Claims:

1. A processor implemented method comprising:
(a) obtaining, by a hardware processor, one or more entities and one or more parameters and corresponding parameter values from one or more sources, wherein the one or more parameters being mapped across the one or more entities;
(b) querying, by said hardware processor, a database to determine at least one of inconsistent entity, new entity, missing entity, redundant entity, inconsistent parameter, new parameter, missing parameter, redundant parameter, inconsistent parameter value, new parameter value, missing parameter value, redundant parameter value;
(c) updating, by said hardware processor, said database based on at least one of the determined inconsistent entity, new entity, missing entity, redundant entity, inconsistent parameter, new parameter, missing parameter, redundant parameter, inconsistent parameter value, new parameter value, missing parameter value, redundant parameter value to obtain at least one of an updated list of entities, an updated list of parameters, and an updated list of parameters values;
(d) generating, by said hardware processor, one or more dimensions based on said updated list of parameters;
(e) dynamically mapping, by said hardware processor, each of said one or more dimensions to one or more corresponding parameters from said updated list of parameters;
(f) processing, by said hardware processor, a selection of one or more entities from said updated list of entities to obtain one or more selected entities;
(g) identifying, by said hardware processor, a set of dimensions for said one or more selected entities based on a corresponding parameter value from said updated list of parameters to obtain one or more selected dimensions;
(h) processing, by said hardware processor, a selection of one or more parameters from said updated list of parameters to obtain one or more selected parameters for said one or more selected entities;
(i) computing and applying, by said hardware processor, a scaling parameter score to each of said one or more parameter values corresponding to each of said one or more selected parameters for each of said one or more selected entities to obtain one or more scaled parameter values corresponding to each of said one or more selected parameters for each of said one or more selected entities;
(j) computing, by said hardware processor, a variance among said one or more scaled parameter values for each of said one or more selected parameters for said one or more selected entities;
(k) assigning, by said hardware processor, a weight to each of said one or more selected parameters pertaining to each of said selected set of dimensions based on said computed variance among each of said one or more selected parameter values for each of said one or more selected parameters for said one or more selected entities;
(l) computing, by said hardware processor, an entity dimension score for each of said one or more selected entities and each of said one or more selected dimensions based on the number of said one or more selected parameters for each of said one or more selected dimensions, said weight assigned to each of said one or more selected parameters, and said one or more scaled parameter values corresponding to said one or more selected parameters for said one or more selected one or more entities;
(m) computing, by said hardware processor, a prioritization threshold value for each of the one or more selected dimensions;
(n) iteratively performing, by said hardware processor, an analysis on said one or more selected entities across the two or three selected dimensions to obtain one or more decision data reports, wherein said one or more decision data reports comprises one or more selected entities being mapped to said two or three selected dimensions, and a priority assigned to each of said one or more entities based on said entity dimension score for each of said one or more selected entities and for said two or more selected dimensions having a value above or below said prioritization threshold value; and
(o) generating, by said hardware processor, an optimized decision data report based on said one or more decision data reports, wherein said optimized decision data report comprises a priority list of entities.

2. The processor implemented method of claim 1, wherein said prioritization threshold value for each of the one or more selected dimensions is computed based on said computed entity dimension score for said one or more selected entities and each of said one or more selected dimensions.

3. The processor implemented method of claim 1, wherein said priority list of entities categorizes said one or more selected entities into optimal and non-optimal categories based on at least one of rank, spatial positioning in quadrant or generation of intersection sets into high, medium and low priorities, or color coding that is indicative of an order for prioritization.

4. A system comprising:
a memory storing instructions and a database; and
a hardware processor coupled to said memory, wherein said hardware processor is configured by said instructions to:
(a) obtain one or more entities and one or more parameters and corresponding parameter values from one or more sources, wherein the one or more parameters being mapped across the one or more entities;
(b) query said database to determine at least one of inconsistent entity, new entity, missing entity, redundant entity, inconsistent parameter, new parameter, missing parameter, redundant parameter, inconsistent parameter value, new parameter value, missing parameter value, redundant parameter value;
(c) update said database based on at least one of the determined inconsistent entity, new entity, missing entity, redundant entity, inconsistent parameter, new parameter, missing parameter, redundant parameter, inconsistent parameter value, new parameter value, missing parameter value, redundant parameter value to obtain at least one of an updated list of entities, an updated list of parameters, and an updated list of parameters values;
(d) generate one or more dimensions based on said updated list of parameters;
(e) dynamically map each of said one or more dimensions to one or more corresponding parameters from said updated list of parameters;
(f) process a selection of one or more entities from said updated list of entities to obtain one or more selected entities;
(g) identify a set of dimensions for said one or more selected entities based on a corresponding parameter value from said updated list of parameters to obtain one or more selected dimensions;
(h) process a selection of one of more parameters from said updated list of parameters to obtain one or more selected parameters for said one or more selected entities;
(i) compute and apply a scaling parameter score to each of said one or more parameter values corresponding to each of said one or more selected parameters for each of said one or more selected entities to obtain one or more scaled parameter values corresponding to each of said one or more selected parameters for each of said one or more selected entities;
(j) compute a variance among said one or more scaled parameter values for each of said one or more selected parameters for said one or more selected entities;
(k) assign a weight to each of said one or more selected parameters pertaining to each of said selected set of dimensions based on said computed variance among each of said one or more selected parameter values for each of said one or more selected parameters for said one or more selected entities;
(l) compute an entity dimension score for each of the one or more selected entities and each of the one or more selected dimensions based on the number of the one or more selected parameters for each of the one or more selected dimensions, said weight assigned to each of the one or more selected parameters, and the one or more scaled parameter values corresponding to the one or more selected parameters for said one or more selected one or more entities;
(m) compute a prioritization threshold value for each of the one or more selected dimensions based on said computed entity dimension scores for said one or more selected entities and each of said one or more selected dimensions;
(n) iteratively perform an analysis on said one or more selected entities across said one or more selected dimensions to obtain one or more decision data reports, wherein said one or more decision data reports comprises one or more selected entities being mapped to said two or three selected dimensions, and a priority assigned to each of said one or more entities based on said entity dimension score for each of said one or more selected entities and for said two or more selected dimensions having a value above or below said prioritization threshold value; and
(o) generate an optimized decision data report based on said one or more decision data reports, wherein said optimized decision data report comprises a priority list of entities.

5. The system of claim 4, wherein said prioritization threshold value for each of the one or more selected dimensions is computed based on said computed entity dimension scores for said one or more selected entities and each of said one or more selected dimensions.

6. The system of claim 4, wherein the priority list of entities categorizes one or more entities into optimal and non-optimal categories based on at least one of rank, spatial positioning in quadrant or generation of intersection sets into high, medium and low priorities, or color coding that is indicative of an order for prioritization.
, Description:As Attached