Large enterprises compile and analyze large amounts of data on a daily basis. Typically, the collected raw data is processed by financial analysts to produce reports. Executive personnel use these reports to oversee the operations and make decisions based on the data. Some of the processing performed by can be easily automated by currently available computational tools. These tasks mostly make use of standard transformations on the raw data including visualizations and aggregate summaries. On the other hand, automating some of the manual processing requires more involved AI techniques. In our project, we aim to solve one of these harder to automate tasks. In fact, analyzing textual data using NLP is one of the standardized methods of data processing in modern software tools. However, vast majority of NLP methods primarily aim to analyze textual data, rather than generate meaningful narratives. Since generation of text is a domain dependent and non-trivial task, automated generation of narratives requires novel research to be useful to an enterprise environment. In this project, we focus on using numerical financial and supply chain data to generate useful textual reports that can be used in executive level companies. Upon successful completion of this project, financial analysts will spend less time on repetitive tasks and have more time to focus on reporting tasks requiring higher level data fusion skills.

Industry Partner(s):Unilever Canada

PI & Academic Institution:John Maidens, Ryerson University

Co-PI Name: Ayse Bener

# of HQPs: 3

Focus Areas/Industry Sector: Advanced Manufacturing, Digital Media

Platforms: Cloud, GPU

Technology: Artificial Intelligence, Real-Time Analytics