At the SAS Powered Educators meetup in March, I spoke to Monica Dragoicea, a full professor at the Faculty of Automatic Control and Computers at the University Politehnica of Bucharest. Her background is in automatic control, but her more recent studies and interests lie in systems and service systems engineering, analytics and smart service design, computational intelligence and mobile robotics. We talked about her experience using SAS, and how the democratization of analytics can help academics and business users to generate more reliable results.
How did your journey with SAS begin?
My journey with SAS began purely by chance, when you came to our university to expand SAS’s academic collaboration and I was invited by the Dean of our Faculty to get involved. Sometimes in life, you find yourself looking for something without realizing what that was. The collaboration with SAS was one of those things. As we developed our collaboration, I started to explore the SAS offering for academia, including both tools and support for learning, and that of course meant SAS Viya.
How have you found using SAS Viya?
What is different about SAS Viya is the whole learning ecosystem around it: tools, support, and expert collaboration. Viya is a complex tool but it can be customized for various levels of use. You can go from basic level competences right up to advanced analytics, and it is really easy to develop and understand real-world use cases. I have often said that the value of Viya is that it brings the world of algorithms to life right in front of you.
Do you see a need for SAS competencies in the market?
Yes, definitely. In the last few years, with the COVID crisis and beyond, we have seen high volatility in business systems, and unforeseen shortages of both human resources and raw materials. Businesses and society have been forced to change focus from demand to availability—or rather unavailability—of resources. The short-term solutions that were developed rapidly are not going to be sustainable in the long term. Business ecosystems must become resilient, and increase their capacity to absorb shocks. We are going to need new skills for this, and I think SAS skills—and analytics more generally—are a vital part of that picture.
How important do you think democratization of analytics will be in future?
We have to make technology work for people. Every day, all of us come across various data sources, data processes, and data-intensive activities. Our interactions are mediated more and more by technology and more “informed” by data-driven processes. We need to create a critical mass of people who can recognize these data encounters, and develop new innovations based on data. Democratization of analytics is essential for this.
What do you think are the most appealing topics for students around analytics?
As you know, we offer two programs linked to SAS and analytics, to give students both short and long-term options for learning. We have seen plenty of interest in both the two-module postgraduate continuous learning program and the master’s program. At SAS Lab, we have seen a lot of interest in topics like programming, machine learning algorithms, and the application of AI-type technologies. Students seem especially interested in topics related to social media such as sentiment and topic analysis, probably because they see it going on around them all the time.
What do you plan to do next with SAS Lab at UPB?
We see SAS Lab as a way to develop skills and create actionable knowledge. I think everything we do has be rooted in the idea of benefitting people and society. Current plans include projects on the implications of using advanced data analytics, advances in computational intelligence, and automation with various use cases. We are also going to organize seminars and workshops involving public and commercial institutions to generate some use cases for students to use as research projects. This will help us to build strong links between academia and the real world.
Finally, how do you see jobs and skills changing in the future?
We are currently witnessing a race to develop digital technologies such as generative AI. It is not clear how or even whether these technologies will prove to be useful. However, I think we can be confident that future jobs will be closely linked to technology, and also the evolution of the planet. We are only going to be able to address challenges such as climate change by harnessing analytics—and that is why democratization of analytics is so essential.
