21 Aug 2017Lessons from the Classroom

Companies Love Big Data But Lack the Strategy To Use It Effectively

by Dina Gerdeman

Big data is a critical competitive advantage for companies that know how to use it. Harvard Business School faculty share insights that they teach to executives.

Source: peterhowell

Big data has shifted the ground under every business, enough so that many managers are waking up to the fact that they are already behind in developing a smart data strategy.

Data has always been important in business, of course. But with the arrival of digital data—its volume, depth, and accessibility—it has become clear it is key to helping companies develop sustainable competitive advantage.

“The new attention being given to data today is because suddenly, everywhere, it’s become much cheaper to measure,” says John A. Deighton, the Baker Foundation Professor of Business Administration at Harvard Business School. “Used well, it changes the basis of competition in industry after industry.”

The problem is that, in many cases, big data is not used well. Companies are better at collecting data–about their customers, about their products, about competitors– than analyzing that data and designing strategy around it.

That’s one reason eight HBS professors pooled resources in June to launch the Competing on Business Analytics and Big Data Executive Education program. “It was unprecedented to engage eight faculty in a single program,” says Deighton, “and it reflects the fact that data questions touch every part of the enterprise.”

The program drew C-suite executives and senior managers to look at how big data affects the supply chain, marketing, human resources, and other key business functions. Attendees studied how market-leading companies are harnessing data to reshape their companies, and explored how they can put data to work for them in ways that create value for their own businesses.

The data advantage in sports

Big data is already being used heavily in the sports world, students heard from Karim Lakhani, co-chair of the program and part of the Technology and Operations Management Unit. He discussed with students how German soccer team TSG Hoffenheim deploys analytics in scouting and player development. He also noted how New Zealand’s yacht designers and crew prepared for the 1995 Americas Cup with a radical, data-intensive experimental design.

Deighton discussed how new sources of data starting to be generated by the Internet of Things will impact the advertising-based hegemony of Google, Amazon, and Facebook. “The best picture we have today of an industry running on data is seen in advertising, where at least a third of all spending by brands goes to digital media. What happens when products with sensors generate such volumes of customer experience data that advertising may be a less significant factor?”

Jeff Polzer, of the Organizational Behavior faculty, introduced “people analytics,” the fast-growing field in which business managers, HR specialists, and data scientists work together to use data to improve employee-related decisions and practices. New analytic approaches and new sources of digital data are starting to revolutionize this field, he said, such as algorithmic approaches to hiring and promotion; real-time data streams that track performance feedback and organizational culture; and analyses of digital trace data to map and shape organizational networks.

“As managers and employees work through these challenges and tradeoffs, the potential gains they can accrue from using data, including benefits to employees who strive for feedback and self-improvement, mean that day-to-day managerial life will increasingly be infused with employee-related analytics,” says Polzer.

Turning to how data is analyzed, Dennis Campbell, of the Accounting and Management Unit, discussed the data strategy of the MGM Grand hotel in Las Vegas, and challenges to distinguishing between correlation and causation in inferences drawn from large data sets.

Ariel Dora Stern, of the Technology and Operations Management Unit, challenged the class to think about what couldn’t have been done without recent advances in data. She took the class toward her personal passion, precision medicine.

“Health care is rife with examples like these—and such applications of big data will only expand over the coming years. I spend a lot of time thinking about questions like, How will artificial intelligence change the medical diagnostics industry? How will better data collection transform the ways in which we do clinical trials for new cancer drugs? It is both staggering and exciting to imagine how data and analytic capabilities will transform entire industries and it was thrilling to engage in these conversations with folks from such rich and diverse backgrounds.”

"It is both staggering and exciting to imagine how data and analytic capabilities will transform entire industries"

Kris Ferreira, also a Technology and Operations Management Unit professor, sketched her approach to operationalizing a data strategy using discussions around case studies.

“First, students learned a framework for combining intuition and data/analytics (including regression and optimization) into a comprehensive decision-making strategy. Second, students developed an implementation plan to transform an intuition-based company into one that relied more heavily on data and analytics; this included discovering important barriers to change that require a broader understanding of the organizational culture and incentives.”

Ferreira also shared results of a survey of about 350 companies in four industries about their analytics capabilities. “Results show strong correlations between business performance metrics and analytics capabilities, and highlight a variety of tasks in which top performing companies use analytics…”

Feng Zhu, who teaches Digital Innovation and Transformation, illustrated how big data is making new business models possible. “Most organizations today use data analytics to optimize or improve their existing businesses. But to take full advantage of data analytics, it will be important for them to consider the following two strategic questions: 1) Can I use my data to offer new products or services to my existing customers? 2) Can I leverage data to serve those customers who are currently not served by me or my competitors?”

In class after class during the program, across the range of disciplines that make up a business school, instructors emphasized the importance of connecting analysis to an overarching data strategy. In Lakhani’s final case discussion, which explored internal and customer transformation at GE, it became clear that a data strategy wasn’t just about gathering and analyzing information—it can be the unifying principle in corporate reinvention.