Thought Leadership At UC Davis Graduate School of Management: Professor Hemant Bhargava On Technology-Based Business & Markets

Professor Hemant K. Bhargava at UC Davis Graduate School of Management

Byrne: And what are you finding about digital addiction?

Bhargava: I’m still working on this, but I have a working research hypothesis, which I’m trying to prove. These platforms use attention-driven models, meaning the more time you spend the more they earn, so it drives them toward addictive design. And that’s occurs in the form of content algorithms that promote things that engage people more. So things that are more brutal, gory, violent, fake, sensational.

Byrne: Provocative, to be sure.

Bhargava: They tend to get more eyeballs. And these algorithms are scanning content for those things and pushing it deeper and deeper. So that’s sort of understood. What I started looking at was whether competition between platforms would alleviate some of this bad design because ultimately competition forces firms to look after the interests of users. And particularly if there are some platforms that try to charge for information or for the experience rather than depend on attention. Some of that seems to hold true, but then we add in the fact that these platforms are subject to network effects, meaning the more users on the platform, the more attractive it is to everyone. 

Competition does not work as well as it should. I’m still working on this and trying to prove everything, but that’s what I’m leading to.

Byrne: You have also conceived and designed and are the head of the Master’s in Business Analytics program. It seems to me like decision sciences has morphed into business analytics. Is that right?

Bhargava: I think that’s true.

Byrne: Is there a difference between business analytics and decision sciences?

Bhargava: I think there is a big difference. When we worked on decision analysis 30 years ago, we had decision problems and we had models and we rarely had data. And even when we did have data, the cycle time between identifying a problem, building a model, collecting data to run it, and then actually producing insights, almost made it certain that your insights were not quite current. Today we have a very different environment where you can actually run it in real time.

That has led to machine learning and AI, ways of analyzing data that go beyond the older statistical modeling techniques. But as someone who came in from the field of decision sciences, I feel that we’re still missing something. You have to close the loop and convert all of this data analysis into decision support and also really analytical literacy for decision makers, which I feel there is still a gap in the education marketplace and in practice.

Byrne: So you’ve gone from a period in decision sciences when you had too little data to one in data analytics where our access to data is overwhelming.

Bhargava: That’s right. And yet not enough intent about what to use the data for. I was at a Business Roundtable meeting a couple years ago when we asked two sets of questions to the technology leaders at the table: “How ready is your organization to do decision analysis in terms of data readiness? And how well are you doing it? Do you know what to do?” 

The answer to the first question was not bad. On a scale of one to 10, organizations believed they were somewhere between five and eight. But they did not have people who could ask the right questions and drive the right analysis, too. I think that’s still a problem.

Byrne: That will probably will remain a problem for some time.

Bhargava: I think so. And I think our ability to make meaningful use of data is exceeded by the amount of data.

Byrne: Do you think AI and machine learning will help us get our arms around the overwhelming amounts of data out there?

Bhargava: Three years ago, I would’ve liked to say no. I think we’re being constantly surprised by the capabilities of AI. It is viable now, that given enough data and past experience, machines can start constructing decision models for us. I think that issue of intentionality is still going to be at the center. How does the machine know my intent in what I want to do with my business? Can it somehow learn and create models that match the intent of the corporation? We haven’t seen that yet, but I would not rule it out.

Byrne: Hemant, you’ve had a remarkable educational journey and a remarkable academic journey. I wonder how it’s changed you as a person.

Bhargava: I remember in my early years contemplating what I was doing, why I was doing it, and asking if I’d ever be successful at it. You learn to overcome those imposter syndrome questions. I think academics has really taught me that. So part of it comes from when you can really change someone’s life as a student by either turning on the light bulb in them in a class or making a meaningful difference to people’s lives. And that’s not something I had understood for a long time.

Byrne: And there are different parts of the job that are rewarding in different ways, whether it’s teaching, mentoring others, running things, or doing research. Well, it’s been a real pleasure. Thank you for joining us.


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