wFlow: Machine Learning Assisted Flowchart Generation Module for the wDesk Platform
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Date
2017-04
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Montana State University
Abstract
wDesk, a financial services reporting platform created by Workiva, allows users to aggregate a wide variety of data about internal processes and requirements within the platform. Despite the presence of this data within wDesk, users still frequently use this same data outside of the platform to manually create process flow diagrams for reporting. In order to improve this workflow, Workiva partnered with the MSU Software Factory to create a module that will allow users to generate a flowchart from the existing data within the wDesk system that meets the high product standards of the company. This new module, called wFlow, was developed using Workiva’s standard development practices and technologies. Following an agile development cycle, wFlow was implemented using Dart and React, and utilizes Workiva’s publicly available front-end frameworks, w_module and w_flux. To optimize the generated flowcharts, the use of machine learning algorithms was explored for the purposes of creating a clear and readable graph layout without user oversight, when given only the data that comprises the nodes and connections of the graph. The integration of wFlow into the wDesk ecosystem will significantly improve the workflow of its users and open the door for further machine learning integration into Workiva products.