The target market for this prototype is Business Analyst users working at enterprise-level companies. The concept is to make Advanced Analytical models available and accessible to users who are not necessarily data analysts. Advanced analytical models, such as predictive analytics, are valuable tools that impact operations, marketing, store location planning and layout, etc. The biggest challenge in this case was to make such an advanced modeling concept accessible and understandable to a Business Analyst type of persona. 

Through interviews, I discovered that a huge hurdle in adapting advanced analytical models is cleaning up data sets. These data sets have millions of rows, and with that much data, it is inevitable that errors such as null values, outliers, and other types of errors occur in the data. Moreover, these data sets are updated on a frequent basis, usually nightly but as often as hourly. The ability to automate how to handle non-compliant data would be a huge help in these situations unique to Enterprise-level customers. 

This prototype demonstrates an initial data upload with assigning rules for cleaning up large data sets. 

Video demo is below and full prototype is available here. Thanks for looking!