A Conversation on Design Thinking With Harvard Professor Srikant Datar

Transcript

Dan LeClair: [00:00] Srikant, we're here in the Innovation Lab, the i-lab, where you teach one of the most popular courses at Harvard Business School, on design thinking. But you're an accounting professor!

Srikant Datar: [00:19] Used to be.

Dan: [00:20] Used to be an accounting professor! How did you find yourself in this space of Design Thinking?

Srikant: [00:28] It came, Dan, from the work that David Garvin, Patrick Cullen, and I did on Rethinking the MBA. One of the things we had heard from a lot of executives was the need to train our students to work on unstructured problems. Think more innovatively about solutions, break their fixed ways of thinking.

[00:53] Among the recommendations that we had made, that came back to us a number of times as one that people were perhaps having the most difficulty with. The difficulty came for two reasons. One is the pedagogy is different. You've got to do this in a different way, and how do we actually implement that pedagogy? And then there's the skill. You need to develop the skill to do it. A little bit similar to what we had talked about before.

[01:24] I was due for a sabbatical and I said to myself, we made this recommendation based on what we heard at the design school. We are seeing people have great interest in implementing this particular way of thinking and way of teaching innovation. But there doesn't seem to be a way in which people can actually do it.

[01:58] I know we had started a few seminars in that area but I wanted to just say let's learn it for myself. The best way to learn something of course is to teach it. I went on sabbatical. I traveled to all countries, all continents other than, not Antarctica and not Greenland, but every other continent. Just to try and understand what this whole field is about.

[02:24] As I went into it, I got more and more and more excited about it and said, this is going to be something that I think management schools ought to really embrace a lot more. It's something that I had to be convinced can be learned. I don't think it can be taught but it can be learned, that if in fact we use these pedagogies people can learn to be more innovative, more creative, more empathetic.

[02:58] The course basically has, I'd say, four big components. The first is deep understanding and insight. So you'll think about if you're designing a new product—by the way, if you're redesigning a curriculum, and in fact in some of my sessions that I've now led on curriculum redesigning I've actually done it using a design thinking approach.

[03:24] I have tried to combine, how do you think about the content of curriculum redesign with the process of using design thinking to think about that content? One of the most powerful experiences I had was one of my students using design thinking to think about the impact they have on others. Like redesigning how I'm coming across to other people.

[03:47] You can apply it to business models, services, products, anything. There are four components. First is how do I deeply understand—let's say I'm designing a product—how that product might be used by consumer or a customer. That requires empathy, understanding, and insight.

[04:06] The first model is very much of "being" skills, if you will, that we had talked about, Rethinking the MBA. The second is around problem framing: How do you frame a problem correctly, and how do you break fixed ways of thinking?

[04:28] There are many kinds of fixednesses that we have. The more expert you are on anything the more fixed you become, because you have a particular approach that you're constantly going to use.

[04:40] You have fixedness that is referred to as functional fixedness, where you think that certain things, certain functions are only to be attributed to certain kinds of products. For example, if I own a car then I might have a function of fixedness that says the function of the car is to only transport my family. [05:03] If I break that functional fixedness and say, "No, maybe if I own a car I might use it to transport somebody else." You might be able to come up with the idea of Uber or Lyft, where you're using the product in a completely different way than the function that you are always assumed that product to be used for.

[05:25] You might have structural fixedness, which says that everything ought to be structured in a particular way. For example, in the earlier discussion when we were talking about the flipped classroom, the technique we use is division, where we actually take a step that comes later in the process and move it up front and see, does that result in something different? Might it be helpful to actually have someone read before they come to class, even though they've not learned that material, and have a discussion in class?

[05:57] You might have relational fixedness that says that there must be a certain relationship that must always exist between how things happen. For instance, in the case of insurance products, the relational fixedness is being that I paid my premium. If I'm well, nobody worries about anything I do, but if I'm unwell, then the insurance company will pay for doctors' bills and hospital stays and so on.

[06:24] Might I break that? Might I say that I want to create an incentive for people to actually stay well, and if you try to stay well I will give you a reward for it. I am breaking a relational fixedness that says that insurance is only there to look after me when I'm unwell, but it doesn't impose on me any requirement that I stay well.

[06:44] That's cognitive fixedness. There are many fixednesses. The beauty of some of the work that I saw at different design firms is how they have systematically broken that kind of fixedness. I thought, how powerful a way of thinking about getting at these different ways of thinking. That's just talking about knowing.

[07:03] The next step, the third step in... So breaking fixedness is the second. Deep understanding, insight is the first; breaking fixedness is the second. The third step is rapid prototyping, that all too often, whether redesigning curricular, redesigning products, we want to make the perfect product and then go out and figure out whether in fact that product has worked very well.

[07:25] Though design thinking argues, and innovative thinking argues, is that you prototype stuff. You ask a critical question that you want to have answered, and go test it, and learn from it. The IDU has this very interesting phrase that you fail early and often to succeed sooner because you're learning all the time.

[07:46] For instance, when Google was designing the Google Glass, where the idea was that if I'm looking at my cell phone now I'm out of the real world. I'm in my virtual world. But they thought, if I have something here, right in my eyesight, maybe I can be in the real world and the virtual world at the same time. That's a major idea.

[08:12] But they didn't wait to develop the whole Google Glass to figure out if this was possible, they prototyped it, and the prototype took them one day. Within one day they were able to see whether in fact it is even possible that you could be in the real world and the virtual world at the same time. Once they figured that out, then there are other things. It might be too heavy. If it's too heavy, then will it hurt my nose?

[08:36] I don't have to develop the product. I can figure out how much weight that my nose can take. They have this amazing thing where they put more weight at the back of the ears so that there's less pressure. The ears can take a lot more weight than the nose.

[08:48] So then, how do you move it? There are many things you can test, but it just fundamentally breaks the idea that if you're trying to innovate, you don't necessarily wait for the whole product to come, then launch it. You keep doing and testing, doing and testing, so that you got at this doing skill rather than just imagining or conceptualizing. Of course that's important, but what design thinking talks about is test as fast as you can.

[09:18] Then the last stage is one of implementation and execution, which is a skill that we've already done. But these first three, I don't think we do very much in MBA programs.

Dan: [09:28] You were inspired by this work on Rethinking the MBA.

Srikant: [09:30] It came entirely from Rethinking the MBA, then going on sabbatical. Even when we were writing the book and writing the case studies on it, there was a very powerful set of ideas that fundamentally changes.

[09:44] The way I would close this last point that I'm making is, if you think about what we do in management schools and business schools, there's this whole operation cycle. Very important part of what we do might involve a little bit of design, of course, but there's manufacturing, marketing, distribution. These are all important parts of operations.

[10:04] Sitting here is the innovation cycle that actually interacts with the operation cycle, and a large part of what we end up doing in either recruiting people, training people, developing people, is developing them for the operation world. It has a very interesting set of rules in the operation world.

[10:23] It's decision-making, rational DQM, procedures, checklists, process, these are all good. But as you start applying that to the innovation world, it doesn't work so well. The innovation world is experimenting, connecting, connecting dots, exploring, prototyping, trying, and that's a different set of skills.

[10:43] What I learned as I was designing this course is that you need both sets of skills. You can't only run with these—you need to apply the skills appropriately. When I'm thinking of innovating I got to think about these skills, and I got to develop my way of thinking to develop those skills.

[11:05] When I'm in the operational world, I need to use these other skills that I was talking about, rational DQM, decision-making. We had spent a lot of time developing very good skills for the operation world, but had we spent enough time on developing the skills needed for the innovation world?

[11:23] Of course, Rethinking the MBA argued that we had not. As we kept doing more and more of that work and seeing and talking to a lot of design firms and companies that were doing it, I became more and more convinced that this was a very important skill for management schools and business schools to develop, and that led to the course.

Dan: [11:44] I hear that you're trying to combine design thinking with big data, with data analytics. Tell us about your vision there and how you see that evolving.

Srikant: [11:56] That's another thing, as you were describing in our earlier conversation. The world has moved to lots of data getting collected. That led to very interesting things on data analytics that has gone all the way from predicting how someone might want to buy products and goods, to of course understanding things like spam that we automatically see. That's basically a machine learning algorithms that suggest when something comes in, move it to the spam folder rather than serve it to me in my inbox.

[12:32] IBM Watson, for instance, is doing this amazing stuff on how you might be able to use machine learning and data analytics to treat diseases. In fact there's some fabulous work they've done around cancer, and how might you identify the correct treatments for cancer depending upon how patients are presenting themselves to the doctor.

[12:53] As I saw this work in several of companies that I work closely with, either on their boards or otherwise, I had seen this big move towards data coming in. How do I use this data? This data-rich source is a very valuable asset that a company has.

[13:15] In my design-thinking work, that was a different world. I was talking about empathy and understanding, the "being" part. I was talking about prototyping, the "doing" part. I was talking about breaking fixedness, the "knowing" part.

[13:26] It didn't involve all this heavy data usage. I said, "There's all this heavy data coming in over here, but as I'm doing this design thinking, it's more human-centered learning." This whole topic is one of machine learning. This is driven by computer scientists, people who understand a lot of statistics. This is more people who have more liberal arts education, more understanding of humans, anthropology, sociology, that kind of skill.

[14:02] I said, "Is there an opportunity to actually think that both types of learning will in fact for a particular problem be extremely valuable?" Maybe, in a particular problem, there are areas where one can use machine learning, which would be a very powerful way to understand what is happening. Maybe there are areas where I need to use human-centered learning.

[14:28] In fact, although these skills appear to be very different, and they are, the opportunity to combine them to get much more powerful insights into what's happening in organizations, and helping organizations do what they do, might be great.

[14:45] I said one more of those things, "Let me try and see if I might learn machine learning and big data and data analytics and so on." Just as I said in the case of design thinking, I talked to a lot of design firms. Several of them came to Harvard to help me design that course. A person I knew at LinkedIn was very interested in teaching this course at Harvard with me, so the two of us hooked up together to offer this course on big data at Harvard.

[15:16] What is very interesting about it though, Dan, is that as you think about big data and machine learning, the critical thinking parts of it—which we also talked about in Rethinking the MBA—I had wondered whether as a result of all this machine learning going on, does that mean that the need for critical thinking decreases? Because the machine's already doing all that learning and coming up with these judgments.

[15:59] Does that mean critical thinking that we had written about in Rethinking the MBA, we need less of? Which, of course, you need lots of critical thinking. If you don't have much data, you've got to think through what the possibilities might be. Here I've got lots of data, do I need it?

[16:04] What was fascinating as we were teaching this course is, how important it is that if you are going to do any course on data analytics and machine learning, you need to teach even more of critical thinking than one might have thought, because there's data coming at you. It's coming up with certain kinds of rules or decisions. I've got to really think about whether, in my particular context, those rules and decisions make sense or not.

[16:32] I'm now not surprised by the predictions that as this data explosion continues, the number of managers we will need who understand data is going to be ... I don't know whether it's five times, eight times, 10 times ... but it's going to be that kind of number more than the people who we would just call as data scientists who can actually organize the data.

[16:59] The people who need to use the data, most statistics and most estimates suggest that we're going to need many, many more managers who know how to work with and use the data and apply critical thinking to the data than we would need data scientists who are actually going to develop the algorithms that will give us the data, or the conclusions from the data, or the insights from the data, that they can work with.

[17:28] But then managers are going to have to take all this data, understand exactly what the data scientist did, and use that to make decisions. We're going to need far more of those managers than we're going to need the people who are actually going to be the data scientists.

[17:43] That became very clear to me as I kept doing this work, and also became very clear as to why, as we think of this new era, business schools are going to figure out, how do I actually combine this whole field, this whole plethora of data we are getting in and train managers who can really look at that data and be comfortable making decisions with it.