EP3063719A1

EP3063719A1 - Optimizing a consulting engagement

Info

Publication number: EP3063719A1
Application number: EP13896374.9A
Authority: EP
Inventors: Jaye Darren HICKS; Bradley Scott MOELLER; Jonathan David Gibson
Original assignee: Hewlett Packard Enterprise Development LP
Current assignee: Hewlett Packard Enterprise Development LP
Priority date: 2013-10-28
Filing date: 2013-10-28
Publication date: 2016-09-07
Also published as: CN105874496A; EP3063719A4; WO2015065319A1

Abstract

Optimizing a consulting engagement includes, with a processor, clustering a consulting engagement with a number of reference consulting engagement models based on similarities between the consulting engagement and the number of reference consulting engagement models, with the processor, presenting engagement procedures to a user for the consulting engagement, and, with the processor, determining the engagement procedures to be executed based on a regression analysis to optimize the consulting engagement.

Description

OPTIMIZING A CONSULTING ENGAGEMENT

BACKGROUND

[0001] A consulting engagement is used to help an organization's information technology (IT) operations improve in performance and alignment to a business concern through the analysis of issues, goals, and objectives for the organization. Through the analysis of an organization's issues, goals, and objectives, the consulting engagement allows an organization to refine the organization's overall IT processes and services. As a result, the consulting engagement may serve as a mechanism to improve IT processes and services within the organization.

BRIEF DESCRIPTION OF THE DRAWINGS

[0002] The accompanying drawings illustrate various examples of the principles described herein and are a part of the specification. The examples do not limit the scope of the claims.

[0003] Fig. 1 is a diagram of an example of a system for optimizing a consulting engagement, according to the principles described herein.

[0004] Fig. 2 is a diagram of an example of an optimizing system, according to the principles described herein.

[0005] Fig. 3 is a diagram of an example of an engagement and objectives clustering, according to the principles described herein.

[0006] Fig. 4 is a flowchart of an example of a method for optimizing a consulting engagement, according to one example of principles described herein. [0007] Fig. 5 is a flowchart of an example of a method for optimizing a consulting engagement, according to one example of principles described herein.

[0008] Fig. 6 is a diagram of an example of an optimizing system, according to the principles described herein.

[0009] Fig. 7 is a diagram of an example of an optimizing system, according to the principles described herein.

[0010] Throughout the drawings, identical reference numbers designate similar, but not necessarily identical, elements.

DETAILED DESCRIPTION

[0011] To design a consulting engagement, prior consulting engagement models may be used to model an organization's processes and services. A prior consulting engagement model may be stored in a repository. In one example, a user navigates through the repository to gather the appropriate information to design the consulting engagement based on the prior consulting engagement models.

[0012] Navigating through a repository that is sizeable and extensive can be challenging for a user if the user is not experienced with the information contained in the repository. As a result, allowing the user to navigate through the repository can result in inconsistent and non-optimal approaches for the consulting engagement. Further, as an organization and technology continues to evolve, the organization's prior consulting engagement models, the organization's processes and services, and the organization's resources may become outdated. As a result, the outcome of a consulting engagement based on outdated resources may result in an inaccurate or unsuitable consulting engagement.

[0013] The principles described herein include a method for optimizing a consulting engagement. Such a method includes with a processor, clustering a consulting engagement with a number of reference consulting engagement models based on similarities between the consulting engagement and the number of the reference consulting engagement models, with the processor, presenting engagement procedures to a user for the consulting engagement, and with the processor, determining the engagement procedures to be executed based on a regression analysis to optimize the consulting engagement. Such a method allows the engagement procedures to optimize the consulting engagement by aligning the engagement procedures to the organization's issues, goals and objectives as a user executes the consulting engagement. As a result, the consulting engagement is optimized to improve processes and services within the organization.

[0014] Further, the method can include updating information for the consulting engagement for future consulting engagements. More information about updating information for the consulting engagement for future consulting engagements will be described in more detail below.

[0015] In the present specification and in the appended claims, the term "consulting engagement" is meant to be understood broadly as a consulting mechanism for all processes and services that are provisioned by IT operations, for a department in the organization, to their internal and/or external clients used to run an organization. In one example, the IT operations may include an organization's management, envisioning, planning, designing, implementation, construction, deployment, distribution, verification, installation, instantiation, execution, maintenance, other IT operations, or combinations thereof of processes and services.

[0016] In the present specification and in the appended claims, the term "objectives" is meant to be understood broadly as goals, procedures, and issues to be addressed in the consulting engagement to improve an

organization's processes and services and the alignment of the organization's IT operations to a business concern. In one example, an objective may include an enhancement of technology for the organization, implementing a new function for the organization, implementing new procedures for the organization, other objectives, or combinations thereof.

[0017] Further, as used in the present specification and in the appended claims, the term "a number of or similar language is meant to be understood broadly as any positive number comprising 1 to infinity; zero not being a number, but the absence of a number.

[0018] In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough

understanding of the present systems and methods. It will be apparent, however, to one skilled in the art that the present apparatus, systems, and methods may be practiced without these specific details. Reference in the specification to "an example" or similar language means that a particular feature, structure, or characteristic described in connection with that example is included as described, but may not be included in other examples.

[0019] Referring now to the figures, Fig. 1 is a diagram of an example of a system for optimizing a consulting engagement, according to the principles described herein. As will be described below, an optimizing system is in communication with a user device over a network to optimize a consulting engagement. Further, engagement procedures are used to further optimize the consulting engagement by aligning the engagement procedures to the organization's objectives as a user executes the consulting engagement. As a result, the consulting engagement is optimized to improve processes and services within the organization. As will be described in this specification, this is accomplished by utilizing clustering, artificial intelligence, and learning techniques.

[0020] In one example, the system (100) includes a user device (102) with a display (104). In this example, a user using the user device (102) is connected to a network (106). Further, the user device (102) is used to accesses an optimizing system (108). In one example, the optimizing system (108) obtains, from the user device (102), core data, other data, or combinations thereof about an organization.

[0021] In one example, the core data includes a consulting

engagement history for a client. In keeping with the given example, the other data includes the client's culture, market position, global footprint, political basis, alignment with business partners, trends in a market place, regulations in the marketplace, industries that the client aligns with from a global perspective, business goals, business objectives, or combinations thereof. More information about the core data and the other data will be described in other parts of this specification.

[0022] The system (100) further includes an optimizing system (108). In one example, the optimizing system (108) clusters a consulting engagement with a number of reference consulting engagement models based on similarities between the consulting engagement and the number of the reference consulting engagement models. More information about clustering the consulting engagement with a number of reference consulting engagement models based on similarities between the consulting engagement and the number of the reference consulting engagement models will be described in more detail later on in this specification.

[0023] The optimizing system (108) presents engagement procedures to a user for the consulting engagement. In one example, the engagement procedures may be presented to a user via a display (104) on a user device (102). As will be described in other parts of this specification, the engagement procedures may include engagement clusters, engagement procedures, objective clusters, or combinations thereof.

[0024] The optimizing system (108) further determines engagement procedures to be executed based on a regression analysis to optimize the consulting engagement. In one example, the regression analysis may be a statistical process for estimating the relationships among variables. The regression analysis includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and an independent variable. More specifically, the regression analysis helps one understand how a value of the dependent variable changes when any one of the independent variables is varied, while the other independent variables are held fixed. More information about the regression analysis will be described in later parts of this specification.

[0025] As a result, the optimizing system (108) allows the

engagement procedures to optimize the consulting engagement by aligning the engagement procedures to the organization's objectives as a user executes the consulting engagement. Further, the consulting engagement is optimized to improve processes and services within the organization.

[0026] While this example has been described with reference to the optimizing system being located over the network, the optimizing system may be located in any appropriate location according to the principles described herein. For example, the optimizing system may be located in the user device, a serve, a database, or combinations thereof.

[0027] While this example has been described with reference to the core data and the other data being located in a user device, the core data and the other data may be located in any appropriate location according to the principles described herein. For example, the core data and the other data may be located in a repository, a database, a server, the optimizing system, or combinations thereof.

[0028] Fig. 2 is a diagram of an example of an optimizing system (200), according to the principles described herein. As mentioned above, an optimizing system (200) uses engagement procedures to optimize the consulting engagement by aligning the engagement procedures to the organization's objectives as a user executes the consulting engagement. As a result, the consulting engagement is optimized to improve processes and services within the organization. As will be described below, the optimizing system (200) uses clustering, artificial intelligence and learning techniques to further optimize the consulting engagement.

[0029] In the example of Fig. 2, the optimizing system (200) is seeded with core data (202) about a client's environment, applications, industry, business objectives, and regulatory best practices. Further, the optimizing system (200) is seeded with other data (204) from such sources, but not limited to experienced consultants and practitioners with prior engagement history, clients themselves, social media, Internet, or combinations thereof. Further, the other data (204) includes the client's culture, market position, global footprint, political basis, alignment with business partners, trends in a market place, trends and evolution of IT technology, regulations in the marketplace, industries that the client aligns with from a global perspective, business goals, business objectives, or combinations thereof. In one example, information for the core data (202) and the other data (204) may be templates the user, such as an experienced consultant and practitioner, leverages on a similar consulting engagement.

[0030] The information from the core data (202) and the other data (204) is sent to a mathematical engine (206). In one example, the mathematical engine (206) may include mathematical functions to produce a consulting engagement clustering engine (208). In one example, the consulting

engagement clustering engine (208) may include components such as consulting engagements (210), reference consulting engagement models (212), and engagement clusters (214).

[0031] In one example, the consulting engagements (210) are defined by the appropriate context and resources. The optimizing system (200) defines each consulting engagement as a d-dimensional real vector, where each dimension represents a discrete consulting engagement attribute. This approach provides some flexibility relative to each client's context and environment permitting differences in the amount of information available, in order for this approach to be effective. The d-dimensional real vector for each consulting engagement is defined as:

A=[n, ... n+d] (Equation 1) where A is the d-dimensional real vector for each consulting engagement, d is the consulting engagement dimension, and n is the number of consulting engagement dimensions. In one example, the consulting engagement dimension includes attributes such as: business context, industry context, application context, information context and technology context. Further, the number of consulting engagement dimensions may be specific to the client environment, based on the information and scope of the consulting

engagement. In one example, the optimizing system (200) learns through an updating engine (236) and adds additional consulting engagement dimensions when appropriate. [0032] In one example, the reference consulting engagement models (212) often leverage and use similar artifacts, techniques and processes, as the consulting engagements (210). However, the optimizing system (200) takes this premise further through the definition of reference consulting engagement models (212). In one example, a reference consulting engagement model may be viewed as a perfect state consulting model based on contextual situations. While most consulting engagements will not fully match this perfect state, reference consulting engagement model similarity is extremely useful in optimizing and aligning artifacts, resources, and processes based on similar reference consulting engagement model for the consulting engagement to be performed.

[0033] In one example, the engagement clusters (214) use the location of a reference consulting engagement model as an identically dimensioned real vector to that of the current consulting engagement, to define a centroid around which the consulting engagements are clustered. As will be described in other parts of this specification, clustering is accomplished using a modified k-means clustering function, where the centroid and k values are defined by the type and number of reference consulting engagement models.

[0034] The optimizing system (200) creates an objectives model (220) using an objectives clustering engine (232). In one example, the objectives model (220) represents objectives such as goals, issues, and other objectives to be addressed based on the consulting engagement in progress and their collateral form the current data (216) as well as relevant historical engagement data from the historical data (218).

[0035] In one example, similar consulting engagements will likely use similar engagement procedures to execute the consulting engagements based on similar objectives to be resolved by the consulting engagements, based on, but not limited to historical context. In one example, the optimizing system (200) forms a k-means cluster on a three-dimensional vector of an abstract objective to be resolved. In this example, the three-dimensional vector of an abstract objective to be resolved is defined as: l=[l_s l_o l_r] (Equation 2) where (l_s) defines a priority, (l₀) provides a context and (l_r) represents a historical outcomes based on previously successful consulting engagements.

[0036] The optimizing system (200) initiates the creation of an alignment of consulting engagement objectives (224) along with the

engagement procedures (226). In one example, engagement procedures (226) are discrete operations used to execute the consulting engagement. In one example, the engagement procedures are positioned by a nearest determining engine (238). In one example, the nearest determining engine (238) positions the engagement procedures within a d-dimensional space, as a result of their relationship between a given objectives cluster and a given engagement cluster.

[0037] The optimizing system (200) uses a regression analysis from a regression analysis engine (222) which is fed into an engagement procedure determining engine (226) to determine and establish subsequent engagement procedures as the consulting engagement progresses. As the consulting engagement is executed and results processed by the engagement execution and processed results engine (230), the output may be presented to a user via a presenting engine (234). Further, the output from the engagement execution and processed results engine (228) is captured by an updating engine (236). In one example, the updating engine (236) uses the output from the engagement execution and processed results engine (230) to update information for the consulting engagement for the future consulting engagements. As will be described in other parts of this specification, updating the information for the consulting engagement for the future consulting engagements includes updating artifacts, client feedback, internal social interaction, external social media, or combinations thereof. Further, in this example, the output from the engagement execution and processed results engine (230) is stored as historical data (218).

[0038] Fig. 3 is a diagram of an example of engagement and objectives clustering (300), according to the principles described herein. As mentioned above, engagement procedures such as engagement and objectives clustering (300) for the consulting engagement (318) are presented to a user. In one example, the engagement and objectives clustering (300) are presented to the user via a display (318). Further, the engagement and objectives clustering (300) may be presented in n-space. In another example, the engagement and objectives clustering (300) may be presented in another type of space.

[0039] In the example, of Fig. 3, the engagement and objectives clustering (300) may include engagement cluster A (302) and engagement cluster B (304). In one example, engagement cluster A (302) is created by clustering a consulting engagement (318) with a number of reference consulting engagement models (312) based on similarities between the consulting engagement (318) and the number of the reference consulting engagement models (312). For example, the clustering for engagement cluster A (302) is accomplished using a modified k-means clustering function, where centroid A (308) and k values are defined by the type and number of reference consulting engagement models. In one example, the clustering of consulting engagements provides the desired consulting engagement similarity which the optimizing system uses to identify the reference consulting engagement model.

[0040] In keeping with the given example, engagement cluster A may include centroid A (308). In this example, the location of the reference consulting engagement models (312) is used as an identically dimensioned real vector to that of the consulting engagement (318), to define centroid A (308) around which the consulting engagement (318) is clustered. Further, the example of engagement cluster A (302) may be mirrored for engagement cluster B (304). Further, engagement cluster B (304) may include centroid B (310).

[0041] As mentioned above, the engagement and objectives clustering (300) may create an objectives cluster (306). In one example, the objectives cluster (306) is created by clustering similar objectives for the consulting engagement (318). For example, similar consulting engagements will likely use similar engagement procedures to execute the consulting engagements based on similar objectives to be resolved by the consulting engagements, based on, but not limited to historical context. In this example, the optimizing system forms traditional k-means cluster on a three-dimensional vector of an abstract objective to be resolved. In one example, the vector may be Equation 2.

[0042] Further, the engagement and objectives clustering (300) can include a number of engagement procedures (316). For example, engagement procedure one (316-1), engagement procedure two (316-2), engagement procedure three (316-3), engagement procedure four (316-4), and engagement procedure five (316-5). In one example, the engagement procedures (316) are discrete operations used to execute the consulting engagement (318). In this example, the engagement procedures (316) are positioned within a d- dimensional space, as a result of the engagement procedure's relationship between a given objectives cluster (306) and engagement cluster such as engagement cluster A (302). In one example, this is done through the use of both industry standard data, as well as client-specific historical data, to attain the learning aspect of the optimizing system.

[0043] Further, a number of procedural links (314) are presented to the user in the engagement and objectives clustering (300). In one example, the procedural links (314) may be represented by solid lines. Further, the procedural links (314) link the engagement clusters (302, 310) to the engagement procedures (316). Further, the procedural links (314) link the objective clusters (306) to the engagement procedures (316). For example, procedural link one (314-1) links engagement cluster A (302) to engagement procedure one (316-1). Procedural link two (314-2) links engagement procedure one (316-1) to engagement cluster B (310). Further, procedural link three (314-3) links engagement cluster B (310) to engagement procedure five (316-5). Finally, procedural link four (314-4) links engagement procedure five (316-5) to the objectives cluster (306). As a result, the procedural links link engagement clusters, engagement procedures, and objectives cluster such that the engagement procedures may be executed to meet the objectives cluster to optimize a consulting engagement.

[0044] Fig. 4 is a flowchart of an example of a method for optimizing a consulting engagement, according to one example of principles described herein. In this example, the method (400) includes with a processor, clustering (401) a consulting engagement with a number of reference consulting engagement models based on similarities between the consulting engagement and the number of the reference consulting engagement models, with the processor, presenting (402) engagement procedures to a user for the consulting engagement, and with the processor, determining (403) the engagement procedures to be executed based on a regression analysis to optimize the consulting engagement.

[0045] As mentioned above, the method (400) includes with a processor, clustering (401 ) a consulting engagement with a number of reference consulting engagement models based on similarities between the consulting engagement and the number of the reference consulting engagement models. In one example, clustering is accomplished using a modified k-means clustering function, where a centroid and k values are defined by the type and number of reference consulting engagement models. In one example, the clustering of consulting engagements provides the desired consulting engagement similarity which the optimizing system uses to identify the reference consulting engagement model.

[0046] The method (400) further includes with the processor, presenting (402) engagement procedures to a user for the consulting engagement. In one example, the process of presenting engagement procedures is performed within a computer system and not in physical space. In one example, the engagement procedures are presented to the user via a display. Further, the engagement procedures may be presented in n-space. In another example, the engagement procedures may be presented in another space.

[0047] As mentioned above, the engagement procedures may include engagement clusters, consulting engagements, engagement procedures, objectives clusters, or combinations thereof. Further, the engagement procedures may include procedural links. In one example, the procedural links link engagement clusters, engagement procedures, and objectives cluster to each other such that the engagement procedures may be executed to meet the objectives cluster to optimize a consulting engagement.

[0048] As mentioned above, the method (400) further includes with the processor determining (403) engagement procedures to be executed based on a regression analysis to optimize the consulting engagement. In one example, once an initial engagement procedure has been determined and executed, a regression analysis is used to determine the next engagement procedure. In one example, the regression analysis may be based on current data, historical data or combinations thereof for objectives of the consulting engagement.

[0049] Further, a historical success factor of an engagement procedure contributing to meet a given objective is used as a way to determine which engagement procedure to use. In one example, the optimizing system is configurable in the determination of the threshold for the proximity of the engagement procedure to the given objectives cluster and the consulting engagement. For example, if a calculated engagement procedure does not fall within a specific threshold, the likelihood that the engagement procedure would be effective is low, and therefore the optimizing system would not use the engagement procedure. In this example, a user determination would have to be made and appropriate action taken for the engagement procedure. Further, as the engagement procedures are executed the objectives may be collected as to the efficacy of the engagement procedures for the given situation.

[0050] Fig. 5 is a flowchart of an example of a method for optimizing a consulting engagement, according to one example of principles described herein. In this example, the method (500) includes clustering (501) a consulting engagement with a number of reference consulting engagement models based on similarities between the consulting engagement and the number of the reference consulting engagement models, clustering (502) objectives for the consulting engagement, presenting (503) engagement procedures to a user for the consulting engagement, determining (504) a nearest objective, a nearest engagement cluster, or combinations thereof for the consulting engagement, determining (505) the engagement procedures to be executed based on a regression analysis to optimize the consulting engagement, and updating (506) information for the consulting engagement for future consulting engagements. Further, in the method (500) of Fig. 5, each procedure may be executed using a processor.

[0051] As mentioned above, the method (500) includes clustering (502) objectives for the consulting engagement. In one example, an objectives cluster is created by clustering similar objectives for the consulting engagement. For example, similar consulting engagements will likely use similar engagement procedures to execute the consulting engagements based on similar objectives to be resolved by the consulting engagements, based on, but not limited to historical context. In this example, the optimizing system forms traditional It- means cluster on a three-dimensional vector of an abstract objective to be resolved. In one example, the vector may be Equation 2. Further, the objectives cluster in n-space includes data of how objectives were met and/or resolved from previous consulting engagements and how they are being addressed in current consulting engagements.

[0052] As mentioned above, the method (500) further includes determining (504) a nearest objective, a nearest engagement cluster, or combinations thereof for the consulting engagement. In one example, the nearest engagement procedure to execute is determined by the optimizing system through identification of the k-nearest neighbor engagement procedure to a centroid for the engagement cluster and the objectives cluster. In one example, the k-nearest neighbor may be k=3. In another example, the k- nearest neighbor may be k=5.

[0053] As mentioned above, the method (500) includes updating (506) information for the consulting engagement for future consulting engagements. In one example, as the engagement procedures are executed the information for the objectives is collected as to the efficacy of the engagement procedures for the given situation. Further, the objectives cluster information related to meeting the objectives for the consulting engagements are continuously updated based on the results. [0054] Fig. 6 is a diagram of an example of an optimizing system, according to the principles described herein. The optimizing system (600) includes a consulting engagement clustering engine (602), a presenting engine (604), and an engagement procedures determining engine (606). In this example, the optimizing system (600) also includes an objectives clustering engine (608), a nearest determining engine (610), and an updating engine (612). The engines (602, 604, 606, 608, 610, 612) refer to a combination of hardware and program instructions to perform a designated function. Each of the engines (602, 604, 606, 608, 610, 612) may include a processor and memory. The program instructions are stored in the memory and cause the processor to execute the designated function of the engine.

[0055] The consulting engagement clustering engine (602) clusters a consulting engagement with a number of reference consulting engagement models based on similarities between the consulting engagement and the number of the reference consulting engagement models. In one example, the consulting engagement clustering engine (602) may create one engagement cluster. In another example, the consulting engagement clustering engine (602) may create multiple engagement clusters.

[0056] The presenting engine (604) presents engagement procedures to a user for the consulting engagement. In one example, the engagement procedures may be presented in n-space to the user. In another example, the engagement procedures may be presented in another space to the user.

[0057] The engagement procedures determining engine (606) determines engagement procedures to be executed based on a regression analysis to optimize the consulting engagement. In one example, the engagement procedures determining engine (606) determines one engagement procedure to be executed based on the regression analysis. In another example, the engagement procedures determining engine (606) determines multiple engagement procedures to be executed based on the regression analysis.

[0058] The objectives clustering engine (608) clusters objectives for the consulting engagement. In one example, the objectives clustering engine (608) clusters one objective to be met for the consulting engagement. In another example, the objectives clustering engine (608) clusters multiple objectives to be met for the consulting engagement.

[0059] The nearest determining engine (610) determines a nearest objective, a nearest engagement cluster, or combinations thereof for the consulting engagement. In one example, the nearest determining engine (610) determines multiple nearest objectives, multiple nearest engagement clusters, or combinations thereof for the consulting engagement.

[0060] The updating engine (612) updates information for the consulting engagement for future consulting engagements. In one example, the updating engine (612) updates artifacts, client feedback, internal social interaction, external social media, or combinations thereof.

[0061] Fig. 7 is a diagram of an example of an optimizing system (700), according to the principles described herein. In this example, optimizing system (700) includes processing resources (702) that are in communication with memory resources (704). Processing resources (702) include at least one processor and other resources used to process programmed instructions. The memory resources (704) represent generally any memory capable of storing data such as programmed instructions or data structures used by the optimizing system (700). The programmed instructions shown stored in the memory resources (704) include a consulting engagement clusterer (706), a reference consulting engagement model clusterer (708), an objectives clusterer (710), an engagement procedures presenter (712), a nearest objective determiner (714), a nearest engagement cluster determiner (716), a regression analyses executor (718), an engagement procedures determiner (720), an engagement procedures executor (722), and a consulting engagement information updater (724).

[0062] The memory resources (704) include a computer readable storage medium that contains computer readable program code to cause tasks to be executed by the processing resources (702). The computer readable storage medium may be tangible and/or physical storage medium. The computer readable storage medium may be any appropriate storage medium that is not a transmission storage medium. A non-exhaustive list of computer readable storage medium types includes non-volatile memory, volatile memory, random access memory, write only memory, flash memory, electrically erasable program read only memory, or types of memory, or combinations thereof.

[0063] The consulting engagement clusterer (706) represents programmed instructions that, when executed, cause the processing resources (702) to cluster a consulting engagement. The reference consulting

engagement model clusterer (708) represents programmed instructions that, when executed, cause the processing resources (702) to cluster a reference consulting engagement model. The objectives clusterer (710) represents programmed instructions that, when executed, cause the processing resources (702) to cluster objectives. The engagement procedures presenter (712) represents programmed instructions that, when executed, cause the processing resources (702) to present engagement procedures to a user. The nearest objective determiner (714) represents programmed instructions that, when executed, cause the processing resources (702) to determine the nearest objective. The nearest engagement cluster determiner (716) represents programmed instructions that, when executed, cause the processing resources (702) to determine the nearest engagement cluster.

[0064] The regression analyses executor (718) represents programmed instructions that, when executed, cause the processing resources (702) to execute a regression analyses to determine engagement procedures to execute. The engagement procedures determiner (720) represents

programmed instructions that, when executed, cause the processing resources (702) to determine engagement procedures. The engagement procedures executor (722) represents programmed instructions that, when executed, cause the processing resources (702) to execute engagement procedures. The consulting engagement information updater (724) represents programmed instructions that, when executed, cause the processing resources (702) to update information for the consulting engagement.

[0065] Further, the memory resources (704) may be part of an installation package. In response to installing the installation package, the programmed instructions of the memory resources (704) may be downloaded from the installation package's source, such as a portable medium, a server, a remote network location, another location, or combinations thereof. Portable memory media that are compatible with the principles described herein include DVDs, CDs, flash memory, portable disks, magnetic disks, optical disks, other forms of portable memory, or combinations thereof. In other examples, the program instructions are already installed. Here, the memory resources can include integrated memory such as a hard drive, a solid state hard drive, or the like. In some examples, the processing resources (702) and the memory resources (704) are located within the same physical component, such as a server, or a network component. The memory resources (704) may be part of the physical component's main memory, caches, registers, non-volatile memory, or elsewhere in the physical component's memory hierarchy. Alternatively, the memory resources (704) may be in communication with the processing resources (702) over a network. Further, the data structures, such as the libraries, may be accessed from a remote location over a network connection while the programmed instructions are located locally. Thus, the optimizing system (700) may be implemented on a user device, on a server, on a collection of servers, or combinations thereof.

[0066] The optimizing system (700) of Fig. 7 may be part of a general purpose computer. However, in alternative examples, the optimizing system (700) is part of an application specific integrated circuit.

[0067] The preceding description has been presented to illustrate and describe examples of the principles described. This description is not intended to be exhaustive or to limit these principles to any precise form disclosed. Many modifications and variations are possible in light of the above teaching.

Claims

CLAIMS WHAT IS CLAIMED IS:

1. A method for optimizing a consulting engagement, the method comprising:

with a processor, clustering a consulting engagement with a number of reference consulting engagement models based on similarities between the consulting engagement and the number of the reference consulting

engagement models;

with the processor, presenting engagement procedures to a user for the consulting engagement; and

with the processor, determining the engagement procedures to be executed based on a regression analysis to optimize the consulting

engagement.

2. The method of claim 1 , in which the regression analysis is based on current data, historical data, or combinations thereof for objectives of the consulting engagement.

3. The method of claim 1 , in which clustering the consulting engagement based on the reference consulting engagement model comprises obtaining core data, other data or combinations thereof;

in which the core data comprises a consulting engagement history for a client; and

in which the other data comprises the client's culture, market position, global footprint, political basis, alignment with business partners, trends in a marketplace, regulations in the marketplace, industries that the client aligns with from a global perspective, business goals, business objectives, or combinations thereof.

4. The method of claim 1 , further comprising clustering objectives for the consulting engagement.

5. The method of claim 1 , in which determining the engagement procedures to be executed based on the regression analysis to optimize the consulting engagement further comprises determining a nearest objective, a nearest engagement cluster, or combinations thereof for the consulting engagement.

6. The method of claim 1 , further comprising updating information for the consulting engagement for future consulting engagements.

7. The method of claim 6, in which updating the information for the consulting engagement for the future consulting engagements comprises updating artifacts, client feedback, internal social interaction, external social media, or combinations thereof.

8. A system for optimizing a consulting engagement, the system

comprising:

a processor; and

a memory coupled to the processor, in which the memory comprises: a consulting engagement clustering engine to cluster a consulting engagement with a number of reference consulting engagement models based on similarities between the consulting engagement and the number of the reference consulting engagement models;

a clustering objectives engine to cluster objectives for the consulting engagement;

a presenting engine to present engagement procedures to a user for the consulting engagement;

an engagement procedures determining engine to determine the engagement procedures to be executed based on a regression analysis to optimize the consulting engagement; in which the regression analysis is based on current data and historical data for the objectives of a the consulting engagement; and an updating engine to update information for the consulting engagement for future consulting engagements.

9. The system of claim 8, in which clustering the consulting engagement based on the reference consulting engagement model comprises obtaining core data, other data or combinations thereof;

in which the other data comprises the client's culture, market position, global footprint, political basis, alignment with business partners, trends in a market place, regulations in the marketplace, industries that the client aligns with from a global perspective, business goals, business objectives, or combinations thereof.

10. The system of claim 8, in which determining the engagement procedures to be executed based on the regression analysis to optimize the consulting engagement further comprises determining a nearest objective.

11. The system of claim 8, in which determining the engagement procedures to be executed based on the regression analysis to optimize the consulting engagement further comprises determining a nearest engagement cluster.

12. The system of claim 8, in which updating the information for the consulting engagement for the future consulting engagements comprises updating artifacts, client feedback, internal social interaction, external social media, or combinations thereof.

13. A computer program product for optimizing a consulting engagement, comprising:

a tangible computer readable storage medium, the tangible computer readable storage medium comprising computer readable program code embodied therewith, the computer readable program code comprising program instructions that, when executed, causes a processor to:

cluster a consulting engagement with a number of reference consulting engagement models based on similarities between the consulting engagement and the number of the reference consulting engagement models;

cluster objectives for the consulting engagement; and determine engagement procedures to be executed based on a regression analysis to optimize the consulting engagement.

14. The product of claim 13, further comprising program instructions to, when executed, cause the processor to present the engagement procedures to a user for the consulting engagement.

15. The product of claim 13, further comprising program instructions to, when executed, cause the processor to update information for the consulting engagement for future consulting engagements.