Deep Learning Applications in Enterprise Data Science

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Type and Duration

PhD-Thesis, since September 2018

Coordinator

Hilti Chair of Business Process Management

Main Research

Business Process Management

Field of Research

Process Management

Description

Deep Neural Networks (DNNs) are a powerful machine learning tool, loosely inspired by the structure of the human brain. Improvements in the field of DNNs, combined with an increase in computational power and available data, played an essential part in the recent rise of artificial intelligence applications. So far, the impact of deep learning has been most prevalent in a few specific areas, like image recognition, text- and speech processing. However, DNNs ability to handle large amounts of structured as well as unstructured data give them a considerable potential to create new value adding solutions in data analytics for enterprises. Big data initiatives and the rise of mobile and internet of things technologies leave companies with an enormous amount of raw data. DNNs seem to be the ideal technology to turn these data into useful knowledge.

This dissertation project uses action design research to explores the potential and challenges of applying deep learning methods for data analytics in enterprises. It will explore how deep learning methods can create new ways of analyzing enterprise data and how they can lead to value adding applications that make use of these data.

Keywords

Data analytics, Machine Learning, Neural Networks