due to the one or more configuration changes and actual changes to the volume of support tickets received regarding the one or more configuration changes in at least one enterprise. An estimate of the net financial result of implementing one or more configuration changes in the specified enterprise is generated based at least in part on the actual impact of the one or more configuration changes in at least one enterprise.

[0111] In a first aspect of the example method, generating an estimate of the net financial result includes: determining, at least in part based on actual impact, the cumulative amount of time that the productivity of end users in a specified enterprise is statistically likely to change due to one or more configuration changes. According to the first aspect, generating an estimate of the net financial result includes: generating an estimate of the net financial result of implementing one or more configuration changes in a specified enterprise, at least in part based on the sum of a first part and a second part, the first part being equal to the product of the cumulative amount of time and the cost per unit time for each end user in the specified enterprise. Further according to the first aspect, the second part is equal to the estimated cost of resolving support tickets related to one or more configuration changes in the specified enterprise.

[0112] In an implementation of the first aspect of the example method, the cumulative amount of time includes a first amount of time and a second amount of time. According to this implementation, the first part is equal to the sum of (a) the product of the first amount of time and the first common cost per unit time for each end user in a first subset of end users in the specified enterprise and (b) the product of the second amount of time and the second common cost per unit time for each end user in a second subset of end users in the specified enterprise. Further according to this implementation, the first common cost per unit time for each end user in the first subset of end users and the second common cost per unit time for each end user in the second subset of end users are different.

[0113] In a second aspect of the example method, generating an estimate of the net financial result of implementing one or more configuration changes in a specified enterprise takes into account an estimated initial cost associated with implementing one or more configuration changes during the implementation of the one or more configuration changes, and also takes into account an estimated financial benefit predicted to result from the completion of the implementation. According to the second aspect, the estimated initial cost is due to at least one of the following: a predicted decrease in the performance of machines in the specified enterprise during the implementation of the one or more configuration changes or a predicted increase in the volume of support tickets received regarding one or more configuration changes in the specified enterprise. Further according to the second aspect, the estimated financial benefit is due to at least one of the following: a predicted increase in the performance of machines in the specified enterprise after the completion of the implementation of the one or more configuration changes or a predicted decrease in the volume of support tickets received in the specified enterprise. The second aspect of the example method can be implemented in combination with the first aspect of the example method, but the example embodiments are not limited in this regard.

[0114] In a third aspect of the example method, the enterprise information further includes inventory information that indicates at least one of hardware or software associated with machines in the specified enterprise. According to the third aspect, the anonymized information further includes anonymized inventory information that indicates at least one of hardware or software associated with machines in a plurality of enterprises. The third aspect of the example method can be implemented in combination with the first and / or second aspects of the example method, but the example embodiments are not limited in this regard.

[0115] In a fourth aspect of the example method, the performance information indicates the amount of time that an end user of a machine in a specified enterprise is unable to interact with the machine in response to a configuration change. The fourth aspect of the example method may be implemented in combination with the first, second, and / or third aspects of the example method, but the example embodiments are not limited in this regard.

[0116] In a fifth aspect of the example method, the performance information indicates that an end user switches from using a first machine to using a second machine due to a configuration change to a specified enterprise. The fifth aspect of the example method may be implemented in combination with the first, second, third, and / or fourth aspects of the example method, but the example embodiments are not limited in this regard.

[0117] In a sixth aspect of the example method, the performance information indicates the amount of time that an end user interacts with a specified application on one or more machines in a specified enterprise. The sixth aspect of the example method may be implemented in combination with the first, second, third, fourth, and / or fifth aspects of the example method, but the example embodiments are not limited in this regard.

[0118] In a seventh aspect of the example method, the performance information indicates the productivity of an end user, which is determined by applications accessed by the end user on one or more machines in a specified enterprise. The seventh aspect of the example method may be implemented in combination with the first, second, third, fourth, fifth, and / or sixth aspects of the example method, but the example embodiments are not limited in this regard.

[0119] In an eighth aspect of the example method, the performance information indicates when an end user interacts with a machine in a specified enterprise. The eighth aspect of the example method may be implemented in combination with the first aspect, second aspect, third aspect, fourth aspect, fifth aspect, sixth aspect, and / or seventh aspect of the example method, but the example embodiments are not limited in this regard.

[0120] In a ninth aspect of the example method, the actual impact of one or more configuration changes in at least one enterprise includes an actual decrease in the productivity of end users in at least one enterprise. According to the ninth aspect, the example method further includes providing a recommendation to modify at least one aspect of the implementation of one or more configuration changes in a specified enterprise to at least partially compensate for a predicted decrease in the productivity of end users in the specified enterprise, which is at least partially based on the actual decrease in the productivity of end users in at least one enterprise. The ninth aspect of the example method may be implemented in combination with the first aspect, second aspect, third aspect, fourth aspect, fifth aspect, sixth aspect, seventh aspect, and / or eighth aspect of the example method, but the example embodiments are not limited in this regard.

[0121] An example computer program product includes a computer-readable storage medium having instructions recorded thereon for causing a processor-based system to perform operations. The operations include: collecting enterprise information, which includes (a) configuration information indicating configuration changes made to a specified enterprise, (b) ticket information related to the volume of support tickets received regarding the configuration changes, and (c) performance information regarding the performance of machines in the specified enterprise in response to the configuration changes. The operations further include combining the enterprise information with anonymous information received from multiple enterprises to provide combined information. The anonymous information includes (a) anonymous configuration information indicating configuration changes made to multiple enterprises, (b) anonymous ticket information related to the volume of support tickets received regarding the configuration changes made to multiple enterprises, and (c) anonymous performance information regarding the performance of machines in multiple enterprises in response to the configuration changes made to multiple enterprises. The operations also include determining the actual impact of one or more configuration changes on a first subset of machines in the specified enterprise. The actual impact includes the actual change in the performance of the first subset of machines in the specified enterprise due to the one or more configuration changes and the actual change in the volume of support tickets received in response to the one or more configuration changes regarding the first subset of machines in the specified enterprise. The operations also include inferring the predicted impact of one or more configuration changes on a second subset of machines in the specified enterprise by analyzing the combined information using an adaptive algorithm. The predicted impact includes the predicted change in the performance of the second subset of machines in the specified enterprise due to the one or more configuration changes and the predicted change in the volume of support tickets received in response to the one or more configuration changes regarding the second subset of machines in the specified enterprise. The operations further include generating an estimate of the net financial result of implementing one or more configuration changes regarding the second subset of machines in the specified enterprise, at least in part based on the actual impact of the one or more configuration changes on the first subset of machines in the specified enterprise and further at least in part based on the predicted impact of the one or more configuration changes on the second subset of machines in the specified enterprise.

[0122] In a first aspect of the exemplary computer program product, the operations include: determining, at least in part based on the actual impact and further at least in part based on the predicted impact, the cumulative amount of time during which the productivity of the end users of the second subset of machines in the specified enterprise is likely to change statistically due to the one or more configuration changes. According to the first aspect, the operations include: generating an estimate of the net financial result of implementing one or more configuration changes to the second subset of machines in the specified enterprise, at least in part based on the sum of a first part and a second part. The first part is equal to the product of the cumulative amount of time and the cost per end user per unit time of the machines in the second subset of machines in the specified enterprise. The second part is equal to the estimated cost of resolving support tickets related to the one or more configuration changes regarding the second subset of machines in the specified enterprise.

[0123] In an implementation of the first aspect of the example computer program product, the cumulative amount of time includes a first amount of time and a second amount of time. According to this implementation, the first portion is equal to the sum of (a) the product of the first amount of time and the first common cost per unit time for each end user in the first end user subset of the machines in the second machine subset in the specified enterprise and (b) the product of the second amount of time and the second common cost per unit time for each end user in the second end user subset of the machines in the second machine subset in the specified enterprise. Further according to this implementation, the first common cost per unit time for each end user in the first end user subset and the second common cost per unit time for each end user in the second end user subset are different.

[0124] In a second aspect of the example computer program product, the operations include: generating an estimate of the net financial result of implementing one or more configuration changes with respect to a second machine subset in a specified enterprise by considering an estimated initial cost associated with implementing the one or more configuration changes during the implementation of one or more configuration changes and by considering an estimated financial benefit predicted to occur due to the completion of the implementation. According to the second aspect, the estimated initial cost is at least partially based on at least one of the following: a predicted decrease in the performance of the second machine subset in the specified enterprise during the implementation of the one or more configuration changes or a predicted increase in the volume of support tickets received with respect to the one or more configuration changes to the second machine subset in the specified enterprise. Further according to the second aspect, the estimated financial benefit is at least partially based on at least one of the following: a predicted increase in the performance of the second machine subset in the specified enterprise after the completion of the implementation of the one or more configuration changes or a predicted decrease in the volume of support tickets received in the specified enterprise. The second aspect of the example computer program product may be implemented in combination with the first aspect of the example computer program product, but the example embodiments are not limited in this regard.

[0125] IV. Example Computer System

[0126] Figure 8 An example computer 800 is depicted in which embodiments may be implemented. Figure 1 any one or more of the user devices 106A - 106M, any one or more of the management systems 104A - 104M, and / or any one or more of the (multiple) system management servers 102 shown therein; Figure 2 any one or more of the (multiple) system management servers 202, the management system 204, and / or any one or more of the desktop clients 206A - 206C shown therein; and / or Figure 7The computing system 700 shown in the figure can be implemented using a computer 800, including one or more features and / or alternative features of the computer 800. For example, the computer 800 can be a general-purpose computing device in the form of a conventional personal computer, a mobile computer, or a workstation, or the computer 800 can be a special-purpose computing device. The description of the computer 800 provided herein is for illustrative purposes and is not intended to be restrictive. As is known to those skilled in the relevant art(s), embodiments can be implemented in other types of computer systems.

[0127] As Figure 8 shown in the figure, the computer 800 includes a processing unit 802, a system memory 804, and a bus 806 that couples various system components including the system memory 804 to the processing unit 802. The bus 806 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. The system memory 804 includes a read-only memory (ROM) 808 and a random access memory (RAM) 810. A basic input / output system 812 (BIOS) is stored in the ROM 808.

[0128] The computer 800 also has one or more of the following drives: a hard disk drive 814 for reading from and writing to a hard disk, a disk drive 816 for reading from or writing to a removable disk 818, and an optical disk drive 820 for reading from or writing to a removable optical disk 822 (such as a CDROM, DVDROM, or other optical medium). The hard disk drive 814, the disk drive 816, and the optical disk drive 820 are connected to the bus 806 via a hard disk drive interface 824, a disk drive interface 826, and an optical disk drive interface 828, respectively. The drives and their associated computer-readable storage media provide non-volatile storage of computer-readable instructions, data structures, program modules, and other data for the computer. Although hard disks, removable disks, and removable optical disks are described, other types of computer-readable storage media can also be used to store data, such as flash memory cards, digital video disks, random access memory (RAM), read-only memory (ROM), etc.

[0129] Multiple program modules can be stored on a hard disk, disk, optical disc, ROM, or RAM. These programs include an operating system 830, one or more application programs 832, other program modules 834, and program data 836. The application programs 832 or program modules 834 can include, for example, any one or more of those for implementing (e.g., at least a part of) result estimation logic 112, result estimation logic 212, estimation logic 218, event processor 222, event collector 224, performance monitoring agent 232, management portal 244, combinatorial logic 702, determination logic 704, estimation logic 706, result estimation logic 712, actual impact logic 714, predictive impact logic 716, adaptive algorithm 722, time logic 718, generation logic 720, flowchart 400 (including any steps of flowchart 400), flowchart 500 (including any steps of flowchart 500), and / or flowchart 600 (including any steps of flowchart 600), as described herein.

[0130] A user can input commands and information into the computer 800 through input devices such as a keyboard 838 and a pointing device 840. Other input devices (not shown) can include a microphone, joystick, gamepad, dish satellite antenna, scanner, touch screen, camera, accelerometer, gyroscope, etc. These and other input devices are often connected to the processing unit 802 through a serial port interface 842 coupled to the bus 806, but can also be connected through other interfaces, such as a parallel port, game port, or universal serial bus (USB).

[0131] A display device 844 (e.g., a monitor) is also connected to the bus 806 through an interface such as a video adapter 846. In addition to the display device 844, the computer 800 can also include other peripheral output devices (not shown), such as speakers and printers.

[0132] The computer 800 is connected to a network 848 (e.g., the Internet) through a network interface or adapter 850, a modem 852, or other components for establishing communication on the network. The modem 852 can be internal or external and is connected to the bus 806 through the serial port interface 842.

[0133] As used herein, the terms "computer program medium" and "computer-readable storage medium" are generally used to refer to a medium (e.g., a non-transitory medium), such as a hard disk associated with a hard disk drive 814, a removable disk 818, a removable optical disk 822, and other media such as flash memory cards, digital video disks, random access memory (RAM), read-only memory (ROM), etc. Such computer-readable storage media are distinct from and do not overlap with (excluding) communication media. Communication media embody computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave. The term "modulated data signal" means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example and not limitation, communication media include wireless media such as acoustic, RF, infrared, and other wireless media, as well as wired media. Example embodiments also relate to such communication media.

[0134] As noted above, computer programs and modules (including application 832 and other program modules 834) can be stored on a hard disk, disk, optical disk, ROM, or RAM. Such computer programs can also be received via network interface 850 or serial port interface 842. When such computer programs are executed or loaded by an application, they enable computer 800 to implement the features of the embodiments discussed herein. Thus, such computer programs represent the controller of computer 800.

[0135] Example embodiments also relate to a computer program product that includes software (e.g., computer-readable instructions) stored on any computer-usable medium. Such software, when executed in one or more data processing devices, causes the (one or more) data processing devices to operate as described herein. Embodiments may employ any computer-usable or computer-readable medium now known or later developed. Examples of computer-readable media include, but are not limited to, storage devices such as RAM, hard disk drives, floppy disks, CD ROMs, DVD ROMs, zip disks, magnetic tapes, magnetic storage devices, optical storage devices, MEMS-based storage devices, nanotechnology-based storage devices, etc.

[0136] It will be recognized that the disclosed technology is not limited to any particular computer or hardware type. Certain details of suitable computers and hardware are well known and need not be elaborated in detail in this disclosure.

[0137] V. Conclusion

[0138] Although the subject matter has been described in language specific to structural features and / or acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. On the contrary, the specific features and acts described above are disclosed as examples for implementing the claims, and other equivalent features and acts are intended to fall within the scope of the claims.

Claims

1. A system, comprising: a memory; and one or more processors coupled to the memory, the one or more processors being configured to: collect enterprise information, the enterprise information including: configuration information indicating a first configuration change associated with a first machine in a specified enterprise, ticket information related to the volume of support tickets received regarding the first configuration change associated with the first machine in the specified enterprise, and performance information regarding the performance of the first machine in the specified enterprise in response to the first configuration change associated with the first machine in the specified enterprise; combine the enterprise information with anonymized information received from multiple enterprises to provide combined information, the anonymized information including: anonymized configuration information indicating a second configuration change associated with a second machine in the multiple enterprises, anonymized ticket information related to the volume of support tickets received regarding the second configuration change associated with the second machine in the multiple enterprises, and anonymized performance information regarding the performance of the second machine in the multiple enterprises in response to the second configuration change associated with the second machine in the multiple enterprises; infer a predicted impact of one or more third configuration changes on the multiple enterprises by analyzing the combined information using an adaptive algorithm, the predicted impact including: a predicted change in the performance of the second machine in the multiple enterprises due to the one or more third configuration changes, and a predicted change in the volume of support tickets received in the multiple enterprises due to the one or more third configuration changes; generate an estimate of the net financial result of implementing the one or more third configuration changes in the specified enterprise, at least in part based on the predicted impact of the one or more third configuration changes on the multiple enterprises; and increase the efficiency of the computing system by causing the computing system to modify at least one of the one or more third configuration changes for implementation in the specified enterprise, based on the generated estimate of the net financial result, the computing system being configured to perform system management operations for the specified enterprise including the first machine, wherein the at least one third configuration change includes at least one of the following: deploy software on at least one first machine in the first machine in the specified enterprise, change hardware on at least one first machine in the first machine in the specified enterprise, change one or more security settings associated with at least one first machine in the first machine in the specified enterprise, or change compliance settings associated with at least one first machine in the first machine in the specified enterprise.

2. The system according to claim 1, wherein the one or more processors are configured to: determine, at least in part based on the predicted impact, the cumulative amount of time during which the productivity of end users in the specified enterprise is statistically likely to change due to the one or more third configuration changes; and Generate the estimate of the net financial result of implementing the one or more third configuration changes in the specified enterprise, at least in part based on the sum of a first part and a second part, where the first part is equal to the product of the cumulative time amount and the cost per unit time for each end user in the specified enterprise, and the second part is equal to the estimated cost of resolving support tickets related to the one or more third configuration changes in the specified enterprise.

3. The system according to claim 2, wherein the cumulative time amount includes a first time amount and a second time amount; wherein the first part is equal to the sum of: (a) the product of the first time amount and the first common cost per unit time for each end user in a first subset of end users in the specified enterprise, and (b) the product of the second time amount and the second common cost per unit time for each end user in a second subset of end users in the specified enterprise; and wherein the first common cost per unit time for each end user in the first subset of end users and the second common cost per unit time for each end user in the second subset of end users are different.

4. The system according to claim 1, wherein the one or more processors are configured to: Generate the estimate of the net financial result of implementing the one or more third configuration changes in the specified enterprise by considering the estimated initial cost associated with implementing the one or more third configuration changes during the implementation period and by considering the estimated financial benefits predicted to result from the completion of the implementation; wherein the estimated initial cost is at least in part based on at least one of: a predicted decrease in the performance of the first machine in the specified enterprise during the implementation of the one or more third configuration changes or a predicted increase in the volume of support tickets received regarding the one or more third configuration changes in the specified enterprise; and wherein the estimated financial benefits are at least in part based on at least one of: a predicted increase in the performance of the first machine in the specified enterprise after the completion of the implementation of the one or more third configuration changes or a predicted decrease in the volume of support tickets received in the specified enterprise.

5. The system according to claim 1, wherein the enterprise information further includes inventory information indicating at least one of hardware or software associated with the first machine in the specified enterprise; and wherein the anonymized information further includes anonymized inventory information indicating at least one of hardware or software associated with the second machine in the plurality of enterprises.

6. The system according to claim 1, wherein the performance information indicates at least one of: The amount of time that end users of the first machine in the specified enterprise are unable to interact with the first machine in response to the first configuration change; Due to the first configuration change made to the specified enterprise, end users switch from using an initial machine to using a subsequent machine; The amount of time the end user interacts with a specified application on one or more of the first machines in the specified enterprise; The productivity of the end user as determined by the applications accessed by the end user on one or more of the first machines in the specified enterprise; Or When the end user interacts with the first machines in the specified enterprise.

7. The system according to claim 1, wherein the predicted impact of the one or more third configuration changes on the multiple enterprises includes a predicted decrease in the productivity of end users in the multiple enterprises; and Wherein the one or more processors are further configured to: Provide a recommendation to modify at least one aspect of the implementation of the one or more third configuration changes in the specified enterprise to at least partially compensate for the predicted decrease in the productivity of end users in the specified enterprise, the predicted decrease in the productivity of end users in the specified enterprise being at least partially based on the predicted decrease in the productivity of end users in the multiple enterprises.

8. The system according to claim 1, wherein the one or more processors are configured to: Determine the actual impact of the one or more third configuration changes in at least one enterprise, the actual impact including an actual change in the performance of one or more third machines in the at least one enterprise and an actual change in the volume of support tickets received regarding the one or more third configuration changes in the at least one enterprise; and Generate the estimate of the net financial result of implementing the one or more third configuration changes in the specified enterprise based at least in part on the predicted impact of the one or more third configuration changes on the multiple enterprises and further at least in part on the actual impact of the one or more third configuration changes in the at least one enterprise.

9. The system according to claim 1, wherein the one or more processors are configured to: Infer the predicted impact based at least in part on the amount of time the implementation of the one or more third configuration changes is to be performed in the specified enterprise.

10. A method performed by a computing system, Comprising: Collecting enterprise information, the enterprise information including: Configuration information indicating a first configuration change associated with a first machine in a specified enterprise, Ticket information related to the volume of support tickets received regarding the first configuration change associated with the first machine in the specified enterprise, and Performance information regarding the performance of the first machine in the specified enterprise in response to the first configuration change associated with the first machine in the specified enterprise; Combining the enterprise information with anonymized information received from multiple enterprises to provide combined information, the anonymized information including: Anonymized configuration information indicating a second configuration change associated with a second machine in the multiple enterprises, Anonymized ticket information related to the volume of support tickets received regarding the second configuration change associated with the second machine in the multiple enterprises, and Anonymous performance information regarding the performance of the second machine in the plurality of enterprises in response to the second configuration change associated with the second machine in the plurality of enterprises; Determine the actual impact of one or more third configuration changes in at least one enterprise based at least in part on the combined information, the actual impact including: The actual change in the performance of the third machine in the at least one enterprise due to the one or more third configuration changes, and The actual change in the volume of support tickets received regarding the one or more third configuration changes in the at least one enterprise; Generate an estimate of the net financial result of implementing the one or more third configuration changes in the specified enterprise based at least in part on the actual impact of the one or more third configuration changes in the at least one enterprise; and Based on the generated estimate of the net financial result, increase the efficiency of the processor-based system by causing the processor-based system to modify at least one of the one or more third configuration changes for implementation in the specified enterprise, the processor-based system being configured to perform system management operations for the specified enterprise including the first machine, wherein the at least one third configuration change includes at least one of the following: Deploy software on at least one of the first machines in the first machine in the specified enterprise, Change the hardware on at least one of the first machines in the first machine in the specified enterprise, Change one or more security settings associated with at least one of the first machines in the first machine in the specified enterprise, or Change the compliance settings associated with at least one of the first machines in the first machine in the specified enterprise.

11. The method according to claim 10, wherein generating the estimate of the net financial result Includes: Determine, at least in part based on the actual impact, the cumulative amount of time by which the productivity of end users in the specified enterprise is statistically likely to change due to the one or more third configuration changes; And Generate the estimate of the net financial result of implementing the one or more third configuration changes in the specified enterprise based at least in part on the sum of a first part and a second part, the first part being equal to the product of the cumulative amount of time and the cost per unit time of each end user in the specified enterprise, and the second part being equal to the estimated cost of resolving support tickets related to the one or more third configuration changes in the specified enterprise.

12. The method according to claim 11, wherein the cumulative amount of time includes a first amount of time and a second amount of time; Wherein the first part is equal to the sum of: (a) the product of the first amount of time and the first common cost per unit time of each end user in the first subset of end users in the specified enterprise, and (b) the product of the second amount of time and the second common cost per unit time of each end user in the second subset of end users in the specified enterprise; and The first common cost per unit time for each end user in the first subset of end users and the second common cost per unit time for each end user in the second subset of end users are different.

13. The method according to claim 10, wherein generating the estimate of the net financial result of implementing the one or more third configuration changes in the specified enterprise takes into account an estimated initial cost associated with implementing the one or more third configuration changes during the implementation of the one or more third configuration changes, and also takes into account an estimated financial benefit predicted to result from the completion of the implementation; wherein the estimated initial cost is due to at least one of: a predicted decrease in the performance of the first machine in the specified enterprise during the implementation of the one or more third configuration changes or a predicted increase in the volume of support tickets received regarding the one or more third configuration changes in the specified enterprise; and wherein the estimated financial benefit is due to at least one of: a predicted increase in the performance of the first machine in the specified enterprise after the completion of the implementation of the one or more third configuration changes or a predicted decrease in the volume of support tickets received in the specified enterprise.

14. The method according to claim 10, wherein the enterprise information further includes inventory information indicating at least one of hardware or software associated with the first machine in the specified enterprise; and wherein the anonymized information further includes anonymized inventory information indicating at least one of hardware or software associated with the second machine in the plurality of enterprises.

15. The method according to claim 10, wherein the performance information indicates at least one of: the amount of time that end users in the specified enterprise are unable to interact with the first machine in response to the first configuration change; due to the first configuration change made to the specified enterprise, end users switching from using an initial machine to using a subsequent machine; the amount of time that end users interact with a specified application on one or more of the first machines in the specified enterprise; the productivity of the end users determined by the applications accessed by the end users on one or more of the first machines in the specified enterprise; or when the end users interact with the first machines in the specified enterprise.

16. The method according to claim 10, wherein the actual impact of the one or more third configuration changes in the at least one enterprise includes an actual decrease in the productivity of end users in the at least one enterprise; and wherein the method further comprises: providing a recommendation to modify at least one aspect of the implementation of the one or more third configuration changes in the specified enterprise to at least partially compensate for a predicted decrease in the productivity of end users in the specified enterprise, the predicted decrease in the productivity of end users in the specified enterprise being at least partially based on the actual decrease in the productivity of end users in the at least one enterprise.

17. A computer program product comprising a computer-readable storage medium having instructions recorded thereon for causing a processor-based system to perform operations, the operations comprising: Collecting enterprise information, the enterprise information including: Configuration information indicating a first configuration change associated with a first machine in a specified enterprise, Ticket information related to the volume of support tickets received regarding the first configuration change associated with the first machine in the specified enterprise, and Performance information regarding the performance of the first machine in the specified enterprise in response to the first configuration change associated with the first machine in the specified enterprise; Combining the enterprise information with anonymized information received from multiple enterprises to provide combined information, the anonymized information including: Anonymized configuration information indicating a second configuration change associated with a second machine in the multiple enterprises, Anonymized ticket information related to the volume of support tickets received regarding the second configuration change associated with the second machine in the multiple enterprises, and Anonymized performance information regarding the performance of the second machine in the multiple enterprises in response to the second configuration change associated with the second machine in the multiple enterprises; Determining the actual impact of one or more third configuration changes on a first subset of the first machine in the specified enterprise, the actual impact including: An actual change in the performance of the first subset of the first machine in the specified enterprise due to the one or more third configuration changes, and An actual change in the volume of support tickets received in response to the one or more third configuration changes regarding the first subset of the first machine in the specified enterprise; Inferring the predicted impact of the one or more third configuration changes on a second subset of the first machine in the specified enterprise by analyzing the combined information using an adaptive algorithm, the predicted impact including: A predicted change in the performance of the second subset of the first machine in the specified enterprise due to the one or more third configuration changes, and A predicted change in the volume of support tickets received in response to the one or more third configuration changes regarding the second subset of the first machine in the specified enterprise; and Generating an estimate of the net financial result of implementing the one or more third configuration changes on the second subset of the first machine in the specified enterprise based at least in part on the actual impact of the one or more third configuration changes on the first subset of the first machine in the specified enterprise and further at least in part on the predicted impact of the one or more third configuration changes on the second subset of the first machine in the specified enterprise; and Based on an estimate of the generated net financial result, increase the efficiency of the computing system by causing the computing system to modify at least one of the one or more third configuration changes implemented for the second subset of the first machines in the specified enterprise, the computing system being configured to perform system management tasks for the specified enterprise including the first machines, wherein the at least one third configuration change includes at least one of the following: Deploy software on at least one of the first machines in the second subset; Change hardware on at least one of the first machines in the second subset; Change one or more security settings associated with at least one of the first machines in the second subset, or Change compliance settings associated with at least one of the first machines in the second subset.

18. The computer program product according to claim 17, wherein the operation comprises: Determine, at least in part based on the actual impact and further at least in part based on the predicted impact, the cumulative amount of time that the productivity of the end users of the second subset of the first machines in the specified enterprise is likely to change statistically due to the one or more third configuration changes; and Generate the estimate of the net financial result of implementing the one or more third configuration changes for the second subset of the first machines in the specified enterprise, at least in part based on the sum of a first part and a second part, the first part being equal to the product of the cumulative amount of time and the cost per unit time of each end user in the first machines of the second subset, and the second part being equal to the estimated cost of resolving support tickets related to the one or more third configuration changes for the second subset of the first machines in the specified enterprise.

19. The computer program product according to claim 18, wherein the cumulative amount of time comprises a first amount of time and a second amount of time; wherein the first part is equal to the sum of: (a) the product of the first amount of time and the first common cost per unit time of each end user in a first subset of the end users of the first machines in the second subset of the first machines in the specified enterprise, and (b) the product of the second amount of time and the second common cost per unit time of each end user in a second subset of the end users of the first machines in the second subset of the first machines in the specified enterprise; and wherein the first common cost per unit time of each end user in the first subset of the end users and the second common cost per unit time of each end user in the second subset of the end users are different.

20. The computer program product according to claim 17, wherein the operation comprises: Generate the estimate of the net financial result of implementing the one or more third configuration changes to the second subset of the first machines in the designated enterprise by considering the estimated initial cost associated with implementing the one or more third configuration changes during the implementation of the one or more third configuration changes, and by considering the estimated financial benefit predicted to result from the implementation being completed; Wherein the estimated initial cost is at least partially based on at least one of the following: a predicted decrease in the performance of the second subset of the first machines in the designated enterprise during the implementation of the one or more third configuration changes, or a predicted increase in the volume of support tickets received regarding the one or more third configuration changes to the second subset of the first machines in the designated enterprise; and Wherein the estimated financial benefit is at least partially based on at least one of the following: a predicted increase in the performance of the second subset of the first machines in the designated enterprise after the implementation of the one or more third configuration changes is completed, or a predicted decrease in the volume of support tickets received in the designated enterprise.

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