As the ‘commoditization’ of algorithms continues, buy-side traders who use these electronic trading tools are dissatisfied, according to Greenwich Associates.
Benjamin Franklin’s 1748 writing “Advice to a Young Tradesman” is often credited with coining the phrase “time is money.”1) Unbeknownst to Franklin, his quote fits the world of modern electronic trading perfectly. The speed by which a trader can access market information, place an order upon the market and have that order filled, are of paramount importance to attempting to achieve long-term profitability.
What Is Latency?
In simplest terms, “latency” is defined as being any delay or lapse of time between a request and a response.2) As it pertains to trading, latency directly influences the amount of time it takes for a trader to interact with the market. The timely reception of pertinent market information and the ability to act upon its receipt are often greatly impacted by latency issues.
As trading-based technology has grown, the possibility of a trader gaining a competitive advantage or disadvantage due to latency issues has become intensified. As far as the active trader is concerned, latency needs to be quantified and managed in order to maximize the odds of success.
Latency In The Marketplace
In order to compete in the near-light-speed digital markets of today, it is a necessity that surplus latency is reduced as it appears in each facet of the trading operation.
The flow of market-pricing data originates at the exchange or marketplace, and it’s passed on to the trader for interpretation via the online trading platform. Streaming data-transfer speeds are typically measured in milliseconds from origin to client.3)
Excess latencies can be present in the following parts of the data stream:
- Exchange or market based servers
- Brokerage servers
- Internet connectivity
- Client computer hardware and software
“Data lag” is a common problem and the result of inefficiencies present in the data-streaming process. Many causes of data lag are out of the trader’s control. Occasional problems with hardware located at the exchange or brokerage firm in addition to internet “bottlenecks” arise without warning and often go unrecognized by the trader.
Order Routing And Execution
Order routing and order execution are areas of electronic trading where the playing field can be skewed directly because of latency issues. Consistent order fills and low slippage are key components of profitability that depend on an order arriving at market ahead of the competition.
Depending on whether or not the market being traded is exchanged-based or over-the-counter (OTC), the typical routing of a retail trader’s market order is as follows:
- Order is entered by trader remotely via online trading platform
- Order is received by brokerage servers
- Order is relayed by broker to exchange or market
- Order is placed in queue at exchange or market
At no fault to the trader, latency can plague each stage of the order-routing process. Unfortunately, an order that arrives late to the market is likely to be filled at a disadvantageous price, increasing the potential for loss due to slippage.
Different types of trading operations address the issue of latency in vastly different fashions. Institutional trading firms have the ability to invest large amounts of capital into low-latency infrastructures, while small retail traders are faced with the challenge of minimizing latency issues wherever they can.
One manner in which institutional investment firms gain a competitive advantage in the marketplace is through securing direct market access (DMA). It is the direct connection of a buy-side entity to the order book at the exchange. The implementation of DMA can be a costly undertaking, and until recently has been exclusive to well-capitalized trading operations.
The order routing process under DMA:
- Order is entered by the trader via connectivity to exchange or market
- Order is placed in queue for execution at the exchange or market
Through the elimination of several steps in the routing process, the order reaches the exchange and is executed ahead of the competition. Commonly, institutional investors center trading operations on DMA capabilities.
From the standpoint of the retail trader, the use of DMA services is limited. However, as electronic trading has evolved, services claiming to provide DMA to retail traders have begun to address the demand from within the industry. Until the time in which DMA is readily available to the independent retail trader, several steps can be taken to increase the overall efficiency of the trading operation. In order to optimize performance and limit latency when interacting within the marketplace, a trader is well-advised to perform the following tasks:
- Update and maintain computer hardware
- Perform internet connectivity tests with regularity. “Ping” tests with brokerage servers can be a valuable tool used to measure internet performance.
- Evaluate trading platform performance daily. Lag issues can manifest themselves in the timely updating of charting applications and pricing quotes.
The topic of “latency” as it pertains to electronic trading is a complex one. Ph.Ds. in physics and computer science are seemingly prerequisites for one to gain a comprehensive understanding of the subject. However, effective trading and function are what is important, and addressing the variables which affect performance is a necessary part of competing in the digital marketplace.
The once lively grains trading pits at the Chicago Board of Trade building will go silent after Monday, as the oldest futures exchange in the United States moves one step closer to strictly electronic trading.
Several floor traders fought the closure by the CME Group that represents the Chicago Mercantile Exchange and Board of Trade, and were able to postpone it from July 2, Reuters reported. The closings – also happening in New York at all CME Group futures pits – are the latest in a trend over the past 15 years that began as electronic trading soared.
“This is not something that happened over night; it’s been happening for a long time,” Dr. Peter Alonzi, a professor of economics at Dominican University in River Forest, Illinois and the former senior manager of the Chicago Board of Trade’s educational programs group in the 1990s, told PBS NewsHour.
For more than 80 years, Chicago Board of Trade members shouted and flashed hand signals to establish futures contracts – agreements to buy or sell a certain amount of a commodity like wheat, corn or oats at a predetermined price and date – in the pits of the landmark art deco building on LaSalle and Jackson streets.
“The prices discovered through the competitive trading activity in the pits were an integral part of the agricultural industry,” Alonzi said. Later, when they started trading futures on treasury bonds, notes and federal funds, action in the floor pits impacted world financial markets.
Open outcry has been the main method of trading futures throughout the Chicago Board of Trade’s history and accounted for the majority of trading volume until 2003.
Passed down over generations of traders, the “craft” – as Alonzi characterized it – has fallen to represent one percent of futures volume for the CME Group, the Associated Press reported. Most futures trading now happens online.
The S&P 500 futures and options pits in Chicago will remain open. The CME Group plans to sublease the grain room and its 10 octagonal pits, a spokesperson told PBS NewsHour.
Click through the timeline to learn more about the Chicago Board of Trade and its 167 years of existence.
To analyze the organization of trading in the era of quantitative finance we conduct an ethnography of arbitrage, the trading strategy that best exemplifies finance in the wake of the quantitative revolution. In contrast to value and momentum investing, we argue, arbitrage involves an art of association—the construction of comparability across different assets. In place of essential or relational characteristics, the peculiar valuation that takes place in arbitrage is based on an operation that makes something the measure of something else—associating securities to each other. The process of recognizing opportunities and the practices of making novel associations are shaped by the specific socio-spatial and socio-technical configurations of the trading room. Calculation is distributed across persons and instruments as the trading room organizes interaction among diverse principles of valuation.
Throughout our careers, we are taught to conform — to the status quo, to the opinions and behaviors of others, and to information that supports our views. The pressure only grows as we climb the organizational ladder. By the time we reach high-level positions, conformity has been so hammered into us that we perpetuate it in our enterprises. In a recent survey I conducted of more than 2,000 employees across a wide range of industries, nearly half the respondents reported working in organizations where they regularly feel the need to conform, and more than half said that people in their organizations do not question the status quo. The results were similar when I surveyed high-level executives and midlevel managers. As this data suggests, organizations consciously or unconsciously urge employees to check a good chunk of their real selves at the door. Workers and their organizations both pay a price: decreased engagement, productivity, and innovation (see the exhibit “The Perils of Conformity”).
Drawing on my research and fieldwork and on the work of other scholars of psychology and management, I will describe three reasons for our conformity on the job, discuss why this behavior is costly for organizations, and suggest ways to combat it.
Of course, not all conformity is bad. But to be successful and evolve, organizations need to strike a balance between adherence to the formal and informal rules that provide necessary structure and the freedom that helps employees do their best work. The pendulum has swung too far in the direction of conformity. In another recent survey I conducted, involving more than 1,000 employees in a variety of industries, less than 10% said they worked in companies that regularly encourage nonconformity. That’s not surprising: For decades the principles of scientific management have prevailed. Leaders have been overly focused on designing efficient processes and getting employees to follow them. Now they need to think about when conformity hurts their business and allow — even promote — what I call constructive nonconformity: behavior that deviates from organizational norms, others’ actions, or common expectations, to the benefit of the organization.
Why Conformity Is So Prevalent
Let’s look at the three main, and interrelated, reasons why we so often conform at work.
We fall prey to social pressure. Early in life we learn that tangible benefits arise from following social rules about what to say, how to act, how to dress, and so on. Conforming makes us feel accepted and part of the majority. As classic research conducted in the 1950s by the psychologist Solomon Asch showed, conformity to peer pressure is so powerful that it occurs even when we know it will lead us to make bad decisions. In one experiment, Asch asked participants to complete what they believed was a simple perceptual task: identifying which of three lines on one card was the same length as a line on another card. When asked individually, participants chose the correct line. When asked in the presence of paid actors who intentionally selected the wrong line, about 75% conformed to the group at least once. In other words, they chose an incorrect answer in order to fit in.
Organizations have long exploited this tendency. Ancient Roman families employed professional mourners at funerals. Entertainment companies hire people (“claques”) to applaud at performances. And companies advertising health products often report the percentage of doctors or dentists who use their offerings.
Conformity at work takes many forms: modeling the behavior of others in similar roles, expressing appropriate emotions, wearing proper attire, routinely agreeing with the opinions of managers, acquiescing to a team’s poor decisions, and so on. And all too often, bowing to peer pressure reduces individuals’ engagement with their jobs. This is understandable: Conforming often conflicts with our true preferences and beliefs and therefore makes us feel inauthentic. In fact, research I conducted with Maryam Kouchaki, of Northwestern University, and Adam Galinsky, of Columbia University, showed that when people feel inauthentic at work, it’s usually because they have succumbed to social pressure to conform.
We become too comfortable with the status quo. In organizations, standard practices — the usual ways of thinking and doing — play a critical role in shaping performance over time. But they can also get us stuck, decrease our engagement, and constrain our ability to innovate or to perform at a high level. Rather than resulting from thoughtful choices, many traditions endure out of routine, or what psychologists call the status quo bias. Because we feel validated and reassured when we stick to our usual ways of thinking and doing, and because — as research has consistently found — we weight the potential losses of deviating from the status quo much more heavily than we do the potential gains, we favor decisions that maintain the current state of affairs.
But sticking with the status quo can lead to boredom, which in turn can fuel complacency and stagnation. Borders, BlackBerry, Polaroid, and Myspace are but a few of the many companies that once had winning formulas but didn’t update their strategies until it was too late. Overly comfortable with the status quo, their leaders fell back on tradition and avoided the type of nonconformist behavior that could have spurred continued success.
Of more than 1,000 employees surveyed, less than 10% said they worked in companies that regularly encourage nonconformity.
We interpret information in a self-serving manner. A third reason for the prevalence of conformity is that we tend to prioritize information that supports our existing beliefs and to ignore information that challenges them, so we overlook things that could spur positive change. Complicating matters, we also tend to view unexpected or unpleasant information as a threat and to shun it — a phenomenon psychologists call motivated skepticism.
In fact, research suggests, the manner in which we weigh evidence resembles the manner in which we weigh ourselves on a bathroom scale. If the scale delivers bad news, we hop off and get back on — perhaps the scale misfired or we misread the display. If it delivers good news, we assume it’s correct and cheerfully head for the shower.
Here’s a more scientific example. Two psychologists, Peter Ditto and David Lopez, asked study participants to evaluate a student’s intelligence by reviewing information about him one piece at a time — similar to the way college admissions officers evaluate applicants. The information was quite negative. Subjects could stop going through it as soon as they’d reached a firm conclusion. When they had been primed to like the student (with a photo and some information provided before the evaluation), they turned over one card after another, searching for anything that would allow them to give a favorable rating. When they had been primed to dislike him, they turned over a few cards, shrugged, and called it a day.
By uncritically accepting information when it is consistent with what we believe and insisting on more when it isn’t, we subtly stack the deck against good decisions.
Promoting Constructive Nonconformity
Few leaders actively encourage deviant behavior in their employees; most go to great lengths to get rid of it. Yet nonconformity promotes innovation, improves performance, and can enhance a person’s standing more than conformity can. For example, research I conducted with Silvia Bellezza, of Columbia, and Anat Keinan, of Harvard, showed that observers judge a keynote speaker who wears red sneakers, a CEO who makes the rounds of Wall Street in a hoodie and jeans, and a presenter who creates her own PowerPoint template rather than using her company’s as having higher status than counterparts who conform to business norms.
My research also shows that going against the crowd gives us confidence in our actions, which makes us feel unique and engaged and translates to higher performance and greater creativity. In one field study, I asked a group of employees to behave in nonconforming ways (speaking up if they disagreed with colleagues’ decisions, expressing what they felt rather than what they thought they were expected to feel, and so on). I asked another group to behave in conforming ways, and a third group to do whatever its members usually did. After three weeks, those in the first group reported feeling more confident and engaged in their work than those in the other groups. They displayed more creativity in a task that was part of the study. And their supervisors gave them higher ratings on performance and innovativeness.
Six strategies can help leaders encourage constructive nonconformity in their organizations and themselves.
1. Give Employees Opportunities to Be Themselves
Decades’ worth of psychological research has shown that we feel accepted and believe that our views are more credible when our colleagues share them. But although conformity may make us feel good, it doesn’t let us reap the benefits of authenticity. In one study Dan Cable, of London Business School, and Virginia Kay, then of the University of North Carolina at Chapel Hill, surveyed 154 recent MBA graduates who were four months into their jobs. Those who felt they could express their authentic selves at work were, on average, 16% more engaged and more committed to their organizations than those who felt they had to hide their authentic selves. In another study, Cable and Kay surveyed 2,700 teachers who had been working for a year and reviewed the performance ratings given by their supervisors. Teachers who said they could express their authentic selves received higher ratings than teachers who did not feel they could do so.
Here are some ways to help workers be true to themselves:
Encourage employees to reflect on what makes them feel authentic. This can be done from the very start of the employment relationship — during orientation. In a field study I conducted with Brad Staats, of the University of North Carolina at Chapel Hill, and Dan Cable, employees in the business-process-outsourcing division of the Indian IT company Wipro went through a slightly modified onboarding process. We gave them a half hour to think about what was unique about them, what made them authentic, and how they could bring out their authentic selves at work. Later we compared them with employees who had gone through Wipro’s usual onboarding program, which allowed no time for such reflection. The employees in the first group had found ways to tailor their jobs so that they could be their true selves — for example, they exercised judgment when answering calls instead of rigidly following the company script. They were more engaged in their work, performed better, and were more likely to be with the company seven months later.
Leaders can also encourage this type of reflection once people are on the job. The start of a new year is a natural time for employees and their leaders to reflect on what makes them unique and authentic and how they can shape their jobs — even in small ways — to avoid conformity. Reflection can also be encouraged at other career points, such as a performance review, a promotion, or a transition into a new role.
Tell employees what job needs to be done rather than how to do it. When Colleen Barrett was executive vice president of Southwest Airlines, from 1990 to 2001, she established the goal of allowing employees to be themselves. For example, flight attendants were encouraged to deliver the legally required safety announcement in their own style and with humor. “We have always thought that your avocation can be your vocation so that you don’t have to do any acting in your life when you leave home to go to work,” she has said. This philosophy helped make Southwest a top industry performer in terms of passenger volume, profitability, customer satisfaction, and turnover.
Let employees solve problems on their own. Leaders can encourage authenticity by allowing workers to decide how to handle certain situations. For instance, in the 1990s British Airways got rid of its thick customer-service handbook and gave employees the freedom (within reason) to figure out how to deal with customer problems as they arose (see “Competing on Customer Service: An Interview with British Airways’ Sir Colin Marshall,” HBR, November–December 1995).
Another company that subscribes to this philosophy is Pal’s Sudden Service, a fast-food chain in the southern United States. By implementing lean principles, including the idea that workers are empowered to call out and fix problems, Pal’s has achieved impressive numbers: one car served at the drive-through every 18 seconds, one mistake in every 3,600 orders (the industry average is one in 15), customer satisfaction scores of 98%, and health inspection scores above 97%. Turnover at the assistant manager level is under 2%, and in three decades Pal’s has lost only seven general managers — two of them to retirement. Annual turnover on the front lines is about 34% — half the industry average. Pal’s trains its employees extensively: New frontline workers receive 135 hours of instruction, on average (the industry average is about two hours). As a result, employees are confident that they can solve problems on their own and can stop processes if something does not seem right. (They also know they can ask for help.) When I was conducting interviews for a case on Pal’s, a general manager gave me an example of how he encourages frontline workers to make decisions themselves: “A 16-year-old [employee] shows me a hot dog bun with flour on it and asks me if it’s OK. My response: ‘Your call. Would you sell it?’”
Let employees define their missions. Morning Star, a California-based tomato processing company, has employees write “personal commercial mission statements” that reflect who they are and specify their goals for a given time period, ones that will contribute to the company’s success. The statements are embedded in contracts known as “colleague letters of understanding,” or CLOUs, which employees negotiate with coworkers, each spelling out how he or she will collaborate with others. The personal commercial mission of Morning Star’s founder, Chris Rufer, is “to advance tomato technology to be the best in the world and operate these factories so they are pristine.” That of one sales and marketing employee is “to indelibly mark ‘Morning Star Tomato Products’ on the tongue and brain of every commercial tomato product user.” That of one employee in the shipping unit is “to reliably and efficiently provide our customers with marvelously attractive loads of desired product.”
2. Encourage Employees to Bring out Their Signature Strengths
Michelangelo described sculpting as a process whereby the artist releases an ideal figure from the block of stone in which it slumbers. We all possess ideal forms, the signature strengths — being social connectors, for example, or being able to see the positive in any situation — that we use naturally in our lives. And we all have a drive to do what we do best and be recognized accordingly. A leader’s task is to encourage employees to sculpt their jobs to bring out their strengths — and to sculpt his or her own job, too. The actions below can help.
Give employees opportunities to identify their strengths. In a research project I conducted with Dan Cable, Brad Staats, and the University of Michigan’s Julia Lee, leaders of national and local government agencies across the globe reflected each morning on their signature strengths and how to use them. They also read descriptions of times when they were at their best, written by people in their personal and professional networks. These leaders displayed more engagement and innovative behavior than members of a control group, and their teams performed better.
Tailor jobs to employees’ strengths. Facebook is known for hiring smart people regardless of the positions currently open in the company, gathering information about their strengths, and designing their jobs accordingly. Another example is Osteria Francescana, a Michelin three-star restaurant in Modena, Italy, that won first place in the 2016 World’s 50 Best Restaurant awards. Most restaurants, especially top-ranked ones, observe a strict hierarchy, with specific titles for each position. But at Osteria Francescana, jobs and their attendant responsibilities are tailored to individual workers.
Employees who said they could express their authentic selves at work were more committed to their organizations.
Discovering employees’ strengths takes time and effort. Massimo Bottura, the owner and head chef, rotates interns through various positions for at least a few months so that he and his team can configure jobs to play to the newcomers’ strengths. This ensures that employees land where they fit best.
If such a process is too ambitious for your organization, consider giving employees some freedom to choose responsibilities within their assigned roles.
3. Question the Status Quo, and Encourage Employees to Do the Same
Although businesses can benefit from repeatable practices that ensure consistency, they can also stimulate employee engagement and innovation by questioning standard procedures — “the way we’ve always done it.” Here are some proven tactics.
Ask “Why?” and “What if?” By regularly asking employees such questions, Max Zanardi, for several years the general manager of the Ritz-Carlton in Istanbul, creatively led them to redefine luxury by providing customers with authentic and unusual experiences. For example, employees had traditionally planted flowers each year on the terrace outside the hotel’s restaurant. One day Zanardi asked, “Why do we always plant flowers? How about vegetables? What about herbs?” This resulted in a terrace garden featuring herbs and heirloom tomatoes used in the restaurant — things guests very much appreciated.
Leaders who question the status quo give employees reasons to stay engaged and often spark fresh ideas that can rejuvenate the business.
Stress that the company is not perfect. Ed Catmull, the cofounder and president of Pixar Animation Studios, worried that new hires would be too awed by Pixar’s success to challenge existing practices (see “How Pixar Fosters Collective Creativity,” HBR, September 2008). So during onboarding sessions, his speeches included examples of the company’s mistakes. Emphasizing that we are all human and that the organization will never be perfect gives employees freedom to engage in constructive nonconformity.
Excel at the basics. Ensuring that employees have deep knowledge about the way things usually operate provides them with a foundation for constructively questioning the status quo. This philosophy underlies the many hours Pal’s devotes to training: Company leaders want employees to be expert in all aspects of their work. Similarly, Bottura believes that to create innovative dishes, his chefs must be well versed in classic cooking techniques.
4. Create Challenging Experiences
It’s easy for workers to get bored and fall back on routine when their jobs involve little variety or challenge. And employees who find their work boring lack the motivation to perform well and creatively, whereas work that is challenging enhances their engagement. Research led by David H. Zald, of Vanderbilt University, shows that novel behavior, such as trying something new or risky, triggers the release of dopamine, a chemical that helps keep us motivated and eager to innovate.
Leaders can draw on the following tactics when structuring employees’ jobs:
Maximize variety. This makes it less likely that employees will go on autopilot and more likely that they will come up with innovative ways to improve what they’re doing. It also boosts performance, as Brad Staats and I found in our analysis of two and a half years’ worth of transaction data from a Japanese bank department responsible for processing home loan applications. The mortgage line involved 17 distinct tasks, including scanning applications, comparing scanned documents to originals, entering application data into the computer system, assessing whether information complied with underwriting standards, and conducting credit checks. Workers who were assigned diverse tasks from day to day were more productive than others (as measured by the time taken to complete each task); the variety kept them motivated. This allowed the bank to process applications more quickly, increasing its competitiveness.
Variety can be ensured in a number of ways. Pal’s rotates employees through tasks (taking orders, grilling, working the register, and so on) in a different order each day. Some companies forgo defined career trajectories and instead move employees through various positions within departments or teams over the course of months or years.
In addition to improving engagement, job rotation broadens individuals’ skill sets, creating a more flexible workforce. This makes it easier to find substitutes if someone falls ill or abruptly quits and to shift people from tasks where they are no longer needed (see “Why ‘Good Jobs’ Are Good for Retailers,” HBR, January–February 2012).
Continually inject novelty into work. Novelty is a powerful force. When something new happens at work, we pay attention, engage, and tend to remember it. We are less likely to take our work for granted when it continues to generate strong feelings. Novelty in one’s job is more satisfying than stability.
So, how can leaders inject it into work? Bottura throws last-minute menu changes at his team to keep excitement high. At Pal’s, employees learn the order of their tasks for the day only when they get to work.
Leaders can also introduce novelty by making sure that projects include a few people who are somewhat out of their comfort zone, or by periodically giving teams new challenges (for instance, asking them to deliver a product faster than in the past). They can assign employees to teams charged with designing a new work process or piloting a new service.
Identify opportunities for personal learning and growth. Giving people such experiences is an essential way to promote constructive nonconformity, research has shown. For instance, in a field study conducted at a global consulting firm, colleagues and I found that when onboarding didn’t just focus on performance but also spotlighted opportunities for learning and growth, engagement and innovative behaviors were higher six months later. Companies often identify growth opportunities during performance reviews, of course, but there are many other ways to do so. Chefs at Osteria Francescana can accompany Bottura to cooking events that expose them to other countries, cuisines, traditions, arts, and culture — all potential sources of inspiration for new dishes. When I worked as a research consultant at Disney, in the summer of 2010, I learned that members of the Imagineering R&D group were encouraged to belong to professional societies, attend conferences, and publish in academic and professional journals. Companies can help pay for courses that may not strictly relate to employees’ current jobs but would nonetheless expand their skill sets or fuel their curiosity.
Give employees responsibility and accountability. At Morning Star, if employees need new equipment to do their work — even something that costs thousands of dollars — they may buy it. If they see a process that would benefit from different skills, they may hire someone. They must consult colleagues who would be affected (other people who would use the equipment, say), but they don’t need approval from above. Because there are no job titles at Morning Star, how employees influence others — and thus get work done — is determined mainly by how their colleagues perceive the quality of their decisions.
Employees of Semco Group set their own schedules and production quotas. They even choose the amount and form of their compensation.
5. Foster Broader Perspectives
We often focus so narrowly on our own point of view that we have trouble understanding others’ experiences and perspectives. And as we assume high-level positions, research shows, our egocentric focus becomes stronger. Here are some ways to combat it:
Create opportunities for employees to view problems from multiple angles. We all tend to be self-serving in terms of how we process information and generate (or fail to generate) alternatives to the status quo. Leaders can help employees overcome this tendency by encouraging them to view problems from different perspectives. At the electronics manufacturer Sharp, an oft-repeated maxim is “Be dragonflies, not flatfish.” Dragonflies have compound eyes that can take in multiple perspectives at once; flatfish have both eyes on the same side of the head and can see in only one direction at a time.
Jon Olinto and Anthony Ackil, the founders of the fast-casual restaurant chain b.good, require all employees (including managers) and franchisees to be trained in every job — from prep to grill to register. (Unlike Pal’s, however, b.good does not rotate people through jobs each day.) Being exposed to different perspectives increases engagement and innovative behaviors, research has found.
Use language that reduces self-serving bias. To prevent their traders from letting success go to their heads when the market is booming, some Wall Street firms regularly remind them, “Don’t confuse brains with a bull market.” At GE, terms such as “planting seeds” (to describe making investments that will produce fruitful results even after the managers behind them have moved on to other jobs) have entered the lexicon (see “How GE Teaches Teams to Lead Change,” HBR, January 2009).
Hire people with diverse perspectives. Decades’ worth of research has found that working among people from a variety of cultures and backgrounds helps us see problems in new ways and consider ideas that might otherwise go unnoticed, and it fosters the kind of creativity that champions change. At Osteria Francescana the two sous-chefs are Kondo “Taka” Takahiko, from Japan, and Davide diFabio, from Italy. They differ not only in country of origin but also in strengths and ways of thinking: Davide is comfortable with improvisation, for example, while Taka is obsessed with precision. Diversity in ways of thinking is a quality sought by Rachael Chong, the founder and CEO of the startup Catchafire. When interviewing job candidates, she describes potential challenges and carefully listens to see whether people come up with many possible solutions or get stuck on a single one. To promote innovation and new approaches, Ed Catmull hires prominent outsiders, gives them important roles, and publicly acclaims their contributions. But many organizations do just the opposite: hire people whose thinking mirrors that of the current management team.
6. Voice and Encourage Dissenting Views
We often seek out and fasten on information that confirms our beliefs. Yet data that is inconsistent with our views and may even generate negative feelings (such as a sense of failure) can provide opportunities to improve our organizations and ourselves. Leaders can use a number of tactics to push employees out of their comfort zones.
Look for disconfirming evidence. Leaders shouldn’t ask, “Who agrees with this course of action?” or “What information supports this view?” Instead they should ask, “What information suggests this might not be the right path to take?” Mellody Hobson, the president of Ariel Investments and the chair of the board of directors of DreamWorks Animation, regularly opens team meetings by reminding attendees that they don’t need to be right; they need to bring up information that can help the team make the right decisions, which happens when members voice their concerns and disagree. At the Chicago Board of Trade, in-house investigators scrutinize trades that may violate exchange rules. To avoid bias in collecting information, they have been trained to ask open-ended interview questions, not ones that can be answered with a simple yes or no. Leaders can use a similar approach when discussing decisions. They should also take care not to depend on opinions but to assess whether the data supports or undermines the prevailing point of view.
Create dissent by default. Leaders can encourage debate during meetings by inviting individuals to take opposing points of view; they can also design processes to include dissent. When employees of Pal’s suggest promising ideas for new menu items, the ideas are tested in three different stores: one whose owner-operator likes the idea (“the protagonist”), one whose owner-operator is skeptical (“the antagonist”), and one whose owner-operator has yet to form a strong opinion (“the neutral”). This ensures that dissenting views are aired and that they help inform the CEO’s decisions about proposed items.
Identify courageous dissenters. Even if encouraged to push back, many timid or junior people won’t. So make sure the team includes people you know will voice their concerns, writes Diana McLain Smith in The Elephant in the Room: How Relationships Make or Break the Success of Leaders and Organizations. Once the more reluctant employees see that opposing views are welcome, they will start to feel comfortable dissenting as well.
Striking the Right Balance
By adopting the strategies above, leaders can fight their own and their employees’ tendency to conform when that would hurt the company’s interests. But to strike the optimal balance between conformity and nonconformity, they must think carefully about the boundaries within which employees will be free to deviate from the status quo. For instance, the way a manager leads her team can be up to her as long as her behavior is aligned with the company’s purpose and values and she delivers on that purpose.
Morning Star’s colleague letters of understanding provide such boundaries. They clearly state employees’ goals and their responsibility to deliver on the organization’s purpose but leave it up to individual workers to decide how to achieve those goals. Colleagues with whom an employee has negotiated a CLOU will let him know if his actions cross a line.
In the summer of 2015 a team of hackers attempted to take control of an unmanned military helicopter known as Little Bird. The helicopter, which is similar to the piloted version long-favored for US special operations missions, was stationed at a Boeing facility in Arizona. The hackers had a head start: At the time they began the operation, they already had access to one part of the drone’s computer system. From there, all they needed to do was hack into Little Bird’s onboard flight-control computer, and the drone was theirs.
When the project started, a “Red Team” of hackers could have taken over the helicopter almost as easily as it could break into your home Wi-Fi. But in the intervening months, engineers from the Defense Advanced Research Projects Agency had implemented a new kind of security mechanism—a software system that couldn’t be commandeered. Key parts of Little Bird’s computer system were unhackable with existing technology, its code as trustworthy as a mathematical proof. Even though the Red Team was given six weeks with the drone and more access to its computing network than genuine bad actors could ever expect to attain, they failed to crack Little Bird’s defenses.
“They were not able to break out and disrupt the operation in any way,” said Kathleen Fisher, a professor of computer science at Tufts University and the founding program manager of the High-Assurance Cyber Military Systems (HACMS) project. “That result made all of Darpa stand up and say, oh my goodness, we can actually use this technology in systems we care about.”
The technology that repelled the hackers was a style of software programming known as formal verification. Unlike most computer code, which is written informally and evaluated based mainly on whether it works, formally verified software reads like a mathematical proof: Each statement follows logically from the preceding one. An entire program can be tested with the same certainty that mathematicians prove theorems.
“You’re writing down a mathematical formula that describes the program’s behavior and using some sort of proof checker that’s going to check the correctness of that statement,” said Bryan Parno, who does research on formal verification and security at Microsoft Research.
The aspiration to create formally verified software has existed nearly as long as the field of computer science. For a long time it seemed hopelessly out of reach, but advances over the past decade in so-called “formal methods” have inched the approach closer to mainstream practice. Today formal software verification is being explored in well-funded academic collaborations, the US military and technology companies such as Microsoft and Amazon.
The interest occurs as an increasing number of vital social tasks are transacted online. Previously, when computers were isolated in homes and offices, programming bugs were merely inconvenient. Now those same small coding errors open massive security vulnerabilities on networked machines that allow anyone with the know-how free rein inside a computer system.
“Back in the 20th century, if a program had a bug, that was bad, the program might crash, so be it,” said Andrew Appel, professor of computer science at Princeton University and a leader in the program verification field. But in the 21st century, a bug could create “an avenue for hackers to take control of the program and steal all your data. It’s gone from being a bug that’s bad but tolerable to a vulnerability, which is much worse,” he said.
The Dream of Perfect Programs
In October 1973 Edsger Dijkstra came up with an idea for creating error-free code. While staying in a hotel at a conference, he found himself seized in the middle of the night by the idea of making programming more mathematical. As he explained in a later reflection, “With my brain burning, I left my bed at 2:30 a.m. and wrote for more than an hour.” That material served as the starting point for his seminal 1976 book, “A Discipline of Programming,” which, together with work by Tony Hoare (who, like Dijkstra, received the Turing Award, computer science’s highest honor), established a vision for incorporating proofs of correctness into how computer programs are written.
It’s not a vision that computer science followed, largely because for many years afterward it seemed impractical—if not impossible—to specify a program’s function using the rules of formal logic.
A formal specification is a way of defining what, exactly, a computer program does. And a formal verification is a way of proving beyond a doubt that a program’s code perfectly achieves that specification. To see how this works, imagine writing a computer program for a robot car that drives you to the grocery store. At the operational level, you’d define the moves the car has at its disposal to achieve the trip—it can turn left or right, brake or accelerate, turn on or off at either end of the trip. Your program, as it were, would be a compilation of those basic operations arranged in the appropriate order so that at the end, you arrived at the grocery store and not the airport.
The traditional, simple way to see if a program works is to test it. Coders submit their programs to a wide range of inputs (or unit tests) to ensure they behave as designed. If your program were an algorithm that routed a robot car, for example, you might test it between many different sets of points. This testing approach produces software that works correctly, most of the time, which is all we really need for most applications. But unit testing can’t guarantee that software will always work correctly because there’s no way to run a program through every conceivable input. Even if your driving algorithm works for every destination you test it against, there’s always the possibility that it will malfunction under some rare conditions—or “corner cases,” as they’re called—and open a security gap. In actual programs, these malfunctions could be as simple as a buffer overflow error, where a program copies a little more data than it should and overwrites a small piece of the computer’s memory. It’s a seemingly innocuous error that’s hard to eliminate and provides an opening for hackers to attack a system—a weak hinge that becomes the gateway to the castle.
“One flaw anywhere in your software, and that’s the security vulnerability. It’s hard to test every possible path of every possible input,” Parno said.
Actual specifications are subtler than a trip to the grocery store. Programmers may want to write a program that notarizes and time-stamps documents in the order in which they’re received (a useful tool in, say, a patent office). In this case the specification would need to explain that the counter always increases (so that a document received later always has a higher number than a document received earlier) and that the program will never leak the key it uses to sign the documents.
This is easy enough to state in plain English. Translating the specification into formal language that a computer can apply is much harder—and accounts for a main challenge when writing any piece of software in this way.
“Coming up with a formal machine-readable specification or goal is conceptually tricky,” Parno said. “It’s easy to say at a high level ‘don’t leak my password,’ but turning that into a mathematical definition takes some thinking.”
To take another example, consider a program for sorting a list of numbers. A programmer trying to formalize a specification for a sort program might come up with something like this:
For every item j in a list, ensure that the element j ≤ j+1
Yet this formal specification—ensure that every element in a list is less than or equal to the element that follows it—contains a bug: The programmer assumes that the output will be a permutation of the input. That is, given the list [7, 3, 5], she expects that the program will return [3, 5, 7] and satisfy the definition. Yet the list [1, 2] also satisfies the definition since “it is a sorted list, just not the sorted list we were probably hoping for,” Parno said.
In other words, it’s hard to translate an idea you have for what a program should do into a formal specification that forecloses every possible (but incorrect) interpretation of what you want the program to do. And the example above is for something as simple as a sort program. Now imagine taking something much more abstract than sorting, such as protecting a password. “What does that mean mathematically? Defining it may involve writing down a mathematical description of what it means to keep a secret, or what it means for an encryption algorithm to be secure,” Parno said. “These are all questions we, and many others, have looked at and made progress on, but they can be quite subtle to get right.”
Between the lines it takes to write both the specification and the extra annotations needed to help the programming software reason about the code, a program that includes its formal verification information can be five times as long as a traditional program that was written to achieve the same end.
This burden can be alleviated somewhat with the right tools—programming languages and proof-assistant programs designed to help software engineers construct bombproof code. But those didn’t exist in the 1970s. “There were many parts of science and technology that just weren’t mature enough to make that work, and so around 1980, many parts of the computer science field lost interest in it,” said Appel, who is the lead principal investigator of a research group called DeepSpec that’s developing formally verified computer systems.
Even as the tools improved, another hurdle stood in the way of program verification: No one was sure whether it was even necessary. While formal methods enthusiasts talked of small coding errors manifesting as catastrophic bugs, everyone else looked around and saw computer programs that pretty much worked fine. Sure, they crashed sometimes, but losing a little unsaved work or having to restart occasionally seemed like a small price to pay for not having to tediously spell out every little piece of a program in the language of a formal logical system. In time, even program verification’s earliest champions began to doubt its usefulness. In the 1990s Hoare — whose “Hoare logic” was one of the first formal systems for reasoning about the correctness of a computer program — acknowledged that maybe specification was a labor-intensive solution to a problem that didn’t exist. As he wrote in 1995:
Ten years ago, researchers into formal methods (and I was the most mistaken among them) predicted that the programming world would embrace with gratitude every assistance promised by formalization…. It has turned out that the world just does not suffer significantly from the kind of problem that our research was originally intended to solve.
Then came the Internet, which did for coding errors what air travel did for the spread of infectious diseases: When every computer is connected to every other one, inconvenient but tolerable software bugs can lead to a cascade of security failures.
“Here’s the thing we didn’t quite fully understand,” Appel said. “It’s that there are certain kinds of software that are outward-facing to all hackers in the Internet, so that if there is a bug in that software, it might well be a security vulnerability.”
By the time researchers began to understand the critical threats to computer security posed by the Internet, program verification was ready for a comeback. To start, researchers had made big advances in the technology that undergirds formal methods: improvements in proof-assistant programs like Coq and Isabelle that support formal methods; the development of new logical systems (called dependent-type theories) that provide a framework for computers to reason about code; and improvements in what’s called “operational semantics”—in essence, a language that has the right words to express what a program is supposed to do.
“If you start with an English-language specification, you’re inherently starting with an ambiguous specification,” said Jeannette Wing, corporate vice president at Microsoft Research. “Any natural language is inherently ambiguous. In a formal specification you’re writing down a precise specification based on mathematics to explain what it is you want the program to do.”
In addition, researchers in formal methods also moderated their goals. In the 1970s and early 1980s, they envisioned creating entire fully verified computer systems, from the circuit all the way to the programs. Today most formal methods researchers focus instead on verifying smaller but especially vulnerable or critical pieces of a system, like operating systems or cryptographic protocols.
“We’re not claiming we’re going to prove an entire system is correct, 100 percent reliable in every bit, down to the circuit level,” Wing said. “That’s ridiculous to make those claims. We are much more clear about what we can and cannot do.”
The HACMS project illustrates how it’s possible to generate big security guarantees by specifying one small part of a computer system. The project’s first goal was to create an unhackable recreational quadcopter. The off-the-shelf software that ran the quadcopter was monolithic, meaning that if an attacker broke into one piece of it, he had access to all of it. So, over the next two years, the HACMS team set about dividing the code on the quadcopter’s mission-control computer into partitions.
The team also rewrote the software architecture, using what Fisher, the HACMS founding project manager, calls “high-assurance building blocks”—tools that allow programmers to prove the fidelity of their code. One of those verified building blocks comes with a proof guaranteeing that someone with access inside one partition won’t be able to escalate their privileges and get inside other partitions.
Later the HACMS programmers installed this partitioned software on Little Bird. In the test against the Red Team hackers, they provided the Red Team access inside a partition that controlled aspects of the drone helicopter, like the camera, but not essential functions. The hackers were mathematically guaranteed to get stuck. “They proved in a machine-checked way that the Red Team would not be able to break out of the partition, so it’s not surprising” that they couldn’t, Fisher said. “It’s consistent with the theorem, but it’s good to check.”
In the year since the Little Bird test, Darpa has been applying the tools and techniques from the HACMS project to other areas of military technology, like satellites and self-driving convoy trucks. The new initiatives are consistent with the way formal verification has spread over the last decade: Each successful project emboldens the next. “People can’t really have the excuse anymore that it’s too hard,” Fisher said.
Verifying the Internet
Security and reliability are the two main goals that motivate formal methods. And with each passing day the need for improvements in both is more apparent. In 2014 a small coding error that would have been caught by formal specification opened the way for the Heartbleed bug, which threatened to bring down the Internet. A year later a pair of white-hat hackers confirmed perhaps the biggest fears we have about Internet-connected cars when they successfully took control of someone else’s Jeep Cherokee.
As the stakes rise, researchers in formal methods are pushing into more ambitious places. In a return to the spirit that animated early verification efforts in the 1970s, the DeepSpec collaboration led by Appel (who also worked on HACMS) is attempting to build a fully verified end-to-end system like a web server. If successful, the effort, which is funded by a $10 million grant from the National Science Foundation, would stitch together many of the smaller-scale verification successes of the last decade. Researchers have built a number of provably secure components, such as the core, or kernel, of an operating system. “What hadn’t been done, and is the challenge DeepSpec is focusing on, is how to connect those components together at specification interfaces,” Appel said.
Over at Microsoft Research, software engineers have two ambitious formal verification projects underway. The first, named Everest, is to create a verified version of HTTPS, the protocol that secures web browsers and that Wing refers to as the “Achilles heel of the Internet.”
The second is to create verified specifications for complex cyber-physical systems such as drones. Here the challenge is considerable. Where typical software follows discrete, unambiguous steps, the programs that tell a drone how to move use machine learning to make probabilistic decisions based on a continuous stream of environmental data. It’s far from obvious how to reason about that kind of uncertainty or pin it down in a formal specification. But formal methods have advanced a lot even in the last decade, and Wing, who oversees this work, is optimistic formal methods researchers are going to figure it out.
Original story reprinted with permission from Quanta Magazine, an editorially independent publication of the Simons Foundation whose mission is to enhance public understanding of science by covering research developments and trends in mathematics and the physical and life sciences.
A small black device about the size of a pizza box could be the future of financial trading.
Australian company Metamako’s network switch is able to route incoming information through to trading servers in just four nanoseconds, and can lower the time it takes to execute a full trade—from the time it receives market information from a stock exchange to the time it sends out a “buy” or “sell” order—to just 85 nanoseconds, nearly three times faster than Cisco’s general-purpose networking switch.
The lightning-fast gear is just one link in the high-speed trading chain, of course. Servers have to parse incoming information, make a decision, and send the order back to the switch, before it heads along super-fast cables to an exchange. But Metamako’s hardware highlights the lengths that traders are willing to go to lower the “latency” of every component in their setup. The company figures it sells about 100 units a month at around $20,000 a pop—according to the Wall Street Journal’s piece on the company, that’s pretty affordable compared to many of the components in a high-speed trading rig.
This “race to the bottom” is getting pretty close to the limits of what is possible with known physics—light travels about one foot per nanosecond.
Even relativity can’t get in the way of dreaming of making a fast buck, however. When in 2012 scientists briefly thought they’d detected neutrinos that could travel faster than light, for example, high-frequency traders pondered how they might build a system that would execute trades that would, theoretically, occur in the past.
That kind of thinking tells you everything you need to know about just how speed-obsessed the industry has become. But high-frequency trading has also risen to dominate the world of stock trading— about half of all stock that changes hands in the U.S. is through high-speed trades, according to the Journal piece.
Whether or not that’s a good thing is a matter of opinion. Critics of the practice argue that it skews profits to those who can afford sophisticated setups. High-frequency traders are able to make pennies off of individual trades but execute them millions of times a day, while regular investors are left in the dust. And it could be a destabilizing force, where software gone haywire erases huge chunks of a company’s value in a matter of minutes. This has happened enough that it has a name: a flash crash.
Proponents, on the other hand, argue that reaching the physical speed limits of trading will make markets fairer. Once everyone trades at light speed, or near to it, there will be little advantage to be gained by going any faster.
Until, of course, someone figures out how to break the laws of physics.
Have you intentionally linked your b2b marketing, sales, and strategy?
If not, don’t worry. You’re in good company.
Most companies struggle with this according to the Frank Cespedes, author, and Senior Lecturer at Harvard Business School, “selling [or marketing,] no matter how clever and creative can’t generate good financial returns unless it’s connected to strategy.”
I met Frank while we both spoke at an event in Santiago, Chile. We had a memorable time sharing ideas, and research. I thought Frank had a practical approach to aligning sales and marketing. So, I reached out to him and interviewed him about what he’s learned through his research for his most recent book Aligning Strategy and Sales (Harvard Business Review Press).
Writers Note: I edited this interview for length and grammar only.
Brian: What inspired you to write about Aligning Strategy and Sales?
Frank: Despite decades of attention to so-called strategic planning, there is remarkably little research about how to link strategy with the nitty gritty of field execution especially sales efforts [and marketing].
American companies annually spend about $900 billion every year on sales efforts. That’s not marketing. That’s sales compensation, the travel and entertainment expenses, incentives, the infrastructure, etc. To put that in perspective, Brian, that amount is more than three times what companies spend on all media advertising– Super Bowls, everything else. It’s more than about 40 times what they spend on digital marketing and it’s more than 50 times what they currently spend on social media.
That’s a lot of managerial time and effort as well as money, and the reality is that sales forces are, by far, the biggest and most important part of strategy execution for most firms, especially in B2B markets.
How did you develop this idea?
I was an academic at Harvard Business School for about 11 years working my way up the hierarchy and always was doing research in sales related areas. My research started with distribution channels, B2B distribution channels, morphed into sales.
Then I ran a business for 12 years. And then I came back and said, ‘I’m teaching strategy. I know something about sales. Let me see what people have written about the connections between strategy and sales.’
What I found was a gap: lots of books and articles about strategy, and countless books and articles and training programs about selling, but virtually nothing about connecting these activities. So I figured two things.
The world does not need another book about strategy. And I don’t think, to be blunt, the world needs yet another selling methodology, but there just isn’t much if anything about linking the two and that was the gap that I set out to address, practically and based on the research about these topics, not one-off anecdotes.
Brian: If you had only three questions you could ask executives to diagnose sales and strategy alignment what would those be?
Question 1. Do you have a strategy and can you articulate it?
Most companies and most senior executives, confuse strategy with other important but separate things like vision or mission or purpose or values. Those are important, but they are not the same as a strategy.
A strategy at a minimum always has to pass the following three tests:
- External consistency. In other words, does our approach deal with the threats and the opportunities in the external market today, not yesterday?
- Internal consistency. How do we put things together in sales, in marketing, in operations so that two plus two equals at least four? It’s internally consistent but also if we have some competitive advantage, it’s going to take longer or cost more for competitors to imitate us.
- Dynamic consistency. Every strategy has a sell-by date. No strategy is forever. It’s very unlikely that any company is going to get an e-mail from the marketplace that says it’s time to change. And it’s not the responsibility of the market to be kind to any company and its strategy.
It’s the responsibility of managers to understand what’s going on in their market and adapt. That’s what I mean by dynamic consistency. If you have a strategy, you should be able to articulate its core components—Objectives, Scope, Advantage–in 50 words or less.
Question 2. Are we clear about the market segments where we do and don’t play and what are the buying processes?
These questions are fundamental to making decisions about Scope in any strategy. And in those areas where we choose to play what is it that we do or can do that, we believe us some advantage? You have to be clear about that if you’re going to do effective selling because where you play drives the buying processes that your sales people will encounter, and again this is central. Value is created or destroyed in actual interactions with customers and their buying processes, not in meetings or planning documents.
Question 3. Do we know what the important sales tasks our sales people must do to succeed in the marketplace are?
Sales tasks are very actionable things. They ultimately tell us where we should and should not spend, money, time, effort, investments in what areas of the of the conversion process in the sales funnel. And the critical tasks for any company are determined by their strategy and what that strategy means for the segments and buyers the sales force deals with; a generic selling methodology does NOT determine the tasks. This is important because, despite what most sales trainers still preach about selling and sales people, the fact is that the most important thing about selling is the buyer and their buying processes, not the personality or pitch of the seller.
I think my three questions are relatively simple, but I believe that they’re fundamental. Do you have a strategy? Are we clear about where we play and not play in the buying processes in those areas where we play? And what are the important sales tasks?
Brian: What are your best tips for getting sales and b2b marketing aligned around strategy?
Frank: Well, I think the questions I just articulated in answer to your earlier question are also relevant here because again there is no such thing as effective marketing or effective selling if it’s not connected to strategic goals and business objectives.
And many, many marketing and sales managers are very often either unaware or indifferent to the strategic and financial goals and objectives in their companies. They need to align with those goals and, conversely, they need to speak up and earn a place at the C-Suite table. Then, they have to be accountable.
Using the Seller’s Compass
Let me just say one thing about marketing, one thing about sales. The figure “Seller’s Compass” helps explain the process of aligning sales, marketing, and strategy (used with permission).
In marketing, I think your work [Lead Generation for the Complex Sale] is very relevant to lead generation. We all know the data. This data has been remarkably consistent for years, so there’s obviously something systemic going on what many people call the lead generation black hole.
How many leads from marketing are used? Lots of money is wasted, and that’s the right verb. It’s wasted on social media and other chic tools, and many marketers are ironically proud of that. They’re proud of the lack of metrics currently.
I wrote something about this last year in Harvard Business Review, Is Social Media Actually Helping Your Company’s Bottom Line? That gets us right back to one of the core aspects of marketing in many businesses, at least in relationship to sales and lead generation: what’s a good lead and are we clear about that? No company or marketing manager or sales manager can correctly answer that question independent of their firm’s business strategy.
That strategy helps to determine the necessary sales tasks (including relevant and efficient lead generation) and, as the “Seller’s Compass” figure indicates, the job in sales management is to get actual selling behaviors to align with required sales tasks.
Three Levers to Align B2B Selling Behaviors with Tasks
In turn, managers basically have three levers to do that: People (hiring, training, development), Sales Force Control Systems (including organization, metrics, and compensation plans), and Sales Force Environment (how sales managers manage and apply the control systems, including performance reviews and links with other functions in the company).
On sales, I think what they need to talk about compensation and incentives. The data has been remarkably consistent, at least during my lifetime. According to the surveys, once we get beyond fixed salary, about 70% of sales comp plans base incentives on sales volume. In other words, based on [sales] volume regardless of the profit margin of that sale or the cost to serve that customer.
When you have an incentive system like that, either the implicit or explicit message to the sales force is that any customer is a good customer. The message is a variation on the old biblical aphorism, “Go forth and multiply.” That’s what [sales] people do. They go forth, and they bring back a diverse set of customers that have very, very different implications for product, for the selling cycle, for post-sale service, for capacity utilization and operations, for the cash flow profile of the company.
All of this flows ultimately in any business from what sales people sell at what price and how fast. At some point, it doesn’t matter what senior executives think their strategy is. The de facto strategy of the company is those aggregate sales that are coming in. Conversely, those executives should continually ask, Why do we pay people the way we do and do we understand the daily behavioral implications of our comp system?
Again, companies spend massively more on Sales than Marketing and, as Mark Twain once said, “If you put a lot of eggs in one basket, then keep your eyes on that basket!” The most pressing things executives need to talk about for [aligning] business strategy with daily sales efforts are those questions I mentioned earlier and, in most firms, there are vast areas for improvement in all of those topics.
By Jan 13th 2018, EU banks will be required under the Revised Payment Service Directive (PSD2) to open payment and account services to third-party providers. Fintechs will only need simple licenses to plug into banks via Open Banking APIs for payments and client data, without resorting to traditional methods which are often clunky and bank specific.
Plain boring? Maybe, but the directives are absolutely disruptive. They will redefine how the industry works as a whole in a banks+Fintechs world. This is the breakthrough that massively scales collaboration, and redefines business models.
Banks may have been blind to Fintech at first, and Fintechs may have said they’ll take down banks, but now both parties have come to a simple conclusion: They’re each equally just as bad at what the other is doing, and they need to collaborate to create value. Since then, banks and Fintechs started to live their happily-ever-after in castles built in regulatory sandboxes: innovation labs everywhere, hackathons boom, four feet on the accelerator. Partnerships, Collaboration, Love. But no common language. And that’s where EU regulators are helping.
In a recent survey, 53% of banks said the PSD2 regulations will be a driver to change their business model. Some banks have even moved ahead of the regulation to capture these new opportunities. Yet, these regulatory changes went fairly unnoticed by the wider audience and were barely mentioned in mainstream media. There were always more catchy headlines on blockchain technology and robo-advisory startups, leaving minimal bandwidth for the less fancy aspect of banking and Fintech (try talking regulatory changes over dinner).
So, let’s bring back the attention to what really matters today. Let me share a disruption analogy so as to quantify the magnitude of the upcoming revolution. Not digital. But surprisingly very related. Keywords of both stories: API, open standards, disruption, disintermediation, massive investments, business models overhaul, and unprecedented business growth.
The APIs and the Shipping Container: A Tale of Two Industries
For hundreds of years, friction surrounding any type of transaction has been hindering business growth: goods, money and data are never transferred fast enough. For hundreds of years the same problem, and for hundreds of years the same answer: “More”. More manpower for more goods, more bank counters for more money, more keyboards for more data. Simple. Costly. Absolutely not practical. Completely unscalable.
Fintechs are now on a mission to solve the “money and data problems”. Similarly, just sixty years back, a truck driver thought he could solve the “goods” problem. His solution? An Open API. Most likely the first ever, and some thirty years before the internet.
Malcom McLean was an entrepreneur who built one of the major logistics trucking fleets in the U.S, starting with one truck. McLean was very frustrated with the amount of time his trucks were spending at ports waiting for loads to be transferred to ships, and so he did what any entrepreneur would do: He got an idea, invested in a ship, and took action. On April 26th 1956, McLean’s “Ideal X” left port Newark, New Jersey to deliver goods some 1,600 miles south-west, in Houston. On its deck, the load looked nothing like what anyone had seen before: 58 metal boxes.
The shipping container that McLean had invented may have looked at first like a large metal box, but it was much more than that. More than 30 years before the internet, it was created as a global collaboration platform running on a steel protocol, i.e an old fashioned physical API for seamless trade. Businesses could trade anything, in any quantity, and on any distance with very minimal manual intervention between them as long as it was containerized. When McLean invented the container (i.e the “McLean API”, in today’s lingo), he actually allowed truck drivers and shipping lines to talk the same language. And that caught the world off guard.
The McLean API had disrupted a historically labor intensive industry overnight: thanks to containers, ship loading suddenly became insanely faster, and 40 times cheaper. “More goods” didn’t necessarily mean “more loading time, or more dockers in the port”. A new word was coined: Intermodalism, i.e passing containerized goods between two “modes” of transport seamlessly. It meant that ports could do away with manual batches and start running as on-demand platforms for ship loading. Intermodalism triggered a massive wave of disruptions that wiped out jobs, disintermediated entire industries, and pushed major historical trading hubs to obsolescence. But more importantly, after investments in new capabilities and a wide adoption of the container as the industry standard, it had an incommensurable contribution to global economic growth.
As PSD2 regulations will now containerize money and data via standard APIs, here are some comparison points and lessons the old fashioned steel container can teach us:
1 Removing friction via APIs has a much bigger impact on the economy than what you can imagine.
The widespread adoption of the McLean API (i.e the container) had ruthlessly disintermediated dockers and disconnected old ports, but it also contributed to the global economy way beyond expectations. In fact, it fueled globalization more than all free trade agreements did over the last half century. That’s now a proven fact. Yet, back in the 60s, this concept of intermodalism was only applied for goods. Imagine the impact that PSD2 regulations and Open APIs will bring about as they remove all friction from money and data transactions between Fintechs and banks in a globalized economy.
2 APIs remove operational bottlenecks not by automating manual tasks, but by redefining how businesses collaborate. This inevitably leads to a dramatic drop in the manpower required to transact, and to massive disintermediation.
Until the widespread use of the McLean API, shipping had historically employed tens of thousands of dockers. But its standardization also drove its extremely fast adoption worldwide, and disintermediated even the most unionized dockers. Immediate costs of restructuration were largely compensated by long-term savings produced by this new technology. One example: In the 1960s the port of New York employed about 35,000 longshoremen. They are only 3,500 today. That’s 90% less, despite a notable increase of the volume of cargo handled at the port due to globalization. Banks today are the ports to regulations and finance, and they hire office longshoremen. Adding a layer of standards will remove the need for manual data loading, downloading, and reconciliations between banks and their partners.
3 If your legacy infrastructure cannot be upgraded to use APIs, your organization is in danger.
Most historical ports could not keep up with this new technology as they were not able to accommodate large container ships. The Port of San Francisco had been a major marine terminal, and London upstream docks were once hosting the world’s biggest port. But for both, no upgrade was possible on their legacy infrastructure, meaning both could not use the McLean API. As a result, they became disconnected from trade. Their historical legacy and size simply did not matter anymore. No port was too big to fail. It was simply the end of an era. Now, you are probably thinking the majestic ports of yesterday could be the banks of today and their legacy systems. For some banks, you are probably right.
4 Early adopters catching the network effect of APIs will be the biggest winners.
Countries did not only invest in the McLean API for cost savings and efficiency. They invested primarily to connect to a world of new trade relationships, and there was a premium at being the first. When Taiwan and Korea adopted it, their exports tripled within 3 years. In this race for connectivity, investments flocked into new port infrastructures and shipbuilding. Soon, a global network of seamless trade connections providers emerged: Hong-Kong, Rotterdam, Singapore, Shanghai, Hamburg, Los Angeles overtook New York. In 1966 around 1% of countries had container ports. This rose to 90% by 1983. A number of banks are moving ahead of regulations today. In fact, some banks like SolarisBank in Germany are being built on a complete Open APIs architecture and are calling this Banking-as-a-Platform. They’re starting from scratch, but their aim is to capture a first mover advantage.
Today, Banks and Fintechs still transfer data or money between them the same way that loading/unloading of ships was handled before 1956. With PSD2 regulations, transactional data sets (digital containers) will flow back and forth between parties and their own systems: This is Digital Intermodalism. Seamless flow between banks and Fintechs front-ends, robo-advisory engines, mobile payment providers, frictionless user experience with 100% regulatory compliance and all KYC/AML checks in place (most probably automated by regtechs plugged to the banks), in one word: The Future.
Why Doesn’t Anyone Talk About PSD2 if it is Indeed SO Big?
That is the last lesson from the McLean API: Persistent beliefs and misplaced spotlights (blockchain) are causing us to miss out on big changes.
Everyone knew that the McLean API was coming and how disruptive it could be (it took 13 long years for the container to get its global ISO standard, the trigger point being the open source release of its patent by McLean). Yet, the world was caught off guard by the rapidity of its adoption once its global ISO standard was rolled-out. The 13 long years taken to agree on a standard had created a persistent belief that worldwide adoption would remain slow. Most stakeholders had underestimated one basic rule of B2B innovations: standards are a much stronger driver for adoption than word-of-mouth.
So, despite what you may have read here and there, disruption is not always ten years away. It is almost always one B2B standard away. And today, we know the standard for containerization of money and data will be rolled out from Jan 13th 2018.
Ironically, it is the banking regulators of the old commercial ports that are leading the way today, London being ahead of all. The question that remains is whether the major container ports of today, like Singapore or Hong-Kong, will embrace a similar mindset to strengthen the global economic relevance that they’ve acquired over the last 60 years.
We all want to be more successful.
But everything you read probably sounds like a lot of work. Isn’t there a scientifically proven method that’s a little more… fun? There is.
He gave an extremely popular (and, in my opinion, the all-time funniest) TED talk.
And his ideas even attracted the attention of Oprah Winfrey, who filmed an interview with him.
What’s so special about Shawn’s work? His research shows that success doesn’t bring happiness — happiness brings success.
He did what a lot of researchers never do: instead of scrubbing the freak outliers from the data he aggressively studied them.
He wanted to know what people with happiness superpowers do that we don’t.
Instead of deleting those people that are weirdos in the data what we do is we intentionally study them. We try and find out why it is that while an entire sales force has low numbers, we’re finding three or four people whose sales are skyrocketing. Or we’re looking at a low socioeconomic school in Chicago, where the academic scores are below average, there are a couple students whose grades are skyrocketing. By studying those outliers, what we’re doing is we’re gleaning information not on how to move subpar performers up toward that average point, but how to move people from average to superior.
Shawn believes (and his research shows) that you can do things to be happier. And being happier will make you more successful.
I gave Shawn a call to find out what he’s learned. Want more joy and success in your life? Here’s what Shawn had to say.
1) Success Brings Happiness? No. Happiness Brings Success.
We all chase success hoping it will make us happy:
- I’ll be happy once I get that promotion.
- I’ll be happy once I get that raise.
- I’ll be happy once I lose 15 pounds.
But the research shows that isn’t true. You achieve a goal and you’re briefly happier… but then you’re looking toward the next big thing.
What Shawn’s research showed was when you flip the formula and focus on increasing happiness, you end up increasing success.
If we can get somebody to raise their levels of optimism or deepen their social connection or raise happiness, turns out every single business and educational outcome we know how to test for improves dramatically. You can increase your success rates for the rest of your life and your happiness levels will flatline, but if you raise your level of happiness and deepen optimism it turns out every single one of your success rates rises dramatically compared to what it would have been at negative, neutral, or stressed.
MET Life saw such great results among happy salespeople that they tried an experiment: they started hiring people based on optimism.
And that was even if those people performed poorly on the standard industry “aptitude test.” What was the result?
It turns out that the optimistic group outsold their more pessimistic counterparts by 19% in year one and 57% in year two.
How can this be? Shawn explained that intelligence and technical skills only predict 25% of success:
If we know the intelligence and technical skills of an employee, we can actually only predict about 25% of their job success. 75% of long term job success is predicted not by intelligence and technical skills, which is normally how we hire, educate and train, but it’s predicted by three other umbrella categories. It’s optimism (which is the belief that your behavior matters in the midst of challenge), your social connection (whether or not you have depth and breadth in your social relationships), and the way that you perceive stress.
And students who want success in their future should worry a little less about grades and more about optimism.
Shawn found that rolling a pair of dice was as predictive of your future income as your college GPA is. (And millionaires agree.)
(For more on how to be more optimistic, click here.)
So your attitude has a huge effect on how successful you are. What was the most powerful thing Shawn learned from looking at those happiness outliers?
2) See Problems As Challenges, Not Threats
Shawn did a study of bankers right after the huge banking crisis hit. Most of them were incredibly stressed. But a few were happy and resilient.
What did those guys have in common? They didn’t see problems as threats; they saw them as challenges to overcome.
What these positive outliers do is that when there are changes that occur in the economic landscape or the political landscape or at an educational institution, they see those changes not as threats, but as challenges.
So those people are just wired differently and our duty is to envy them, right? Nope. Shawn did an experiment that proved this attitude can be learned.
Just by showing the normal bankers a video explaining how to see stress as a challenge, he turned sad bankers into super-bankers.
And we watched those groups of people over the next three to six weeks, and what we found was if we could move people to view stress as enhancing, a challenge instead of as a threat, we saw a 23% drop in their stress-related symptoms. It produced a significant increase not only in levels of happiness, but a dramatic improvement in their levels of engagement at work as well.
(For more on what the happiest people do every day, click here.)
But what about when there’s just too much to do? Maybe there are more “challenges” than you can handle.
Should we just give up on any chance of work-life balance? Cancel those plans with friends and spend more hours at the office?
Once again the answer is the exact opposite.
3) Twice As Much Work Means You Need Friends Twice As Much
After doing his undergraduate work at Harvard, Shawn was a proctor there, helping freshman adapt to the often stressful, competitive environment.
Many students would respond to the workload by living in the library and eating meals in their bedrooms so they could keep studying.
Did those students perform better? No. Those were the ones who burned out; the ones who ended up wanting to transfer to another school.
Shawn would tell them what they had unknowingly done was cut themselves off from the greatest predictor of happiness.
The people who survive stress the best are the ones who actually increase their social investments in the middle of stress, which is the opposite of what most of us do.
Turns out that social connection is the greatest predictor of happiness we have when I run them in my studies. When we run social support metrics, they trump everything else we do, every time.
And what did we just learn about happiness? It predicts success. And it was no different here:
We found that social connection is extremely important for predicting academic achievement.
Want to resist stress, increase productivity and get a promotion? Then don’t just seek social support — provide it to others.
Confirming the research of top Wharton professor Adam Grant, people who provide social support get some of the greatest benefits.
Shawn saw this not only with his students at Harvard but he’s since advised over a third of the Fortune 100 companies — and it worked there too.
Work altruists were ten times more likely to be engaged than the bottom quartile of that list and the top quartile was significantly happier and 40% more likely to receive a promotion over the next 2-year period of time.
(For more on how work altruism can benefit you, click here.)
Some of you might be thinking, “Alright already, happiness makes you more successful. I get it. But how do I get happier?”
It’s simpler than you think.
4) Send A “Thank You” Email Every Morning
So Shawn believes rather than focusing on big boosts like vacations, it’s smarter to build little, consistent habits akin to brushing your teeth.
What little habit gives a big happiness boost over time? Send a 2-minute “thank you” email or text as soon as you get into the office.
The simplest thing you can do is a two-minute email praising or thanking one person that you know. We’ve done this at Facebook, at US Foods, we’ve done this at Microsoft. We had them write a two-minute email praising or thanking one person they know, and a different person each day for 21 days in a row. That’s it. What we find is this dramatically increases their social connection which is the greatest predictor of happiness we have in organizations. It also improves teamwork. We’ve measured the collective IQ of teams and the collective years of experience of teams but both of those metrics are trumped by social cohesion.
What other little daily happiness habits does Shawn recommend?
(For more on five emails that can improve your life, click here.)
Over 120,000 people receive my weekly email. And it’s sent from my real email address. People can reply. And they do.
What’s one of the most common things readers email me to say?
Eric, you suggest all these great things. I read them. I agree with them. But I don’t end up doing any of them. How can I follow through?
Shawn has a great answer for this too.
5) The 20-Second Rule
What stops you from making the changes you know you should? Shawn says it’s “activation energy.”
You know, like the activation energy it takes to initially get your butt off the couch and to the gym. The hard part is getting started.
If you reduce the amount of activation energy required, tough things become easy. So make new habits 20 seconds easier to start.
Shawn would sleep in his gym clothes and put his sneakers next to the bed and it made him much more likely to exercise when he woke up.
If you can make the positive habit three to 20 seconds easier to start, you’re likelihood of doing it rises dramatically.
And you can do the same thing by flipping it for negative habits. Watching too much television? Merely take out the batteries of the remote control creating a 20 second delay and it dramatically decreases the amount of television people will watch.
(For more easy ways to build new habits, click here.)
So how do we pull all this together? And what was the most inspiring thing Shawn told me about happiness and success?
Here’s what we can all learn from Shawn:
- Success doesn’t bring happiness. Happiness brings success.
- See problems as challenges, not threats.
- More work means you need more social support. And giving support is better than receiving.
- Send a 2-minute “thank you” email every morning.
- Use the 20-second rule to build the habit.
Some people might think it’s too hard to get happier. Maybe they’ve suffered from depression.
Or they’ve seen the research that we have a “happiness set point”, and our genetics ultimately decide how happy we can be.
You know what the most inspiring thing Shawn told me was? The latest research shows good habits might trump genes.
When you look at outliers on the graph, you find people who actually break the tyranny of genes and environment by creating these conscious positive habits that actually cause them to interact with life in a more positive way with higher levels of success, lower levels of stress, and higher levels of resilience. They do it by changing their mindset and changing their habits, and by doing so they actually trump their genes.
Most people accept that they’re just born some way and that’s how they’re going to be the rest of their life, and whatever they were last year is what they’re going to be this year. I think positive psychology shows us that that doesn’t actually have to be the case.
Send a gratitude email right now. It only takes 2 minutes. And send another one tomorrow.
That habit will make you happier. And being happier will make you more successful and deepen your relationships.
Happiness. Success. Strong relationships. What else really matters?