WO2010056201A1

WO2010056201A1 - System for computing compensation data

Info

Publication number: WO2010056201A1
Application number: PCT/SG2008/000436
Authority: WO
Inventors: Samuel Jones; Daren Kemp
Original assignee: Samuel Jones; Daren Kemp
Priority date: 2008-11-17
Filing date: 2008-11-17
Publication date: 2010-05-20

Abstract

A method, a machine-readable medium and a system for computing compensation data. The method comprises receiving input data and extracting a plurality of group compensation data from a plurality of reference compensation data. The input data comprises at least one of input compensation data and a plurality of input boundary conditions. Each of the plurality of group compensation data has a plurality of group boundary conditions associated therewith. The plurality of group boundary conditions substantially matches the plurality of input boundary conditions. The input compensation data is ranked against the plurality of group compensation data to obtain output data therefrom.

Description

SYSTEM FOR COMPUTING COMPENSATION DATA

Field Of Invention

The present invention generally relates to data comparison. More particularly, the invention relates to computation of compensation data in various industries.

Background

Human resources, such as an employee, are vital to an organization. Often, health of the human resource within the organization is affected by movement of employee. Employee movement is sometimes prompted by compensation considerations via peer to peer comparison. An example of a compensation consideration is monetary compensation such as salary or bonus. For example, an employee may choose to leave an organization for another due to better salary compensation from the other organization.

It is therefore to an organization's advantage to be able to keep abreast of current compensation considerations offered by competitors so as to maintain a healthy human resource. However, such compensation data may often not be readily accessible due to confidentiality. On the other side, for the current employee of the organization to make an informed choice of whether to join another organization, the current employee, traditionally, has to approach the other organization to undergo a process of interview before obtaining the desired compensation data. Furthermore, such compensation data available to the current employee is usually limited and generalized. For example, the current employee wishing to migrate to another country or work in a different industry may have difficulty obtaining compensation data pertaining to specific queries as such.

Therefore it is readily appreciated that parties, such as the organization or the current employee, may have difficulty obtaining information pertaining to the compensation data of interest and hence have no avenue for making a comparison to derive an informed decision. Hence a solution for addressing the foregoing problems is desired. Summary

In accordance with a first aspect of the invention, there is disclosed a method for computing compensation data. The method comprises receiving input data and extracting a plurality of group compensation data from a plurality of reference compensation data. The input data comprises at least one of input compensation data and a plurality of input boundary conditions. Each of the plurality of group compensation data has a plurality of group boundary conditions associated therewith. The plurality of group boundary conditions substantially matches the plurality of input boundary conditions. The input compensation data is ranked against the plurality of group compensation data to obtain output data therefrom.

In accordance with a second aspect of the invention, there is disclosed a machine- readable medium having stored therein a plurality of programming instructions, which when executed, the instructions cause the machine to receive input data and extract a plurality of group compensation data from a plurality of reference compensation data. The input data comprises at least one of input compensation data and a plurality of input boundary conditions. Each of the plurality of group compensation data has a plurality of group boundary conditions associated therewith. The plurality of group boundary conditions substantially matches the plurality of input boundary conditions. The input compensation data is ranked against the plurality of group compensation data to thereby obtain an index. The index is at least one of providable and displayable by the machine.

In accordance with a third aspect of the invention, there is disclosed a system comprising means for receiving input data and means for extracting a plurality of group compensation data from a plurality of reference compensation data. The input data comprises at least one of input compensation data and a plurality of input boundary conditions. Each of the plurality of group compensation data has a plurality of group boundary conditions associated therewith. The plurality of group boundary conditions substantially matches the plurality of input boundary conditions. The system further comprises means for ranking the input compensation data against the plurality of group compensation data to obtain output data therefrom. Brief Description Of The Drawings

The invention is described hereinafter with reference to the following drawings, in which:

FIGs. Ia-Ic show a system diagram of a system for computing compensation data in accordance with an embodiment of the invention; and

FIG. 2 shows a process flow chart of computation method employed by the system of Figs. Ia-Ic.

Detailed Description

For purposes of brevity and clarity, the description of the present invention is limited hereinafter to a method and a system for computing compensation data is provided. This however does not preclude various embodiments of the invention from other applications where fundamental principles prevalent among the various embodiments of the invention such as operational, functional or performance characteristics are required.

An exemplary embodiment of the invention, a system 100 for computing compensation data for addressing the foregoing problems of conventional compensation data implementations, is described hereinafter with reference to Fig. la-c. The system 100, as shown in Fig. Ia, comprises a computation module 110. The computation module 110 receives at least one input 120 and produces at least one output 130.

The input 120 is preferably an input data received by the computation module 110. The input data is preferably a textual data and comprises at least one of a compensation data and a boundary list. The compensation data is preferably input compensation data comprising at least one personal particular from a user. The boundary list comprises at least one boundary condition. The boundary condition is preferably an input boundary condition selectable by the user from a compilation of boundary conditions. The compilation of boundary conditions comprises boundary conditions such as industry type, position within corporate hierarchy, number of years of related experience in the industry, academic qualification type, institutions attended and professional qualification type. The compilation of boundary conditions further comprises other boundary conditions such as company name, company size, geographical locality, bonuses, value of commission, value of benefits, salary and salary supplements. Each of the boundary conditions comprises at least one option selectable by the user.

For example, the options available for the industry type comprise industries such as finance, engineering and medical. The options available for the position within corporate hierarchy, preferably, comprise positions such as chief executive officer, director, manager and senior executive. The options available for the number of years of related experience in the industry comprise a plurality of year ranges, for example, less than a year, one to five years, five to seven years or eight to ten years. Alternatively, the option to specify other year ranges or number of years is also available. The options available for academic qualification type preferably comprise recognized academic qualification conventions such as pre-graduate qualifications, graduate degrees or post graduate degrees. An example of a pre-graduate qualification is a diploma, examples of graduate degrees are Bachelor of Science or Bachelor of Engineering and examples of graduate degrees are Masters of Science, Masters of Engineering and Doctor of philosophy. Alternatively, academic qualification types having additional merits, for example honors degree of differing divisions or a degree with merit, are also available options. The options available for institutes attended preferably include academic institutions such as Yale University or University of Oxford. Alternatively, professional or executive training institutes are also included. The options available for professional qualification type preferably include qualifications issued by a recognized professional body of the relevant industry. Examples of the professional qualifications are Certified Public Accountant (CPA) and Certified Financial Analyst (CFA). Alternatively, professional qualification types recognized by specific employers are also included. The options available for the geographical locality comprise locations of interest to the user. The locations are, for example, location of a company in different countries around the world, location of a company within a specific country or location of residence of the user. Additionally, the options available for the company name are preferably existing companies well known in the relevant industry. Alternatively, the option to specify a company name by the user is also available. The options available for company size comprise headcount ranges of employees for the company. The headcount ranges are, for example, less than ten, eleven to fifty, fifty to hundred and more than hundred. Alternatively, the option to specify the headcount range or headcount number by the user is also available. Preferably, the options available for the wage comprise annual income salary range or monthly income salary range, in a desired currency, for the industry of interest.

The personal particulars from the user preferably comprise the personal particulars corresponding to the boundary conditions selected by the user. For example, the user, earning an annual income of eighty thousand United State Dollars (USD) as a software engineer and having 5 years experience working in the related field, when using the system 100, will preferably include 'engineering', '5 years' and 'USD eighty thousand' as user personal particulars when the corresponding boundary conditions such as industry type, number of years of related experience in the industry and salary are selected by the user. Alternatively, the personal particulars from the user comprise personal particulars independent of the boundary conditions selectable by the user, for example society membership.

The input 120 is preferably received and processed by the computation module 110. Preferably, as shown in Fig. Ib, the computation module 110 comprises comparison module HOa and a database module HOb. The database module 110b comprises a database. The database comprises a plurality of reference compensation data. Information is extractable from the database for comparison with the boundary conditions of the input 120. Preferably, the extracted information is relevant to the boundary conditions selected by the user. The extracted information is otherwise termed as group compensation data. The comparison module 11 Oa receives the input 120 and the group compensation data from the database module 110b. The computation module 110 preferably processes the input 120 by weighting the boundary conditions of the input at the comparison module 110a and comparing the weighted boundary conditions with the group compensation data provided by the database HOb to produce the output 130. Alternatively, as shown in Fig. Ic, the database is stored directly in a storage area 11 Oc within the comparison module 11 Oa for comparison with the input 120 received by the comparison module 110a.

The input data received is preferably stored as a component of the reference compensation data and is preferably weighted or ranked in accordance to the source of the input and the content of the input.

The information in the database is preferably sorted and categorized into one or more group compensation data. Preferably, the categories of the group compensation data are defined by the boundary conditions available for selection, hence associating the group compensation data with the boundary conditions available for selection. The boundary conditions associated for each group compensation data are preferably form a plurality of group boundary conditions. Preferably, the plurality of group boundary conditions substantially matches the boundary conditions and each of the plurality of group compensation data is associated with a plurality of group boundary conditions. Alternatively, the categories of the group compensation data are customizable. An example of customizing the categories of the group compensation data is defining the categories according to the user preference regardless of the boundary conditions selectable. The one or more group compensation data are preferably grouped into a plurality of compensation brackets. Preferably, at least one of the compensation brackets substantially matches with the input compensation data, hence associating the compensation bracket with the input compensation data.

The computation module 110, preferably, produces the output 130 after comparing the input 120 with the reference compensation data from the database module 110b. The output is preferably an index, signifying rating results of the input 120 when compared with the reference from the database module HOb. Alternatively, the output 130 is a plurality of indices or a profile. The indices represent various rating results of each of the boundary conditions of the input 120 and the profile is generated from the plurality of group boundary conditions associated with the extracted information from the database. The system 100 preferably employs at least one of a static implementation and a dynamic implementation. An exemplary static implementation of the system 100 is a machine readable medium stored with a plurality of programming instructions. The machine for reading the readable medium is, for example, a computer (not shown). The readable medium is preferably the storage area HOc. The storage area 110c is preferably an internal memory storage device such as a hard disk drive (HDD) of the computer. Alternatively, the storage area HOc is an external memory device such as an external HDD or a compact disk (CD). The computer comprises an input module and a central processing unit (CPU). The CPU corresponds to the computation module 110 and the input module of the computer is preferably a keyboard or a storage medium reader for generating the input 120 to the CPU. The CPU receives the input 120 and processes the input 120 to produce the output 130. The storage medium reader is preferably an appropriate device provided for receiving and reading the readable medium. For example, a CD-read only memory (CD-ROM) drive is provided for receiving and reading the CD. The computer further comprises an output module for displaying the output 130. The output module is preferably a display unit such as a monitor screen. The programming instructions stored in the storage area 110c when executed, causes the CPU of the computer to receive compensation data from the input 120. Preferably the CPU, after receiving the input 120, processes the input 120 by weighting the boundary conditions and, following that, comparing the weighted boundary conditions with the reference. The reference is updatable via static update. Static update is preferably a manual update performed via the input 120 by the user of the computer. Preferably, the manual update is performed by storing the received input data in the database. Alternatively, the manual update is performed, by the user, by introducing data to the computer from the CD via the CD-ROM drive and sending commands or instructions to the computer to execute the update via the keyboard.

An exemplary dynamic implementation of the system 100 is incorporating the computer in a network. The network is preferably an internet network. Alternatively, the network is an intranet network or a plurality of the computer linked together to form the network. The reference of the database module 110b is updatable via at least one of manual update and dynamic update. Manual update is preferably performed as performed in the static implementation of the system 100. Dynamic update is preferably performed via the network. For example, the user of the computer may send instructions or commands from another computer via the internet to execute the update.

In an exemplary application, the system 100 is used in a web-based application over the internet. The web-based application is preferably a website comprising components such as advertisements from sponsors or businesses, peer review or account management. The peer review component preferably comprises referrals and write-ups of the industry or of a specific company from users of the website. The write-ups preferably comprise of pros and cons of the industry and data pertaining to the boundary conditions from the compilation of boundary conditions. Preferably the write-ups originate from users who are savvy, for example employees or ex- employees of a company in the relevant industry. The system 100, preferably, updates the database of the database module HOb dynamically, based on the write- ups by the user. The website further comprises a moderator component for moderating the write-ups by the user. Preferably the moderator component filters inaccurate data in the write-up from the system 100. Alternatively, the moderator component moderates data in the write-up which deviates from industrial norms. Moderating the data includes informing other users of the system 100 that the deviated data is a deviation from the industrial norm and weighting the user data. The weighted user data is preferably used for assessments of updates provided by the user data to the database of the database module 110b. Assessments of the update include ranking, reliability or accuracy of the user data in the database of the database module HOb. The peer review component further comprises forums or other commonly used interactive tools such as blogs. The peer review component is also implementable for weighting the user data. The account management component comprises log-in account for capturing user data and identifying the user by the user data. The input data is preferably associated with the user data. The log-in account is implementable via password and email log-in with email activation.

The website further comprises components such as news feed. The news feed comprises news articles such as market intelligence feed and recruitment data feed. The market intelligence feed preferably comprises latest market and industry trends or other general news regarding the relevant industries. The recruitment feed preferably comprises advertisements of career openings in various companies. The news feed is preferably stored as market data segments in the database of the database module 110b. Preferably, the news feed are processed such that contents of the news feed are archived for obtaining intelligence data, such as the news articles or recruitment details, when the input data is received by the system 100. Preferably, the news feed is processed by extracting keywords from the contents, such as textual information, of the news feed. Following this, the input data to the system 100 is compared against the extracted keywords for identifying at least one textual occurrence of the input data in the news feed. In an event where textual occurrence is positively identified, intelligence data is retrieved and, hence, obtained from news feed. The textual occurrence is preferably positively identified when the input data matches the extracted keywords. Alternatively, the news feed is processed by assigning at least one keyword to each of the market data segments. The assigned keyword, similarly to the extracted keywords, is then used for obtaining intelligence data from the news feed. The intelligence data from the news feed is preferably available to the users of the website. Alternatively, the intelligence data from the news feed is available to the users via email based on subscription by the user.

Preferably, the system 100 employs at least one of a first weighting scheme and a second weighting scheme. The first weighting scheme comprises a fixed weighting scheme. The fixed weighting scheme is implementable by predefining weighting factors on each of the boundary conditions available to the user for selection. The total of the weighting factors of the boundary conditions selected by the user are preferably normalized, for example, to provide a total index of a hundred. Total indices of other values, such as one, are also providable upon normalization of the weighting factors of the selected boundary conditions. Alternatively, a total index based on the total of the weighting factors of the boundary conditions, without any normalization, is provided. The boundary conditions are preferably weighted according to the user preference. For example, the user may prefer to increase the weighting factor of a particular boundary condition such as the salary with respect to the weighting factor of another boundary condition such as the company name. Alternatively, the boundary conditions are weighted according to the CPU. For example the weighting factor of each of the boundary conditions is preprogrammed into the CPU. The fixed weighting scheme is further implementable by predefining weighting factors on each category of the group compensation data, as implemented in the predefmition of the weighting factors of the boundary conditions, hence obtaining a plurality of weighted compensation data. The input compensation data is preferably ranked against the weighted compensation data to obtain the output 130.

The second weighting scheme comprises a variable weighting scheme. The variable weighting scheme is implementable by varying weighting factors of the boundary conditions dynamically. Preferably, the system 100, taking into consideration the boundary conditions selected by the user and the boundary conditions most commonly selected, automatically varies the weighting factor by increasing the weighting factor of the boundary condition most commonly selected and reducing the weighting factor of the boundary condition least commonly selected. Alternatively, each of the boundary conditions is initially rated by order of preference by the user. The order of preference is then stored in the CPU of the computer. Following this, when a set of boundary conditions is selected by the user, the system 100 considers the rating of each of the boundary conditions within the set of boundary conditions selected and automatically varies the weighting factor by increasing the weighting factor of the boundary condition most highly rated and reducing the weighting factors of the boundary conditions with lower ratings accordingly, the boundary condition with the lowest rating having the lowest weighting factor. The variable weighting scheme is further implementable by varying weighting factors on each category of the group compensation data, as implemented in the dynamic variance of the weighting factors of the boundary conditions, hence obtaining a plurality of weighted compensation data. The input compensation data is preferably ranked against the weighted compensation data to obtain the output 130.

In an exemplary scenario of the application of the weighting schemes, a manager in the finance industry, who attended Yale University, earns an annual salary of USD 100,000 with an annual bonus of four months, uses the system 100 over the internet. The manager selects a combination of boundary conditions such as industry type, position within corporate hierarchy, institutions attended, bonuses and salary. The manager, preferably, enters "finance", "manager", "Yale University", "four months" and "USD 100,000" as the personal particulars corresponding to the respective boundary conditions. Following this, the manager applies the first weighting scheme and assigns the weighting factors of 100, 75, 5, 100 and 50 to the respective boundary conditions. In this exemplary scenario, the system 100 ranks the input compensation data of the manager against the group compensation data and the manager obtains the following ranking results, with respect to the weighting factors, from the system 100 for each of the selected boundary conditions: 80 out of 100, 60 out of 75, 5 out of 5, 70 out of 100 and 40 out of 50. The ranking results are then normalized to obtain a total index of 5. Hence the resulting index would be calculated as follows: (80 + 60 + 5 + 70 +40)/ (100 + 75 + 5 + 100 +50) = 255/330 = 3.86/5.0. Therefore, from the boundary conditions the manager selected, the weighting scheme used and considering the weighting factors the manager assigned to each of the boundary conditions, the manager obtains an index of approximately 3.86 based on a normalized index of 5.0, for the output 130 of the system 100.

Preferably, the system 100 employs a computation method 200 as shown in Fig. 2. The computation method 200 comprises receiving the compensation data of the input 120 at step 210 and comparing the received compensation data with the reference compensation data at step 220. The computation method 200 further comprises ranking the compensation data to obtain the index of the output 130 at step 230.

In the foregoing manner, a system and method for computing compensation data is described for addressing at least one of the foregoing disadvantages. The invention is not to be limited to specific forms or arrangements of parts so described and it will be apparent to one skilled in the art in view of this disclosure that numerous changes and/or modification can be made without departing from the scope and spirit of the invention.

Claims

1. A method comprising: receiving input data comprising at least one of input compensation data and a plurality of input boundary conditions; extracting a plurality of group compensation data from a plurality of reference compensation data, each of the plurality of group compensation data having a plurality of group boundary conditions associated therewith, the plurality of group boundary conditions substantially matching the plurality of input boundary conditions; and ranking the input compensation data against the plurality of group compensation data to obtain output data therefrom.

2. The method as in claim 1, ranking the input compensation data against the plurality of group compensation data to obtain output data therefrom comprising: ranking the input compensation data against the plurality of group compensation data to thereby obtain an index.

3. The method as in claim 1, the output data comprising a profile generated from the plurality of group boundary conditions associated with the extracted plurality of group compensation data.

4. The method as in claim 1, further comprising: grouping the plurality of group compensation data into a plurality of compensation brackets; and identifying one of the plurality of compensation brackets whereto the input compensation data is associatable.

5. The method as in claim 1, further comprising: capturing the input compensation data and the plurality of input boundary conditions as constituents of the reference compensation data and a plurality of reference boundary conditions respectively, the plurality of reference boundary conditions being associated with the reference compensation data.

6. The method as in claim 5, further comprising: weighting the captured input compensation data with reference to the plurality of reference compensation data.

7. The method as in claim 1, ranking the input compensation data against the plurality of group compensation data comprising: weighting each of the plurality of group compensation data to obtain a plurality of weighted compensation data therefrom; and ranking the input compensation data against the plurality of weighted compensation data to thereby obtain the index.

8. The method as in claim 1, the input data comprising at least one of salary, bonus, salary supplement, value of commission and value of benefits.

9. The method as in claim 1, the plurality of input boundary conditions comprising at least one of industry type, position within corporate hierarchy, years of related experience, name of company, size of company, geographical locality, academic qualification type, professional qualification type, schools attended and society membership.

10. The method as in claim 1, further comprising: capturing user data, a user being identifiable by the user data; and associating the input data with the user data.

11. The method as in claim 1 , further comprising: providing a database containing a plurality of market data segments; and identifying at least one of the plurality of market data segments containing textual occurrence of at least one of the plurality of input boundary conditions to obtain intelligence data therfrom.

12. The method as in claim 11, generating intelligence data comprising: assigning each of the plurality of market data segments with at least one keyword; and matching at least one of the plurality of input boundary conditions with the at least one keyword assigned to each of the plurality of market data segments to thereby identify the at least one of the plurality of market data segments

13. The method as in claim 11, further comprising: retrieving user data associated with the input data, the user data for identifying a user; and providing the intelligence data to the user using the user data.

14. A machine-readable medium having stored therein a plurality of programming instructions, which when executed, the instructions cause the machine to: receive input data comprising at least one of input compensation data and a plurality of input boundary conditions; extract a plurality of group compensation data from a plurality of reference compensation data, each of the plurality of group compensation data having a plurality of group boundary conditions associated therewith, the plurality of group boundary conditions substantially matching the plurality of input boundary conditions; and rank the input compensation data against the plurality of group compensation data to thereby obtain an index, wherein the index is at least one of providable and displayable by the machine.

15. The machine-readable medium as in claim 14, wherein the plurality of programming instructions, which when executed, the instructions cause the machine to: rank the input compensation data against the plurality of group compensation data to thereby obtain an index.

16. The machine-readable medium as in claim 14, the output data comprising a profile generated from the plurality of group boundary conditions associated with the extracted plurality of group compensation data.

17. The machine-readable medium as in claim 14, wherein the plurality of programming instructions, which when executed, the instructions cause the machine to: group the plurality of group compensation data into a plurality of compensation brackets; and identify one of the plurality of compensation brackets whereto the input compensation data is associatable.

18. The machine-readable medium as in claim 14, wherein the plurality of programming instructions, which when executed, the instructions cause the machine to: capture the input compensation data and the plurality of input boundary conditions as constituents of the reference compensation data and a plurality of reference boundary conditions respectively, the plurality of reference boundary conditions being associated with the reference compensation data.

19. The machine-readable medium as in claim 18, wherein the plurality of programming instructions, which when executed, the instructions cause the machine to: weight the captured input compensation data with reference to the plurality of reference compensation data.

20. The machine-readable medium as in claim 14, wherein the plurality of programming instructions, which when executed, the instructions cause the machine to: weight each of the plurality of group compensation data to obtain a plurality of weighted compensation data therefrom; and rank the input compensation data against the plurality of weighted compensation data to thereby obtain the index.

21. The machine-readable medium as in claim 14, the input data comprising at least one of salary, bonus, salary supplement, value of commission and value of benefits.

22. The machine-readable medium as in claim 14, the plurality of input boundary conditions comprising at least one of industry type, position within corporate hierarchy, years of related experience, name of company, size of company, geographical locality, academic qualification type, professional qualification type, schools attended and society membership.

23. The machine-readable medium as in claim 14, wherein the plurality of programming instructions, which when executed, the instructions cause the machine to: capture user data, a user being identifiable by the user data; and associate the input data with the user data.

24. The machine-readable medium as in claim 14, wherein the plurality of programming instructions, which when executed, the instructions cause the machine to: provide a database containing a plurality of market data segments; and identify at least one of the plurality of market data segments containing textual occurrence of at least one of the plurality of input boundary conditions to obtain intelligence data therfrom.

25. The machine-readable medium as in claim 24, wherein the plurality of programming instructions, which when executed, the instructions cause the machine to: assign each of the plurality of market data segments with at least one keyword; and match at least one of the plurality of input boundary conditions with the at least one keyword assigned to each of the plurality of market data segments to thereby identify the at least one of the plurality of market data segments

26. The machine-readable medium as in claim 24, wherein the plurality of programming instructions, which when executed, the instructions cause the machine to: retrieve user data associated with the input data, the user data for identifying a user; and provide the intelligence data to the user using the user data.

27. A system comprising: means for receiving input data comprising at least one of input compensation data and a plurality of input boundary conditions; means for extracting a plurality of group compensation data from a plurality of reference compensation data, each of the plurality of group compensation data having a plurality of group boundary conditions associated therewith, the plurality of group boundary conditions substantially matching the plurality of input boundary conditions; and means for ranking the input compensation data against the plurality of group compensation data to obtain output data therefrom.

28. The system as in claim 27, ranking the input compensation data against the plurality of group compensation data to obtain output data therefrom comprising: means for ranking the input compensation data against the plurality of group compensation data to thereby obtain an index.

29. The system as in claim 27, the output data comprising a profile generated from the plurality of group boundary conditions associated with the extracted plurality of group compensation data.

30. The system as in claim 27, further comprising: means for grouping the plurality of group compensation data into a plurality of compensation brackets; and means for identifying one of the plurality of compensation brackets whereto the input compensation data is associatable.

31. The system as in claim 27, further comprising: means for capturing the input compensation data and the plurality of input boundary conditions as constituents of the reference compensation data and a plurality of reference boundary conditions respectively, the plurality of reference boundary conditions being associated with the reference compensation data.

32. The system as in claim 31 , further comprising: means for weighting the captured input compensation data with reference to the plurality of reference compensation data.

33. The system as in claim 27, ranking the input compensation data against the plurality of group compensation data comprising: means for weighting each of the plurality of group compensation data to obtain a plurality of weighted compensation data therefrom; and means for ranking the input compensation data against the plurality of weighted compensation data to thereby obtain the index.

34. The system as in claim 27, the input data comprising at least one of salary, bonus, salary supplement, value of commission and value of benefits.

35. The system as in claim 27, the plurality of input boundary conditions comprising at least one of industry type, position within corporate hierarchy, years of related experience, name of company, size of company, geographical locality, academic qualification type, professional qualification type, schools attended and society membership.

36. The system as in claim 27, further comprising: means for capturing user data, a user being identifiable by the user data; and means for associating the input data with the user data.

37. The system as in claim 27, further comprising: means for providing a database containing a plurality of market data segments; and means for identifying at least one of the plurality of market data segments containing textual occurrence of at least one of the plurality of input boundary conditions to obtain intelligence data therfrom.

38. The system as in claim 37, generating intelligence data comprising: means for assigning each of the plurality of market data segments with at least one keyword; and means for matching at least one of the plurality of input boundary conditions with the at least one keyword assigned to each of the plurality of market data segments to thereby identify the at least one of the plurality of market data segments

39. The system as in claim 37, further comprising: means for retrieving user data associated with the input data, the user data for identifying a user; and means for providing the intelligence data to the user using the user data.