Evaluating organizational skills using cognitive computing to determine cost of entering a new market

1. A method for training a machine learning neural network for determining gaps in skills for entering a new market using cognitive computing, comprising:

creating a first data set defining required skills of an organization from a logical representation of a component business model based on a current business strategy;

creating a second data set linking core business capabilities and processes to the required skills and distribution of skill maturity levels from the logical representation of a component business model;

training a machine learning neural network on the first and second data sets to define current skills of an organization based on the current business strategy using a cognitive computing processor device;

determining a required ratio of skills of three technology horizons to define a plurality of skill classes of the required skills of the organization using the cognitive computing processor device;

training the machine learning neural network on the required ratio of skills to define current skill profiles of the organization based on the plurality of skill classes using the cognitive computing processor device;

training the machine learning neural network to determine at least one first skill gap between the required skills of the organization and the current skill profiles, using the cognitive computing processor device;

creating a third data set comprising skills required for a new business strategy for the organization from a logical representation of a component business model based on the new business strategy, using the cognitive computing processor device; and

training the machine learning neural network on the third data set to determine at least one second skill gap between the skills required for the new business strategy and the current skill profiles, using the cognitive computing processor device.

2. The method of claim 1 , wherein the machine learning neural network is trained to consider market and technology evolution factors as well as business domain complexity factors.

3. The method of claim 1 , wherein the machine learning neural network is trained as a multi-class classifier.

4. The method of claim 3 , wherein the multi-class classifier machine learning neural network is trained to suggest skill ratios required for each horizon distribution of the three technology horizons.

5. The method of claim 1 , wherein training the machine learning neural network to define current skills profiles comprises using a person skill profiling algorithm for auto tagging of users with skills based on a multi-layered weighted regressor neural network.

6. The method of claim 5 , wherein determining, using the trained machine learning neural network, at least one first skill gap comprises performing gap analysis to determine gaps between the defined skills profiles determined by the profiling algorithm and the defined business capabilities, processes and required skills.

7. A computer system for training a machine learning neural network for determining gaps in skills for entering a new market using cognitive computing, comprising:

one or more computer processors;

one or more non-transitory computer-readable storage media;

program instructions, stored on the one or more non-transitory computer-readable storage media, which when implemented by the one or more processors, cause the computer system to perform the steps of:

creating a first data set defining required skills of an organization from a logical representation of a component business model based on a current business strategy;

creating a second data set linking core business capabilities and processes to the required skills and distribution of skill maturity levels from the logical representation of a component business model;

training a machine learning neural network on the first and second data sets to define current skills of an organization based on the current business strategy using a cognitive computing processor device;

determining a required ratio of skills of three technology horizons to define a plurality of skill classes of the required skills of the organization using the cognitive computing processor device;

training the machine learning neural network on the required ratio of skills to define current skill profiles of the organization based on the plurality of skill classes using the cognitive computing processor device;

training the machine learning neural network to determine at least one first skill gap between the required skills of the organization and the current skill profiles, using the cognitive computing processor device;

creating a third data set comprising skills required for a new business strategy for the organization from a logical representation of a component business model based on the new business strategy, using the cognitive computing processor device; and

training the machine learning neural network on the third data set to determine at least one second skill gap between the skills required for the new business strategy and the current skill profiles, using the cognitive computing processor device.

8. The computer system of claim 7 , wherein the machine learning neural network is trained to consider market and technology evolution factors as well as business domain complexity factors.

9. The computer system of claim 7 , wherein the machine learning neural network is trained as a multi-class classifier.

10. The computer system of claim 9 ,

wherein the multi-class classifier machine learning neural network is trained to suggest skill ratios required for each horizon distribution of the three technology horizons.

11. The computer system of claim 7 , wherein training the machine learning neural network to define skills profiles comprises using a person skill profiling algorithm for auto tagging of users with skills based on a multi-layered weighted regressor neural network.

12. The computer system of claim 7 , wherein determining, using the trained machine learning neural network, at least one first skill gap comprises performing gap analysis to determine gaps between the defined skills profiles determined by the profiling algorithm and the defined business capabilities, processes and required skills.

13. A computer program product comprising:

program instructions on a computer-readable storage medium, where execution of the program instructions using a computer causes the computer to perform a method for training a machine learning neural network for determining gaps in skills for entering a new market using cognitive computing, comprising:

creating a first data set defining required skills of an organization from a logical representation of a component business model based on a current business strategy;

creating a second data set linking core business capabilities and processes to the required skills and distribution of skill maturity levels from the logical representation of a component business model;

training a machine learning neural network on the first and second data sets to define current skills of an organization based on the current business strategy using a cognitive computing processor device;

determining a required ratio of skills of three technology horizons to define a plurality of skill classes of the required skills of the organization using the cognitive computing processor device;

training the machine learning neural network on the required ratio of skills to define current skill profiles of the organization based on the plurality of skill classes using the cognitive computing processor device;

training the machine learning neural network to determine at least one first skill gap between the required skills of the organization and the current skill profiles, using the cognitive computing processor device;

creating a third data set comprising skills required for a new business strategy for the organization from a logical representation of a component business model based on the new business strategy, using the cognitive computing processor device; and

training the machine learning neural network on the third data set to determine at least one second skill gap between the skills required for the new business strategy and the current skill profiles, using the cognitive computing processor device.

14. The computer program product of claim 13 , wherein the machine learning neural network is trained to consider market and technology evolution factors as well as business domain complexity factors and wherein the machine learning neural network is trained as a multi-class classifier.

15. The computer program product of claim 14 , wherein the multi-class classifier machine learning neural network is trained to suggest skill ratios required for each horizon distribution of the three technology horizons and wherein training the machine learning neural network to define current skills profiles comprises using a person skill profiling algorithm for auto tagging of users with skills based on a multi-layered weighted regressor neural network.

16. The computer program product of claim 14 , wherein determining, using the trained machine learning neural network, at least one first skill gap comprises performing gap analysis to determine gaps between the defined skills profiles determined by the profiling algorithm and the defined business capabilities, processes and required skills.