Mac researcher partners with IBM to improve Watson
McMaster University professor Fei Chiang has received funding from SOSCIP to partner with IBM to improve Watson Analytics!
SOSCIP is a research and development consortium that pairs academic and industry researchers with advanced computing tools to fuel Canadian innovation within the areas of agile computing, cities, mining, health, digital media, energy, cybersecurity, water and advanced manufacturing.
Check out the excerpt below from the full article on the project:
Big data can lend many insights that give an organization an edge in the global marketplace. It can inform decisions that can improve profit margins and help leaders better understand patients, customers and employees, and improve inefficient and costly processes.
Data, however, can also be rife with errors, duplications, inconsistencies and incomplete information. That’s where Fei Chiang and her team comes in. Chiang is an assistant professor in the Department of Computing and Software, Faculty of Engineering at McMaster University. Funded as part of the OCE-SOSCIP Smart Computing R&D Challenge, a collaboration between OCE, NSERC and SOSCIP to provide $7.5M in funding to SOSCIP projects, her SOSCIP project involves working on improving data quality to achieve trusted and accurate results from data analysis tasks.
“Poor data quality costs businesses and organizations millions of dollars a year in operational inefficiencies, poor decision-making and wasted time,” she explained.
“Real data often contains missing, duplicate, inconsistent and empty values. The currency and timeliness of data is also important as many decisions need to be made with the most recent data.”
Chiang is working with IBM to improve the data quality metrics in Watson Analytics, IBM’s cloud-based data analytics platform. The first step is to build a set of detailed quality metrics that provide in-depth information on the data quality problems. The metrics are aggregated to provide customized data quality scores to users based on their data analysis task.
When the research is completed, the aim is to provide organizations with a means of “cleaning” their data, a process which could take weeks to months. Chiang’s research could trim that time down to days, saving the organization money and vital time needed to make big decisions in a fast-paced marketplace.
The research also provides valuable hands-on training for data scientists, which represents a significant gap in the Canadian market.