While in Turkey, Ayse Bener, a professor at Ryerson University in Toronto, Ont., took the time to explain how she has infused IBM into her research.
Bener has been working with the IBM Toronto Labs for 5 years, mainly with the DB2 team. Her research involves building predictive models to help software development managers, developers, and testers make decisions in data driven networks.
She builds projects that learn from past project data, otherwise known as cold data, or data that directly comes from the code developers write. Human related data is also used, such as organizational structure information, the way that various departments communicate with each other as well as some individual information about how people make decisions, and how it impacts their work.
She tries to predict defects in upcoming product releases. This information is useful due to the product release schedules. When developers finish coding, the product goes into testing, and there are sophisticated methodologies that software organizations use when testing their products. However, testing takes a lot of time, in some cases organization allocate almost 50 percent of their time spending it on product testing when looking at a typical product life cycles.
Bener says test teams can allocate scarce resources more efficiently using predictive models. In doing so, they can use the same time to try to test more cases in a more efficient manner. In the end, this is what a successful model is used for.
In a specific case, Bener researched different aspects of the DB2 development life cycle, different issue management systems, in order to come up with with a more meaningful and actionable model for the end-user.
She looked at DB2 products on environmental impacts, green software, and attempted to answer what kind of environmental impact such as C02 emissions that the product would have.
Bener also operates a research lab called the Data Science Lab where PhD and Master’s students research data science. They concentrate on three verticals: software engineering, healthcare and energy and sustainability. She has four students dedicated to software engineering and research is based on what they do with IBM Toronto labs.
Apart from her regular professor day-to-day job, she is also the director of Big Data for Ryerson’s Provost office. Her lab is apart of the Big Data initiative (BDI) high performance RC4 facility, another partner of the SOSIP initiative with Ontario universities for the big data processing environment. Ryerson has invested in big data and entrepreneurship, partnered with the One11 big data incubator.
Bener is also overseeing the launch of the new Certificate in Data Analytics, Big Data, and Predictive Analytics, which will be offered for the first time at Ryerson this Fall. This will be followed by a Data Science and Analytics Master’s program in 2015.
The two programs are the first of their kind in Canada and Bener credits IBM in being an instrumental force in the writing of proposals during the past two years.
Bener is also researching how analytical skill sets can be partnered with Watson. She is interested in using the Watson product as teaching material for the new program this fall.
The biggest challenge for students she says will be how they are able to handle the quantitative data.
“At every level, data analytics is going to be needed and has to be in the DNA of every organization moving forward,” Bener says. “Business-led programs are designed to test the skill set of people when interpreting and understanding numbers, but you need the doers who can generate numbers as well.
“The new breed of data scientists have to know and translate it into data related solution.”