IES 38 ORGANIZATIONS AUTUMN 2013 STANFORD BUSINESS BOUNDAR “ Social scientists are actively mining data to study everything from happiness to social norms to political upheaval.” than a lot of semantic understanding. This implies that in industries where machine learning is crucial to the quality of the product, you would expect to see a lot of concentration, and new firms will have difficulty getting off the ground. Is that why investors sink millions into startups that make no profit but grow troves of data? Take the case of mobile phone services. If you can get a lot of your mobile phone users to use your voice services, you will become better at voice recognition, which creates a higher-quality product for your consumers. It becomes a source of differentiation in your core product, but also it’s a capability you can extend. That data is valuable for learning how to understand speech in a variety of contexts. You might imagine that companies that gather a large corpus of that data could sell it as a standalone product to other companies who are not direct competitors but who need speech recognition. All of the products that are touched by interfacing with humans will have that feature. So, if humans are trying to type text or speak or use touch or handwriting or gestures to interface with a device, then a company that has a large corpus of that input will have an advantage at understanding the input faster and better. Voice recognition is an example of this more general phenomenon in which you see some of the internet firms integrating in a lot of directions because they want to gather more data. They don’t want their competitors to have data that makes the competitors’ products better, and they can also try to monetize the data in other ways, for example through personalization and better-targeted advertising. In what other industries do you expect to see Big Data disrupt business as usual? It will be interesting to see what domains will use data effectively sooner rather than later. You might think, for example, that getting an automated airline reservation system to change a connecting flight reservation when your first flight has been delayed would be simpler than getting a car to drive itself. With caller ID, the airline should know who you are when you call from the tarmac. Yet, many a passenger has been frustrated by the time-consuming airline phone tree encountered in that situation, and only recently have we seen real improvement in that experience. Cars, on the other hand, have demonstrated they can drive themselves safely. Another fascinating use of data sensors is to monitor the parts of a complex machine, such as a car or an airplane, to learn how to improve safety or when to replace worn parts. Medical diagnosis may also be a case where machines poring through petabytes of data might be quicker or more accurate than doctors, particularly for rare conditions, or cases where treatments have unusual side effects for particular populations of patients. Some cases might surprise you. You might have thought it was pretty much impossible to break into something like the taxi business, since it is so highly regulated and local government is sensitive to the industry. Yet companies have succeeded in many cities, and they use real-time demand data to raise prices in times of short supply, ensuring that people who are willing to pay enough can always find a ride. What about finding new uses for old data? There are huge opportunities to answer important policy questions, both public policy and business policy questions, using operational or passively collected data designed for another purpose. All of the social sciences are actively mining data from social media such as Twitter, studying everything from happiness to adolescent social norms to the underpinnings of political upheaval. Both academics and the financial industry have mined sources such as Google Trends, finding that patterns of search behavior can be used to predict flu outbreaks, unemployment statistics, as well as stock returns. Another growing trend is for firms with access to large datasets to partner with researchers from academia, where the firm learns from the researchers’ expertise while the researchers are able to answer questions that can only be studied using proprietary datasets. The resulting published research only produces aggregate statistics, protecting the confidentiality of the data. This is something I’ve done fruitfully myself, with Microsoft Research, but other companies such as eBay and Yahoo have also successfully worked with academics. Consider a less obvious example: cities. New York City, Chicago, and some entire countries, are doing large-scale data collection now. As this data becomes available, it is used for its direct purpose like data-driven policing or traffic and transportation-flow management. But we’re just starting to see the possibilities that can be unlocked through the secondary uses of the data: data concerning things like noise, energy use, and pollution at a very granular level. You might gather noise data to identify violations of a noise ordinance but then find you can use it to study the effect of noise on the health of children. You might gather data about taxi trips to monitor compliance with various regulations, but end up learning about commuting patterns, gaps in public transportation, and even the propensity of different types of customers to tip. I expect to see that in businesses as well. They may be passively collecting information about what their customers are doing with their cars. They may discover, along the way, patterns in how customers are using the cars that have implications for the design of transportation systems generally, for urban planning, as well as for how to design future cars. It can be difficult for companies and governments to enable full utilization of the data they possess because of confidentiality and data-security issues, but more amazing uses will inevitably come. Any industry could be the next one to rethink and innovate in a dramatic way. Δ
39 Rick Wilking/Reuters/Corbis TRUST Warren Buffett high-fives a runner at a May 5k in his hometown of Omaha. GOVERNANCE What Happened to Trust? Why giving corporate managers more autonomy can pay off BY DAVID LARCKER AND BRIAN TAYAN The litany of prominent corporate failures in the last decade or so — Enron, WorldCom, Lehman Brothers, and so on — ushered in an increase in regulatory requirements for corporate governance. The result is that every ery year, companies spend tens of millions of dollars paying for audit fees, internal auditors, and compliance efforts and evaluating incentive compensation and director r salaries in order to satisfy a long list of rules, regulations, and procedures imposed by legislators and the market. It all l raises a critical but too often overlooked oked issue: Would corporate governance improve if companies instead had fewer controls? Would shareholders be better off if organizations instead demonstrated more trust in employees and executives? Research suggests that the answer may be yes, and that companies might benefit by emphasizing trust over regulations. Indeed, high-trust settings are characterized by less bureaucracy, simpler procedures, and higher productivity. For starters, trust replaces the need for written contracts because the two parties commit in advance to abide by a set of actions and behaviors that are mutually beneficial. Both parties in a trusting relationship generally understand the limits of acceptable behavior even when these are not fully specified. And when trust is introduced into the environment, the motivations of each party are known and their behaviors are predictable. That means managers can spend less time monitoring employee actions, and employees can focus on their jobs rather than exerting additional effort simply to demonstrate they are compliant with the firm’s standards. In the extreme case — utopia! — there would be a number of additional benefits to creating a more trust-centered environment. David Larcker is the James Irvin Miller Professor of Accounting at Stanford GSB and the Morgan Stanley Director of Stanford GSB’s Center for Leadership Development and Research. Brian Tayan is a researcher at the Center for Leadership Development and Research and received his MBA from Stanford GSB in 2003. They are the authors of the recent book A Real Look at Real World Corporate Governance.