Patent Publication Number: US-11379777-B2

Title: Estimating a result of configuration change(s) in an enterprise

Description:
CROSS-REFERENCE TO RELATED APPLICATION(S) 
     This application claims the benefit of U.S. Provisional Application No. 62/940,784, filed Nov. 26, 2019 and entitled “Estimating a Result of Configuration Change(s) in an Enterprise,” the entirety of which is incorporated herein by reference. 
    
    
     BACKGROUND 
     Information technology (IT) professionals typically perform actions (a.k.a. IT actions) to facilitate systems management of an organization&#39;s enterprise. One functional component of systems management is configuration management, which involves implementing configuration changes in the enterprise, for example, to maintain integrity of the enterprise. Actions that are performed by the IT professionals often are driven by providing end users of the enterprise with hardware and software they need in a manner that meets the organization&#39;s security and compliance standards. However, many IT actions (or inactions), such as those that are performed to deploy software, hardware, and security and compliance settings, have hidden costs. 
     SUMMARY 
     Various approaches are described herein for, among other things, estimating a result of configuration change(s) in an enterprise. For instance, the estimated result may be indicated (e.g., communicated) via an enterprise management tool that is used by an administrator to perform systems management of the enterprise. Systems management typically refers to enterprise-wide administration of distributed systems (e.g., computer systems). Some example tasks that may 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 a variety of 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 handles changes in a system systematically to maintain integrity of the system. Such changes may be implemented for beneficial purposes, including but not limited to revising capability of the system; increasing performance, reliability, and/or maintainability of the system; extending life of the system; reducing cost, risk, and/or liability of the system; and correcting defect(s) of the system. 
     In an example approach, enterprise information is gathered. The enterprise information may include configuration information, ticket information, and/or performance information. The configuration information indicates configuration changes that are made to a designated enterprise. The ticket information indicates a volume of support tickets that are received with regard to the configuration changes. The performance information indicates performance of machines in the designated enterprise in response to the configuration changes. The enterprise information is combined with anonymized information that is received from multiple enterprises to provide combined information. The anonymized information may include anonymized configuration information, anonymized ticket information, and/or anonymized performance information. The anonymized configuration information indicates configuration changes that are made to the enterprises. The anonymized ticket information indicates a volume of support tickets that are received with regard to the configuration changes that are made to the enterprises. The anonymized performance information indicates performance of machines in the enterprises in response to the configuration changes being made to the enterprises. 
     In a first aspect of this approach, a predictive impact of configuration change(s) across the enterprises is inferred by using an adaptive algorithm to analyze the combined information. The predictive impact includes a predicted change to performance of machines in the enterprises as a result of the configuration change(s) and a predicted change to a volume of support tickets that are received in the enterprises as a result of the configuration change(s). An estimate of a net financial result of implementing the configuration change(s) in the designated enterprise is generated based at least in part on the predictive impact of the configuration change(s) across the enterprises. 
     In a second aspect of this approach, an actual impact of configuration change(s) in at least one enterprise is determined. The actual impact includes an actual change to performance of machines in the at least one enterprise as a result of the configuration change(s) and an actual change to a volume of support tickets that are received with regard to the configuration change(s) in the at least one enterprise. An estimate of a net financial result of implementing the configuration change(s) in the designated enterprise is generated based at least in part on the actual impact of the configuration change(s) in the at least one enterprise. 
     In a third aspect of this approach, an actual impact of configuration change(s) with regard to a first subset of the machines in the designated enterprise is determined. The actual impact includes an actual change to performance of the first subset of the machines in the designated enterprise as a result of the configuration change(s) and an actual change to a volume of support tickets that are received in response to the configuration change(s) with regard to the first subset of the machines in the designated enterprise. A predictive impact of the configuration change(s) with regard to a second subset of the machines in the designated enterprise is inferred by using an adaptive algorithm to analyze the combined information. The predictive impact includes a predicted change to performance of the second subset of the machines in the designated enterprise as a result of the configuration change(s) and a predicted change to a volume of support tickets that are received in response to the configuration change(s) with regard to the second subset of the machines in the designated enterprise. An estimate of a net financial result of implementing the configuration change(s) with regard to the second subset of the machines in the designated enterprise is generated based at least in part on the actual impact of the configuration change(s) with regard to the first subset of the machines in the designated enterprise and further based at least in part on the predictive impact of the configuration change(s) with regard to the second subset of the machines in the designated enterprise. 
     This Summary is provided to introduce a selection of concepts in a simplified form that are 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. Moreover, it is noted that the invention is not limited to the specific embodiments described in the Detailed Description and/or other sections of this document. Such embodiments are presented herein for illustrative purposes only. Additional embodiments will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS/FIGURES 
       The accompanying drawings, which are incorporated herein and form part of the specification, illustrate embodiments of the present invention and, together with the description, further serve to explain the principles involved and to enable a person skilled in the relevant art(s) to make and use the disclosed technologies. 
         FIG. 1  is a block diagram of a result estimation system in accordance with an embodiment. 
         FIG. 2  is a block diagram of an example implementation of a result estimation system shown in  FIG. 1  in accordance with an embodiment. 
         FIG. 3  illustrates an example value report in accordance with an embodiment. 
         FIGS. 4-6  depict flowcharts of example methods for estimating a result of configuration change(s) in an enterprise in accordance with embodiments. 
         FIG. 7  is a block diagram of an example computing system in accordance with an embodiment. 
         FIG. 8  depicts an example computer in which embodiments may be implemented. 
     
    
    
     The features and advantages of the disclosed technologies will become more apparent from the detailed description set forth below when taken in conjunction with the drawings, in which like reference characters identify corresponding elements throughout. In the drawings, like reference numbers 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 number. 
     DETAILED DESCRIPTION 
     I. Introduction 
     The following detailed description refers to the accompanying drawings that illustrate exemplary embodiments of the present invention. However, the scope of the present invention is not limited to these embodiments, but is instead defined by the appended claims. Thus, embodiments beyond those shown in the accompanying drawings, such as modified versions of the illustrated embodiments, may nevertheless be encompassed by the present invention. 
     References in the specification to “one embodiment,” “an embodiment,” “an example embodiment,” or the like, indicate that the embodiment described may include a particular feature, structure, or characteristic, but every embodiment may not necessarily include the particular feature, structure, or characteristic. Moreover, such phrases are not necessarily referring to the same embodiment. Furthermore, when a particular feature, structure, or characteristic is described in connection with an embodiment, it is submitted that it is within the knowledge of one skilled in the relevant art(s) to implement such feature, structure, or characteristic in connection with other embodiments whether or not explicitly described. 
     Descriptors such as “first”, “second”, “third”, etc. are used to reference some elements discussed herein. Such descriptors are used to facilitate the discussion of the example embodiments and do not indicate a required order of the referenced elements, unless an affirmative statement is made herein that such an order is required. 
     II. Example Embodiments 
     Example embodiments described herein are capable of estimating a result of configuration change(s) in an enterprise. Examples of a configuration change include but are not limited to deployment of software (e.g., an update to the software), changing hardware, changing security setting(s), and changing compliance setting(s). For instance, the estimated result may be indicated (e.g., communicated) via an enterprise management tool that is used by an IT professional (e.g., an administrator) to perform systems management of the enterprise. Systems management typically refers to enterprise-wide administration of distributed systems (e.g., computer systems). Some example tasks that may 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 a variety of 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 handles changes in a system systematically to maintain integrity of the system. Such changes may be implemented for beneficial purposes, including but not limited to revising capability of the system; increasing performance, reliability, and/or maintainability of the system; extending life of the system; reducing cost, risk, and/or liability of the system; and correcting defect(s) of the system. 
     Example techniques described herein have a variety of benefits as compared to conventional systems management techniques. For instance, the example techniques may be capable of estimating a result of configuration change(s) in an enterprise. The estimate of the result may include an estimate of a net financial result of implementing the configuration change(s) in the enterprise. The net financial result may take into consideration an initial cost associated with implementing the configuration change(s) (e.g., a cost of decreasing end user productivity during the implementation) and a reduction in on-going cost associated with the enterprise (e.g., a financial benefit of increasing end user productivity once the implementation is completed). The net financial result may be based on any of a variety of factors, including but not limited to a predictive impact of the configuration change(s) across one or more enterprises, the actual impact of the configuration change(s) across one or more enterprises, or a combination thereof. The example techniques may provide insight to an IT professional as to traditionally hidden costs associated with implementation of configuration change(s) in an enterprise. Examples of such a traditionally hidden cost include but are not limited to a cost associated with (e.g., resulting from) a reduction in productivity of end users in the enterprise and a cost associated with an increase in an amount of support tickets (e.g., per unit of time) that are received from end users in the enterprise. The example techniques may lead to more effective planning and decision-making with regard to systems management of an enterprise by providing visibility into the overall cost of implementing configuration change(s) in the enterprise. 
     The example techniques may reduce an amount of time and/or resources (e.g., processor cycles, memory, network bandwidth) that is consumed to manage a system. The example techniques may reduce a cost associated with managing the system. For instance, by generating the estimate of the net financial result of implementing the configuration change(s), implementation of configuration change(s) having a net financial result that is greater than a threshold amount may not be performed, or one or more of the configuration change(s) may be modified to reduce the net financial result. The example techniques may increase efficiency of a computing system that performs systems management operations for the enterprise. For instance, the computing system may be configured to not implement configuration changes if an estimate of a net financial cost of the implementation exceeds a cost threshold, or the computing system may be configured to modify one or more of the configuration changes to reduce the financial cost of the implementation below the cost threshold. 
     Moreover, by generating an estimate of a net financial result of implementing configuration change(s), an IT professional need not necessarily spend time manually performing calculations to estimate the net financial result. Reducing the time spent by the IT professional to manage the system results in a reduced cost associated with the IT professional. The example techniques may increase efficiency of an IT professional. For instance, by generating the estimate of the net financial result of implementing the configuration change(s), the example techniques may reduce a number of steps that are performed by the IT professional, an amount of effort that the IT professional expends, and/or an amount of time that the IT professional takes to oversee the systems management of the enterprise. 
     The example techniques may increase efficiency of an end user. For example, implementation of configuration change(s) in the enterprise may result in substantial downtime of the end user. By generating the estimate of the net financial result of implementing the configuration change(s), mitigating steps may be taken to reduce the downtime of the end user. For instance, implementation of configuration change(s) that consumes more than a threshold amount of time may be delayed until the end user is not interacting with a computing device that is to be impacted by the configuration change(s), one or more of the configuration change(s) may be selectively excluded from the implementation, or one or more of the configuration change(s) may be modified such that the downtime of the end user is reduced. By reducing the downtime of the end user that results from the implementation, a portion of the cost of the implementation that is attributable to the end user is reduced. 
       FIG. 1  is a block diagram of an example result estimation system  100  in accordance with an embodiment. Generally speaking, result estimation system  100  operates to perform system configuration operations with regard to enterprises  114 A- 114 M (e.g., customer environments). As shown in  FIG. 1 , the result estimation system  100  includes systems management server(s)  102  and the enterprises  114 A- 114 M. Communication between the systems management server(s)  102  and the enterprises  114 A- 114 M is carried out over a network  108  using well-known network communication protocols. The network  108  may be a wide-area network (e.g., the Internet), a local area network (LAN), another type of network, or a combination thereof. 
     Each of the enterprises  114 A- 114 M includes an admin system  104 , a network  110 , and user devices  106 . For instance, a first enterprise  114 A includes a first admin system  104 A, a first network  110 A, and first user devices  106 A. A second enterprise  114 B includes a second admin system  104 B, a second network  110 B, and second user devices  106 B. An Mth admin system  104 M includes an Mth admin system  104 M, an Mth network  110 M, and Mth user devices  106 M. In each of the enterprises  114 A- 114 M, communication between the admin system  104  and the user devices  106  is carried out over the network  110  using well-known network communication protocols. The network  110  may be a wide-area network (e.g., the Internet), a local area network (LAN), another type of network, or a combination thereof. 
     In each of the enterprises  114 A- 114 M, the user devices  106  are processing systems that are capable of communicating the admin system  104 . An example of a processing system is a system that includes at least one processor that is capable of manipulating data in accordance with a set of instructions. For instance, a processing system may be a computer, a personal digital assistant, etc. The user devices  106  are configured to provide requests to servers (not shown) for requesting information stored on (or otherwise accessible via) the servers. For instance, a user may initiate a request for executing a computer program (e.g., an application) using a client (e.g., a Web browser or other type of client) deployed on a user device that is owned by or otherwise accessible to the user. In accordance with some example embodiments, user devices  106  are capable of accessing domains (e.g., Web sites) hosted by the servers, so that the user devices  106  may access information that is available via the domains. Such domains may include Web pages, which may be provided as hypertext markup language (HTML) documents and objects (e.g., files) that are linked therein, for example. 
     Each of the user devices  106  may include any client-enabled system or device, including but not limited to a desktop computer, a laptop computer, a tablet computer, a wearable computer such as a smart watch or a head-mounted computer, a personal digital assistant, a cellular telephone, an Internet of things (IoT) device, or the like. It will be recognized that any one or more of the user devices  106  may communicate with any one or more of the servers. 
     The user devices  106  are also configured to provide information to the admin system  104  automatically or in responses to inquiries from the admin system  104 . For instance, such information may pertain to hardware and/or software configurations of the user devices  106  (e.g., updates thereof), workloads of the user devices  106 , resource utilization on the user devices, etc. 
     The admin system  104  is a processing system that is capable of communicating with the user devices  106 . The admin system  104  is configured to perform operations to facilitate systems management of the enterprise  114  in response to instructions that are received from the IT administrator of the enterprise  114 . For example, the admin system  104  may provide commands to the systems management server(s)  102 , indicating configuration changes that are to be implemented by the systems management server(s)  102  to perform systems management of the enterprise  114 . In another example, the admin system  104  may provide requests for information to the user devices  106 . The IT administrator may make decisions regarding systems management of the user devices  106  and/or the network  110  based at least in part on the information that is received from the user devices  106 . It will be recognized that at least some (e.g., all) of the information may be gathered by the systems management server(s)  102 , rather than by the admin system  104 . 
     The systems management server(s)  102  are processing systems that are capable of communicating with enterprises  114 A- 114 M. For instance, the systems management server(s)  102  may be capable of communicating with the admin system  104  and/or the user devices  106  in each enterprise  114 . The systems management server(s)  102  include result estimation logic  112 . The result estimation logic  112  is configured to provide information to and/or gather information from the admin server  104  and/or the user devices  106  in each enterprise  114  for purposes of performing systems management of the enterprise  114 . For instance, the result estimation logic  112  may push information to the user devices  106  or provide the information in response to requests that are received from the user devices  106 . The requests may be user-generated or generated without user involvement. For example, policies that are applied to a user device are done without explicit user requests. In accordance with this example, the policies are applied in the background even if no user is logged onto the user device. In further accordance with this example, the user device (e.g., an agent thereon) may poll a server for policy on a schedule (e.g., once per hour) or on events (e.g., device wakeup, user unlock, etc.). In further accordance with this example, the server may push the policy to the user device (e.g., an agent thereon) via an open HTTP endpoint. 
     In accordance with example embodiments described herein, the result estimation logic  112  gathers enterprise information. The enterprise information may include configuration information, ticket information, and/or performance information. The configuration information indicates configuration changes that are made to a designated enterprise (e.g., any one of the enterprises  114 A- 114 M). The ticket information indicates a volume of support tickets that are received with regard to the configuration changes. The performance information indicates performance of machines in the designated enterprise (e.g., first user devices  106 A in enterprise  114 A, second user devices  106 B in enterprise  114 B, . . . , or Mth user devices  106 M in enterprise  114 M) in response to the configuration changes. For instance, the performance information may indicate a boot time, login time, application launch time, crash rate, network latency, and/or resource consumption (e.g., CPU, disk, or battery consumption) for any one or more of the machines in the designated enterprise. The result estimation logic  112  combines the enterprise information with anonymized information that is received from multiple enterprises (e.g., any two or more of the enterprises  114 A- 114 M) to provide combined information. The multiple enterprises may or may not include the designated enterprise. The anonymized information may include anonymized configuration information, anonymized ticket information, and/or anonymized performance information. The anonymized configuration information indicates configuration changes that are made to the enterprises. The anonymized ticket information indicates a volume of support tickets that are received with regard to the configuration changes that are made to the enterprises. The anonymized performance information indicates performance of machines in the enterprises in response to the configuration changes being made to the enterprises. The anonymized information may be averaged across the enterprises, though the example embodiments are not limited in this respect. For example, such averaged information may serve as a baseline against which to compare the enterprise information. In another example, previously gathered enterprise information regarding the designated enterprise may serve as a baseline against which to compare the enterprise information. 
     In a first example implementation, the result estimation logic  112  infers a predictive impact of configuration change(s) across the enterprises by using an adaptive algorithm to analyze the combined information. The predictive impact includes a predicted change to performance of machines in the enterprises as a result of the configuration change(s) and a predicted change to a volume of support tickets that are received in the enterprises as a result of the configuration change(s). In an example scenario, the configuration change(s) may include deployment of a security feature, and the predictive impact may indicate that deployment of the security feature is likely to reduce boot performance (e.g., increase boot times), negatively impact login attempts (e.g., increase login times) due to multi-factor authorization prompts, and/or increase the volume of support tickets in the enterprises. The result estimation logic  112  generates an estimate of a net financial result of implementing the configuration change(s) in the designated enterprise based at least in part on the predictive impact of the configuration change(s) across the enterprises. 
     In a second example implementation, the result estimation logic  112  determines an actual impact of configuration change(s) in at least one enterprise (e.g., at least one of the enterprises  114 A- 114 M). The actual impact includes an actual change to performance of machines in the at least one enterprise as a result of the configuration change(s) and an actual change to a volume of support tickets that are received with regard to the configuration change(s) in the at least one enterprise. In an example scenario, the actual impact may indicate that the configuration change(s) have resulted in an application no longer working (e.g., failing to execute) and/or a volume of support tickets in the at least one enterprise increasing. The result estimation logic  112  generates an estimate of a net financial result of implementing the configuration change(s) in the designated enterprise based at least in part on the actual impact of the configuration change(s) in the at least one enterprise. In an example scenario, by translating the actual impact of the configuration change(s) into end user time disruption and then to actual cost based at least in part on the end users&#39; fully loaded cost per hour and an estimated cost (e.g., a historical cost) of handling a support ticket, the estimation logic  112  may generate the estimate of the net financial result. 
     In a third example implementation, the result estimation logic  112  determines an actual impact of configuration change(s) with regard to a first subset of the machines in the designated enterprise. The actual impact includes an actual change to performance of the first subset of the machines in the designated enterprise as a result of the configuration change(s) and an actual change to a volume of support tickets that are received in response to the configuration change(s) with regard to the first subset of the machines in the designated enterprise. The result estimation logic  112  infers a predictive impact of the configuration change(s) with regard to a second subset of the machines in the designated enterprise by using an adaptive algorithm to analyze the combined information. The predictive impact includes a predicted change to performance of the second subset of the machines in the designated enterprise as a result of the configuration change(s) and a predicted change to a volume of support tickets that are received in response to the configuration change(s) with regard to the second subset of the machines in the designated enterprise. The result estimation logic  112  generates an estimate of a net financial result of implementing the configuration change(s) with regard to the second subset of the machines in the designated enterprise based at least in part on the actual impact of the configuration change(s) with regard to the first subset of the machines in the designated enterprise and further based at least in part on the predictive impact of the configuration change(s) with regard to the second subset of the machines in the designated enterprise. Accordingly, the third example implementation may enable prediction of an incremental financial impact of rolling out the configuration change(s) from pilot machines (e.g., the first subset of the machines) to production machines (e.g., the second subset of the machines) in the designated enterprise. 
     In the first, second, and third example implementations described above, the net financial result may be a net positive amount (e.g., a net financial gain) or a net negative amount (e.g., a net financial loss), depending on whether a combination of the various financial factors on which the net financial result is based is positive or negative, respectively. 
     It will be recognized that at least some of the aforementioned functionality may be implemented by the admin system  104 , rather than by the result estimation logic  112 . For example, if the administrator does not want to provide information to the systems management server(s)  102  and instead wishes to only receive information from the systems management server(s)  102 , the admin system  104  may gather the enterprise information regarding the designated enterprise. The admin system  104  may combine the enterprise information with the anonymized information to provide the combined information. The admin system  104  may determine the actual impact of the configuration change(s) and/or infer the predictive impact of the configuration change(s). The admin system  104  may generate the estimate of the net financial result based at least in part on the actual impact and/or the predictive impact. 
     The result estimation logic  112  may be implemented in various ways to estimate a result of configuration change(s) 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 be executed in one or more processors. In another example, at least a portion of the result estimation logic  112  may be implemented as hardware logic/electrical circuitry. For instance, 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 system (SoC), a complex programmable logic device (CPLD), etc. Each SoC may include an integrated circuit chip that includes one or more of a processor (e.g., a microcontroller, microprocessor, digital signal processor (DSP), etc.), memory, one or more communication interfaces, and/or further circuits and/or embedded firmware to perform its functions. 
     The result estimation logic  112  is shown to be incorporated in the systems management server(s)  102  for illustrative purposes and is not intended to be limiting. It will be recognized that the result estimation logic  112  (or any portion(s) thereof) may be incorporated in the admin system  104  and/or any one or more of the user devices  106 . For example, client-side aspects of the result estimation logic  112  may be incorporated in the admin system  104  and/or one or more of the user devices  106 , and server-side aspects of the result estimation logic  112  may be incorporated in the systems management server(s)  102 . 
       FIG. 2  is a block diagram of a result estimation system  200 , which is an example implementation of a result estimation system  100  shown in  FIG. 1 , in accordance with an embodiment. The result estimation system  200  may be configured to measure key device metrics and changes in an enterprise and to compare those metrics and changes against corporate and global baselines to achieve insights, such as a net financial result of implementing the changes in the enterprise. As shown in  FIG. 2 , the result estimation system  200  includes systems management server(s)  202  and customer environments  214 A- 214 C. Communication between the systems management server(s)  202  and the customer environments  214  may be carried out over a network using well-known network communication protocols, for example. The systems management server(s)  202  include result estimation logic  212 . The result estimation logic  212  is configured to estimate results of configuration changes in customer environments  214 A- 214 M. The result estimation logic  212  includes an admin portal  244 , and enterprise store  216 , estimation logic  218 , a global data store  220 , an event processor  222 , and an event collector  224 . The event collector  224  collects event information, which includes configuration information  236 , support tickets  240 , and performance information  242 . For example, the event collector  224  collects the configuration information  236  from admin systems in the respective customer environments  214 A- 214 M. In accordance with this example, the event collector  240  collects the support tickets  240  and the performance information  242  from desktop clients in the customer environments  214 A- 214 M. The configuration information  236  and the performance information  242  are the same as the configuration information and the performance information described above with reference to the result estimation system  100  of  FIG. 1 . Each of the support tickets indicates a technical issue that an end user has encountered with regard to the desktop client of the end user. 
     The event processor  222  processes the event information that is received from the event collector  224 . For example, the event processor  222  generates ticket information based at least in part on the support tickets  240 . The ticket information indicates a volume of the support tickets  240  that are received with regard to configuration changes that are indicated by the configuration information  236 . The event processor  222  may cross-reference the support tickets  240  with the configuration changes based at least in part on time instance(s) at which the configuration changes are implemented, time instance(s) at which the support tickets  240  are received, aspects of the enterprise that are to be impacted by the configuration changes, and/or aspects of the enterprise that the support tickets  240  indicate to have encountered an issue. By cross-referencing the support tickets  240  with the configuration changes, the event processor  222  may determine a likelihood that each of the support tickets  240  pertains to the configuration changes. The event processor  222  may use the likelihoods to determine the volume of the support tickets  240  that are received with regard to the configuration changes. The event processor  222  may identify enterprise information  210  for each of the enterprises  214 A- 214 M. The enterprise information  210  for each of enterprise includes (e.g., consists of) enterprise specific information for that enterprise. For instance, the event processor  222  may filter the event information to identify portions of the event information that pertain to each enterprise. The event processor  222  may designate the portions of the event information that pertain to each enterprise to be the enterprise information  210  for that enterprise. The event processor  222  may generate anonymized information  208  by aggregating the event information from the various enterprises  214 A- 214 M and removing any information that is capable of being used to identify from which of the customer environments  214 A the various portions of the event information is received. 
     The enterprise data store  216  stores the enterprise information  210 . The global data store  220  stores the anonymized information  208 . The estimation logic  218  generates result estimates  234 A- 234 M for the respective customer environments  214 A- 214 M. The result estimates for a customer environment include an estimate of a net financial result of implementing configuration change(s) in the customer environment based at least in part on an analysis of the enterprise information  210  for that enterprise and the anonymized information  208 . The estimation logic  218  may generate each of the result estimates  234 A- 234 M based at least in part on an actual impact of the configuration change(s) on device metrics (e.g., device downtime, user switching devices, user interaction time, user productivity) and/or a predictive impact of the configuration change(s) in one or more of the customer environments  214 A- 214 M. For instance, the actual impact may be determined based at least in part on the performance information  242  and the support tickets  240  that are received from one or more of the customer environments  214 A- 214 M in response to implementation of the configuration change(s) therein. Because the actual impact may be based on data from fewer than all of the enterprises  234 A- 234 M (e.g., a single enterprise), a set of variables that are used to determine the actual impact may be narrowed in scope. For instance, rather than using a variable corresponding to a relatively broad category, multiple variables corresponding to sub-categories of the relatively broad category may be used. 
     In an example embodiment, the estimation logic  218  trains a machine learning model with data from the customer environments  214 A- 214 M to predict changes to the device metrics and the support ticket volumes due to the configuration change(s). In another example embodiment, the estimation logic  218  includes a customized rule engine that uses the data from the customer environments  214 A- 214 M to predict the changes to the device metrics and the support ticket volumes due to the configuration change(s). In accordance with these embodiments, the data from the customer environments  214 A- 214 M may include the enterprise information and/or the anonymized information described above with reference to  FIG. 1 . The estimation logic  218  may be capable of expressing an expected impact in the form “X % of &lt;devices | users&gt; with &lt;common characteristics&gt; will see a Y % regression in &lt;metric&gt; due to &lt;configuration change&gt;.” The set of all possible configuration changes may be quite broad, which may result in a substantial amount of data and scoping to a manageable subset of predictive variables (characteristics and configuration changes) based on data analysis. The estimation logic  218  may seek metric regressions on all devices and end users, trace back to a set of root causes, and then use a clustering algorithm to identify common attributes of affected devices and end users. 
     The estimation logic  218  may calculate the net financial result of the configuration change(s) by translating the impact (e.g., actual impact and/or predictive impact) of the device metric changes into end user time disruption, determining a per-end-user fully loaded cost per hour, determining an estimated cost of handling each support ticket, and adding (a) a product of the end user time disruption and the per-end-user fully loaded cost per hour and (b) an estimated time to handle the support tickets that result from implementation of the configuration change(s) and the estimated cost of handling each support ticket. A per-end-user fully loaded cost per hour may be equal to a total compensation (e.g., including pay and monetary value of benefits) to be provided to a plurality of end users for working over a period of time divided by a cumulative number of hours worked by the plurality of end users over the period of time. Translating the impact into the end user time disruption may involve determining which portions of the operations that are performed to implement the configuration change(s) occurred when the end users were using their devices and which portions of the operations occurred when the end users were not using their devices. For instance, an update installed overnight may have no impact on the end users, but an update installed during working hours may delay productivity of the end users by 10 minutes or more. 
     The admin portal  244  is an interface via which administrators in the customer environments may communicate with the result estimation logic  212 . The admin portal  244  is configured to provide the result estimates  234 A- 234 M to the respective customer environments  214 A- 214 M. 
     Each of the customer environments  214 A- 214 M includes an admin system and desktop clients. For instance, the customer environment  214 A is shown to include admin system  204  and desktop clients  206 A- 206 C. The admin system  204  is operable in a manner similar to any one of the admin systems  104 A- 104 M described above with reference to  FIG. 1 . The desktop clients  206 A- 206 C are operable in a manner similar to any of the user devices  106 A- 106 M described above with reference to  FIG. 1 . The desktop client  206  is shown to include event logs  226 , a registry  228 , extensions  230 , and a performance monitor agent  232  for non-limiting, illustrative purposes. The event logs  226  include information regarding the events that occur with regard to the desktop client  206 A. For instance, the event logs  226  may include a time stamp to indicate a time instance at which a corresponding event occurred and descriptive information to describe the event. The registry  228  may include a database of configuration settings in an operating system (OS) of the desktop client  206 A. The extensions  230  extend an operating system driver model. For instance, the extensions  230  may provide an OS interface through which instrumented components may provide information and notification. The performance monitor agent  232  reviews the event logs  226 , the configuration settings in the registry  228 , and the information and notifications that are received through the OS interface provided by the extensions  230  and generates the performance information  242  based thereon. 
     It will be recognized that the result estimation system  200  may not include one or more of the components shown in  FIG. 2 . Furthermore, the result estimation system  200  may include components in addition to or in lieu of the components shown in  FIG. 2 . 
       FIG. 3  illustrates an example value report  300  in accordance with an embodiment. The value report  300  indicates an amount of money saved (or that could be saved) by an organization by making optimizations to performance metrics (e.g., boot time and login time) versus a previously measured baseline. The value report  300  includes a baseline menu  302 , a filter menu  304 , a cost-per-hour field  306 , a currency menu  308 , and a virtual create button  310 . The baseline menu  302  enables an administrator of an enterprise to select a date on which a snapshot of metrics associated with the enterprise are captured to establish a baseline regarding the enterprise. The baseline menu  302  is configured to provide a plurality of dates from which the date is selected. The date is specified to be June 19 th  for non-limiting, illustrative purposes. The filter menu  304  enables the administrator to filter events that occur in the enterprise for purposes of determining a net financial result of implementing configuration change(s) in the enterprise. The administrator has selected no filter for non-limiting, illustrative purposes. The cost-per-hour field  306  enables the administrator to specify a cost-per-hour to be associated with each information worker (IW). The currency menu  308  enables the administrator to specify the currency associated with the cost-per-hour specified in the cost-per-hour field  306 . The administrator has specified a cost-per-hour of 100.00 in the cost-per-hour field  306  and selected U.S. dollars (USD) in the currency menu  308  for non-limiting, illustrative purposes. The virtual create button  310  enables the administrator to initiate creation of the net financial result of implementing the configuration changes in the enterprise. 
     The value report  300  shows the result of the administrator selecting the virtual create button  310 . In particular, the value report  300  specifies that implementation of the configuration change(s) will result in a monthly savings of $28,000, which includes a first portion associated with a reduction of boot time that results from implementation of the configuration change(s) and a second portion associated with a reduction in login time that results from implementation of the configuration change(s). The value report  300  indicates that the implementation of the configuration change(s) reduces the boot time by 21 hours, which corresponds to a monthly cost savings of $10,238. The monthly cost savings attributable to the reduction of the boot time is based on analysis of 38,700 reboots corresponding to 17,234 devices for the month. The value report  300  further indicates that the implementation of the configuration change(s) reduces the login time by 38 hours, which corresponds to a monthly cost savings of $17,762. The monthly cost savings attributable to the reduction of the login time is based on analysis of 38,700 logins corresponding to 17,234 devices for the month. The value report  300  further indicates that the implementation of the configuration change(s) remediated 154 issues, corresponding to 17,234 devices and five scripts. Selection of a “Print” user interface element by the administrator causes the value report  300  to be printed. Selection of an “Export” user interface element by the administrator causes the value report  300  to be exported (e.g., saved in a file at a location specified by the administrator). The administrator may share the value report  300  with the chief information officer (CIO) of the organization, for example, to demonstrate the net amount of money saved (or to be saved) by implementing the configuration change(s). 
       FIGS. 4-6  depict flowcharts  400 ,  500 , and  600  of example methods for estimating a result of configuration change(s) in an enterprise in accordance with embodiments. Flowcharts  400 ,  500 , and  600  may be performed by the systems management server(s)  102 , one of the admin systems  104 A- 104 M, or a combination thereof, shown in  FIG. 1 , for example. For illustrative purposes, flowcharts  400 ,  500 , and  600  are described with respect to computing system  700  shown in  FIG. 7 , which may be an example implementation of the systems management server(s)  102 , one of the admin systems  104 A- 104 M, or a combination thereof. As shown in  FIG. 7 , the computing system  700  includes result estimation logic  712 . The result estimation logic  712  includes combining logic  702 , determination logic  704 , and estimation logic  706 . The determination logic  704  includes actual impact logic  714  and predictive impact logic  716 . The predictive impact logic  716  includes an adaptive algorithm  722 . The estimation logic  706  includes time logic  718  and generation logic  720 . Further structural and operational embodiments will be apparent to persons skilled in the relevant art(s) based on the discussion regarding flowcharts  400 ,  500 , and  600 . 
     As shown in  FIG. 4 , the method of flowchart  400  begins at step  402 . In step  402 , enterprise information regarding a designated enterprise is gathered. The enterprise information includes (a) configuration information indicating configuration changes that are made to a designated enterprise, (b) ticket information regarding a volume of support tickets that are received with regard to the configuration changes, and (c) performance information regarding performance of machines in the designated enterprise in response to the configuration changes. In an example implementation, combining logic  702  gathers enterprise information  710 , which includes the configuration information, the ticket information, and the performance information. 
     The performance of any one or more machines may include machine boot time (e.g., by phase of a multi-phase implementation of the configuration change(s)); login time (e.g., by phase); application launch time(s) (e.g., a duration of time between a time instance at which an application launches and a time instance at which a user starts to interact with the application); total duration of CPU, memory, disk input/output (I/O), and/or network I/O consumption that is greater than or equal to a consumption threshold (e.g., 75% of total consumption capacity); machine utilization (e.g., number of hours that each machine is turned on and the user is interacting with the machine); application utilization; battery utilization; disk utilization; network latency and timeout rate (e.g., by application and/or uniform resource identifier (URI) end point; kernel crash (B SOD) mean-usage-time-between-failures; abnormal shutdowns mean-usage-time-between-failures; predictive battery life as mean-usage-time-between-drain; a subset of applications that, when a user interacts therewith, drain the battery at a rate that exceeds a threshold rate; predictive life of a hard drive; and/or frequency of crashes and hangs of drivers and/or applications. 
     The performance of any one or more machines may include an amount of downtime that the one or more machines experience due at least in part to the implementation of the configuration change(s). The downtime of a machine corresponds to downtime of the end user who uses the machine. The downtime of the end user corresponds to a reduction in productivity of the end user. The downtime of the machine may include time that the machine is off-line (e.g., not fully booted) and/or time during which the machine is in a state in which the end user is not able to interact with the machine. For instance, when an end user logs into a machine, the period of time between a time instance at which the end user enters the end user&#39;s credentials and a time instance at which the end user is able to start an application on the machine is an example of time during which the machine is in a state in which the end user is not able to interact with the machine. 
     At step  404 , the enterprise information is combined with anonymized information that is received from multiple enterprises to provide combined information. The anonymized information includes (a) anonymized configuration information indicating configuration changes that are made to the enterprises, (b) anonymized ticket information regarding a volume of support tickets that are received with regard to the configuration changes that are made to the enterprises, and (c) anonymized performance information regarding performance of machines in the enterprises in response to the configuration changes being made to the enterprises. In an example implementation, the combining logic  702  combines the enterprise information  710  with anonymized information  708  that is received from the multiple enterprises to provide combined information  732 . 
     At step  406 , a predictive impact of configuration change(s) across the enterprises is inferred by using an adaptive algorithm to analyze the combined information. The predictive impact includes a predicted change to performance of machines in the enterprises as a result of the configuration change(s) and a predicted change to a volume of support tickets that are received in the enterprises as a result of the configuration change(s). For example, the predictive impact of the configuration change(s) across the enterprises may be inferred based at least in part on an actual impact of the configuration change(s) on one or more of the enterprises. In another example, the predictive impact of the configuration change(s) across the enterprises may be inferred based at least in part on an actual impact of one or more other configuration change on one or more of the enterprises. For instance, a correlation may be made between the one or more other configuration changes, the actual impact of the one or more other configuration changes, and the configuration change(s) to infer the predictive impact. In an example implementation, the predictive impact logic  716  infers the predictive impact of the configuration change(s) across the enterprises by using an adaptive algorithm  722  to analyze the combined information  732 . In accordance with this implementation, the predictive impact logic  716  generates a predictive impact indicator  726  in response to inferring the predictive impact. The predictive impact indicator  726  indicates (e.g., specifies) the predictive impact. 
     In an example embodiment, inferring the predictive impact at step  406  is based at least in part on how the configuration change(s) are being implemented. For example, the determination may be based at least in part on an amount of time over which an implementation of the configuration change(s) is to be performed in the designated enterprise. In accordance with this example, implementing the configuration change(s) over a relatively short amount of time may have a greater impact than implementing the configuration change(s) over a relatively greater amount of time. For instance, pushing out an update rapidly may have, on average, a greater impact than pushing it out more gradually. 
     At step  408 , an estimate of a net financial result of implementing the configuration change(s) in the designated enterprise is generated based at least in part on the predictive impact of the configuration change(s) across the enterprises. In an example implementation, the estimation logic  706  generates a result estimate  736  based at least in part on the predictive impact that is indicated by the predictive impact indicator  726 . The result estimate  736  includes the estimate of the net financial result of implementing the configuration change(s) in the designated enterprise. 
     In an example embodiment, generating the estimate of the net financial result at step  408  includes determining a cumulative amount of time that productivity of end users in the designated enterprise is statistically likely to change as a result of the configuration change(s) based at least in part on the predictive impact. For example, the time logic  718  may determine the cumulative amount of time. In accordance with this example, the time logic  718  may generate a time indicator  734  to indicate the cumulative amount of time. In accordance with this embodiment, generating the estimate of the net financial result at step  408  is based at least in part on a sum of a first portion and a second portion. The first portion is equal to a product of the cumulative amount of time and a cost per unit of time per end user in the designated enterprise. The second portion is equal to an estimated cost of resolving support tickets regarding the configuration change(s) in the designated enterprise. For example, the generation logic  720  may generate the result estimate  736  based at least in part on the sum of the first portion and the second portion. In accordance with this example, the generation logic  720  may generate the result estimate  736  in response to receipt of the time indicator  734 , end user cost information  728 , and ticket cost information  730 . The end user cost information  728  indicates the cost per unit of time per end user in the designated enterprise. The ticket cost information  730  indicates the estimated cost of resolving the support tickets regarding the configuration change(s) in the designated enterprise. In an aspect, the estimated cost of resolving the support tickets may be based at least in part on a predetermined fixed estimated cost that is associated with each support ticket. The fixed estimated cost may be any suitable cost, such as $100, $150, $185, etc. In accordance with this aspect, the estimated cost of resolving the support tickets regarding the configuration change(s) may be calculated by multiplying the fixed estimated cost and a number of the support tickets. For instance, if the fixed estimated cost is $100 and 23 support tickets are attributable to implementation of the configuration change(s), the estimated cost of resolving the support tickets is $100*23=$2300. 
     In an example implementation of this embodiment, the cumulative amount of time includes a first amount of time and a second amount of time. In accordance with this implementation, the first portion is equal to a sum of (a) a product of the first amount of time and a first common cost per unit of time per end user in a first subset of the end users in the designated enterprise and (b) a product of the second amount of time and a second common cost per unit of time per end user in a second subset of the end users in the designated enterprise. In further accordance with this implementation, the first common cost per unit of time per end user in the first subset of the end users and the second common cost per unit of time per end user in the second subset of the end users are different. Different common costs per unit of time per end user for respective subsets of the end users may be referred to as “differential labor costs”. For example, the first subset of the end users may reside in a first geographic region, and the second subset of the end users may reside in a second geographic region that is different from the first geographic region. In accordance with this example, the first geographic region may be China, and the second geographic region may be Europe. In another example, the first subset of the end users may belong to a first hierarchical tier (e.g., pay grade) in a hierarchy of an organization, and the second subset of the end users may belong to a second hierarchical tier in the hierarchy, wherein the second hierarchical tier is different from the first hierarchical tier. In accordance with this example, the first hierarchical tier may correspond to administrative staff, and the second hierarchical tier may correspond to managers. It should be noted that if the product of the first amount of time and the first common cost per unit of time per end user in the first subset of the end users is greater than the product of the second amount of time and the second common cost per unit of time per end user in the second subset of the end users, a determination may be made to implement the configuration change(s) with regard to machines of the end users in the second subset before machines of the end users in the first subset, and vice versa. 
     In another example embodiment, generating the estimate of the net financial result at step  408  takes into consideration an estimated initial cost associated with implementing the configuration change(s) during implementation of the configuration change(s) and further takes into consideration an estimated financial benefit that is predicted to occur as a result of the implementation being completed. The estimated initial cost results from a predicted decrease in the performance of the machines in the designated enterprise and/or a predicted increase in the volume of the support tickets that are received with regard to the configuration change(s) in the designated enterprise during the implementation of the configuration change(s). The estimated financial benefit results from a predicted increase in the performance of the machines in the designated enterprise and/or a predicted decrease in the volume of the support tickets that are received in the designated enterprise after the implementation of the configuration change(s) is completed. For example, the estimated financial benefit may correspond to a payback period over which the estimated initial cost is offset by a reduction in the on-going cost associated with the machines after the implementation of the configuration change(s) is completed. In accordance with this example, on a day-to-day basis after the implementation of the configuration change(s) is completed, the end users may be more productive, fewer support tickets may be received, the machines of the end users may experience less downtime, the machines may be more efficient, the machines may perform operations faster, etc. If the estimated initial cost is greater than the estimated financial benefit that is predicted to occur, the net financial result may be referred to as a net financial loss. If the estimated initial cost is less than the estimated financial benefit that is predicted to occur, the net financial result may be referred to as a net financial gain. 
     In yet another example embodiment, the enterprise information further includes inventory information, which indicates hardware and/or software that is associated with the machines in the designated enterprise. In accordance with this embodiment, the anonymized information further includes anonymized inventory information, which indicates hardware and/or software that is associated with the machines in the plurality of enterprises. Inventory information (e.g., anonymized inventory information) may indicate any suitable information regarding any one or more machines. Examples of inventory information include but are not limited to a manufacturer and/or model of hardware associated with one or more machines; an age of such hardware; memory, central processing unit (CPU), hard drive, network, and/or attached peripheral(s) associated with one or more machines; a version and/or a patch level of an operating system (OS) that is deployed on one or more machines; applications installed on one or more machines; a boot configuration of one or more machines; a version of a basic input/output system (BIOS) that is deployed on one or more machines; an inventory and/or version(s) of driver(s) that are installed on one or more machines; and settings and/or policies associated with one or more machines. 
     In still another example embodiment, the performance information indicates an amount of time that end users of the machines in the designated enterprise are unable to interact with the machines in response to the configuration changes (e.g., in response to implementation of the configuration change(s) or as a result of the implementation of the configuration change(s)). For example, the end users may be unable to interact with the machines during downtime of the machines. In accordance with this example, the end users may be unable to interact with the machines during boot of the machines and/or during sign-in of the end users to the machines. 
     In another example embodiment, the performance information indicates that an end user switches from using a first machine to using a second machine as a result of the configuration changes being made to the designated enterprise. For instance, the end user may have a common end user identity across multiple machines of the end user. A cloud service (e.g., an agent thereof) or an application that is deployed on each of the end user&#39;s machines may detect when the user uses the that machine. For example, the cloud service or application may detect that the end user is actively using the cloud service to perform an operation (e.g., check the end user&#39;s email, reply to an email, interact with a social media account, etc.). Accordingly, a determination may be made 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 indicating that the end user is using the first machine at a first time instance prior to implementation of the configuration change(s) and a second signal from the cloud service or an application deployed on the second machine indicating that the end user is using the second machine at a second time instance after the implementation of the configuration change(s) is completed. In an example scenario, a determination may be made that although downtime of the end user&#39;s machine(s) has not changed, implementation of the configuration change(s) has caused the end user to switch from using the first machine to using the second machine. In accordance with this scenario, it may be known that the end user is X % less productive when using the second machine, as compared to using the first machine. Accordingly, the end user switching from using the first machine to using the second machine may indicate a relatively higher estimated initial cost of implementing the configuration change(s). 
     In yet another example embodiment, the performance information indicates an amount of time that an end user interacts (e.g., engages) with a specified application on one or more of the machines in the designated enterprise. For instance, the amount of time that the end user interacts with a machine may be detected by an application (e.g., a productivity application) that is deployed on the machine or a cloud service agent that is deployed on the machine. For example, the end user and/or an administrator of the designated enterprise may be presented with an option to opt-in to allowing the cloud service agent to monitor interactions of the end user with the machine. In accordance with this example, the cloud service agent may detect the amount of time that the end user interacts with the machine based at least in part on the end user and/or the administrator of the designated enterprise opting-in to allowing the cloud service agent to monitor the interactions of the end user with the machine. 
     In still another example embodiment, the performance information indicates productivity of an end user as determined by an application that is accessed by the end user on one or more of the machines in the designated enterprise. 
     In another example embodiment, the performance information indicates when end users interact with the machines in the designated enterprise. For instance, the performance information may indicate time(s) at which the end users interact with the machines, ranges of times over which the end users are statistically likely to interact with the machines, days of the week on which the end users interact with the machines, days of the week on which the end users are statistically likely to interact with the machines, and so on. For example, implementing configuration change(s) at night may have a substantially different impact on the end users and machines than implementing the configuration change(s) during the day. In another example, implementing configuration change(s) on the weekend may have a substantially different impact on the end users and machines than implementing the configuration change(s) during the work week. Accordingly, knowing when the end users interact with the machines may help to determine an impact that the implementation of the configuration change(s) may have on the end users and machines, which may lead to a more accurate estimate of the net financial result of implementing the configuration change(s) in the designated enterprise. 
     In some example embodiments, one or more steps  402 ,  404 ,  406 , and/or  408  of flowchart  400  may not be performed. Moreover, steps in addition to or in lieu of steps  402 ,  404 ,  406 , and/or  408  may be performed. For instance, in an example embodiment, the predictive impact of the configuration change(s) across the enterprises includes a predicted reduction of productivity of end users in the enterprises. In accordance with this embodiment, the method of flowchart  400  further includes providing a recommendation to modify at least one aspect of an implementation of the configuration change(s) in the designated enterprise to at least partially compensate for a predicted reduction of productivity of end users in the designated enterprise, which is based at least in part on the predicted reduction of productivity of the end users in the enterprises. For instance, providing the recommendation may include providing the recommendation to change a time and/or day on which to implement the configuration change(s) to at least partially compensate for the predicted reduction of productivity of the end users in the designated enterprise. For example, the recommendation may indicate that if a deadline for implementing the configuration change(s) is changed from Friday afternoon to Saturday afternoon, the estimated initial cost of implementing the configuration change(s) may be reduced by X % or $Y because more of the implementation can be performed during the off hours of the end users. In an example implementation, the estimation logic  706  provides the recommendation to modify at least one aspect of the implementation of the configuration change(s). 
     In another example embodiment, the method of flowchart  400  further includes determining an actual impact of the configuration change(s) in at least one enterprise. The actual impact may include an actual change to performance of one or more machines in the at least one enterprise and an actual change to a volume of support tickets that are received with regard to the configuration change(s) in the at least one enterprise. For instance, ticket information and performance information regarding the at least one enterprise prior to implementation of the configuration change(s) may be compared to may compare to ticket information and performance information regarding the at least one enterprise after the implementation of the configuration change(s) to determine the actual impact of the configuration change(s) in the at least one enterprise. The ticket information and performance information regarding the at least one enterprise may be anonymized, though the scope of the example embodiments is not limited in this respect. In an example implementation, the actual impact logic  714  determines the actual impact of the configuration change(s) in the at least one enterprise. For example, the actual impact logic  714  may generate an actual impact indicator  724  that indicates the actual impact of the configuration change(s) in the at least one enterprise. In accordance with this embodiment, generating the estimate of the net financial result at step  408  is based at least in part on the predictive impact of the configuration change(s) in the enterprises and further based at least in part on the actual impact of the configuration change(s) in the at least one enterprise. For instance, the estimation logic  706  may generate the result estimate  736  based at least in part on the predictive impact that is indicated by the predictive impact indicator  726  and further based at least in part on the actual impact that is indicated by the actual impact indicator  724 . The result estimate  736  includes the estimate of the net financial result of implementing the configuration change(s) in the designated enterprise. 
     In yet another example embodiment, steps  406  and  408  of flowchart  400  may be replaced with one or more of the steps shown in flowchart  500  of  FIG. 5 . As shown in  FIG. 5 , the method of flowchart  500  begins at step  502 . In step  502 , an actual impact of configuration change(s) in at least one enterprise is determined. For instance, the actual impact may be determined based at least in part on the combined information. For example, the actual impact may be determined based at least in part on the enterprise information and/or the anonymized information. The actual impact includes an actual change to performance of machines in the at least one enterprise as a result of the configuration change(s) and an actual change to a volume of support tickets that are received with regard to the configuration change(s) in the at least one enterprise. In an example implementation, the actual impact logic  714  determines the actual impact of the configuration change(s) in the at least one enterprise. For instance, the actual impact logic  714  may determine the actual impact based at least in part on the combined information  732 . In accordance with 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. 
     At step  504 , an estimate of a net financial result of implementing the configuration change(s) in the designated enterprise is generated based at least in part on the actual impact of the configuration change(s) in the at least one enterprise. In an example implementation, the estimation logic  706  generates a result estimate  736  based at least in part on the actual impact that is indicated by the actual impact indicator  724 . The result estimate  736  includes the estimate of the net financial result of implementing the configuration change(s) in the designated enterprise. 
     Any of the embodiments described above with respect to steps  406  and/or  408  of flowchart  400  are also applicable to steps  502  and/or  504  of flowchart  500 , though references to predictive impact and predicted change may be replaced with references to actual impact and actual change, respectively. For instance, in an example embodiment, generating the estimate of the net financial result at step  504  includes determining a cumulative amount of time that productivity of end users in the designated enterprise is statistically likely to change as a result of the configuration change(s) based at least in part on the actual impact. For example, the time logic  718  may determine the cumulative amount of time. In accordance with this example, the time logic  718  may generate a time indicator  734  to indicate the cumulative amount of time. In accordance with this embodiment, generating the estimate of the net financial result at step  504  is based at least in part on a sum of a first portion and a second portion. The first portion is equal to a product of the cumulative amount of time and a cost per unit of time per end user in the designated enterprise. The second portion is equal to an estimated cost of resolving support tickets regarding the configuration change(s) in the designated enterprise. For example, the generation logic  720  may generate the result estimate  736  based at least in part on the sum of the first portion and the second portion. 
     In an example implementation of this embodiment, the cumulative amount of time includes a first amount of time and a second amount of time. In accordance with this implementation, the first portion is equal to a sum of (a) a product of the first amount of time and a first common cost per unit of time per end user in a first subset of the end users in the designated enterprise and (b) a product of the second amount of time and a second common cost per unit of time per end user in a second subset of the end users in the designated enterprise. In further accordance with this implementation, the first common cost per unit of time per end user in the first subset of the end users and the second common cost per unit of time per end user in the second subset of the end users are different. 
     In another example embodiment, the actual impact of the configuration change(s) in the at least one enterprise includes an actual reduction of productivity of end users in the at least one enterprise. In accordance with this embodiment, the method of flowchart  500  further includes providing a recommendation to modify at least one aspect of an implementation of the configuration change(s) in the designated enterprise to at least partially compensate for a predicted reduction of productivity of the end users in the designated enterprise, which is based at least in part on the actual reduction of productivity of the end users in the at least one enterprise. For instance, providing the recommendation may include providing the recommendation to change a time and/or day on which to implement the configuration change(s) to at least partially compensate for a predicted reduction of productivity of end users in the designated enterprise that is based at least in part on the actual reduction of productivity of the end users in the at least one enterprise. In an example implementation, the estimation logic  706  provides the recommendation to modify at least one aspect of the implementation of the configuration change(s). 
     In yet another example embodiment, steps  406  and  408  of flowchart  400  may be replaced with one or more of the steps shown in flowchart  600  of  FIG. 6 . As shown in  FIG. 6 , the method of flowchart  600  begins at step  602 . In step  602 , an actual impact of configuration change(s) with regard to (e.g., on) a first subset of the machines in the designated enterprise is determined. The actual impact includes an actual change to performance of the first subset of the machines in the designated enterprise as a result of the configuration change(s) and an actual change to a volume of support tickets that are received in response to the configuration change(s) with regard to the first subset of the machines in the designated enterprise. In an example implementation, the actual impact logic  714  determines the actual impact of the configuration change(s) with regard to a first subset of the machines in the designated enterprise. For instance, the actual impact logic  714  may determine the actual impact based at least in part on the combined information  732 . In accordance with 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. 
     At step  604 , a predictive impact of the configuration change(s) with regard to a second subset of the machines in the designated enterprise is inferred by using an adaptive algorithm to analyze the combined information. The predictive impact includes a predicted change to performance of the second subset of the machines in the designated enterprise as a result of the configuration change(s) and a predicted change to a volume of support tickets that are received in response to the configuration change(s) with regard to the second subset of the machines in the designated enterprise. The second subset of the machines is different from the first subset of the machines. In an example implementation, the predictive impact logic  716  infers the predictive impact of the configuration change(s) with regard to the second subset of the machines in the designated enterprise by using an adaptive algorithm  722  to analyze the combined information  732 . In accordance with this implementation, the predictive impact logic  716  generates a predictive impact indicator  726  in response to inferring the predictive impact. The predictive impact indicator  726  indicates (e.g., specifies) the predictive impact. 
     It will be recognized that the actual impact of configuration change(s) may be determined at step  602  with regard to the first subset of the machines and not with regard to the second subset of the machines. It will be further recognized that the predictive impact of the configuration change(s) may be inferred at step  604  with regard to the second subset of the machines and not with regard to the first subset of the machines. 
     In an example embodiment, the implementation of the configuration change(s) with regard to the first subset of the machines is a pilot implementation of the configuration change(s). In accordance with this embodiment, the implementation of the configuration change(s) with regard to the second subset of the machines is a production implementation of the configuration change(s). 
     At step  606 , an estimate of a net financial result of implementing the configuration change(s) with regard to the second subset of the machines in the designated enterprise is generated based at least in part on the actual impact of the configuration change(s) with regard to the first subset of the machines in the designated enterprise and further based at least in part on the predictive impact of the configuration change(s) with regard to the second subset of the machines in the designated enterprise. In an example implementation, the estimation logic  706  generates a result estimate  736  based at least in part on the actual impact that is indicated by the actual impact indicator  724  and further based at least in part on the predictive impact that is indicated by the predictive impact indicator  726 . The result estimate  736  includes the estimate of the net financial result of implementing the configuration change(s) with regard to the second subset of the machines in the designated enterprise. 
     In an example embodiment, generating the estimate of the net financial result at step  606  includes determining a cumulative amount of time that productivity of end users of the second subset of the machines in the designated enterprise is statistically likely to change as a result of the configuration change(s) based at least in part on the actual impact and further based at least in part on the predictive impact. For example, the time logic  718  may determine the cumulative amount of time. In accordance with this example, the time logic  718  may generate a time indicator  734  to indicate the cumulative amount of time. In accordance with this embodiment, generating the estimate of the net financial result at step  606  is based at least in part on a sum of a first portion and a second portion. The first portion is equal to a product of the cumulative amount of time and a cost per unit of time per end user of a machine in the second subset of the machines in the designated enterprise. The second portion is equal to an estimated cost of resolving support tickets regarding the configuration change(s) with regard to the second subset of the machines in the designated enterprise. For example, the generation logic  720  may generate the result estimate  736  based at least in part on the sum of the first portion and the second portion. In accordance with this example, the generation logic  720  may generate the result estimate  736  in response to receipt of 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 of time per end user of a machine in the second subset of the machines in the designated enterprise. The ticket cost information  730  indicates the estimated cost of resolving the support tickets regarding the configuration change(s) with regard to the second subset of the machines in the designated enterprise. 
     In an example implementation of this embodiment, the cumulative amount of time includes a first amount of time and a second amount of time. In accordance with this implementation, the first portion is equal to a sum of (a) a product of the first amount of time and a first common cost per unit of time per end user in a first subset of the end users of the machines in the second subset of the machines in the designated enterprise and (b) a product of the second amount of time and a second common cost per unit of time per end user in a second subset of the end users of the machines in the second subset of the machines in the designated enterprise. In further accordance with this implementation, the first common cost per unit of time per end user in the first subset of the end users and the second common cost per unit of time per end user in the second subset of the end users are different. 
     In another example embodiment, generating the estimate of the net financial result at step  606  takes into consideration an estimated initial cost associated with implementing the configuration change(s) during implementation of the configuration change(s) and further takes into consideration an estimated financial benefit that is predicted to occur as a result of the implementation being completed. The estimated initial cost results from a predicted decrease in the performance of the second subset of the machines in the designated enterprise and/or a predicted increase in the volume of the support tickets that are received with regard to the configuration change(s) with regard to the second subset of the machines in the designated enterprise during the implementation of the configuration change(s). The estimated financial benefit results from a predicted increase in the performance of the second subset of the machines in the designated enterprise and/or a predicted decrease in the volume of the support tickets that are received in the designated enterprise after the implementation of the configuration change(s) is completed. 
     In yet another example embodiment, the enterprise information further includes inventory information, which indicates hardware and/or software that is associated with the machines in the designated enterprise. In accordance with this embodiment, the anonymized information further includes anonymized inventory information, which indicates hardware and/or software that is associated with the machines in the plurality of enterprises. 
     In still another example embodiment, the performance information indicates an amount of time that end users of the machines in the designated enterprise are unable to interact with the machines in response to the configuration changes. 
     In another example embodiment, the performance information indicates that an end user switches from using a first machine to using a second machine as a result of the configuration changes being made to the designated enterprise. 
     In yet another example embodiment, the performance information indicates an amount of time that an end user interacts with a specified application on one or more of the machines in the designated enterprise. 
     In still another example embodiment, the performance information indicates productivity of an end user as determined by an application that is accessed by the end user on one or more of the machines in the designated enterprise. 
     In another example embodiment, the performance information indicates when end users interact with the machines in the designated enterprise. 
     In yet another example embodiment, the predictive impact of the configuration change(s) with regard to the second subset of the machines in the designated enterprise includes a predicted reduction of productivity of end users of the second subset of the machines in the designated enterprise. In accordance with this embodiment, the method of flowchart  600  further includes providing a recommendation to modify at least one aspect of an implementation of the configuration change(s) with regard to the second subset of the machines in the designated enterprise to at least partially compensate for the predicted reduction of productivity of the end users of the second subset of the machines in the designated enterprise. In an example implementation, the estimation logic  706  provides the recommendation to modify at least one aspect of the implementation of the configuration change(s). 
     Although the operations of some of the disclosed methods are described in a particular, sequential order for convenient presentation, it should be understood that this manner of description encompasses rearrangement, unless a particular ordering is required by specific language set forth herein. For example, operations described sequentially may in some cases be rearranged or performed concurrently. Moreover, for the sake of simplicity, the attached figures may not show the various ways in which the disclosed methods may be used in conjunction with other methods. 
     It will be recognized that the computing system  700  may not include one or more of the combining logic  702 , the determination logic  704 , the estimation logic  706 , the result estimation logic  712 , the actual impact logic  714 , the predictive impact logic  716 , the adaptive algorithm  722 , the time logic  718 , and/or the generation logic  720 . Furthermore, the computing system  700  may include components in addition to or in lieu of the combining logic  702 , the determination logic  704 , the estimation logic  706 , the result estimation logic  712 , the actual impact logic  714 , the predictive impact logic  716 , the adaptive algorithm  722 , the time logic  718 , and/or the generation logic  720 . 
     Any one or more of the result estimation logic  112 , the result estimation logic  212 , the estimation logic  218 , the event processor  222 , the event collector  224 , the performance monitor agent  232 , the admin portal  244 , the combining logic  702 , the determination logic  704 , the estimation logic  706 , the result estimation logic  712 , the actual impact logic  714 , the predictive impact logic  716 , the adaptive algorithm  722 , the time logic  718 , the generation logic  720 , flowchart  400 , flowchart  500 , and/or flowchart  600  may be implemented in hardware, software, firmware, or any combination thereof. 
     For example, any one or more of the result estimation logic  112 , the result estimation logic  212 , the estimation logic  218 , the event processor  222 , the event collector  224 , the performance monitor agent  232 , the admin portal  244 , the combining logic  702 , the determination logic  704 , the estimation logic  706 , the result estimation logic  712 , the actual impact logic  714 , the predictive impact logic  716 , the adaptive algorithm  722 , the time logic  718 , the generation logic  720 , flowchart  400 , flowchart  500 , and/or flowchart  600  may be implemented, at least in part, as computer program code configured to be executed in one or more processors. 
     In another example, any one or more of the result estimation logic  112 , the result estimation logic  212 , the estimation logic  218 , the event processor  222 , the event collector  224 , the performance monitor agent  232 , the admin portal  244 , the combining logic  702 , the determination logic  704 , the estimation logic  706 , the result estimation logic  712 , the actual impact logic  714 , the predictive impact logic  716 , the adaptive algorithm  722 , the time logic  718 , the generation logic  720 , flowchart  400 , flowchart  500 , and/or flowchart  600  may be implemented, at least in part, as hardware logic/electrical circuitry. Such hardware logic/electrical circuitry may include one or more hardware logic components. Examples of a hardware logic component include but are not limited to a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), an application-specific standard product (ASSP), a system-on-a-chip system (SoC), a complex programmable logic device (CPLD), etc. For instance, a SoC may include an integrated circuit chip that includes one or more of a processor (e.g., a microcontroller, microprocessor, digital signal processor (DSP), etc.), memory, one or more communication interfaces, and/or further circuits and/or embedded firmware to perform its functions. 
     III. Further Discussion of Some Example Embodiments 
     An example system comprises a memory and one or more processors coupled to the memory. The one or more processors are configured to gather enterprise information, which includes (a) configuration information indicating configuration changes that are made to a designated enterprise, (b) ticket information regarding a volume of support tickets that are received with regard to the configuration changes, and (c) performance information regarding performance of machines in the designated enterprise in response to the configuration changes. The enterprise information is combined with anonymized information that is received from a plurality of enterprises to provide combined information. The anonymized information includes (a) anonymized configuration information indicating configuration changes that are made to the plurality of enterprises, (b) anonymized ticket information regarding a volume of support tickets that are received with regard to the configuration changes that are made to the plurality of enterprises, and (c) anonymized performance information regarding performance of machines in the plurality of enterprises in response to the configuration changes being made to the plurality of enterprises. A predictive impact of one or more configuration changes across the plurality of enterprises is inferred by using an adaptive algorithm to analyze the combined information. The predictive impact includes a predicted change to performance of machines in the plurality of enterprises as a result of the one or more configuration changes and a predicted change to a volume of support tickets that are received in the plurality of enterprises as a result of the one or more configuration changes. An estimate of a net financial result of implementing the one or more configuration changes in the designated enterprise is generated based at least in part on the predictive impact of the one or more configuration changes across the plurality of enterprises. 
     In a first aspect of the example system, the one or more processors are configured to determine a cumulative amount of time that productivity of end users in the designated enterprise is statistically likely to change as a result of the one or more configuration changes based at least in part on the predictive impact. In accordance with the first aspect, the estimate of the net financial result of implementing the one or more configuration changes in the designated enterprise is generated based at least in part on a sum of a first portion and a second portion. The first portion is equal to a product of the cumulative amount of time and a cost per unit of time per end user in the designated enterprise. The second portion is equal to an estimated cost of resolving support tickets regarding the one or more configuration changes in the designated enterprise. 
     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. In accordance with this implementation, the first portion is equal to a sum of (a) a product of the first amount of time and a first common cost per unit of time per end user in a first subset of the end users in the designated enterprise and (b) a product of the second amount of time and a second common cost per unit of time per end user in a second subset of the end users in the designated enterprise. In further accordance with this implementation, the first common cost per unit of time per end user in the first subset of the end users and the second common cost per unit of time per end user in the second subset of the end users are different. 
     In a second aspect of the example system, the one or more processors are configured to generate the estimate of the net financial result of implementing the one or more configuration changes in the designated enterprise by taking into consideration an estimated initial cost associated with implementing the one or more configuration changes during implementation of the one or more configuration changes and by taking into consideration an estimated financial benefit that is predicted to occur as a result of the implementation being completed. In accordance with the second aspect, the estimated initial cost is based at least in part on at least one of a predicted decrease in the performance of the machines in the designated enterprise or a predicted increase in the volume of the support tickets that are received with regard to the one or more configuration changes in the designated enterprise during the implementation of the one or more configuration changes. In further accordance with the second aspect, the estimated financial benefit is based at least in part on at least one of a predicted increase in the performance of the machines in the designated enterprise or a predicted decrease in the volume of the support tickets that are received in the designated enterprise after the implementation of the one or more configuration changes is completed. The second aspect of the example system may be implemented in combination with the first aspect of the example system, though the example embodiments are not limited in this respect. 
     In a third aspect of the example system, the enterprise information further includes inventory information, which indicates at least one of hardware or software that is associated with the machines in the designated enterprise. In accordance with the third aspect, the anonymized information further includes anonymized inventory information, which indicates at least one of hardware or software that is associated with the machines in the plurality of enterprises. The third aspect of the example system may be implemented in combination with the first and/or second aspect of the example system, though the example embodiments are not limited in this respect. 
     In a fourth aspect of the example system, the performance information indicates an amount of time that end users of the machines in the designated enterprise are unable to interact with the machines in response to the configuration changes. The fourth aspect of the example system may be implemented in combination with the first, second, and/or third aspect of the example system, though the example embodiments are not limited in this respect. 
     In a fifth aspect of the example system, the performance information indicates that an end user switches from using a first machine to using a second machine as a result of the configuration changes being made to the designated enterprise. The fifth aspect of the example system may be implemented in combination with the first, second, third, and/or fourth aspect of the example system, though the example embodiments are not limited in this respect. 
     In a sixth aspect of the example system, the performance information indicates an amount of time that an end user interacts with a specified application on one or more of the machines in the designated enterprise. The sixth aspect of the example system may be implemented in combination with the first, second, third, fourth, and/or fifth aspect of the example system, though the example embodiments are not limited in this respect. 
     In a seventh aspect of the example system, the performance information indicates productivity of an end user as determined by an application that is accessed by the end user on one or more of the machines in the designated enterprise. The seventh aspect of the example system may be implemented in combination with the first, second, third, fourth, fifth, and/or sixth aspect of the example system, though the example embodiments are not limited in this respect. 
     In an eighth aspect of the example system, the performance information indicates when end users interact with the machines in the designated enterprise. The eighth aspect of the example system may be implemented in combination with the first, second, third, fourth, fifth, sixth, and/or seventh aspect of the example system, though the example embodiments are not limited in this respect. 
     In a ninth aspect of the example system, the predictive impact of the one or more configuration changes across the plurality of enterprises includes a predicted reduction of productivity of end users in the plurality of enterprises. In accordance with the ninth aspect, the one or more processors are further configured to provide a recommendation to modify at least one aspect of an implementation of the one or more configuration changes in the designated enterprise to at least partially compensate for a predicted reduction of productivity of end users in the designated enterprise, which is based at least in part on the predicted reduction of productivity of the end users in the plurality of enterprises. The ninth aspect of the example system may be implemented in combination with the first, second, third, fourth, fifth, sixth, seventh, and/or eighth aspect of the example system, though the example embodiments are not limited in this respect. 
     In a tenth aspect of the example system, the one or more processors are configured to determine an actual impact of the one or more configuration changes in at least one enterprise. The actual impact includes an actual change to performance of one or more machines in the at least one enterprise and an actual change to a volume of support tickets that are received with regard to the one or more configuration changes in the at least one enterprise. In accordance with the tenth aspect, the estimate of the net financial result of implementing the one or more configuration changes in the designated enterprise is generated based at least in part on the predictive impact of the one or more configuration changes in the plurality of enterprises and further based at least in part on the actual impact of the one or more configuration changes in the at least one enterprise. The tenth aspect of the example system may be implemented in combination with the first, second, third, fourth, fifth, sixth, seventh, eighth, and/or ninth aspect of the example system, though the example embodiments are not limited in this respect. 
     In an eleventh aspect of the example system, the one or more processors are configured to infer the predictive impact based at least in part on an amount of time over which an implementation of the one or more configuration changes is to be performed in the designated enterprise. The eleventh aspect of the example system may be implemented in combination with the first, second, third, fourth, fifth, sixth, seventh, eighth, ninth, and/or tenth aspect of the example system, though the example embodiments are not limited in this respect. 
     In an example method, enterprise information, which includes (a) configuration information indicating configuration changes that are made to a designated enterprise, (b) ticket information regarding a volume of support tickets that are received with regard to the configuration changes, and (c) performance information regarding performance of machines in the designated enterprise in response to the configuration changes, is gathered. The enterprise information is combined with anonymized information that is received from a plurality of enterprises to provide combined information. The anonymized information includes (a) anonymized configuration information indicating configuration changes that are made to the plurality of enterprises, (b) anonymized ticket information regarding a volume of support tickets that are received with regard to the configuration changes that are made to the plurality of enterprises, and (c) anonymized performance information regarding performance of machines in the plurality of enterprises in response to the configuration changes being made to the plurality of enterprises. An 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 an actual change to performance of machines in the at least one enterprise as a result of the one or more configuration changes and an actual change to a volume of support tickets that are received with regard to the one or more configuration changes in the at least one enterprise. An estimate of a net financial result of implementing the one or more configuration changes in the designated enterprise is generated based at least in part on the actual impact of the one or more configuration changes in the at least one enterprise. 
     In a first aspect of the example method, generating the estimate of the net financial result comprises determining a cumulative amount of time that productivity of end users in the designated enterprise is statistically likely to change as a result of the one or more configuration changes based at least in part on the actual impact. In accordance with the first aspect, generating the estimate of the net financial result comprises generating the estimate of the net financial result of implementing the one or more configuration changes in the designated enterprise based at least in part on a sum of a first portion and a second portion, the first portion being equal to a product of the cumulative amount of time and a cost per unit of time per end user in the designated enterprise. In further accordance with the first aspect, the second portion is equal to an estimated cost of resolving support tickets regarding the one or more configuration changes in the designated enterprise. 
     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. In accordance with this implementation, the first portion is equal to a sum of (a) a product of the first amount of time and a first common cost per unit of time per end user in a first subset of the end users in the designated enterprise and (b) a product of the second amount of time and a second common cost per unit of time per end user in a second subset of the end users in the designated enterprise. In further accordance with this implementation, the first common cost per unit of time per end user in the first subset of the end users and the second common cost per unit of time per end user in the second subset of the end users are different. 
     In a second aspect of the example method, generating the estimate of the net financial result of implementing the one or more configuration changes in the designated enterprise takes into consideration an estimated initial cost associated with implementing the one or more configuration changes during implementation of the one or more configuration changes and further takes into consideration an estimated financial benefit that is predicted to occur as a result of the implementation being completed. In accordance with the second aspect, the estimated initial cost results from at least one of a predicted decrease in the performance of the machines in the designated enterprise or a predicted increase in the volume of the support tickets that are received with regard to the one or more configuration changes in the designated enterprise during the implementation of the one or more configuration changes. In further accordance with the second aspect, the estimated financial benefit results from at least one of a predicted increase in the performance of the machines in the designated enterprise or a predicted decrease in the volume of the support tickets that are received in the designated enterprise after the implementation of the one or more configuration changes is completed. The second aspect of the example method may be implemented in combination with the first aspect of the example method, though the example embodiments are not limited in this respect. 
     In a third aspect of the example method, the enterprise information further includes inventory information, which indicates at least one of hardware or software that is associated with the machines in the designated enterprise. In accordance with the third aspect, the anonymized information further includes anonymized inventory information, which indicates at least one of hardware or software that is associated with the machines in the plurality of enterprises. The third aspect of the example method may be implemented in combination with the first and/or second aspect of the example method, though the example embodiments are not limited in this respect. 
     In a fourth aspect of the example method, the performance information indicates an amount of time that end users of the machines in the designated enterprise are unable to interact with the machines in response to the configuration changes. The fourth aspect of the example method may be implemented in combination with the first, second, and/or third aspect of the example method, though the example embodiments are not limited in this respect. 
     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 as a result of the configuration changes being made to the designated enterprise. The fifth aspect of the example method may be implemented in combination with the first, second, third, and/or fourth aspect of the example method, though the example embodiments are not limited in this respect. 
     In a sixth aspect of the example method, the performance information indicates an amount of time that an end user interacts with a specified application on one or more of the machines in the designated enterprise. The sixth aspect of the example method may be implemented in combination with the first, second, third, fourth, and/or fifth aspect of the example method, though the example embodiments are not limited in this respect. 
     In a seventh aspect of the example method, the performance information indicates productivity of an end user as determined by an application that is accessed by the end user on one or more of the machines in the designated enterprise. The seventh aspect of the example method may be implemented in combination with the first, second, third, fourth, fifth, and/or sixth aspect of the example method, though the example embodiments are not limited in this respect. 
     In an eighth aspect of the example method, the performance information indicates when end users interact with the machines in the designated enterprise. The eighth aspect of the example method may be implemented in combination with the first, second, third, fourth, fifth, sixth, and/or seventh aspect of the example method, though the example embodiments are not limited in this respect. 
     In a ninth aspect of the example method, the actual impact of the one or more configuration changes in the at least one enterprise includes an actual reduction of productivity of end users in the at least one enterprise. In accordance with the ninth aspect, the example method further comprises providing a recommendation to modify at least one aspect of an implementation of the one or more configuration changes in the designated enterprise to at least partially compensate for a predicted reduction of productivity of the end users in the designated enterprise, which is based at least in part on the actual reduction of productivity of the end users in the at least one enterprise. The ninth aspect of the example method may be implemented in combination with the first, second, third, fourth, fifth, sixth, seventh, and/or eighth aspect of the example method, though the example embodiments are not limited in this respect. 
     An example computer program product comprises a computer-readable storage medium having instructions recorded thereon for enabling a processor-based system to perform operations. The operations comprise gather enterprise information, which includes (a) configuration information indicating configuration changes that are made to a designated enterprise, (b) ticket information regarding a volume of support tickets that are received with regard to the configuration changes, and (c) performance information regarding performance of machines in the designated enterprise in response to the configuration changes. The operations further comprise combine the enterprise information with anonymized information that is received from a plurality of enterprises to provide combined information. The anonymized information includes (a) anonymized configuration information indicating configuration changes that are made to the plurality of enterprises, (b) anonymized ticket information regarding a volume of support tickets that are received with regard to the configuration changes that are made to the plurality of enterprises, and (c) anonymized performance information regarding performance of machines in the plurality of enterprises in response to the configuration changes being made to the plurality of enterprises. The operations further comprise determine an actual impact of one or more configuration changes with regard to a first subset of the machines in the designated enterprise. The actual impact includes an actual change to performance of the first subset of the machines in the designated enterprise as a result of the one or more configuration changes and an actual change to a volume of support tickets that are received in response to the one or more configuration changes with regard to the first subset of the machines in the designated enterprise. The operations further comprise infer a predictive impact of the one or more configuration changes with regard to a second subset of the machines in the designated enterprise by using an adaptive algorithm to analyze the combined information. The predictive impact includes a predicted change to performance of the second subset of the machines in the designated enterprise as a result of the one or more configuration changes and a predicted change to a volume of support tickets that are received in response to the one or more configuration changes with regard to the second subset of the machines in the designated enterprise. The operations further comprise generate an estimate of a net financial result of implementing the one or more configuration changes with regard to the second subset of the machines in the designated enterprise based at least in part on the actual impact of the one or more configuration changes with regard to the first subset of the machines in the designated enterprise and further based at least in part on the predictive impact of the one or more configuration changes with regard to the second subset of the machines in the designated enterprise. 
     In a first aspect of the example computer program product, the operations comprise determine a cumulative amount of time that productivity of end users of the second subset of the machines in the designated enterprise is statistically likely to change as a result of the one or more configuration changes based at least in part on the actual impact and further based at least in part on the predictive impact. In accordance with the first aspect, the operations comprise generate the estimate of the net financial result of implementing the one or more configuration changes with regard to the second subset of the machines in the designated enterprise based at least in part on a sum of a first portion and a second portion. The first portion is equal to a product of the cumulative amount of time and a cost per unit of time per end user of a machine in the second subset of the machines in the designated enterprise. The second portion is equal to an estimated cost of resolving support tickets regarding the one or more configuration changes with regard to the second subset of the machines in the designated enterprise. 
     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. In accordance with this implementation, the first portion is equal to a sum of (a) a product of the first amount of time and a first common cost per unit of time per end user in a first subset of the end users of the machines in the second subset of the machines in the designated enterprise and (b) a product of the second amount of time and a second common cost per unit of time per end user in a second subset of the end users of the machines in the second subset of the machines in the designated enterprise. In further accordance with this implementation, the first common cost per unit of time per end user in the first subset of the end users and the second common cost per unit of time per end user in the second subset of the end users are different. 
     In a second aspect of the example computer program product, the operations comprise generate the estimate of the net financial result of implementing the one or more configuration changes with regard to the second subset of the machines in the designated enterprise by taking into consideration an estimated initial cost associated with implementing the one or more configuration changes during implementation of the one or more configuration changes and by taking into consideration an estimated financial benefit that is predicted to occur as a result of the implementation being completed. In accordance with the second aspect, the estimated initial cost is based at least in part on at least one of a predicted decrease in the performance of the second subset of the machines in the designated enterprise or a predicted increase in the volume of the support tickets that are received with regard to the one or more configuration changes with regard to the second subset of the machines in the designated enterprise during the implementation of the one or more configuration changes. In further accordance with the second aspect, the estimated financial benefit is based at least in part on at least one of a predicted increase in the performance of the second subset of the machines in the designated enterprise or a predicted decrease in the volume of the support tickets that are received in the designated enterprise after the implementation of the one or more configuration changes is completed. The second aspect of the example computer program product may be implemented in combination with the first aspect of the example computer program product, though the example embodiments are not limited in this respect. 
     IV. Example Computer System 
       FIG. 8  depicts an example computer  800  in which embodiments may be implemented. Any one or more of user devices  106 A- 106 M, any one or more of admin systems  104 A- 104 M, and/or any one or more of systems management server(s)  102  shown in  FIG. 1 ; any one or more of systems management server(s)  202 , admin system  204 , and/or any one or more of desktop clients  206 A- 206 C shown in  FIG. 2 ; and/or computing system  700  shown in  FIG. 7  may be implemented using computer  800 , including one or more features of computer  800  and/or alternative features. Computer  800  may be a general-purpose computing device in the form of a conventional personal computer, a mobile computer, or a workstation, for example, or computer  800  may be a special purpose computing device. The description of computer  800  provided herein is provided for purposes of illustration, and is not intended to be limiting. Embodiments may be implemented in further types of computer systems, as would be known to persons skilled in the relevant art(s). 
     As shown in  FIG. 8 , computer  800  includes a processing unit  802 , a system memory  804 , and a bus  806  that couples various system components including system memory  804  to processing unit  802 . Bus  806  represents one or more of any 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. System memory  804  includes read only memory (ROM)  808  and random access memory (RAM)  810 . A basic input/output system  812  (BIOS) is stored in ROM  808 . 
     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 magnetic disk drive  816  for reading from or writing to a removable magnetic disk  818 , and an optical disk drive  820  for reading from or writing to a removable optical disk  822  such as a CD ROM, DVD ROM, or other optical media. Hard disk drive  814 , magnetic disk drive  816 , and optical disk drive  820  are connected to bus  806  by a hard disk drive interface  824 , a magnetic disk drive interface  826 , and an optical drive interface  828 , respectively. The drives and their associated computer-readable storage media provide nonvolatile storage of computer-readable instructions, data structures, program modules and other data for the computer. Although a hard disk, a removable magnetic disk and a removable optical disk are described, other types of computer-readable storage media can be used to store data, such as flash memory cards, digital video disks, random access memories (RAMs), read only memories (ROM), and the like. 
     A number of program modules may be stored on the hard disk, magnetic disk, optical disk, ROM, or RAM. These programs include an operating system  830 , one or more application programs  832 , other program modules  834 , and program data  836 . Application programs  832  or program modules  834  may include, for example, computer program logic for implementing any one or more of (e.g., at least a portion of) the result estimation logic  112 , the result estimation logic  212 , the estimation logic  218 , the event processor  222 , the event collector  224 , the performance monitor agent  232 , the admin portal  244 , the combining logic  702 , the determination logic  704 , the estimation logic  706 , the result estimation logic  712 , the actual impact logic  714 , the predictive impact logic  716 , the adaptive algorithm  722 , the time logic  718 , the generation logic  720 , flowchart  400  (including any step of flowchart  400 ), flowchart  500  (including any step of flowchart  500 ), and/or flowchart  600  (including any step of flowchart  600 ), as described herein. 
     A user may enter commands and information into the computer  800  through input devices such as keyboard  838  and pointing device  840 . Other input devices (not shown) may include a microphone, joystick, game pad, satellite dish, scanner, touch screen, camera, accelerometer, gyroscope, or the like. These and other input devices are often connected to the processing unit  802  through a serial port interface  842  that is coupled to bus  806 , but may be connected by other interfaces, such as a parallel port, game port, or a universal serial bus (USB). 
     A display device  844  (e.g., a monitor) is also connected to bus  806  via an interface, such as a video adapter  846 . In addition to display device  844 , computer  800  may include other peripheral output devices (not shown) such as speakers and printers. 
     Computer  800  is connected to a network  848  (e.g., the Internet) through a network interface or adapter  850 , a modem  852 , or other means for establishing communications over the network. Modem  852 , which may be internal or external, is connected to bus  806  via serial port interface  842 . 
     As used herein, the terms “computer program medium” and “computer-readable storage medium” are used to generally refer to media (e.g., non-transitory media) such as the hard disk associated with hard disk drive  814 , removable magnetic disk  818 , removable optical disk  822 , as well as other media such as flash memory cards, digital video disks, random access memories (RAMs), read only memories (ROM), and the like. Such computer-readable storage media are distinguished from and non-overlapping with communication media (do not include communication media). Communication media embodies 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 includes wireless media such as acoustic, RF, infrared and other wireless media, as well as wired media. Example embodiments are also directed to such communication media. 
     As noted above, computer programs and modules (including application programs  832  and other program modules  834 ) may be stored on the hard disk, magnetic disk, optical disk, ROM, or RAM. Such computer programs may also be received via network interface  850  or serial port interface  842 . Such computer programs, when executed or loaded by an application, enable computer  800  to implement features of embodiments discussed herein. Accordingly, such computer programs represent controllers of the computer  800 . 
     Example embodiments are also directed to computer program products comprising software (e.g., computer-readable instructions) stored on any computer-useable medium. Such software, when executed in one or more data processing devices, causes data processing device(s) to operate as described herein. Embodiments may employ any computer-useable or computer-readable medium, known now or in the future. Examples of computer-readable mediums include, but are not limited to storage devices such as RAM, hard drives, floppy disks, CD ROMs, DVD ROMs, zip disks, tapes, magnetic storage devices, optical storage devices, MEMS-based storage devices, nanotechnology-based storage devices, and the like. 
     It will be recognized that the disclosed technologies are not limited to any particular computer or type of hardware. Certain details of suitable computers and hardware are well known and need not be set forth in detail in this disclosure. 
     V. Conclusion 
     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. Rather, the specific features and acts described above are disclosed as examples of implementing the claims, and other equivalent features and acts are intended to be within the scope of the claims.