The Importance of Innovation with Business Analytics
Karim Lakhani is a professor at Harvard Business School (HBS), co-director of Harvard’s Laboratory for Innovation Science and co-chair of its new Business Analytics Program. In partnership with organizations like NASA, Lakhani has researched the efficacy of crowdsourcing to solve organizations’ data analytics problems.
With his specialization in the management of technological innovation in organizations, Lakhani will explore with students in the Business Analytics Program how companies’ business models, use of analytics and business strategies are evolving. We spoke to Lakhani about his goals for the program and the pressing need for data analytics experts in the business world today.
As students begin the Harvard Business Analytics Program, you’ll co-teach the first course, Digital Strategy and Innovation, with Professor Marco Iansiti. What do you think will surprise students about what they learn in this course?
I think how the trends that they’re seeing now have roots back 30 years ago in terms of the broad digitization of the economy, and with digitization there’s more data being produced. In the last 30 years the tech sector has been transformed to be highly data and analytics driven. But now the same forces that affected the tech sector are affecting all industries.
So, the surprising thing is that we’ve seen this story before. We know how it rolls out, and there are some real lessons to be learned as the rest of the economy is digitized and data analytics start to play a really important role in how decisions get made in the economy.
The Harvard Business Analytics Program offers courses through the Harvard Business School, the John A. Paulson School of Engineering and Applied Sciences (SEAS), and the Faculty of Arts and Sciences (FAS). Why did faculty decide to take this interdisciplinary approach to designing the program?
Business analytics requires both fundamental knowledge and understanding of quantitative analysis, programming, data architecture and data science, so we need to develop a deep sense of the technical aspects. But beyond that there is the need to apply them to the business context. It’s inherently an interdisciplinary field, where the business side and the economic side are complementary.
Our hypothesis is that leaders of organizations that are data centric will require both the technical skills and the understanding of their application to the business side; hence, all three parts of Harvard have come together to work on this.
We see it in our own training and how our students are being trained in our PhD programs. For example, many students in doctoral programs in business schools now have to learn machine learning and AI and apply that knowledge to the study of business. And five of the top ten Fortune 100 companies have a core strength in business analytics, which is really combining the engineering part with the business side.
Who are the ideal students for the Business Analytics Program?
We anticipate mid-career executives that are finding themselves in roles that require managing business analytics functions inside organizations. They may have come from either a technical background or a business background, but now they have to learn how to integrate the two. I think there’s a clear need there.
And what we’re observing more broadly is that business analytics is now moving into the center of organizations. Before it tended to be seen as a retrospective recording function. Now it’s a prospective decision-making function. This move from the peripheral to the center means that the people who are now responsible for these functions need to understand the technology and the economics and the strategy around them, as well as what the best ways to employ these elements are. A whole range of managers now have to up-skill, retooling both their knowledge about the techniques and the strategies that go with them.
I also think that a range of senior executives who see that data analytics and algorithms are going to be important for the future of their companies now want to get up to speed on those topics themselves and learn about them so that they can actually better guide the teams that are reporting to them.
In your recent article for the Harvard Business Review, you highlight how “the high degree of digital connectivity has dramatically sped up the transformation” in industries like phone service providers and e-commerce. For Harvard students working in the “hub economy” of tech giants, how do they stay competitive with the speed of digital change?
What we’re seeing are innovations happening in business models and operating models of organizations. When we define a business model, we think of it in terms of value creation and value capture, and now there are many more ways to create value and many more ways to capture value. Students need to be ready for an explosion of new business models that are now being created in their industries. Preparation requires awareness that the battleground is around business model innovation through digital and data, and that the competitors are both well-established companies as well as startups that are trying to shake up the existing industry architectures.
Secondly, we also know that there are returns to investing in digitizing a firm’s operations, and that, in fact, operating model changes are critical to changing a business model. Students must learn from the best in terms of how operating model changes need to be made, what types of changes have to be made and how to implement these changes to create a new business model. This course and program will expose students to both sides.
You serve as co-director of the Laboratory for Innovation Science at Harvard University. How has the lab grown and changed since its founding in 2010?
We have a unique position as a social science laboratory, where we are solving innovation problems for our partners and at the same time, researching how best to organize the innovation process. We have really specialized. A large portion of our work has been running data analytics contests for our partners at NASA, Harvard Medical School, the Broad Institute and other partners in the government to both help them solve these problems and create breakthrough algorithms in data analytics. At the same time, we have studied the best design of crowdsourcing programs, contests and communities.
So, our lab has become a Harvard-wide facility with partners in the engineering school and medical school who have this dual mission to help design and solve problems and also solve innovation management challenges.
Based on your work at the lab, what trends have you noted about the evolving field of data science?
We’ve been at the forefront of work with online platforms that do data science and machine learning, and so part of my education has been to understand the varieties of ways in which data science and analytics can be applied to a whole range of core problems. One of the big things we’ve noted is that there’s a serious shortage of talent in data science. Everybody—in all the industries—needs data scientists, so crowdsourcing and our lab’s work can be one solution to finding talent anywhere in the world to work on these.
This interview was edited for clarity and brevity.