I-Corps: Creating data sets that are distilled into collections of information that may be used to answer questions in real-time

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The broader impact/commercial potential of this I-Corps project is the development of low-cost, industry agnostic, artificial intelligence resources. Presently, data analysts and data science teams face one primary problem: data cleanliness. Data cleanliness is a measure of corrupt or inaccurate records from a record set or table. To clean a database one must identify incomplete or incorrect parts of the data and then replace, modify, or delete the bad data. Machine learning models often perform well in lab conditions but fail in the real world due to dirty data. Artificial intelligence solutions are inhibited in real world applications due to too little signaling data amidst the noise. The proposed technology solves the problems that face many data scientists who dependent on the dirty data at hand. There is a demand for faster, better decision making across virtually every industry. The goal is to strategically solve the data cleanliness problem, with protectable intellectual property and processes, for various industries after refining internal processes, user interface, and value propositions for the initial technology target: financial institutions.This I-Corps project is based on the development of novel artificial intelligence resources to automate data preparation, augmentation, and governance processes. These resources include proprietary machine learning algorithms, unique data librarying processes, dynamic training sets, next generation database technology, and intelligent rules engines to reimagine the expensive, high-effort, and highly technical space of data science. The novel approach of this project will automate over 60% of a data scientists’ workload, reduce cost, minimize internal IT resource demands, and increase information access.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.