Marina Gorbis's Blog, page 1342

How the Market Ruined Twitter

There was a time when Twitter could be described as “plumbing.” Now the best description might be, “giant bank account with a company attached.” It’s hard not to see this as a big step backwards, and to wonder whether the standard venture-capital-to-public-company trajectory is turning out to be entirely wrong for an enterprise like Twitter.

Let me explain, starting with the plumbing. The quote comes from author, tech thinker, and now public-TV personality Steven Johnson: “The history of the Internet suggests that there have been cool Web sites that go in and out of fashion and then there have been open standards that become plumbing,” he told David Carr of The New York Times in January 2010. “Twitter is looking more and more like plumbing, and plumbing is eternal.”

As Johnson had described it in much more depth in a Time cover story a few months before, what made Twitter so promising and interesting and important was “the fact that many of its core features and applications have been developed by people who are not on the Twitter payroll.” Most of its conventions (the hashtag, for example) had been developed by users. And “the vast majority of its users interact with the service via software created by third parties.” It was basically an open-source enterprise, and seemed to owe most of its remarkable success to that openness.

Of course, that “success” didn’t come with a lot of revenue. For its first four years, Twitter was able to keep the servers running thanks to mainly to $150 million in funding from venture capitalists and angel investors. Then, after a few of those investors ousted co-founder and CEO Ev Williams in a boardroom coup late in 2010, Twitter raised another $1.2 billion in less than a year. Not surprisingly, the company stopped glorying in the openness of its ecosystem not long after that. Spooked by investor/entrepreneur Bill Gross’s attempt to build a sort of shadow Twitter by buying up the most popular third-party apps, Twitter began cracking down on those third-party software purveyors and taking control of its relationship with users (in order to better “monetize” them). It’s still the users whose creating and sharing gives Twitter its value as a business, but their activities are now mostly channeled and managed by the company itself. And while Twitter has taken some limited steps lately to win back outside app developers, the bigger news has been its apparent intent to move away from its simple chronological timeline to use algorithmic methods to determine what users see, as rival Facebook has done for years.

This incipient change has met with a hugely negative reaction among Twitter users, who seem to want to keep things just the way they are. As a pretty regular Twitter user, I have that kneejerk reaction, too. But keeping things just the way they are seems like a bad idea in today’s rapidly evolving digital environment. If Twitter were still open to such contributions, entrepreneurs and hackers would probably be experimenting feverishly with new ways to present the timeline, and users would likely be trying them out rather than complaining. But when Twitter itself does this, it feels to users like Big Brother is messing with their world. It’s no longer a user-created ecosystem. It’s just a company, trying to make some money.

Now it is true that Facebook acts in Big-Brother-like fashion all the time, and despite incessant complaints over the years it has proved remarkably successful at keeping users engaged. Twitter started in a different place, though, and its users have different expectations. The company has piles of money — $3.6 billion in cash and short-term investments — and my sense from looking at the numbers for the past couple of quarters is that it could probably be making some money, too (that is, generating positive free cash flow), if that were a priority. But the priority is instead growing fast enough to generate adequate returns on the $4 billion-plus (the company raised another $1.8 billion in its 2013 IPO) that investors have plowed into it. And the company’s latest (Oct. 27) earnings report doesn’t really show that kind of acceleration. “They’re not yet able to generate the kind of topline growth we’d normally expect to see in a company with the asset size they’ve got,” analyst  James Gellert of Rapid Ratings International told Forbes after the earnings came out. As I write this, the company’s stock price is down 44% from its peak last December.

In the early days, Twitter clearly owed much of its growth to its open, ecosystem-like approach. That growth would have slowed eventually in any case, but it’s hard not to think Twitter’s prospects as a network and as a societal force would be much greater if it had remained more like an ecosystem and less like a conventional corporation. I think there’s at least a chance that Twitter, Inc., would have brighter prospects under that scenario, too, but that’s easy for me to say. I’m not one of those investors who poured $4 billion into Twitter over the past four years and now understandably want the company to figure out how to make lots of money, pronto.

Could Twitter have chosen not to follow the standard VC-to-IPO path that has brought it to this pass? It would have taken a great degree of self-abnegation on the part of the founders, and a remarkable ability to resist Silicon Valley peer pressure. But there are alternatives. Many companies over the decades that have simply chosen to take things a bit slower and not become entirely beholden to outside investors. Ello, the new anti-Facebook that has gotten a lot of attention and $5.5 million in VC funding in the past few weeks, has organized itself as a public-benefit corporation with a charter that says it will never accept advertising. WordPress operates as an open-source ecosystem with a venture-backed corporation, Automattic, at its heart. The Internet itself is a giant cooperative endeavor that allows lots of companies to make gobs of money but most likely wouldn’t work at all if it were controlled by one of them.

At every corporation there are tensions between the demands and needs of shareholders and those of employees, customers, and other stakeholders. But many of the most important new enterprises of the digital age are especially dependent on their users — a group of people who are neither customers nor employees but often seem to generate almost all the enterprise’s value.  Fitting such organizations into the shareholder-dominated straitjacket that is the publicly traded corporation could be more than just irritating to these users. It might also be really bad business.

It’s Not HR’s Job to Be Strategic

Human-capital issues are top-of-mind for CEOs around the world — but their regard for the HR function remains perilously low: In a PwC study, only 34% said that HR is well prepared to capitalize on transformational trends (compared with 56% for finance).

Sadly, chief executives aren’t the only ones with this negative perception. It’s pervasive in organizations — and to make matters worse, HR practitioners have inadvertently played into it. In its “State of Human Capital” report, McKinsey found that people in HR still largely have “a support-function mindset, a low tolerance for risk, and a limited sense of strategic ‘authorship’” — all of which has led to “low status among executive peers, no budget for innovation, and a ‘zero-defects’ mentality.”

Though many HR managers would take exception to those findings, they do, overwhelmingly, want more of a strategic voice than they have now. Look at any HR discussion forum, and you’ll find some version of this question: How can HR get a “seat at the table” and become a strategic business partner?

I’m going to suggest that HR — at least in its current form — shouldn’t be a strategic partner. A few months ago, Ram Charan proposed splitting HR into two parts: one to oversee leadership and organization, and one to handle administration. That was a useful conversation starter. But companies should dice up the function even more finely. Instead of grouping all the people-related activities together under HR, businesses should organize them according to types of service provided — and move a couple of them to other functions altogether.

Consider Deloitte’s service delivery model, which divides activities into four categories: site support, transaction processing, center of expertise, and business partner. In this model, the bulk of HR services fall into the first three categories, where a cost-cutting mindset makes a good deal of sense. Examples include payroll, benefits, risk and compliance, and labor relations.

But talent acquisition and learning and development are altogether different — and they should never be done on the cheap. These areas fall under the fourth rubric, business partner, because their managers need a strong understanding of strategic priorities in order to recruit, prepare, and engage employees to meet them. These managers also bring a valuable perspective to the table. Together, they understand labor market trends and instructional design, which can inform a company’s strategy to “build” or “buy” talent.

First let’s look at talent acquisition. It’s critical to bring in the right people to drive the business forward. The thing is, the labor markets and relevant skills vary widely from function to function. Smart recruiting requires an intimate understanding of the work to be done and the skills needed, as well as the function’s business plan (to forecast demand). That’s the specialized insight required to develop the right sourcing and recruiting strategy. HR often centralizes talent acquisition in order to minimize costs, and that’s short-sighted. Saving a few hundred dollars per hire may seem like a quick win, but a bad hire can cost more than $50,000.

For its part, learning and development should enhance employees’ ability to further the company’s mission, mold future leaders, and build strong teams. But companies aren’t seeing it as the strategic opportunity it is — and that’s because of its placement in HR. I recently spoke with an HR executive at a Fortune 500 packaging firm in the Midwest whose annual budget for the entire high-potential development program was $10,000. That is not a vote of confidence.

You can see this low-value mindset play out in other ways. HR managers adore e-learning, for instance, since they have been conditioned to evaluate everything on cost and scalability. Indeed, according to the Association for Talent Development, nearly 40% of corporate training in 2013 was delivered through technology, and that number is projected to grow. Unfortunately, a lot of e-learning is just plain awful. My company recently surveyed 525 Millennials (people born after 1979) to understand their views on learning and leadership development. Though e-learning was among the most prevalent forms of leadership training, it ranked among those with the least impact — and it was the least desired among all other options. Tech-savvy Millennials are the most likely leaders-in-training to embrace e-learning, yet even they don’t.

Companies will really start feeling the consequences over the next decade. Millennial Branding and Monster.com found that one-third of Millennials rank training and development opportunities as a prospective employer’s top benefit. Cutting corners in this area may jeopardize employee engagement and retention in a demographic that will represent 75% of the U.S. labor force by 2025.

A centralized HR department is ill equipped to address this. But embedding learning and development — along with talent acquisition — within each business function can solve the problem because it will shift the focus from cost reduction to value creation.

By reorganizing HR activities along the lines of the service delivery model, companies can free their cost-focused services to provide excellent support without having to grapple with illusions of strategic grandeur. And they can empower the truly strategic services — talent acquisition and learning and development — to create value without having to view every decision through a cost-cutting lens. At the moment, business leaders are searching for a strategic partner to help them navigate the critical human-capital issues that will make or break their companies. The time has come to give them not one partner, but two.

Identifying the Biases Behind Your Bad Decisions

By now the message from decades of decision-making research and recent popular books such as Daniel Kahneman’s Thinking, Fast and Slow should be clear: The irrational manner in which the human brain often works influences people’s decisions in ways that they and others around them fail to anticipate. The resulting errors prevent us from making sound business and personal decisions, even when we’ve accumulated abundant work experience and knowledge.

Unfortunately, even though we know a lot about how biases like overconfidence, confirmation bais, and loss aversion affect our decisions, people still struggle to counter them in a systematic fashion so they don’t cause us to make ineffective, or poor, decisions. As a result, even when executives think they are taking appropriate steps to correct or overcome employee bias, their actions often don’t work.

What’s the solution? Behavioral economics — the study of how people make decisions, drawing on insights from the fields of psychology, judgment and decision making, and economics — can provide an answer. Since it is so difficult to rewire the human brain in order to fundamentally undo the patterns that lead to biases, behavioral economics advocates that we accept human decision-making errors as given and instead focus on altering the decision-making context in ways that lead to better outcomes. Managers can use this knowledge to improve the effectiveness of a process or system inside their organizations.

Just as an architect thinks carefully about how to best design environments and physical spaces to avoid inefficiencies, managers can adopt choice architecture. Choice architecture, a term used by Richard Thaler and Cass Sunstein in their 2008 book Nudge: Improving Decisions about Health, Wealth, and Happiness, refers to the way in which people’s decisions can be influenced by how choices are presented to them. Once managers consciously recognize the flawed thinking that is part of human nature, they can find ways to better design decision-making contexts.

But how to do this? Let’s consider an example. Maybe you remember how on Seinfeld, George Costanza would leave his car parked at the office on purpose, so that his boss would think he was working long hours. That’s an attempt to take advantage of what psychologist’s call input bias — the tendency to use signs of effort to judge outcomes, when actually the two may have little to do with each other. In this case, Costanza uses the bias to his advantage, to change the way his boss judged his productivity.

But knowing about this bias can also help managers enhance organizational effectiveness. For instance, by identifying important elements of the “choice architecture” that improves customer experience. In a recent paper, scholars Ryan Buell and Mike Norton (both at Harvard Business School) studied ways in which service organizations could improve customer satisfaction. They found that when a company visually showed the effort it exerted during transactions, customers were more likely to be satisfied while waiting for the service. When people can see the effort expended on their behalf in the delivery of a service — what Buell and Norton call “operational transparency” — they not only mind waiting less, but they actually value the service more.

Here’s how it works. In one of their studies, Buell and Norton created a fictitious travel website and asked people to search for a flight from Boston to Los Angeles. Some people saw a typical progress bar slowing being colored in, but others experienced operational transparency: The site showed each airline it was searching — “Now searching delta.com… Now searching jetblue.com…” — and created a dynamic running tally of the most affordable flights. Although all participants then received the same list of flights and fares, those who experienced this transparency rated the service much more highly than those who simply viewed the progress bar. And when asked to choose between a site that delivered instant results or one that made them wait, but showed its work, most people chose the latter.

To take another example, consider the default bias: To avoid the discomfort of complex choices, individuals usually opt for the default supplied to them even when choosing the alternative does not require much effort. Knowledge of this bias has led to a growing trend among employers to use defaults when presenting their employees with the choice of whether or not to save for retirement in an employer-sponsored savings plan. Companies are increasingly enrolling new hires in pension schemes automatically; individuals need to explicitly opt out if they are not interested in saving for retirement. Because automatic enrollment policies recognize the human tendency to procrastinate taking an important action, even when that action is personally beneficial, such policies lead to large increases in participation in retirement plans.

What these examples suggest is that insidious biases are often the main cause of ineffectiveness in organizations. But they also highlight that knowing about the existence of these biases and how they operate can lead to effective solutions to organizational problems. We commonly think of leaders as managers. But managers should also be architects who look for opportunities in the way work is structured to improve behavior to the benefit of individuals, customers, and the organization. (See our previous articles “To Change Employee or Customer Behavior, Start Small” and “Experiment with Organizational Change Before Going All In.”)

There are two steps to follow in order to accomplish this systematically. First, it is important to understand the main source of the organizational problem under consideration. Is the problem primarily driven by insufficient motivation or by the presence of cognitive biases?

For instance, let’s imagine your team is late in delivering a product to an important customer. Talking to those working on the team may reveal that they do not feel engaged at work (pointing to a motivation issue). But it may also reveal members made overconfident predictions on their ability to deliver on time (thus pointing to a cognitive-bias issue). If the latter is the case, the solution may be to automatically increase the time that a team predicts it will take it to carry out the work — an approach that has succeeded at Microsoft.

Second, managers need to carefully consider the costs and benefits of possible ways to change the choice architecture, in order to reduce or eliminate the bias. In some cases, the solution may consist of changing the process in order to force the individuals in question to deliberate more before making a decision. For instance, in the case of group decisions, the leader may assign a member to be a devil’s advocate or the person who asks tough questions (e.g., Is there any data suggesting that the course of action we want to take is not the right one?). Or, the leader could just create opportunities for the members to reflect and examine whether their actions are aligned with their plans. In other cases, it may be best to create a new process — like the default discussed above — that automatically takes care of the bias.

These two steps can help executives mitigate biases that prevent their businesses from achieving greater success.

Is the Corporate Campus Dying?

Jennifer Magnolfi, Founder & Principal Investigator at Programmable Habitats LLC, on how digital work, and the Internet of Things will fundamentally change the how we use the buildings and neighborhoods we work in. For more, read the article Workspaces That Move People.

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Put the “and” Back in “Sales and Marketing”

Nowhere else in the executive suite of a typical corporation are two functions as closely intertwined as sales and marketing. Yet for all the shared responsibility, the marketing and sales relationship has often been a contentious and lopsided one, with sales dominating in B2B sectors while marketing leads in B2C ones.

The joint challenge today for CMOs and heads of sales (or CSOs – Chief Sales Officers) is how they can work together to discover insights that matter, design the right offers and customer experiences based on those insights, and then deliver them effectively to the right people across multiple channels to drive growth. McKinsey research shows that companies with advanced marketing and sales capabilities tend to grow their revenue two to three times more than the average company within their sector.

But to get to that top tier, marketing and sales executives can no longer afford the inefficient silos that have long characterized the relationship. Here are three important elements of the CMO-CSO partnership to get right:

1. Build a joint local strategy. CMOs and sales leaders need to become experts at identifying and tapping micromarkets where there are often significant overlooked growth opportunities. But the real power of the partnership comes from their ability to bring the best of each of their departments—as well as pricing, operations, and other groups—to bear in exploiting those micromarket opportunities.

While that might sound obvious, heads of sales tend to set their goals geographically while CMOs often target segments, making it difficult to have a common baseline for comparing and checking progress. Leaders need to focus on how to create meaningful targets that use the best of each approach.

Consider the case of an Asian telecommunications company that found 20 percent of its marketing budget was being squandered in markets with the lowest lifetime customer value. The company shifted resources to its most lucrative markets, where two-thirds of the opportunity lay. Marketing then partnered with sales to reset customer acquisition goals at each micromarket, basing them on each market’s potential. They set, and met, revenue targets that were 10 percent higher than in previous years.

The CMO and head of sales should take the lead in pulling their departments together to jointly identify the best growth opportunities and translate the resulting insights into tools and plans the marketing and sales teams can use.

One important way to focus the effort is by managing the sales pipeline together. “It is very important for the head of sales and the CMO to have ongoing discussions about pipeline strategy and how the pipeline gets built,” says Linda Crawford, EVP and GM, salesforce.com. “People nailing that are taking the lion’s share of the business these days.”

We have found that when this process works well, marketing often takes on an expanded role by, for example, providing sales with data analytics and by supporting the development and testing of sales plays for a specific micromarket or customer peer group.

2. Collaborate around the customer decision journey. “Because customer expectations have changed so much, it’s even more important that marketing, sales and even service work closely together,” says Lynn Vojvodich, CMO for salesforce.com “Ultimately, you want to create personalized customer journeys that seamlessly integrate touch points across these functions.” The best CMOs and sales leaders are putting mechanisms in place to create a consistent experience for their customers, and identifying which marketing and sales investments will yield the greatest returns. That starts with developing a deep understanding of how customers behave and make decisions. While hardcore data analysis will get you partway, interviewing sales reps is also crucial to uncovering what customers want. “You’ve got to listen to the guys who are taking calls 24/7 and dealing with a customer every two or three minutes,” says Gary Booker, CMO for Dixons Retail. “They really know what the customer wants.”

Marketers and sales people should together be spending a significant percentage of their time with customers to understand current and emerging needs. One well-known product company, for instance, bypassed its distributors and embedded some of its engineers in paint shops because customers had reported having trouble keeping the walls clean. While there, they discovered dust in paint bays was causing defects. So they created a new system for their distributors that reduced paint job defects by 49 percent.

For this sort of collaboration to succeed, the CMO and head of sales need to be deliberate and visible in working with each other. This needs to go further than simply sending out joint emails and joining each other’s meetings. The CMO and head of sales should map out skills and capabilities needed to reach their goals, identify the skills that currently exist and where they reside in the organization, and identify and plan to redress talent gaps.  In addition, the two leaders need to identify disconnection points between the two groups and develop processes to bridge them.

When it comes to data, marketing insights teams have to adopt more of a customer service mentality, approaching sales reps on the front lines more like customers. From the sales side, teams need to be trained to take the insights generated by marketing and act on them. Teams from each function can also participate in joint assignments, and team members can be rotated through each other’s departments. Field marketing can also bring marketing closer to the sales force — and the customer. One European retail bank, for example, set up “opportunity labs” in its branches and agencies—i.e., at the point of delivery to the customer—where marketing could come together with sales to develop new customer programs.

3. Create a technology engine that powers the front lines. Investing in better and more useful technologies is critical for sales to move more quickly and effectively on the leads that marketing can uncover. That means investing in technologies to help turn ubiquitous mobile devices into sales tools and becoming more sophisticated about collecting data. In some industries (e.g. high tech), marketing can work with sales to define what data would be valuable then work with product development to create sensors that provide that data. Products can then provide feedback on when to get maintenance and when the product will have reached the end of its useful life.

But for all the potential technology provides, it’s important not to lose sight of what the point is. The fundamental truth about technological innovation is that it needs to help sales people make better decisions on the front lines. In the rush of excitement to build great tools, the resulting analysis is often either too complex for sales people to use or isn’t relevant to the immediate business opportunity. The challenge for the CMO is to reduce all the heavy backend analysis to a set of simple actions and guidelines that front-line sales people can use. And the challenge for the head of sales is to effectively articulate what insights are needed to make better decisions.

Caesars has taken that point of view to heart. When a guest has entered one of their hotels or casinos and interacted with it (through use of their loyalty card, or increasingly, based on beacons and similar technology throughout our properties), a host (the person responsible for helping and serving customers) will be alerted on their Blackberry or iPhone. That alert displays their historical behavior, what they have been interested in, what experience they had when they were last there, what food s/he likes, and where to find that person.

A cargo airline provides another example. Their marketing team developed a complex model that took all the frequently changing dynamics of the cargo industry, as well as opportunities for different negotiation strategies based on supply and demand, into account. But that wasn’t the real win. The company then took all that complexity and hid it behind a simple dashboard that it gave to the sales force. This dashboard provided simple guidelines on flight capacity, corresponding pricing, and competitor options. The result? A 20 percent boost in share of wallet.

The CMO and head of sales stand on the front lines of growth. They are best positioned to spot and understand emerging trends, build strong bonds with customers, and distill new opportunities into real action. But finding above-market growth will remain elusive until CMOs and heads of sales take the lead in developing a more cohesive approach to the marketplace.

Why Tim Cook’s Coming Out Matters for Apple, and Business

Ellen. Anderson Cooper. Michael Sam. All three broke barriers by coming out in their respective industries – comedy, television news, and football. Now they’re joined by Apple CEO Tim Cook, who just announced that he’s “proud to be gay” and, in the process, became the first Fortune 500 CEO to come out. Earlier this year, two CEOs of publicly traded – yet much smaller – firms came out. But until Tim Cook’s statement, “don’t ask, don’t tell” reigned at the highest echelons of corporate America – almost shocking in 2014, given that 91% of Fortune 500 firms prohibit discrimination based on sexual orientation.

As Cook notes, Apple has long taken a corporate stand in support of LGBT rights and has spoken up against discriminatory laws. But his announcement gives new heft to their commitment. Cook’s sexuality was long an open secret; as he acknowledges, “For years, I’ve been open with many people about my sexual orientation. Plenty of colleagues at Apple know I’m gay, and it doesn’t seem to make a difference in the way they treat me.” But it creates a sort of cognitive dissonance when a company is advocating for equality, yet its leader remains publicly quiet about his own identity.

Cook’s new openness shows that Apple is walking its talk on diversity – positioning them even more favorably in the never-ending Silicon Valley talent wars. It’s also likely to make him a more effective CEO. As Sylvia Ann Hewlett and Karen Sumberg reported in the Harvard Business Review, despite fears to the contrary, being out in the workplace actually has significant advantages – notably, that workers can concentrate on excelling at their jobs, and not “managing” their identity. (And remember, there are still 29 states where it’s legal to fire someone because they are gay.)

Indeed, even for those like Tim Cook, who was out to colleagues but not to “the world” at large, the stress of downplaying one’s identity can take a toll. Research by the Deloitte University Leadership Center for Inclusion showed that 83% of gay employees “covered” at work – i.e., even if they were technically out, they still felt the need to minimize their differences by, for instance, not bringing their partner to work functions, or not displaying family photographs at the office. Cook’s coming out demonstrates powerfully to executives at Apple – and elsewhere in the corporate world – that covering is no longer required to succeed at the top.

“I consider being gay among the greatest gifts God has given me,” says Cook, because it’s increased his empathy toward others and helped him learn to follow his own path – an important asset in a company that prizes innovation and built its brand on the strength of breakthrough ideas such as the iPhone.

“We’ll continue to fight for our values,” he says, “and I believe that any CEO of this incredible company, regardless of race, gender, or sexual orientation, would do the same.” That’s probably true, but it means a great deal for Apple, and the many companies who hope to emulate its success, that Cook is willing not just to speak up for equality in general, but also to stand up and be counted.

When Real-Time Intel Still Isn’t Fast Enough

We now live in a world where both man and machine can access data on almost any topic at any moment. Documentation of our world happens in real time, through a constant, autonomous torrent of ones and zeroes — and research and recall of that information have been reduced to mere mouse clicks. With all data available at all times, opportunities — and adversaries — can also move in real time. So we should ask ourselves, “How do we move faster?” This is the domain of predictive analytics — a concept that isn’t new, but the potential of which, in a world not limited by data or processing power, is expanding rapidly.

I’m at Lockheed Martin where we focus relentlessly on expanding and improving the technology and tradecraft to remain ahead of adversaries. Our investments in predictive analytics primarily serve the goal of anticipating threats emerging from dynamic environments, and being able to do so faster than others. (it would be naïve to think that our adversaries are not finding opposing uses for these technologies). From predicting the locations of roadside bombs to pinpointing the next government collapse, exploiting available data requires high-performance collection and rapid, thorough, and transparent analysis.

It is fascinating, however, that the solutions we’ve developed have also turned out to be effective in fighting other threats to safety and wellbeing – among them, criminal networks and bacterial infection.

Granted, the political and military turmoil right now in Syria and Iraq is a more typical focus of the analysts using something called LM Wisdom, the solution we developed to automate the collection of data and subject it to advanced processing and analysis. LM Wisdom is being used to monitor events in real-time, and correlate, aggregate, and index massive sets of multi-language data. By using processing steps and filters, analysts can collect information and integrate everything from locations to tonality of messages to modes by which people are communicating with one another. Once a model of what’s happening right now is created, correlation algorithms tailored to specific problem sets enable the prediction what might possibly happen next. Information processed through LM Wisdom augments traditional intelligence gathering, so decision makers can understand various threats and what they could rapidly become.

For over half a century, the aerospace and defense industry has been at the forefront of defining advanced analytic techniques, because we needed them to address highly complex engineering challenges. Some of these challenges are well known, such as propelling a man faster than the speed of sound and safely landing a man on the moon. Countless others may never fully be appreciated by the public at large. Perhaps the most impressive aspect of these early solutions was that they all relied on a multi-disciplinary approach, combining mathematics, engineering, computer science, and physical sciences.

What has become abundantly clear across the decades is that any application of analytics to a complex problem relies on three essential components. Analysts need to acquire meaningful and abundant data sets, often from multiple sources internal and external to the organization. Algorithms are then needed to weed out the noise from high-value information and “connect the dots” across the information. Lastly, analysts must rely on their tradecraft – the investigative skills required to ask the right questions of big data.

What is new, however, is that we are no longer limited by data or processing power. Data is enormous and available in real-time — we are now, as many have observed, in the era of Big Data. Processing power, meanwhile, is now so immense that we can capitalize on this abundance. It might seem that more data would increase the unlikelihood of finding the proverbial needle in the haystack, but this challenge is largely overcome by the sheer processing power available in modern computing platforms. The true value of expansive data is in the enablement of analytic prospecting — quickly identifying and recognizing patterns and connections within the data. We can look beyond finding the needle to finding patterns that might indicate the presence of a needle. We can truly start going faster than real-time.

Moreover, the same multi-disciplinary approach and computational ideas used to simulate airflows of fighter jets or predict missile trajectories can now be applied to harness data and unearth actionable intelligence in previously intractable areas. For example, we have employed data analytics to assist in the discovery and identification of criminal networks responsible for producing and distributing counterfeit drugs. Using essentially the same tools we use to make sense of political and military turmoil, we were able to discover the true identities and aliases of key players as well as the flow of money through the illicit network.

It turns out, too, that the same toolkit can be applied in medical settings.  We found that the signals that human bodies constantly emit can be tracked just like a missile or satellite. For example, our team developed an algorithm that detects sepsis, a potentially fatal blood condition, in patients’ bloodstreams up to 14 hours faster than currently employed techniques. Our bodies give off signals like temperature, blood pressure and white cell count, and using these signals, the algorithm can help health care systems and providers deliver more personalized medicine with higher likelihood of improved outcomes.

The power and applications will only continue to grow and spread. Big data will only get bigger. The more computing devices we connect to the internet of things and the more areas to which we apply complex algorithms will only expand the information we have prior to making decisions. As data and processing power cease to be a limiting factor such analysis will revolutionize the way we interact with the world and measure the risks of our decisions.

Of course, these growing capabilities are also available to people who mean to cause us harm. Meeting the challenges that they will present will always be a matter of staying ahead. In a world not limited by data or processing power, real-time awareness will not be sufficient. We will need to be faster.

For more expert insights on the power of predictive analytics, see HBR’s Insight Center, Predictive Analytics in Practice.

Imagining Productivity Apps for the Apple Watch

App developers from Stockholm to San Francisco are anxiously counting down the days til the November release of the Apple Watch SDK (or “Software Development Kit”), which will give them the tools to begin building their own concepts.

I’d argue that these developers stand at a crossroads for the Internet of Things (IoT). Following one path, they can design the familiar types of apps that we already see on tablets and phones, simply scaled-down for a smaller screen. In doing so, they would treat the watch — and by default its wearer — as just another connected data-collecting “thing” among “things.”

Following an alternative path, developers would design for the humanity of the wearer, prioritizing individual intelligence over collective intelligence. For example, they would prioritize my needs (say to make a smart decision) over the data aggregator’s needs (desire to sell tailored ads, for instance), offering the user radically new insights and privacy safeguards — a non-negotiable trait for such an intimate device. On this path, developers would mine insights from cognitive science and UX about the distinctive ways body, brain, and things interact. People wouldn’t be seen as just another IoT node. Below, I describe some strategies and insights to get the conversation (and prototyping) started:

What the Apple Watch does that other connected things don’t do. Many analyst predictions and early use cases, including those from Apple, place watch apps in a continuous narrative; one in which software is adapted from one generation to the next. First there was email on your PC, then on your mobile phone, and soon there will be apps that tweak e-mail for the watch. The storyline appears not only with productivity tools like email, but also with predictive apps (which use algorithms to make recommendations based on context or behavior, like Pandora) and social apps (like Pinterest), too.

By contrast, discontinuous opportunities will arise for those who see the watch as distinctive. For instance, what does it mean to wear a computer, sensors, and accelerometers on your wrist?* How might one build new value-producing offerings based on the natural physical and cognitive behaviors that are characteristic of the way people move their arms and wrists?

Gesture-based productivity apps. Consider the way we naturally gesture as we casually chat with colleagues, deliver presentations, or brainstorm with engineers. Research shows that our gestures and brains work together as a so-called “coupled system” to advance thinking. “By materializing thought in physical gesture, we create a stable physical presence that… productively impacts the neural elements of thought and reason,” observes one study. In other words, these gestures actually help to reinforce neural pathways to and from our brains. Moreover, gesturing can have the effect of freeing up our mental resources to take on new tasks.

On the flip side, taking away a person’s ability to gesture has a drastic negative effect on a test subject’s ability to remember new information.

Potential Uses: One of the perennial challenges for any executive or entrepreneur is how to measure their own knowledge work productivity. This measurement is especially difficult on the frequent occasions when the user is not working at a computer: for example, when presenting at a strategy offsite or giving feedback to a direct report at the break station. Rather than appearing in digital format, these “offsite” cognitive outputs seemingly disappear into thin air. Using gesture as a possible indicator of productive thinking, a variety of apps could offer personalized feedback on away-from-screen trends to support a leader’s capacity to self-reflect and improve with data.

Gesture-driven predictive apps. Experimental research also shows that gestures are predictive. Increasing gestural movement can signal that a mental task or decision is becoming tougher to solve or understand — a dynamic suggested when we call a concept “tough to grasp.”

In these instances, gestures seem to warm-up (or preshape) our thinking for the mental heavy-lifting and learning ahead.

Potential Uses: More and more smartphone apps learn a user’s behavior and proactively make suggestions based on context and predicted needs. For example, when it’s time to get ready to catch a return flight, an app can suggest a cab service near your hotel and then pull up your boarding pass as you arrive at the airport. Many such apps, which essentially help users outsource thinking to a cloud’s predictive intelligence, will soon be rerendered for the Apple Watch, offering similar wine in a new bottle.

By looking to the predictive powers of gesture, however, developers can enable human intelligence in practical and radical new ways. In what situations (one-on-ones, conferences, or team meetings) am I learning new and challenging things, according to the data? Are there certain days or times when I am better at tacking tough mental tasks, which would help me reorganize my schedule and work routines in a less ad hoc way? Apps that begin to answer these sorts of question will create significant value for business users.

Social-gesture apps. One person’s gesturing can have measurable influence on the brains of others, creating a social-intellectual stimulus by activating “mirror neurons” in people nearby. For instance, when scientists collaborate, researchers find that gesture is particularly helpful for highlighting and exploring potential connections between different data visualizations, quantitative charts, and CT scans.

In this sense, a gesture acts as material anchor for groups trying to turn abstract information sources into well-grounded practical insights.

Potential Uses: You can expect all the big online social networks to have an Apple Watch app ready for download, but do you really need your Twitter feed or location-based Facebook offers on your wrist? A truly differentiated offering will give users insight derived from their real-world interactions and collaborations involving data visualization — an increasingly critical skill for most workers and entrepreneurs. Here’s a fundamental question a social-gesture app may begin to address:, What type of data visualizations drive the best sales team discussions and decisions? By combining gestural data with the ability to manually input the type of data viz used, an app could indicate that the bubble chart at Monday’s meeting resulted in a quantifiably more engaging discussion than the scatter graphs and heat maps from Thursday and Friday’s meetings respectively. Moving forward, you can then test the hypothesis that your team works best around bubble charts by incorporating them more frequently into sales meetings and tracking the impact on monthly revenue production. Of course, this is just one possible use of many in the area of information sharing and analysis.

The initial wave of development for the IoT has focused on reducing the need for mental resources, especially in industrial and operational processes, that could otherwise be automated with new smart devices. When considering the potential for the IoT and wearables in corporate or start-up settings, however, the focus needs to shift back to helping people make the most of the heads on their shoulders — in some cases through the things on their the wrists.

*I’d like to thank my colleague, Keith Rollag, who explored this question with me in a recent discussion. I’d also like to refer readers to this book from Oxford University Press, which is an excellent compendium of some of the key experimental studies cited above.

Why Superstars Struggle to Bond with Their Teams

From the moment you start each workday, you’re subject to two basic human impulses: to excel and to conform.

If people in your immediate environment are amazing performers, you might be able to do both at once: By excelling, you fit the norm of your spectacular coworkers. But that’s rare. I’m pretty sure that in most work environments, as soon as you excel, you stop conforming. If you choose a high-performance path, you separate yourself from your coworkers. You’re not quite one of the bunch anymore. No matter how proud you are of your achievements, tell me it doesn’t hurt when you see your old group of friends coming back from a lunch that you somehow hadn’t known about.

I was thinking about this while reading research on the psychological and social effects not of being a high performer but of experiencing an extraordinary event, because the two situations share a few things in common. When something exciting and unusual happens to us, even if it’s random, we’ve excelled, in a way. We’re special. We no longer conform.

The research, by Gus Cooney and Daniel T. Gilbert of Harvard and Timothy D. Wilson of the University of Virginia, shows that after we go through an extraordinary experience, we assume that we’ll really enjoy telling the tale. But when we try, we often don’t feel so good about it. We feel separate. We sense that the group resents our excellent adventure. The study focused on experiences that are really only slightly extraordinary, such as watching an interesting video, but the results are pretty clear: A special experience distances us from other people, and the responses we see in our peers makes us feel excluded.

Jaclyn M. Jensen, an assistant professor in the Richard H. Driehaus College of Business at DePaul University, has put a different lens on what divides us from our coworkers and why. Along with Pankaj C. Patel of Ball State University and Jana L. Raver of Queen’s University in Canada, Jensen studied a large Midwestern field office of a U.S. financial services firm, using surveys to find out what was going on among coworkers — in the workrooms, during team meetings, in the lunchroom, and on email.

The researchers found that even in a collegial, well-behaved workplace, not only are you perceived as different if you’re a high performer; you’re also sometimes victimized. High-performing employees in this environment scored 3.37 on a 1-to-5 scale of victimization frequency, with 1 representing “never” and 5 representing “once a week or more.” They scored significantly higher on this measure of being victimized than average and poorly performing workers.

Mostly, the victimization was subtle, which is understandable, given the risks of being called out as a bully. So instead of being overtly nasty, people avoid you or withhold resources. Or they schedule important meetings when you happen to be out of town.

It probably goes without saying that there’s no rational logic to the victimization of high performers. After all, if you’re a high performer, by definition you have an outsized impact on the organization, and you help make the workgroup shine. Your victimizers’ incentive pay is probably even based (at least in part) on your achievements.

Still, what’s rational about human behavior? As Jensen pointed out to me, human beings have a pronounced tendency to punish those who violate unspoken norms. Average performers worry that you’re making them look bad. If they can bring you down a notch, they can alleviate (or at least they think they can alleviate) their negative feelings by reminding you what an “acceptable” level of performance looks like.

But one of the more interesting aspects of Jensen’s research is that the covert victimization is spotty — it doesn’t apply to all high performers. Certain achievers are spared the worst of the victimization. These are what Jensen and her colleagues call “benevolent” high performers.

Benevolent high performers are sensitive to what’s fair for other people; they put others’ needs ahead of their own. They’re cooperative, even altruistic at times.

OK, no great news there. But the reality is that high performers too often slip into what Jensen would call “non-benevolence” without even realizing it. They start to feel entitled to their hard-won authority. Sometimes they step on or manipulate others, telling themselves that all’s fair in pursuit of the greater good. Pretty soon they’re consistently putting their own needs first. To measure this, the researchers used the surveys to place employees along a continuum of behavior, with “entitled” at one end and “benevolent” at the other. Here “entitled” means having “low equity sensitivity” — a poor sense of what’s fair to others. (As you can see from the chart, low achievers are victimized too, but the researchers found that there’s a different rationale: Weak performers are punished for jeopardizing their coworkers’ success. Benevolence doesn’t help them much.)

So if you’re a high performer who’s being excluded or cold-shouldered, maybe it’s not so much your excellence that your coworkers are reacting to but your creeping non-benevolence. If they’re not looping you into lunch invites, maybe it’s because they’re starting to sense that you’re putting your own needs ahead of theirs.

If that’s the case, you know what to do. Jensen’s research shows that practicing thoughtfulness and cooperativeness really does work to defuse your colleagues’ impulse to take you down.

Cooney et al frame the issue as black and white. They write that there’s a basic conflict between our desires to “do what other people have not yet done and to be just like everyone else,” so that if we satisfy our impulse to stand out, we can’t conform any longer, and failure to conform leads to feelings of exclusion.

Jensen’s view suggests a different way of looking at it: Even if your high performance puts you on another plane, separating you from your old bunch, that nonconformity doesn’t have to come with the punishments of rejection or sniping. If you make an effort to be altruistic, the group will reward you. If not with lunch invitations, then at least with acceptance —  a kind of benevolence of its own.

The Internet of Things Will Change Your Company, Not Just Your Products

I have had a front row seat as companies have struggled to enter the emerging world of the Internet of Things — first, 10 years ago as a vice president at Ambient Devices, an MIT Media Lab spinoff that was a pioneer in commercializing IoT devices, and then as a consultant.

One of the biggest obstacles is that traditional functional departments often can’t meet the needs of IoT business models and have to evolve. Here are some of the challenges that I’ve observed:

Product management. Successful IoT plays require more than simply adding connectivity to a product and charging for service — something many companies don’t immediately understand. Building an IoT offering requires design thinking from the get-go. Specifically, it requires reimagining the business you are in, empathizing with your target customers and their challenges, and creatively determining how to most effectively solve their problems.

A company that understood this was Vitality, which reimagined the pill box as a smart service to get patients to take their medications in accordance with their physicians’ instructions. So instead of creating another pill dispenser, it launched a compliance-enhancing system.

In addressing the billion-dollar adherence problem, Vitality (since acquired by NANTHEALTH) considered the interests of the players in the diverse ecosystem, including pharmaceutical companies, retail pharmacies, and health care providers. It also took into account their roles in changing patient behavior.

The resulting GlowCap offering provides continuous, real-time communication to users and caregivers via a wireless connection. Changes in light and sound indicate when it’s time to take medication. Weight sensors in the pill cylinders indicate when the medication has been removed. Accounts can be set so that text notifications or a phone call are sent if a dose is missed. By pushing a button on the device, an individual can easily order a refill from his or her pharmacy. Weekly e-mails with detailed reports can also be set up, creating a comprehensive system of medication management.

Finance. Finance teams, which are not known for their flexibility to begin with, often have trouble changing their traditional planning, budgeting, and forecasting processes to accommodate radically new IoT business models. I saw this when traditional manufacturers tried to build internet intelligence into products like refrigerators, office products, and health management devices. The finance departments of these companies struggled to account in the same set of books for both one-time revenues for product sales and the recurring subscription revenues for IoT-related services.

Finance departments also had trouble dealing with the fact that the cost of services and the resulting subscription revenues can accrue in a complex manner. Prices for IoT-related services may be based on utilization, thus creating a sliding scale of costs and revenues based on bandwidth utilization, volume of API calls, or changing hosting costs.

Forecasting and planning for product upsells, service additions, increased utilization, and churn across both products and services can also be difficult. Finally, changes in the focus of the business can quickly upend long-held key performance indicators (such as average revenue per unit or customer lifetime value) that may be core to the company’s management culture and how the business is understood.

Operations. When product-based companies add services and connectivity, operational requirements increase. The resulting challenges may include new contract-manufacturing relationships, which can be a complicated and disorienting process for the uninitiated.

The addition of third-party services and shared customer ownership can introduce tiers of customer-support challenges. Inventory requirements, warranties, and returns may change. In addition, companies may suddenly find themselves having to comply with unfamiliar laws and regulations, including those related to the U.S. Federal Communications Commission, the U.S. Health Insurance Portability and Accountability Act (HIPAA),  and customer “Personally Identifiable Information” (confidential data such as names, addresses, contact information).

Sales. In IoT businesses, sales departments often struggle to determine how to best take a combined product and service to market. New skills may be required, new distribution options may emerge, and field conflict (direct and channel) is not uncommon. Sales operations must consider changes to market segmentation, territory management, and resource allocation. Numerous opportunities may arise for distribution partnerships, and determining how best to approach partnerships and compensation can be complicated. (Channel compensation for subscription services with recurring revenue can be a particular challenge.)

Human resources. HR has the job of developing the human capabilities needed to capture the IoT opportunity. These may involve new areas for the company (e.g., telemetry, communications and connectivity protocols, electrical hardware engineering). Building them can be an especially daunting task when the business itself is unsure of what capabilities are required.

Sometimes in IoT businesses, the answer is actually less tech and more traditional execution. My favorite example of this is iRobot, the maker of the innovative Roomba vacuum.

Rodney Brooks, a former director of the MIT Computer Science & Artificial Intelligence Lab and the co-founder of iRobot, told me that the company initially believed that robotics specialists could best explain its robotics-based offerings to the market. But it then discovered that the folks who could best distribute the offerings were vacuum industry veterans who knew the industry lingo, had existing relationships, and understood and managed the channels of distribution.

Engineering. It is rare for a single company to have all the required engineering capabilities under one roof. Consider the breadth and scope that may involve communications and connectivity technologies (telemetry, WiFi, Bluetooth, Zigbee), electrical hardware engineering (sensor technologies, chips, firmware, etc.), and design and user experience. Developing these engineering skills is one big challenge; integrating them into a functional, integrated engineering effort is another.

Since new companies built from the ground up as IoT businesses lack the departmental baggage of older firms trying to make the transition into the IoT world, the former’s learning curve is often shorter. However, the immaturity of the IoT industry means that the practices and capabilities that suffice today will not tomorrow. An ability to evolve — and to do so quickly — is a prerequisite for success.