WO2021075950A1 - Data processing for performance assessment and management - Google Patents

Abstract

The present invention relates to system and method for performance assessment and management of employees in an organization. The method includes receiving a plurality of scores against different growth dimensions for at least one entity by one or more scorers, computing by a processor an aggregate of the plurality of scores in a role of the one or more scorers and at least one growth dimension level, aggregating by an AI engine, the plurality of scores based on role types of the at least one entity to generate a data matrix of scores, and computing by the processor, a compounded score for the entity using a plurality of data models and the data matrix of scores to assess performance of the entity wherein the data matrix scores include a work score, a great experience score and a critical driver score.

Description

DATA PROCESSING FOR PERFORMANCE ASSESSMENT AND MANAGEMENT

FIELD OF THE INVENTION

The present invention relates to data processing for performance assessment and management in organizations. More particularly, the invention relates to system and method for AI (artificial intelligence) based data processing to assess employee performance.

BACKGROUND

In any organization performance assessment plays vital roles in the overall performance of employees and thereby overall growth of the organization. Performance appraisal systems have been a major subject of discussion to the extent that organization experts and thinkers have been advocating to reengineer it as a leveraging tool to tap maximum potentials of the workforce. Over the years, there have been discussions to move away from the traditional bell curve principles to something that can appease to the newer generations.

Understanding human performance is a basic premise in comprehending human operators' ability to perform their tasks, activities, or processes. Features of these operators' abilities to complete their tasks, activities, or processes, such as their accuracy, reliability, timeliness, minimized variation, etc., can then be analyzed to determine a system's functionality and integrity in light of the human elements required by the system.

Human elements are also often the driving force behind the customer satisfaction in a service- driven industry. The success of service industries is not only dependent on the quality of the service product, but on the quality of those delivering the service. Irrespective of whether a service is delivered face-to-face or online, customer satisfaction and customer loyalty become fundamentally dependent on customer interactions with staff members. In general, a service provider is not necessarily limited to a business-to-customer relationship and set of interactions, but may also incorporate inter-organizational (e.g., business-to-business) and intra-organizational (e.g., department-to-department) relationships and interactions.

Any technology to support real-time assessment include multiple issues with respect to the parameters assessed, valuation of performance of one individual by another, reliability of the assessment, bias in judgement etc. Even on gathering data, to make sense of that data requires appropriate processing.

In view of the above, there exists a need of improved systems and methods that overcome the shortcomings associated with existing technologies and prior arts.

SUMMARY OF THE INVENTION

Accordingly, the present invention provides a data processing method for performance assessment of at least one entity. The method includes receiving a plurality of scores against different growth dimensions for at least one entity by one or more scorers; computing by a processor an aggregate of the plurality of scores in a role of the one or more scorers and at least one growth dimension level; aggregating by an AI engine, the plurality of scores based on role types of the at least one entity to generate a data matrix of scores; and computing by the processor, a compounded score for the entity using a plurality of data models and the data matrix of scores to assess performance of the entity wherein the data matrix scores include a work score, a great experience score and a critical driver score.

In an embodiment, the present invention provides a data processing system for performance assessment of at least one entity. The system includes an electronic user interface configured for operating on a configurable performance assessment application; at least one entity database configured for storing a plurality of entity data attributes and a set of associated parameters of the entity data; at least one data model database configured for storing a plurality of data models related to growth dimension parameter and associated score types; a controller encoded with instructions enabling the controller to function as a bot for facilitating aggregation of a plurality of scores based on role types of the at least one entity to generate a data matrix of scores; and a processor coupled to the controller and an AI engine for computing a compounded score of the entity based on processing of the data matrix and the data models to assess performance of the entity wherein the data matrix scores include a work score, a great experience score and a critical driver score.

In an embodiment, the present invention provides a computer-readable non-transitory storage medium storing executable program instructions for data processing to assess performance of at least one entity by a configurable application which when executed by a computer causes the computer to perform operations as described above. BRIEF DESCRIPTION OF THE DRAWINGS

Fig. 1 shows a system architecture of data processing for performance assessment in accordance with an embodiment of the present invention.

Fig. 2 shows a flowchart depicting a method of data processing for performance assessment in accordance with an embodiment of the present invention.

Fig. 3 shows a flowchart depicting an agile methodology of data processing for performance assessment in accordance with an embodiment of the present invention.

Fig. 4 shows a flowchart depicting a method of data processing for performance assessment in accordance with an embodiment of the present invention.

Fig. 5 shows an example showing how the aggregation of score is done in accordance with an embodiment of the present invention.

Fig. 6 shows another example showing how the aggregation of score is done in accordance with an embodiment of the present invention.

BRIEF DESCRIPTION OF THE INVENTION

Various embodiment of the present invention provides system and method of data processing for performance assessment and feedback in an organization. The following description provides specific details of certain embodiments of the invention illustrated in the drawings to provide a thorough understanding of those embodiments. It should be recognized, however, that the present invention can be reflected in additional embodiments and the invention may be practiced without some of the details in the following description.

The various embodiments including the example embodiments are described more fully with reference to the accompanying drawings, in which the various embodiments of the invention are shown. The invention may, however, be embodied in different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure is thorough and complete, and fully conveys the scope of the invention to those skilled in the art. In the drawings, the sizes of components may be exaggerated for clarity.

It should be understood that when an element or layer is referred to as being “on” “connected to” or “coupled to” another element or layer, it can be directly on, connected to, or coupled to the other element or layer or intervening elements or layers that may be present. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items.

Spatially relative terms, such as “work data score,” “experience data score”, “critical driver data score” and the like, may be used herein for ease of description to describe one element or feature's relationship to another element(s) or feature(s) as illustrated in the figures. It should be understood that the spatially relative terms are intended to encompass different orientations of the structure in use or operation in addition to the orientation depicted in the figures.

Embodiments described herein refer to plan views and/or cross-sectional views by way of ideal schematic views. Accordingly, the views may be modified depending on simplistic assembling or manufacturing technologies and/or tolerances. Therefore, example embodiments are not limited to those shown in the views but include modifications in configurations formed on basis of assembling process. Therefore, regions or regions of elements exemplified in the figures have schematic properties and shapes, and do not limit the various embodiments including the example embodiments.

The subject matter of example embodiments, as disclosed herein, is described with specificity to meet statutory requirements. However, the description itself is not intended to limit the scope of this patent. Rather, the inventors have contemplated that the claimed subject matter might also be embodied in other ways, to include different features or combinations of features similar to the ones described in this document, in conjunction with other technologies. Generally, the various embodiments including the example embodiments relate to system and method of data processing for performance assessment and feedback. Referring to Fig. 1, a data processing system application architecture 100 for performance assessment and feedback is shown in accordance with an embodiment of the present invention. The system 100 include at least one computing device 110, a server support architecture 120, a data processing and control support architecture/mechanism 130, and a database 140a. The server support architecture may include server 120a and mainframe 120b. The data processing and control support architecture/mechanism 130 may include a processor 130a and a controller 130b. The system may include a data model database 140b.

In an exemplary embodiment, the application has the list of parameters, its reference values, data scores related to work, experience score, critical drivers score data and data models as underlying seeded data models in the database (140a). This is used by the logic of performance management for processing - by mapping the received input values against these seeded data models. Any addition or change in parameters/reference values/ in database is made by an interface.

In an exemplary embodiment the system and method of the present invention captures multi dimensional feedback such as actual work or core work score; the great experience score whereby team members score each other; and critical drivers score which gauges on future readiness. The scores are given against different growth dimensions. The system includes an AI based aggregation engine that aggregates the scores in the role of the scorer and growth dimension level. Further, the engine aggregates the scores based on the role types. The roles are divided into mandatory and non-mandatory roles. The scores given by mandatory roles are aggregated and the scores given by the non-mandatory roles are aggregated separately.

In an example embodiment, the AI based aggregation engine uses three different data models to get a final score for the event based on the team. After every event is over an overall score is calculated and the same is published as the overall employee score for the current assessment period. Based on the comments given by the career friends, the employee can see a word cloud which helps him to understand the most commonly used adjectives to describe about him or the feedback given to him. Employee get to see a mathematical score with an option to deep dive into various growth dimensions which finally aggregate one’s performance score. In an embodiment, the at least one entity database is configured for storing a plurality of entity/employee data and a set of associated parameters of the entity data including characteristic data.

In a related embodiment, a seed data model of the application reconfigures an underlining seeded mapping structure of the application in response to determination of a received data as a new or modified performance data.

In an embodiment the controller is encoded with instructions enabling the controller to function as a bot where the controller is configured to perform functions of analyzing a set of data models stored in the data model database to identify at least one data model for processing assessment data received from multiple sources related to performance of an entity/employee. The data model enables processing of the received data and AI based processing of a data matrix to determine by the controller at least one performance data, work score, experience score, critical drivers score for performance assessment and feedback.

In an exemplary embodiment, the received data are data parameters received as an input from multiple sources for the same entity and used to arrive at an overall performance score.

In another embodiment, the components are various parts of performance assessment application wherein the performance parameter is a composite entity. The application has model identification component, and score component.

In yet another embodiment, the valuation is in an order in which these components interact with each other to arrive at performance assessment requirement. The protocols or rules are instructions to be followed during presence or absence of certain factors/combination of factors. The modified factors/ rules for model identification and scores are updated and configured using the user interface to these components which then follow an order to arrive at the overall score. These components consume the inputs so updated or configured without code change, as methods with relevant integrations and configurable dependencies, are embedded within in the application. Hence, the modified or new inputs which are thus configured are auto identified by the components for data processing for performance assessment. In an example embodiment, the application of the present invention is explained in terms of the multiple factors. The system and method of the present invention provide performance acceleration and career enrichment through data processing. The system of the present invention is configured as per the practices of an organization and can understand the star employees based on a threshold limit of performance. The system works as a single source to understand the capabilities of various departments for the future and even the employee engagements. The system disrupts the conventional thinking around performance assessment and looks beyond mere performance feedback.

• Disrupt the usual: The system and method as disclosed herein enable one not to be bothered by the conventional appraisal system, but to take control of one’s productivity and growth

• Holistic: The system and method as disclosed herein help to receive feedback that is no longer hierarchy driven, but multidimensional; The system and method are holistic as they cover not only work, but also future growth, learning, fun and other contribution towards creating “Great Experiences”

• Real time: The system and method as disclosed herein provide not just feedback but feed forward. They are real time and help employee understand the feedback of their contributions every day unlike a year end activity.

• Multi-dimensional: The system and method as disclosed herein enable one to understand one’s own the effectiveness and contribution from multiple dimensions such as the career friends, work and other critical drivers.

The data processing system and method of the present invention accepts the plurality of entity data attributes and processes them with appropriate data models to determine performance of the entity and provide feedback.

In an advantageous aspect, the invention addresses the vacuum in the field of employee feedback arena and assists to move away from the traditional bell curve principles to something that can appease to the newer generations. The system and method capture multidimensional and real time feedback so that the application assesses the experience one provides to the fellow employees and thus the impact on collaboration. It also helps to minimize the elements of subjectivity as the feedback captured are beyond the unidimensional manager - reportee relationship.

In an advantageous aspect, since the system and method provide real-time feedback as the feedback for every event is captured in which the employee is involved, and it is based on the role one play in an event. Hence, the feedback is readily available at any given point in time and clearly removes all hurdles of appraisal, time frame -based waiting periods. Thus, the system and method of the present invention helps to mitigate all traditional errors of performance management system, like, bias, recency errors, central tendency errors, severity errors, halo effect, stereotyping, etc. The system transforms the feedback process into a feed forward process. It eliminates the need of a manger to provide feedback, and it promotes a “self-drive” culture in an organization. The system provides multi -dimensional feedback such as actual work or core work score; the great experience score whereby team members score each other; and critical drivers score which gauges on future readiness.

In an embodiment of the invention, the processor is coupled to the controller for reconfiguring the set of associated parameters, the set of data models and the data matrix for determination of performance assessment parameters of the at least one entity based on the evaluation by colleagues, managers and the application itself.

In an embodiment, the system 100 includes an electronic user interface configured for operating the system to determine performance management and feedback.

In an example embodiment the server 120a may include electronic circuitry for enabling execution of various steps by the processor. The electronic circuity may have various elements including but not limited to a plurality of arithmetic logic units (ALU) and floating point Units (FPU), and/or the equivalents thereof. The ALU enables processing of binary integers to assist in generating a plurality of data models to be stored in the data model database 140 to identify at least one data model for processing the received entity data and AI based processing of the data matrix and the identified data model to assess performance. In an example embodiment the server electronic circuitry, includes at least one arithmetic logic unit, floating point units (FPU), other processors, memory, storage devices, high-speed interfaces connected through buses for connecting to memory and high-speed expansion ports, and a low speed interface connecting to low speed bus and storage device. Each of the components of the electronic circuitry, are interconnected using various busses, and may be mounted on a common motherboard or in other manners as appropriate. The processor can process instructions for execution within the server 120a, including instructions stored in the memory or on the storage devices to display graphical information for a GUI on an external input/output device, such as display coupled to high speed interface. In other implementations, multiple processors and/or multiple busses may be used, as appropriate, along with multiple memories and types of memory. Also, multiple servers may be connected, with each server providing portions of the necessary operations (e.g., as a server bank, a group of blade servers, or a multi-processor system).

The processor 130a may communicate with a user through a control interface and display interface coupled to a display. The display may be, for example, a TFT LCD (Thin-Film- Transistor Liquid Crystal Display) or an OLED (Organic Light Emitting Diode) display, or other appropriate display technology. The display interface may comprise appropriate circuitry for driving the display to present graphical and other information to an entity/user. The control interface may receive commands from a user and convert them for submission to the processor. In addition, an external interface may be provided in communication with processor 130a, so as to enable near area communication of device with other devices. External interface may be suitable, for example, for wired communication in some implementations, or for wireless communication in other implementations, and multiple interfaces may also be used.

The database 140a is part of a data storage support architecture 140 that may include memory units that may be a volatile, a non-volatile memory or memory may also be another form of computer-readable medium, such as a floppy disk device, a hard disk device, an optical disk device, a tape device, a flash memory or other similar solid state memory device, or an array of devices, including devices in a storage area network or other configurations. In an embodiment, the artificial intelligence-based AI engine is configured for processing a plurality of data models associated with the entity data including work score on multiple criteria for performance assessment.

In an embodiment, the scores are real-time multi-dimensional feedback obtained from scorers for every event in which the at least one entity is involved based on role in the event.

In an embodiment, the system of the present invention includes a natural language processing (NLP) server configured for processing the protocols based on the identified data models to determine the compounded score.

In an embodiment, the user interface and an underlying logic of the performance management application is based on the seeded mapping wherein on introduction of the new or modified scores the system automatically reconfigures the underlining seeded mapping structure of the application to include the impacted performance assessment factors, the components, the sequence and the protocol for determination of the performance score for an entity.

Referring to Fig. 2, a flowchart 200 depicting a data processing method for performance assessment of at least one entity is provided in accordance with an embodiment of the present invention. The method includes steps of (S210) receiving a plurality of scores against different growth dimensions for at least one entity by one or more scorers; (S220) computing by a processor an aggregate of the plurality of scores in a role of the one or more scorers and at least one growth dimension level; (S230) aggregating by an AI engine, the plurality of scores based on role types of the at least one entity to generate a data matrix of scores; and (S240) computing by the processor, a compounded score for the entity using a plurality of data models and the data matrix of scores to assess performance of the entity wherein the data matrix scores include a work score, a great experience score and a critical driver score.

Referring to Fig. 3, a flowchart shows an agile methodology of data processing for performance assessment in accordance with an embodiment of the present invention. Referring to Fig. 4, a flowchart shows a method of data processing for performance assessment in accordance with an embodiment of the present invention.

Referring to Fig. 5, an example is illustrated on how the aggregation of score is done in accordance with an embodiment of the present invention.

Referring to Fig. 6, another example is illustrated on how the aggregation of score is done in accordance with an embodiment of the present invention. The scoring can be done based on the following aspects, scores, and scoring mechanism:

In an embodiment, the application functions on seeded data and generates mapping structures or codes on basis of the seeded data thereby enabling real time processing of received entity data within the application upon receipt of new or modified data.

In an embodiment, the method of the present invention includes generating the data matrix that enables identification of the performance parameters and score, the components, the sequence and triggers the protocols to process the received data for determining the performance score. In an embodiment, the method of the invention includes storing a set of rules or protocols in a memory to be executed by an AI engine coupled to a processor for generating a plurality of data models associated with the entity data to identify the at least one model for performance assessment.

In some implementations, the machine learning data model is configured to map entity data and data models with data processing protocol to compute work scores, critical driver scores etc. For example, output generated over the model may provide an indication of whether a particular object or class of objects is present, and optionally user instructions. In some implementations, the machine learning model is configured to map new or modified scoring parameters/criteria for determining a work score. Accordingly, in those implementations a single pass over a single machine learning model may be utilized to detect whether each of multiple objects is present.

It should be apparent that different aspects of the description provided above may be implemented in many different forms of software, firmware, and hardware in the implementations illustrated in the figures. The actual software code or specialized control hardware used to implement these aspects is not limiting of the invention. Thus, the operation and behavior of these aspects were described without reference to the specific software code — it being understood that software and control hardware can be designed to implement these aspects based on the description herein.

In one example embodiment, the performance score data by multiple entities is fed to the application, which then utilizes this input data to arrive at an appropriate data model to assess overall score for performance assessment. The processor further filters these input data according to the data dependencies of the identified model and arrives at the results for the factors based on the seeded mapping in database. The method for performance assessment and feedback for the entity is as per the mathematical expressions linked to the model, factors and groups of factors, which can involve data received in multiple layers. The methods are designed to handle input data received in various granularity as well. Multi-level rule engine integration utilizes the required input data to handle specific scores. Model level configuration of seeded data for values in case of exceptional criteria’s is provided along with relevant dependencies. Assimilation of these components in sequence succors to arrive at output of the application, which is score for the employee. The input, processing and output data is further stored in database.

In an exemplary embodiment, the system and method compute overall performance score for an employee/entity based on definable factors. It considers multiple parameters and their interdependencies for computation. Various protocols are imbibed in the process to arrive at the overall score. Thus, multiple parameters for the entity are factored-in, thereby catering to diverse requests to arrive at overall score. The parameters, rules and dependencies considered for this computation are fully definable. Any future change of these underlying factors and rules used for computation can be absorbed by this service without code change. The invention provides flexibility and ease of use of the application coupled with a design which reflects performance score in its entirety.

In an advantageous aspect, the system and method of the present invention is robust to absorb multiple real time changes and function without interruptions in case of update of factors as well as their interdependencies. The real time changes are due to modification in external factors or regulatory requirements. Moreover, the interdependencies of the entity parameters considered for determination of performance factors are also subject to change, leading to an expected variation in the score.

The invention provides a configurable application, which allows configuration of multiple models of processing distinct parameters. The variation of interdependencies and its effect on the overall score is handled by protocols. New models and the protocols are configured at any juncture, thereby allowing addition or update of entity parameters and its interdependencies dynamically.

Further, certain portions of the invention may be implemented as a “component” or “system” that performs one or more functions. These components/systems may include hardware, such as a processor, an ASIC (Application Specific Integrated Circuit), or a FPGA (Field Programmable Gate Array), or a combination of hardware and software. The word “exemplary” is used herein to mean “serving as an example.” Any embodiment or implementation described as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments or implementations.

No element, act, or instruction used in the present application should be construed as critical or essential to the invention unless explicitly described as such. Also, as used herein, the article “a” and “one of’ is intended to include one or more items. Further, the phrase “based on” is intended to mean “based, at least in part, on” unless explicitly stated otherwise.

Each of the above identified processes corresponds to a set of instructions for performing a function described above. The above identified programs or sets of instructions need not be implemented as separate software programs, procedures or modules, and thus various subsets of these modules may be combined or otherwise re-arranged in various embodiments. For example, embodiments may be constructed in which steps are performed in an order different than illustrated, steps are combined, or steps are performed simultaneously, even though shown as sequential steps in illustrative embodiments. Also, the terminology used herein is for the purpose of description and should not be regarded as limiting. The use of “including,” “comprising,” “having,” “containing” and variations thereof herein, is meant to encompass the items listed thereafter and equivalents thereof as well as additional items.

The above-described embodiments of the present invention may be implemented in any of numerous ways. For example, the embodiments may be implemented using various combinations of hardware and software and communication protocol(s). Any standard communication or network protocol may be used and more than one protocol may be utilized. For the portion implemented in software, the software code may be executed on any suitable processor or collection of processors, whether provided in a single computer or distributed among multiple computers. Such processors may be implemented as integrated circuits, with one or more processors in an integrated circuit component, or any other suitable circuitry. Further, it should be appreciated that a computer may be embodied in any of a number of forms, such as a rack-mounted computer, a desktop computer, a laptop computer, single board computer, micro-computer, or a tablet computer. Additionally, a computer may be embedded in a device not generally regarded as a computer but with suitable processing capabilities, including a Personal Digital Assistant (PDA), a smart phone or any other suitable portable or fixed electronic device.

Also, the various methods or processes outlined herein may be coded as software that is executable on one or more processors that employ any one of a variety of operating systems or platforms. Additionally, such software may be written using any of a number of suitable programming languages and/or programming or scripting tools or a combination of programming languages, and also may be compiled as executable machine language code or intermediate code that is executed on a framework or a virtual machine. In this respect, the invention may be embodied as a computer readable storage medium (or multiple computer readable media) (e.g., a computer memory, one or more floppy discs, compact discs (CD), optical discs, digital video disks (DVD), magnetic tapes, flash memories, circuit configurations in Field Programmable Gate Arrays or other semiconductor devices, or other tangible computer storage medium) encoded with one or more programs that, when executed on one or more computers or other processors, perform methods that implement the various embodiments of the invention discussed above. As is apparent from the foregoing examples, a computer readable storage medium may retain information for a sufficient time to provide computer-executable instructions in a non-transitory form.

The terms “program” or “software” are used herein in a generic sense to refer to any type of computer code or set of computer-executable instructions that may be employed to program a computer or other processor to implement various aspects of the present invention as discussed above. Additionally, it should be appreciated that according to one aspect of this embodiment, one or more computer programs that when executed perform methods of the present invention need not reside on a single computer or processor but may be distributed in a modular fashion amongst a number of different computers or processors to implement various aspects of the present invention. Computer-executable instructions may be in many forms, such as program modules, executed by one or more computers or other devices. Also, data structures may be stored in computer-readable media in any suitable form. Any suitable mechanism may be used to establish a relationship between information in fields of a data structure, including the use of pointers, tags, or other mechanisms that establish relationship between data elements. It is to be understood that the above-described embodiments are only illustrative of the application of the principles of the present invention. The illustrative discussions above are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Various modifications and alternative applications may be devised by those skilled in the art in view of the above teachings and without departing from the spirit and scope of the present invention are intended to cover such modifications, applications, and embodiments.

Claims

1. A data processing method for performance assessment of at least one entity, the method comprises the step of: receiving a plurality of scores against different growth dimensions for at least one entity by one or more scorers; computing by a processor an aggregate of the plurality of scores in a role of the one or more scorers and at least one growth dimension level; aggregating by an AI engine, the plurality of scores based on role types of the at least one entity to generate a data matrix of scores; and computing by the processor, a compounded score for the entity using a plurality of data models and the data matrix of scores to assess performance of the entity wherein the data matrix scores include a work score, a great experience score and a critical driver score.

2. The method of claim 1 wherein the role types include mandatory and non-mandatory roles wherein scores provided by mandatory and non-mandatory roles are aggregated separately.

3. The method of claim 1 wherein the engine is configured to determine an overall score of the entity after every event.

4. The method of claim 1 wherein the scores are real-time multi-dimensional feedback obtained from scorers for every event in which the at least one entity is involved based on role in the event.

5. The method of claim 1 further comprises storing a set of rules or protocols in a memory to be executed by the AI engine coupled to the processor for generating the plurality of data models associated with the entity data to identify at least one model for enabling computation of the combined score.

6. A data processing system for performance assessment of at least one entity, the system comprises: an electronic user interface configured for operating on a configurable performance assessment application; at least one entity database configured for storing a plurality of entity data attributes and a set of associated parameters of the entity data; at least one data model database configured for storing a plurality of data models related to growth dimension parameter and associated score types; a controller encoded with instructions enabling the controller to function as a bot for facilitating aggregation of a plurality of scores based on role types of the at least one entity to generate a data matrix of scores; and a processor coupled to the controller and an AI engine for computing a compounded score of the entity based on processing of the data matrix and the data models to assess performance of the entity wherein the data matrix scores include a work score, a great experience score and a critical driver score.

7. The system of claim 6 wherein the artificial intelligence-based AI engine is configured for processing the plurality of data models associated with the growth dimension parameter and score types for the entity to identify at least one data model to process the sores based on the data matrix.

8. The system of claim 7 further comprises a natural language processing (NLP) server configured for processing protocols based on the data models to determine the compounded score.

9. The system of claim 7 wherein the user interface and an underlying logic of the performance assessment application is based on seeded mapping wherein on introduction of any new or modified assessment parameter the system automatically reconfigures the underlining seeded mapping structure of the application.

10. The system of claim 9 wherein the application functions on a seeded data and generates mapping structures or codes on basis of the seeded data thereby enabling real time processing of received entity data from scorers within the application.

11. The system of claim 7 wherein a prediction algorithm enables computation of the critical drivers score for performance assessment of the entity.

12. The system of claim 7 wherein a set of rules or protocols is stored in a memory to be executed by the AI engine coupled to a processor for generating the plurality of data models associated with the growth dimension parameter and the score types to identify at least one model for processing.

13. A computer-readable non-transitory storage medium storing executable program instructions for data processing to assess performance of an entity by a configurable application which when executed by a computer cause the computer to perform operations comprising: receiving a plurality of scores against different growth dimensions for at least one entity by one or more scorers; computing by a processor an aggregate of the plurality of scores in a role of the one or more scorers and at least one growth dimension level; aggregating by an AI engine, the plurality of scores based on role types of the at least one entity to generate a data matrix of scores; and computing by the processor, a compounded score for the entity using a plurality of data models and the data matrix of scores to assess performance of the entity wherein the data matrix scores include a work score, a great experience score and a critical driver score.

14. The computer-readable storage medium of claim 15 further comprises executable program instructions in a memory to be executed for generating the plurality of data models.

15. The computer-readable storage medium of claim 16 further storing instructions that cause the processor to automatically add storage for storing the data models.