IDENTIFICATION OF SIMILAR PROCESSES IN ENTERPRISE
An example computer system for identifying similar processes can include: one or more processors; and non-transitory computer-readable storage media encoding instructions which, when executed by the one or more processors, causes the computer system to create: a similarity engine programmed to use machine learning to analyze a plurality of processes for an enterprise; a models engine programmed to identify similarities between the plurality of processes using the machine learning; and a display engine programmed to display the similarities between the plurality of processes.
1 . A computer system for identifying similar processes, comprising:
one or more processors; and
non-transitory computer-readable storage media encoding instructions which, when executed by the one or more processors, causes the computer system to create:
a similarity engine programmed to use machine learning to analyze a plurality of processes for an enterprise;
a models engine programmed to identify similarities between the plurality of processes using the machine learning; and
a display engine programmed to display the similarities between the plurality of processes.
2 . The computer system of claim 1 , wherein the similarity engine is further programmed to compare metadata associated with the plurality of processes, with the metadata including: (i) a process name providing a name for each of the plurality of processes; and (ii) a process summary providing a summary of a function for each of the plurality of processes.
3 . The computer system of claim 2 , wherein the metadata further includes: (iii) a process scope providing a scope for each of the plurality of processes; (iv) a process purpose providing a purpose for each of the plurality of processes; (v) a controls description providing a summary of controls associated with each of the plurality of processes; and (vi) a risk description providing a summary of risks associated with each of the plurality of processes.
4 . The computer system of claim 1 , wherein the models engine is selected from a group consisting of Doc2Vec, Bidirectional Encoder Representations from Transformers, and Text-to-Text Transfer Transformer.
5 . The computer system of claim 1 , wherein the models engine is further programmed to estimate a similarity score for pairs of the plurality of processes.
6 . The computer system of claim 5 , wherein the display engine is further programmed to show the pairs of the plurality of processes having the similarity score meeting a threshold.
7 . The computer system of claim 1 , comprising further instructions which, when executed by the one or more processors, causes the computer system to create a taxonomy engine programmed to compare taxonomies between the plurality of processes.
8 . The computer system of claim 1 , comprising further instructions which, when executed by the one or more processors, causes the computer system to create a roles engine programmed to compare roles between the plurality of processes.
9 . The computer system of claim 1 , wherein the display engine is further programmed to display the plurality of processes on a matrix, wherein the matrix visually shows clusters of processes that are similar.
10 . The computer system of claim 9 , wherein the display engine is further programmed filter the matrix based upon inputs from a user, wherein the inputs include: (i) a level of similarity defining a specific amount of similarity between the plurality of processes; (ii) a line of business defining one or more lines of business associated with the plurality of processes; (iii) a product defining one or more products associated with the plurality of processes.
11 . A method for identifying similar processes, the method comprising:
using machine learning to analyze a plurality of processes for an enterprise;
identifying similarities between the plurality of processes using the machine learning; and
displaying the similarities between the plurality of processes.
12 . The method of claim 11 , further comprising comparing metadata associated with the plurality of processes, with the metadata including: (i) a process name providing a name for each of the plurality of processes; and (ii) a process summary providing a summary of a function for each of the plurality of processes.
13 . The method of claim 12 , wherein the metadata further includes: (iii) a process scope providing a scope for each of the plurality of processes; (iv) a process purpose providing a purpose for each of the plurality of processes; (v) a controls description providing a summary of controls associated with each of the plurality of processes; and (vi) a risk description providing a summary of risks associated with each of the plurality of processes.
14 . The method of claim 11 , wherein the machine learning is selected from a group consisting of Doc2Vec, Bidirectional Encoder Representations from Transformers, and Text-to-Text Transfer Transformer.
15 . The method of claim 11 , further comprising estimating a similarity score for pairs of the plurality of processes.
16 . The method of claim 15 , further comprising showing the pairs of the plurality of processes having the similarity score meeting a threshold.
17 . The method of claim 11 , further comprising comparing taxonomies between the plurality of processes.
18 . The method of claim 11 , further comprising comparing roles between the plurality of processes.
19 . The method of claim 11 , further comprising displaying the plurality of processes on a matrix, wherein the matrix visually shows clusters of processes that are similar.
20 . The method of claim 19 , further comprising filtering the matrix based upon inputs from a user, wherein the inputs include: (i) a level of similarity defining a specific amount of similarity between the plurality of processes; (ii) a line of business defining one or more lines of business associated with the plurality of processes; (iii) a product defining one or more products associated with the plurality of processes.