Process analysis feedback mapping framework
A process data store may contain a process model (e.g., a process graph, with process graph elements that include nodes and edges, as generated via process mining or a BPMN representation). A process server may retrieve information from the process data store and receive user feedback data. The server may determine if the information retrieved from the process data store is associated with a prior mapping of survey questions to the information retrieved from the process data store. If the information retrieved from the process data store is not associated with a prior mapping of survey questions, embodiments may utilize Machine Learning (“ML”) to automatically map the user feedback data to the information retrieved from the process data store. The server may then automatically assign, group, and analyze the user feedback data to generate a recommended alteration.
1 . A system associated with an enterprise, comprising:
a process data store containing a process model that represents an analytical representation of the enterprise's business processes and includes a process graph with ordered process step elements as generated via process mining; and
a process server, associated with the enterprise and coupled to the process data store, including:
a computer processor, and
a computer memory storing instructions that, when executed by the computer processor, cause the process server to:
retrieve information from the process data store,
receive user feedback data from customers external to the enterprise and employees internal to the enterprise,
determine if the information retrieved from the process data store is associated with a prior mapping of survey questions to the information retrieved from the process data store,
if the information retrieved from the process data store is not associated with a prior mapping of survey questions, utilize Machine Learning (“ML”) to automatically map the user feedback data to the information retrieved from the process data store,
automatically assign the user feedback data in accordance with the prior mapping or the automatic mapping,
automatically group the assigned user feedback data to generate aggregated and clustered data using node and edge tags indicating whether process step elements are internal or external to the enterprise,
analyze the aggregated and clustered user feedback data to automatically generate a recommended alteration of at least one process step element in the process data store, wherein the recommended alteration is associated with deleting the process step element or reordering process step elements in the process graph, and
transmit the recommended alteration of the at least one process step.
2 . The system of claim 1 , wherein the analysis of the user feedback data includes automatically clustering and abstracting user feedback for subsets of elements internal to the enterprise separately from subsets of elements external to the enterprise.
3 . The system of claim 1 , wherein the user feedback data comprises user responses to survey questions including at least one of: (i) numerical sentiment ratings, (ii) free text, and (iii) other elicitation techniques.
4 . The system of claim 1 , wherein the information in the process data store is associated with a process-driven scenario and no prior mapping exists for the feedback data to the process model.
5 . The system of claim 4 , wherein the prior mapping of survey questions is associated with a design of the survey questions.
6 . The system of claim 1 , wherein the information in the process data store is associated with an experience-driven scenario and the feedback data is associated with a prior mapping of the feedback data to the process model.
7 . The system of claim 6 , wherein the automatic mapping is performed via a Large Language Model (“LLM”).
8 . The system of claim 6 , wherein the user feedback data comprises social media information.
9 . The system of claim 1 , wherein the process server is further to display graphical representations of process elements, and selection of a graphical element results in an automatic display of aggregated and clustered data for that element.
10 . A computer-implemented method associated with an enterprise, comprising:
retrieving, by a computer processor of a process server, information from a process data store, wherein the process data store contains a process model that represents an analytical representation of the enterprise's business processes and includes a process graph with ordered process step elements as generated via process mining;
receiving user feedback data from customers external to the enterprise and employees internal to the enterprise;
determining if the information retrieved from the process data store is associated with a prior mapping of survey questions to the information retrieved from the process data store;
if the information retrieved from the process data store is not associated with a prior mapping of survey questions, utilizing Machine Learning (“ML”) to automatically map the user feedback data to the information retrieved from the process data store;
automatically assigning the user feedback data in accordance with the prior mapping or the automatic mapping;
automatically grouping the assigned user feedback data to generate aggregated and clustered data using node and edge tags indicating whether process step elements are internal or external to the enterprise;
analyzing the aggregated and clustered user feedback data to automatically generate a recommended alteration of at least one process step element in the process data store, wherein the recommended alteration is associated with deleting the process step element or reordering process step elements in the process graph; and
transmitting the recommended alteration of the at least one process step.
11 . The method of claim 10 , wherein the user feedback data comprises user responses to survey questions including at least one of: (i) numerical sentiment ratings, (ii) free text, and (iii) other elicitation techniques.
12 . The method of claim 10 , wherein the information in the process data store is associated with a process-driven scenario and no prior mapping exists for the feedback data to the process model.
13 . A non-transitory, machine-readable medium comprising instructions thereon that, when executed by a processor, cause the processor to execute operations to perform a method associated with an enterprise, the method comprising:
retrieving, by a computer processor of a process server, information from a process data store, wherein the process data store contains a process model that represents an analytical representation of the enterprise's business processes and includes a process graph with ordered process step elements as generated via process mining;
receiving user feedback data from customers external to the enterprise and employees internal to the enterprise;
determining if the information retrieved from the process data store is associated with a prior mapping of survey questions to the information retrieved from the process data store;
if the information retrieved from the process data store is not associated with a prior mapping of survey questions, utilizing Machine Learning (“ML”) to automatically map the user feedback data to the information retrieved from the process data store;
automatically assigning the user feedback data in accordance with the prior mapping or automatic mapping;
automatically grouping the assigned user feedback data to generate aggregated and clustered data using node and edge tags indicating whether process step elements are internal or external to the enterprise;
analyzing the aggregated and clustered user feedback data to automatically generate a recommended alteration of at least one process step element in the process data store, wherein the recommended alteration is associated with deleting the process step element or reordering process step elements in the process graph; and
transmitting the recommended alteration of the at least one process step.
14 . The system of claim 1 , wherein event logs at the process server correlate the process model with the user feedback data to fuse data of various granularity and provide an inside-out map of the enterprise's operations based on process and journey models with connections between different data analysis views.