CLIAMS:1. A computer implemented method of determination of an optimal deployment solutions for a plurality of users in an enterprise, the method comprising:
determining, by a processor (102), for each of the plurality of users, an initial user fingerprint (UFP) score set comprising scores that quantify enterprise-level demands of the each user, wherein the enterprise-level demands are associated with one or more features of the plurality of users for working in the enterprise, wherein each of the one or more features is associated with a factor indicating priority of the feature as compared to other features for achieving objectives of the enterprise;
identifying, by the processor (102), relationship types between pairs of each of the one or more features based on the factor associated with the one or more features, wherein each of the relationship types is associated with a weight;
computing, by the processor (102), a final UFP score set for each of the plurality of users, wherein the final UFP score set is computed based on the weight;
identifying, by the processor (102), one or more dominant features based on the final UFP scores, wherein a dominant feature indicates enterprise-level demands;
grouping, by the processor (102), the users into user segments based on the one or more dominant features, wherein each of the user segments comprises a set of users having the features closer to the dominant feature of the user segment than the dominant features of other user segments; and
determining, by the processor (102), the optimal deployment solution for each of the user segments based on the one or more dominant features.
2. The computer-implemented method as claimed in claim 1 further comprising:
receiving, by the processor (102), user data associated with the plurality the users, wherein the user data is indicative of expectations of the users corresponding to an IT infrastructure of the enterprise;
identifying, by the processor (102), from the user data, the one or more features indicative of the objectives of the enterprise; and
identifying, from the user data, attributes of each of the features and for each of the users, wherein an attribute defines the enterprise-level demands of a user and is selected from pre-defined combinations of attributes that define types of the each feature, and wherein the scores in the initial UFP score set are associated with the attributes of the one or more features.
3. The computer-implemented method as claimed in claim 1, wherein the determining the initial UFP score set for the each user comprises identifying the scores for the one or more features from pre-defined look-up tables, wherein each pre-defined look-up table comprises normalized scores for the pre-defined combinations of the attributes defining types of a feature.
4. The computer-implemented method as claimed in claim 1 further comprising computing, by the processor (102), for each of a plurality of deployment solutions of the enterprise, a deployment fingerprint (DFP) score set comprising scores that quantify levels of capabilities of the each deployment solution to meet enterprise-level demands of the users, wherein the deployment solutions define the enterprise infrastructure provided to the users.
5. The computer-implemented method as claimed in claim 1, wherein the grouping of the users into the user segments comprise:
determining a centroid vector of the final UFP score sets for the users, wherein the centroid vector is indicative of central points of distribution of the final UFP score sets;
identifying a final UFP score set as a center vector which is farthest from the centroid vector;
grouping the final UFP score sets in a cluster, for which a distance with respect to the center vector is less than a distance with respect to the centroid vector, and finding a cluster centroid vector of the cluster;
replacing the center vector with the cluster centroid vector when the center vector is not equal to the cluster centroid vector;
iteratively repeating of steps of grouping further the final UFP score sets in the cluster based on distance of the points with respect to the center vector and with respect to the centroid vector, till the center vector and the cluster centroid vector are equal;
when the center vector and the cluster centroid vector are equal, creating a user segment comprising users associated with the final UFP score sets in the cluster;
identifying a final UFP score set in the cluster which is nearest to the center vector of the cluster as the definition of the user segment, and adding that final UFP score set as an element of a segment centroid set;
revising the final UFP score sets by eliminating the final UFP score sets that are grouped in the cluster; and
iteratively repeating of steps of identifying a farthest final UFP score set with respect to the centroid vector, creating further user segments with left-over final UFP score sets.
6. The computer implemented method as claimed in claim 1, wherein the relationship type comprises one of a singleton relationship, a dependent relationship, and a composite relationship.
7. A segmentation system (100) for determination of the optimal deployment solutions for a plurality of users in an enterprise:
a processor (102);
a feature identification module (112), coupled to the processor (102), to determine for each of the plurality of users, an initial user fingerprint (UFP) score set comprising scores that quantify enterprise-level demands of the each user, wherein the enterprise-level demands are associated with one or more features of the plurality of users for working in the enterprise, wherein each of the one or more features is associated with a factor indicating priority of the feature as compared to other features for achieving objectives of the enterprise;
a weight computation module (114), coupled to the processor (102), to:
identify relationship types between pairs of each of the one or more features based on the factor associated with the one or more features, wherein each of the relationship types is associated with a weight;
compute a final UFP score set for each of the plurality of users, wherein the final UFP score set is computed based on the weight; and
identify one or more dominant features based on the final UFP scores, wherein a dominant feature indicates enterprise-level demands;
a segmentation module (116), coupled to the processor (102), to:
create user segments by grouping users based on the one or more dominant features, wherein each of the user segments comprises a set of users having the features related to the dominant feature of the user segment.
8. The segmentation system (100) as claimed in claim 7 further comprises a cost analysis module (120), coupled to the processor (102), to determine the optimal deployment solution for each of the user segments based on the one or more dominant features.
9. The segmentation system (100) as claimed in claim 7, wherein the feature identification module (112) further,
collects user data associated with the plurality the users, wherein the user data is indicative of expectations of the users corresponding to an IT infrastructure of the enterprise;
identifies from the user data, the one or more features indicative of the objectives of the enterprise; and
identifies from the user data, attributes of each of the features and for each of the users, wherein an attribute defines the enterprise-level demands of a user and is selected from pre-defined combinations of attributes that define types of the each feature, and wherein the scores in the initial UFP score set are associated with the attributes of the one or more features.
10. The segmentation system (100) as claimed in claim 7, wherein the feature identification module (112) identifies the scores for the one or more features from pre-defined look-up tables to determine the initial UFP score set for the each user, wherein each pre-defined look-up table comprises normalized scores for the pre-defined combinations of the attributes defining types of a feature.
11. The segmentation system (100) as claimed in claim 7 further comprises a deployment solution module (118), coupled to the processor (102), to compute, for each of a plurality of deployment solutions of the enterprise, a deployment fingerprint (DFP) score set comprising scores that quantify levels of capabilities of the each deployment solution to meet enterprise-level demands of the users, wherein the deployment solutions define the enterprise infrastructure provided to the users.
12. The segmentation system (100) as claimed in claim 11, wherein the deployment solution module (118) further:
collects enterprise data associated with the capabilities of the deployment solutions for meeting the enterprise-level demands of the users; and
identifies, from the enterprise data, capability attributes under each of the features and for each of the deployment solutions, wherein the capability attributes under the each feature define the capabilities of the deployment solutions and are selected from the pre-defined combinations of attributes that define types of the each feature, and wherein the rating scores in the DFP score set are associated with the capability attributes under the features.
13. The segmentation system (100) as claimed in claim 12, wherein the deployment solution module (118) determines the scores under the features in the DFP score sets from predefined look-up tables, wherein one of the predefined look-up tables corresponds to one of the features and comprises normalized scores for the pre-defined combinations of capability attributes that define types of that category feature, and wherein one of the scores corresponds to one of the features, which is determined based on matching of the attributes with one of the pre-defined combinations of capability attributes in the corresponding pre-defined look-up table.
14. A non-transitory computer-readable medium comprising instructions executable by a processor (102) to:
determining, by the processor (102), for each of the plurality of users, an initial user fingerprint (UFP) score set comprising scores that quantify enterprise-level demands of the each user, wherein the enterprise-level demands are associated with one or more features of the plurality of users for working in the enterprise, wherein each of the one or more features is associated with a factor indicating priority of the feature as compared to other features for achieving objectives of the enterprise;
identifying, by the processor (102), relationship types between pairs of each of the one or more features based on the factor associated with the one or more features, wherein each of the relationship types is associated with a weight;
computing, by the processor (102), a final UFP score set for each of the plurality of users, wherein the final UFP score set is computed based on the weight;
identifying, by the processor (102), one or more dominant features based on the final UFP scores, wherein a dominant feature indicates enterprise-level demands;
grouping, by the processor (102), the users into user segments based on the one or more dominant features, wherein each of the user segments comprises a set of users having the features related to the dominant feature of the user segment; and
determining, by the processor (102), an optimal deployment solution for each of the user segments based on the one or more dominant features.
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