Tag Archives: advice

My thoughts on AI, Big Data, and IS Research

Last update: June 10th, 2021

Recently, I had a chance to share my thoughts on how Big Data Analytics and AI will impact Information Systems (IS) research. Thanks to ever-growing datasets (public and proprietary) and powerful computational resources (cloud API, open-source projects), AI and Big Data will be important in IS research in the foreseeable future. If you are an aspiring IS researcher, I believe that you should be able to embrace this and take advantage of this.

First, AI and Big Data are powerful “tools” for IS research. It could be intimidating to see all the fancy new AI techniques. But they are just tools to analyze your data. You don’t need to reinvent the wheel to use them. There are many open-source projects in Python and R that you can use to analyze your data. Also, many cloud services (e.g., Amazon Rekognition, Google Cloud ML, Microsoft Azure ML) allow you to use pre-trained AI models at a modest cost (that your professors can afford). What you need is some working knowledge in programming languages like Python and R. And a high-level understanding of the idea behind algorithms.

Don’t shy away from hands-on programming. Using AI and Big Data tools may not be a competitive advantage in the long run because of the democratization of AI tools. However, I believe it will be the new baseline. So you need to have it in your research toolbox. Specifically, I believe that IS researchers should have a working knowledge of Python/R programming and Linux environment. I recommend these online courses: Data ScienceMachine LearningLinuxSQL, and NoSQL.

Second, AI and Big Data Analytics are creating a lot of interesting new “phenomenon” in personal lives, firms, and societies. How AI and robots will be adopted in the workplace and how that will affect the labor market? Are we losing our jobs? Or can we improve our productivity with AI tools? How AI will be used in professional services by the experts? What are the unintended consequences (such as biases, security, privacy, misinformation) of AI adoptions in the organization and society? And how can we mitigate such issues? There are so many new and interesting research questions.

In order to conduct relevant research, I think that IS researchers should closely follow the emerging technologies. Again, it could be hard to keep up with all the advances. I try to keep up to date by reading industry reports (from McKinsey and Deloitte) and listening to many podcasts (e.g., Freakonomics Radio, a16 Podcasts by Andreessen Horowitz, Lex Fridman Podcast, Stanford’s Entrepreneurial Thought Leaders, HBR’s Exponential View by Azeem Azhar).

I hope this post may help new IS researchers shape their research strategies. I will try to keep updating this post. Cheers!