Claims:1. A method for career navigation, the method comprising:
receiving, by a processor, user current data associated with a user in an organization and target data associated with a target role of the user in the organization, wherein the user current data comprises at least a user skill, and wherein the target data comprises at least a target skill;
generating, by the processor, a skill match score and a skill gap based on comparison of the user current data and the target data, and a weighted decision matrix methodology, wherein the skill match score is indicative of similarity between the user skill and the target skill; and
displaying, by the processor, the skill match score and the skill gap, thereby assisting the user for career navigation.

2. The method of claim 1, wherein the user current data further comprises a role of the user in the organization, a user skill level associated with the user skill, a current benchmark score associated with the user skill, wherein the role is indicative of job of the user in the organization, wherein the user skill level is indicative of expertise of the user in the user skill, wherein the current benchmark score indicates level of excellence associated with the user skill, and wherein the target data further comprises a target skill level associated with the target skill, a weightage assigned to the target skill, and a target benchmark score associated with the target skill.

3. The method of claim 1, wherein the weighted decision matrix methodology comprises:
computing, by the processor, a first difference between the user skill level and the target skill level;
determining, by the processor, a first multiplication based on a multiplication of the weightage and the first difference;
computing, by the processor, a total multiplication based on aggregation of the first multiplication;
determining, by the processor, a primary target skill level based on a multiplication of the weightage and the target skill level;
computing, by the processor, a total skill level based on aggregation of the primary target skill level;
computing, by the processor, a third difference between the total multiplication and the total skill level; and
generating, by the processor, the skill match score based on division of the third difference and the total skill level.

4. The method of claim 1, wherein the user current data is received from at least one of a performance appraisal system, a manager of the user, colleagues of the user in the organization and the user.

5. A system for career navigation, the system comprising:
a memory;
a processor coupled to the memory, wherein the processor is configured to execute programmed instructions stored in the memory to:
receive user current data associated with a user in an organization and target data associated with a target role of the user in the organization, wherein the user current data comprises at least a user skill, and wherein the target data comprises at least a target skill;
generate a skill match score and a skill gap based on comparison of the user current data and the target data and a weighted decision matrix methodology, wherein the skill match score is indicative of similarity between the user skill and the target skill; and
display the skill match score and the skill gap, thereby assisting the user for career navigation.

6. The system of claim 5, wherein the user current data further comprises a role of the user in the organization, a user skill level associated with the user skill, a current benchmark score associated with the user skill, wherein the role is indicative of job of the user in the organization, wherein the user skill level is indicative of expertise of the user in the user skill, wherein the current benchmark score indicates level of excellence associated with the user skill in the organization, and wherein the target data further comprises a target skill level associated with the target skill, a weightage assigned to the target skill, and a target benchmark score associated with the target skill.

7. The system of claim 5, wherein the weighted decision matrix methodology comprises:
computing, by the processor, a first difference between the user skill level and the target skill level;
determining, by the processor, a first multiplication based on a multiplication of the weightage and the primary difference;
computing, by the processor, a total multiplication based on aggregation of the first multiplication;
determining, by the processor, a primary target skill level based on a multiplication of the weightage and the target skill level;
computing, by the processor, a total skill level based on aggregation of the primary target skill level;
computing, by the processor, a third difference between the total multiplication and the total skill level; and
generating, by the processor, the skill match score based on division of the third difference and the total skill level.

8. The system of claim 5, wherein the user current data is received from at least one of a performance appraisal system, a manager of the user, colleagues of the user in the organization and the user.

9. A computer program product having embodied thereon a computer program for career navigation, the computer program product comprises:

a program code for receiving user current data associated with a user in an organization and target data associated with a target role of the user in the organization, wherein the user current data comprises at least a user skill, and wherein the target data comprises at least a target skill;
a program code for generating a skill match score and a skill gap based on comparison of the user current data and the target data and a weighted decision matrix methodology, wherein the skill match score is indicative of similarity between the user skill and the target skill; and
a program code for displaying the skill match score and the skill gap, thereby
assisting the user for career navigation.
, Description:FORM 2

THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENT RULES, 2003

COMPLETE SPECIFICATION
(See Section 10 and Rule 13)

Title of invention:
SYSTEM AND METHOD FOR CAREER NAVIGATION

Applicant:
Skills Alpha Learning Private Limited
a company incorporated in India under the Companies Act, 1956
having address:
Unit No. 401, 4th Floor, Zero One IT Park, Ghorpadi-Mundhwa Road, Mundhwa,
Pune – 411036, Maharashtra, India

The following specification describes the invention and the manner in which it is to be performed.

CROSS-REFERENCE TO RELATED APPLICATIONS AND PRIORITY
The present application does not claim priority from any patent application.
TECHNICAL FIELD
The present disclosure in general relates to the field of data processing. More particularly, the present invention relates to a system and method for career navigation.
BACKGROUND
Nowadays, employees are career conscious and tend to leave the organization where they feel that they do not have an opportunity to showcase their talent, grow to the maximum possible potential and achieve their goals. However, in order to grow, employees need to develop up-skill so that they are fit for promotion and reach to a higher level or acquire newer role in the organization. Although it is employee’s responsibility to plan their career, there exists uncertainty on the type of new and better skills the employee needs to develop in order to grow, within the organization. Moreover, the uncertainty on the type of skills required, makes it difficult for the employee to build an effective plan for growth. Furthermore, the organization generally fail to effectively communicate the skills required in its employees to grow within the organization, resulting in dissatisfaction of the employees and in turn high attrition rate.
SUMMARY
This summary is provided to introduce aspects related to a system and method for career navigation and the aspects are further described below in the detailed description. This summary is not intended to identify essential features of the claimed subject matter nor is it intended for use in determining or limiting the scope of the claimed subject matter.
In one embodiment, a method for career navigation is illustrated. The method may comprise receiving user current data associated with a user in an organization and target data associated with a target role of the user in the organization. The user current data may comprise at least a user skill and the target data may comprise at least a target skill associated with the target role. Upon receiving the user current data and the target data, the method may comprise generating a skill match score and a skill gap based on comparison of the user current data and the target data, and using a weighted decision matrix methodology. The skill match score may be indicative of similarity between the user skill and the target skill. Further to generation of the skill match score and the skill gap, the method may comprise displaying the skill match score and the skill gap to the user.
In another embodiment, a system for career navigation is illustrated. The system comprises a memory and a processor coupled to the memory, further the processor may execute programmed instructions stored in the memory. In one embodiment, the processor may execute programmed instructions stored in the memory for receiving user current data associated with a user in an organization and target data associated with a target role of the user in the organization. The user current data may comprise at least a user skill and the target data may comprise at least a target skill associated with the target role. Upon receiving the user current data and the target data, the processor may execute programmed instructions stored in the memory for generating a skill match score and a skill gap based on comparison of the user current data and the target data, and using a weighted decision matrix methodology. The skill match score may be indicative of similarity between the user skill and the target skill. Further to generation of the skill match score and the skill gap, the processor may execute programmed instructions stored in the memory for displaying the skill match score and the skill gap to the user.
In yet another embodiment, a computer program product having embodied computer program for career navigation is disclosed. The program may comprise a program code for receiving user current data associated with a user in an organization and target data associated with a target role of the user in the organization. The user current data may comprise at least a user skill and the target data may comprise at least a target skill. Upon receiving the user current data and the target data, the program may comprise a program code for generating a skill match score and a skill gap based on comparison of the user current data and the target data, and using a weighted decision matrix methodology. The skill match score may be indicative of similarity between the user skill and the target skill. Further to generation of the skill match score and the skill gap, the program may comprise a program code for displaying the skill match score and the skill gap to the user.

BRIEF DESCRIPTION OF DRAWINGS
The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The same numbers are used throughout the drawings to refer like features and components.
Figure 1 illustrates a network implementation of a system for career navigation, in accordance with an embodiment of the present subject matter.
Figure 2 illustrates the system for career navigation, in accordance with an embodiment of the present subject matter.
Figure 3 illustrates a method for career navigation, in accordance with an embodiment of the present subject matter.
Figure 4 illustrates a flowchart of an example for determining a skill match score, in accordance with an embodiment of the present subject matter.
DETAILED DESCRIPTION
Some embodiments of the present disclosure, illustrating all its features, will now be discussed in detail. The words “receiving”, “generating”, “displaying”, “computing”, “determining”, and “capturing”, and other forms thereof, are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise. Although any systems and methods similar or equivalent to those described herein can be used in the practice or testing of embodiments of the present disclosure, the exemplary, systems and methods for career navigation are now described. The disclosed embodiments of the system and method for career navigation are merely exemplary of the disclosure, which may be embodied in various forms.
Various modifications to the embodiment will be readily apparent to those skilled in the art and the generic principles herein may be applied to other embodiments. However, one of ordinary skill in the art will readily recognize that the present disclosure for career navigation is not intended to be limited to the embodiments illustrated, but is to be accorded the widest scope consistent with the principles and features described herein.
The present subject matter relates to a system and method for career navigation. In one embodiment, user current data associated with a user in an organization and target data associated with a target role of the user in the organization may be received. Upon receiving the user current data and the target data, a skill match score and a skill gap may be generated based on comparison of the user current data and the target data, and using a weighted decision matrix methodology. Further to generation of the skill match score and the skill gap, the skill match score and the skill gap may be displayed to the user. Further, the user may use the skill match score and the skill gap to build a plan for achieving the target role.
Referring now to Figure 1, a network implementation 100 of a system 102 for career navigation is disclosed. Although the present subject matter is explained considering that the system 102 is implemented on a server, it may be understood that the system 102 may also be implemented in a variety of computing systems, such as a laptop computer, a desktop computer, a notebook, a workstation, a mainframe computer, a server, a network server, and the like. In one implementation, the system 102 may be implemented in a cloud-based environment. It will be understood that the system 102 may be accessed by multiple users through one or more user devices 104-1, 104-2…104-N, collectively referred to as user device 104 hereinafter, or applications residing on the user device 104. Examples of the user device 104 may include, but are not limited to, a portable computer, a personal digital assistant, a handheld device, and a workstation. The user device 104 may be communicatively coupled to the system 102 through a network 106. Further, the system 102 may be connected to a database 108 by a wired or wireless communication channel.
In one implementation, the network 106 may be a wireless network, a wired network or a combination thereof. The network 106 may be implemented as one of the different types of networks, such as intranet, local area network (LAN), wide area network (WAN), the internet, and the like. The network 106 may either be a dedicated network or a shared network. The shared network represents an association of the different types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), and the like, to communicate with one another. Further, the network 106 may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, and the like.
In one example, the database 108 may be part of the system 102. The database 108 may be a relational database, a real-time database, a cloud database, a distributed database and the like. The database 108 may be a local database or a global database. In one example, the local database may comprise data associated with one organization and the global database may comprise data associated with multiple organizations.
In one embodiment, data associated with one or more roles existing in an organization may be stored in the database 108. Each role may correspond to a target role. The data associated with the target role may be updated by a manager of the target role, Human Resource (HR) executive of the organization and the like.
In one embodiment, the system 102 may receive user current data associated with a user in an organization and target data associated with a target role of the user in the organization. In one example, the user current data may be received from the user device 104 via the network 106 and the target data may be received from the database 108. The user current data may comprise a role of the user in the organization, a user skill, a user skill level associated with the user skill, and a current benchmark score associated with the user skill. The target data may comprise a target skill, a target skill level associated with the target skill, a weightage assigned to the target skill, and a target benchmark score associated with the target skill. Upon receiving the user current data and the target data, the system 102 may generate a skill match score and a skill gap. In one embodiment, the skill match score and the skill gap may be generated based on comparison of the user current data and the target data, and a weighted decision matrix methodology. The skill match score may indicate similarity between the user skill and the target skill, and the skill gap may indicate the target skill different from the user skill. Further to the generation of the skill match score and the skill gap, the system 102 may display the skill match score and the skill gap to the user. The user may use the skill match score and the skill gap to build a plan for achieving the target role. Further, the system 102 for career navigation is elaborated with respect to the figure 2.
Referring now to figure 2, the system 102 for career navigation is illustrated in accordance with an embodiment of the present subject matter. In one embodiment, the system 102 may include at least one processor 202, an input/output (I/O) interface 204, and a memory 206. The at least one processor 202 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, at least one processor 202 may be configured to fetch and execute computer-readable instructions stored in the memory 206.
The I/O interface 204 may include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like. The I/O interface 204 may allow the system 102 to interact with the user directly or through the user device 104. Further, the I/O interface 204 may enable the system 102 to communicate with other computing devices, such as web servers and external data servers (not shown). The I/O interface 204 may facilitate multiple communications within a wide variety of networks and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. The I/O interface 204 may include one or more ports for connecting a number of devices to one another or to another server.
The memory 206 may include any computer-readable medium known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. The memory 206 may include modules 208 and data 210.
The modules 208 may include routines, programs, objects, components, data structures, and the like., which perform particular tasks, functions or implement particular abstract data types. In one implementation, the module 208 may include a data receiving module 212, a score generating module 214, a score displaying module 216 and other modules 218. The other modules 218 may include programs or coded instructions that supplement applications and functions of the system 102.
The data 210, amongst other things, serve as a repository for storing data processed, received and generated by one or more of the modules 208. The data 210 may also include a repository 222 and other data 224. The other data 224 may include data generated as a result of execution of one or more modules in the other modules 218.
In one implementation, at first, a user may access the system 102 via the I/O interface 204. The user may register using the I/O interface 204 in order to use the system 102. In one aspect, the user may access the I/O interface 204 of the system 102 for obtaining information, providing input information or configuring the system 102.
DATA RECEIVING MODULE 212
In one embodiment, the data receiving module 212 may receive user current data associated with the user in an organization. In one example, the user current data may indicate skill proficiency of the user. The user may be an employee in the organization, a manager of the employee and the like. The user current data may comprise a role of the user in the organization, a user skill, a user skill level associated with the user skill, and a current benchmark score associated with the user skill. The role may indicate a job of the user in the organization, for example, a business analyst, a software developer and the like. The user skill level may be an expertise of the user skill. The user skill may be associated with the role of the user in the organization and current skills of the user. In one embodiment, the user skill may comprise domain specific skill of the user, such as software programming, software testing, software designing and the like., and soft skills of the user, such as communication skills, leadership, and the like. The domain specific skill may be associated with the role of the user. In one embodiment, the current benchmark score may indicate level of excellence associated with the user skill. In one example, the user skill level, associated with the user skill, may be equal to or less than the current benchmark score, associated with the user skill. The user may need to improve the user skill, when the user skill level is less than the current benchmark score.
In another embodiment, the user current data may be received from at least one of a performance appraisal system, a manager of the user, colleagues of the user in the organization, the user and the like. The performance appraisal system may comprise evaluation of user skills, performance of the user in the organization, achievements of the user in the organization and the like. The performance appraisal may be referred as performance review of the user in the organization, performance evaluation of the user in the organization and the like.
Further, the data receiving module 212 may receive target data associated with a target role of the user in the organization from the database 108. The target data may comprise a target skill, a target skill level associated with the target skill, a weightage assigned to the target skill, and a target benchmark score associated with the target skill.
In one embodiment, the target role may be selected by the user based on aspiration of the user, suggestions from a manager of the user, suggestions from colleagues of the user in the organization and the like. In another embodiment, the user may select multiple target roles from multiple organizations and same organization. The target role may indicate job in the organization, such as a software engineer, a business analyst, and the like. In one embodiment, the target skill may comprise domain specific skills, such as software programming, software testing and the like., and soft skills, such as communication skills, leadership and the like. The target skill level may indicate expertise of the target skill. In one embodiment, the weightage may be assigned to the target skill by a manager of the target role, a Human Resource (HR) executive of the organization and the like. The weightage assigned to the target skill may indicate level of importance of the target skill. In one example, the weightage may be in a form of percentage. In one embodiment, the target benchmark score may indicate level of excellence required for the target skill in the organization. Upon receiving the user current data and the target data, the data receiving module 212 may store the user current data and the target data in the repository 222.
SCORE GENERATING MODULE 214
Upon receiving the user current data and the target data, the score generating module 214 may compare the user skill and the target skill. In one embodiment, the user skill level associated with the user skill may be zero (0), when the user skill corresponding to the target skill is not available. In another embodiment, the score generating module 214 may not use the user skill and the user skill level, associated with the user skill, for computation of a skill match score, when the target skill corresponding to the user skill is not available. Further, the weightage may be zero (0), when the target skill corresponding to the user skill is not available. Further to the comparison of the user skill and the target skill, the score generating module 214 may compute a first difference based on a subtraction of the user skill level from the target skill level. The score generating module 214 may store the first difference in the repository 222.
Further to the computation of the first difference, the score generating module 214 may perform a weighted decision matrix methodology to generate the skill match score and a skill gap. In one example, the weighted decision matrix methodology may include computation of a first multiplication, a total multiplication, a primary skill level, a total skill level and a third difference. In one embodiment, the score generating module 214 may determine the first multiplication based on a multiplication of the weightage, associated with the target skill, and the first difference. Once the first multiplication is determined, the score generating module 214 may generate the total multiplication based on an aggregation of the first multiplication. Upon generating the total multiplication, the score generating module 214 may determine the primary skill level based on a multiplication of the weightage, associated with the target skill, and the target skill level. Further, the score generating module 214 may generate the total skill level based on an aggregation of the primary skill level. Upon generating the total multiplication and the total skill level, the score generating module 214 may generate the third difference based on a subtraction of the total multiplication and the total skill level.
Further, the score generating module 214 may generate the skill match score and the skill gap based on the comparison of the user current data and the target data, and the weighted decision matrix methodology. In one example, the score generating module 214 may generate the skill match score based on a division of the third difference and the total skill level. The skill match score may be in a form of percentage. The skill match score may indicate similarity between the target skill and the user skill and the skill gap may indicate the target skill that are different from the user skill. In one embodiment, the score generating module 214 may store the skill match score in the repository 222.
In one embodiment, the score generating module 214 may compute the skill match score using equations (I), (II), (III) and (IV) as shown below:
Di=TSLi-USLi …………………………(I)
M1i=Wi*TSLi …………………………(II)
M2i=Wi*Di …………………………(III)
S=((?_(i=1)^(i=N)¦M1i-?_(i=1)^(i=N)¦?M2i)?)/(?_(i=1)^(i=N)¦M1i) ………………………(IV)
Wherein,
USL- User Skill Level associated with a user skill
TSL- Target Skill Level associated with the target skill
D- First Difference between a user skill level and the target skill level
W- Weightage associated with a target skill
M1- Multiplication of the weightage and the target skill level
M2- Multiplication of the weightage and the first difference
S- Skill match score
N – Total number of the target skill level
SCORE DISPLAYING MODULE 216
Upon generating the skill match score and the skill gap, the score displaying module 216 may display the skill match score and the skill gap to the user. In one example, the score displaying module 216 may prioritize each target role from one or more target roles selected by the user. The priority of each target role may be based on comparison of the skill match score associated with each target role from the one or more target roles. In another example, the score generating module 216 may build a plan to guide the user for adopting the target skill.
Once the skill match score and the skill gap are displayed to the user, the user may use the skill match score and the skill gap to build a plan for achieving the target role. In one example, the user may use the skill match score for improving the user skill and the skill gap for adopting the target skill. The user may identify one or more activities that the user may perform to adopt the target skill.
In one example, the user may select four target roles, for example, target role 1, target role 2, target role 3, target role 4, from the organization. Further, a percentage of skill match score associated with each target role may be (a) target role 1- 45%, (b) target role 2- 35%, (c) target role 3- 67%, and (d) target role 4- 20%. Further, the percentage of skill match score associated with target role 3 may be more than the percentage of skill match score associated with the target role 1, the target role 2 and the target role 4. The user may further build a plan for adopting skills associated with the target role 3.
In one exemplary embodiment, a user of the system 102 may be Ms. Aditi Sharma. The system 102 may generate a table 1 for computing a skill match score associated with Ms. Aditi Sharma. The table 1 may comprise a user role, a user skill, a user skill level, a target role, a target skill, a target skill level, a weightage of the target skill, a first difference, a first multiplication, and a primary skill level in different columns.

Table 1: Skill match score Computation

User Role User Skill User Skill Level Target Role Target Skill Target Skill Level Weightage First Difference First Multiplication Primary Skill Level
Business Analyst - 0 Sales Associate Sales report 3 30% 3 0.9 0.9
Lead Generation 3 Lead Generation 3 20% 0 0 0.6
Documentation 3 - 0 0 -3 0 0
Effective Communication 2 Effective Communication 4 20% 2 0.4 0.8
Virtual team management 2 Virtual team management 4 30% 2 0.6 1.2
Total 10 Total 14 100% - 1.9 3.5
- - - - - - - -

Third Difference =1.6 Skill match score= 0.4571 Percentage score = 45. 71

In one embodiment, the data receiving module 212 may receive user current data and target data associated with the target role selected by Ms. Aditi Sharma. The user current data may comprise the user role, the user skill and the user skill level and the target data may comprise the target role, the target skill and the target skill level. Referring now to the table 1, the user role may be business analyst. The user skill and the user skill level, associated with the user skill, may be- (a) Lead Generation- 3, (b) Documentation- 3, (c) Effective Communication- 2 and (d) Virtual Team Management-2. The target role of the user is sales associate. The target skill and the target skill level, associated with the target skill, may be- (a) Sales Support- 3, (b) Lead Generation- 3, (d) Effective Communication- 4, (d) Virtual Team Management-4. The weightage associated with the target skill may be- (a) Sales Support- 30%, (b) Lead Generation- 20%, (c) Effective Communication- 20% and (d) Virtual Team Management- 30%. Further, the user skill level may be 0 (As shown in column 3 and row 1), as the user skill corresponding to the target skill (Sales support) is not available. Furthermore, the user skill (Documentation) may not be used for computation of the skill match score, as the target skill corresponding to the user skill (Documentation) is not available. Furthermore, the weightage (As shown in column 7 and row 3) may be zero (0), as the target skill corresponding to the user skill (Documentation) is not available.
Referring now to figure 4, a flowchart 400 for determining a skill match score, is disclosed. Further, the above mentioned example of Ms. Aditi Sharma is described below with reference of the flowchart 400 and the table 1.
At block 402, the score generating module 214 may compute the first difference (column 8) based on subtracting the user skill level from the target skill level (As shown in the table 1: (3-0=3), (3-3=0), (0-3=-3), (4-2=2) and (4-2=2)).
At block 404, the score generating module 214 may determine the first multiplication (column 9) by multiplying the weightage of the target skill (column 7) with the first difference (As shown in the table 1: (30%*3=0.9), (20%*0=0), (0*(-3) =0), (20%*2=0.4) and (30%*2=0.6)).
At block 406, the score generating module 214 may compute the total multiplication (row 8, column 9) based on an aggregation of the first multiplication (As shown in the table 1: ((0.9+0+0+0.4+0.6) = 1.9).
At block 408, the score generating module 214 may determine the primary skill level (column 10) based on multiplying the weightage associated with the target skill level (As shown in the table 1: ((30%*3=0.9), (20%*3=0.6), (0*0=0), (20%*4=0.8), (30%*4= 1.2)).
At block 410, the score generating module 214 may compute the total skill level (row 8, column 10) may be determined based on an aggregation of the primary skill level (As shown in the table 1: ((0.9+0.6+0+0.8+1.2) = 3.5).
At block 412, the score generating module 214 may compute the third difference based on subtracting the total multiplication from the total skill level (As shown in the table 1: ((3.5-1.9) = 1.6).
At block 414, the score generating module 214 may determine the skill match score based on a division of the third difference by the total skill level (As shown in the table 1: ((1.6/ 3.5) = 0.4571). In one embodiment, the score generating module 214 may calculate percentage of the skill match score (As shown in the table 1: ((0.4571*100) = 45.71%).
In one embodiment, the score generating module 214 may compute the skill match score using the equations (I), (II), (III) and (IV). Referring to the table 1, N=5.
According to equation (I) –
D1= TSL1-USL1= 3-0=3,
D2= TSL2-USL2= 3-3=0,
D3= TSL3-USL3= 0-3= -3,
D4= TSL4-USL4= 4-2=2, and
D5= TSL5-USL5= 4-2=2.
According to equation (II) –
M11=W1*TSL1= 30%*3=0.9,
M12= W2*TSL2= 20%*3=0.6,
M13= W3*TSL3= 0*0=0,
M14= W4*TSL4=20%*4=0.8, and
M15= W5*TSL5= 30%*4=1.2.
According to equation (III)-
M21= W1*D1=30%*3=0.9,
M22= W2*D2=20%*0=0,
M23= W3*D3=0*(3) =0,
M24= W4*D4=20%*2=0.4, and
M25= W5*D5=30 %*2=0.6.
According to equation (IV)-
S=((?_(i=1)^(i=5)¦M1i-?_(i=1)^(i=5)¦?M2i)?)/(?_(i=1)^(i=5)¦M1i)=0.4571=45.71%
Upon generating the skill match score, the score displaying module 216 may display the skill match score (0.4571) to the user. In one example, percentage of skill match score (45.71%) may be displayed to the user. Further, the method for career navigation is elaborated with respect to the block diagram of figure 3.
Referring now to figure 3, a method 300 for career navigation, is disclosed in accordance with an embodiment of the present subject matter. The method 300 may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, functions, and the like, that perform particular functions or implement particular abstract data types. The method 300 may also be practiced in a distributed computing environment where functions are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, computer executable instructions may be located in both local and remote computer storage media, including memory storage devices.
The order in which the method 300 is described is not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method 300 or alternate methods. Additionally, individual blocks may be deleted from the method 300 without departing from the spirit and scope of the subject matter described herein. Furthermore, the method 300 can be implemented in any suitable hardware, software, firmware, or combination thereof. However, for ease of explanation, in the embodiments described below, the method 300 may be considered to be implemented in the above described system 102.
At block 302, user current data associated with a user in an organization and target data associated with a target role of the user in the organization may be received. In one embodiment, the data receiving module 212 may receive the user current data associated with the user in the organization and the target data associated with the target role of the user in the organization. The user current data may comprise a role of the user in the organization, a user skill, a user skill level associated with the user skill, and a current benchmark score associated with the user skill. The target data may comprise a target skill, a target skill level associated with the target skill, a weightage assigned to the target skill, and a target benchmark score associated with the target skill.
In one embodiment, the user current data may indicate skill proficiency of the user. The user may be an employee in the organization, a manager of the employee and the like. The role may indicate a job of the user in the organization, for example, a software engineer, a business analyst, a software developer and the like. The user skill level may be an expertise of the user skill. The user skill may be associated with the role of the user in the organization and current skills of the user. In one embodiment, the user skill may comprise domain specific skill of the user, such as software programming, software testing, software designing and the like., and soft skills of the user, such as communication skills, leadership, and the like. The domain specific skill may be associated with the role of the user. In one embodiment, the current benchmark score may indicate level of excellence associated with the user skill. In one example, the user skill level, associated with the user skill, may be equal to or less than the current benchmark score, associated with the user skill. The user may need to improve the user skill, when the user skill level is less than the current benchmark score. Further, the user current data may be received from at least one of a performance appraisal system, a manager of the user, colleagues of the user in the organization, the user and the like. The performance appraisal system may comprise evaluation of user skills, performance of the user in the organization, achievements of the user in the organization and the like. The performance appraisal may be referred as performance review of the user in the organization, performance evaluation of the user in the organization and the like.
In one embodiment, the target role may be selected by the user based on aspiration of the user, suggestions from a manager of the user, suggestions from colleagues of the user in the organization and the like. In another embodiment, the user may select multiple target roles from multiple organizations and same organization. The target role may indicate job in the organization, such as a software engineer, a business analyst, and the like. In one embodiment, the target skill may comprise domain specific skills, such as software programming, software testing and the like., and soft skills, such as communication skills, leadership and the like. The target skill level may indicate expertise of the target skill. In one embodiment, the weightage may be assigned to the target skill by a manager of the target role, a Human Resource (HR) executive of the organization and the like. The weightage assigned to the target skill may indicate level of importance of the target skill. In one example, the weightage may be in a form of percentage. In one embodiment, the target benchmark score may indicate level of excellence required for the target skill in the organization.
At block 304, the user skill may be compared with the target skill. In one embodiment, the user skill level associated with the user skill may be zero (0), when the user skill corresponding to the target skill is not available. In another embodiment, the user skill and the user skill level, associated with the user skill, may not be used for computation of a skill match score, when the target skill corresponding to the user skill is not available. Further, the weightage may be zero (0), when the target skill corresponding to the user skill is not available. Further to the comparison of the user skill and the target skill, a first difference may be computed based on a subtraction of the user skill level and the target skill level. Further to the computation of the first difference, a weighted decision matrix methodology may be performed to generate the skill match score and a skill gap. In one example, the weighted decision matrix methodology may include computation of a first multiplication, a total multiplication, a primary skill level, a total skill level and a third difference. In one embodiment, the first multiplication may be determined based on a multiplication of the weightage, associated with the target skill, and the first difference. Once the first multiplication is determined, the total multiplication may be generated based on an aggregation of the first multiplication. Upon generating the total multiplication, the primary skill level may be determined based on a multiplication of the weightage, associated with the target skill, and the target skill level. Further, the total skill level may be generated based on an aggregation of the primary skill level. Upon generating the total multiplication and the total skill level, the third difference may be generated based on a subtraction of the total multiplication and the total skill level. Further, the skill match score and the skill gap may be generated based on the comparison of the user current data and the target data, and the weighted decision matrix methodology. In one embodiment, the score generating module 214 may generate the skill match score and the skill gap. In one example, the skill match score may be generated based on a division of the third difference and the total skill level. The skill match score may be in a form of percentage. The skill match score may indicate similarity between the target skill and the user skill and the skill gap may indicate the target skill that are different from the user skill.
At block 306, once the score and the skill gap are generated, the skill match score and the skill gap may be displayed to the user. In one embodiment, the score displaying module 216 may display the skill match score and the skill gap to the user. In one embodiment, the user may use the skill match score to build a plan for improving the user skill and adopting the target skill.
Although implementations for systems and methods for career navigation have been described, it is to be understood that the appended claims are not necessarily limited to the specific features or methods described. Rather, the specific features and methods are disclosed as examples of implementations for career navigation.