We all know that relationships take work. They require compromise, balance and playing up one another’s strengths and weaknesses. The same applies to business partnerships. Big or small, personal or professional, everyone is looking for that winning combination that brings out the best each party has to offer.
Although large, established enterprises and rising startups reside on opposite sides of the business spectrum, magic can happen when the two work together. However, the spark in such relationships can quickly fizzle. This is not only because 90 percent of startups fail, but also because the two types of partners have different approaches to innovation – a key ingredient for business survival. So, how can these partners work together more efficiently?
What's mine is yours
Big companies and startups each possess something the other needs to innovate. Frankly, many large corporations struggle with the process of innovation itself. Although equipped with brand recognition, resources and funding, time spent blazing a trail could potentially take focus away from core products and services. Similarly, financial teams are driven by numbers and will often be reluctant to invest in something unproven without clear ROI validation and other metrics. Thus, innovation can mean bottlenecks, uncertainty and shifts in market perception.
On the other hand, while startups crave credibility, cash flow, and better resources, they offer cutting-edge talent, best-of-breed expertise, less bureaucracy, agility, and faster time-to-market than their corporate counterparts. But, when large and small players bring their complementary strengths to the table, they fill in the other’s gaps and create new ideas, opportunities and revenue growth.
Feel the fear and do it anyway
Smaller companies and startups sometimes fear that an enterprise partner is really out to steal their intellectual property. That’s not the case – corporations put their muscle behind scaling ideas, not so much in developing them. In contrast, startups are more focused on their people and culture. Harnessing these invaluable resources to innovate and execute together speaks volumes to established companies and potential partners.
Despite their attraction to startups, big companies must overcome a few challenges in working with their smaller counterparts, and startups need to take note. Often driven by fears of being taken advantage of, startups are reluctant to be fully transparent. This can make it difficult for the bigger companies to accurately assess a product or service’s viability. When a startup holds its cards too close to its chest, it becomes challenging for larger corporations to determine whether the startup is the right investment fit. Add that to the lack of a track record and partnering can be a risky proposition. These barriers – some real, some based on misperceptions and fears – have kept too many potential partners at bay. Akin to “taking the plunge” in marriage, businesses will find that a leap of faith can lead to positive outcomes.
A better engagement model
As with any relationship, big companies and startups must spend time together, far beyond emails and phone calls. For example, large technology companies should offer events for startups, inviting them to industry conferences, hackathons or competitions centered on a particular solution or market theme. For startups developing solutions based on specific business or industry needs, this is a great way to gain visibility. These events and interactions provide opportunities to get to know each other one-on-one, strengthen relationships and build mutual trust – as a vendor, channel partner or even potential acquisition. Startups need to use these types of industry events to find companies that share their philosophies, as well as their passion for ideas and partnership.
Similarly, both types of companies should also get involved in their local technology and innovation communities. This will allow everyone to remain in the know around the latest processes and protocols, and uncover new ways to solve business problems. Partner to offer challenges, mentorships, events and opportunities to meet potential customers. Large companies can also establish or engage with innovation accelerators that give startups access to expertise and allow them to further their solutions.
Most importantly, emphasize open, two-way communication throughout this process – both externally and internally. The last thing big companies want is to appear foreboding to potential entrepreneurs. Be bold in your approach and respectful in your interactions.
True story: A tale of mutual success
When my company, Cisco, decided to partner with Japanese Internet of Things (IoT) firm, smart-FOA, both parties recognized that the value in the partnership far outweighed the risks. FOA, which stands for Flow Oriented Approach software, provides an information-sharing platform that can resolve problems and enable real-time decision-making. This is achieved by creating “information strips” that combine with the raw data collected by manufacturing workplace sensors – letting all of it flow on the network so information can be accessed from anywhere. It is essentially fog computing – decentralized processes at the network edge. With Japan known for its ability to utilize data from manufacturing plants, FOA was a natural evolution.
As we worked together, we made sure to understand how our strengths and solutions complemented one another. From Cisco’s perspective, investing in smart-FOA would generate new value by bringing together goods, people, processes and data. In doing this, it would significantly expand Japan’s IoT solutions across global markets. But to unearth this value, we had to maintain open lines of clear communication, exhibit mutual respect and trust, and relish the quick and nimble pace of innovation that comes almost naturally to a startup.
Every relationship requires compromise, communication and a little faith. When combined in the business realm, these elements establish the trust needed to drive a mutually beneficial partnership, with each side gaining confidence through investment in the other. The result is faster innovation, greater market share, and hopefully, making magic together for many years to come.
A professional employer organization (PEO) provides invaluable services for small businesses throughout the country. According to NAPEO, there are approximately 900 PEOs serving more than 175,000 businesses with more than 3.5 million employees. These businesses have partnered with a PEO to solve one or more business pain points, like dealing with payroll and employee benefits, navigating workers' compensation and other legalities, or recruiting new talent.
Payroll and other benefits
Payroll processing can be one of the most attractive offerings of a PEO. Many small business owners find themselves spending inordinate amounts of time dealing with the various administrative tasks associated with payroll, such as payment processing, accounting, filing W-2s and garnishing wages. All of this can be accomplished with virtually no hands-on involvement from the business owner, which frees up their time to improve the business in more tangible ways.
Offering benefits is something that many small business owners feel is beyond the scope of their financial situation. While this may be true if they were to negotiate benefits individually with insurance and retirement companies, partnering with a PEO exponentially increases their ability to offer attractive benefits to their employees. In addition, small business owners are relieved of the tedious task of administering these benefits to their employees. The PEO will take over providing enrollment, negotiating benefits and handling COBRA events as they occur.
Editor's note: Looking for a PEO for your business? Fill out the below questionnaire to have our vendor partners contact you with free information.
To recruit and keep the best employees, it's necessary for small businesses to offer more comprehensive benefits packages. Exceptional employees are difficult to find and cultivate. Providing incentives to remain with the business builds loyalty and improves the caliber of employees the owner can choose from during the recruitment process.
Workers' compensation and other legalities
Workers' compensation claims can be time-consuming and costly for small business owners. In addition to providing coverage, a PEO will direct the administrative tasks that occur if a claim is made. Claims include facilitating the company's compliance with federal laws, associated paperwork, audits and certifications.
Because of the complexity of various state and federal employment laws, human resources can be a nightmare for small business owners. Failing to keep up with the ever-changing tide of requirements can be costly. However, a PEO has an entire staff dedicated to this. This can be invaluable for small businesses with employees in various states, which is common in today's world of telecommuting.
Wage equity and paid sick leave laws are other potential hotspots for small business owners. While these can be difficult enough for companies hiring locally, the difficulty in managing a remote team can make the process nearly impossible for a small company to remain current and in compliance without outside assistance.
One of the most overlooked perks of working with a PEO is the assistance a small business owner can receive in the event of a lawsuit. Many PEOs share the liability associated with specific employment-related lawsuits, such as for wrongful termination. They also have teams of experts to help small business owners ensure they remain compliant with state and federal laws relating to discrimination and harassment. While this is not a replacement for legal counsel or various forms of liability insurance, it's an important tool in the prevention of issues that could lead to costly litigation.
Recruiting new talent
It can be incredibly difficult to find highly qualified people to fill crucial roles within a small business. Many small business owners don't have the time or expertise necessary to reach a wide pool of applicants. In the past they may have found themselves settling for who was available, even if they weren't the best fit for their company.
Many of the top PEOs offer recruitment assistance that can dramatically increase the speed and efficacy of the recruitment process. Services may include writing and placing advertisements, interviewing applicants, and administering background checks. Experienced recruiters are adept at asking the right questions to analyze prospective employees in ways the average small business owner can't. Considering the cost associated with finding and training an employee, this service is certainly worth considering for companies that are anticipating an increase in the employees they will need in the future.
For a business owner to choose the best PEO service, it is necessary to first understand exactly what their expectations should be. By knowing which of the available services are the most important to your business, it is easier to narrow down the field of possibilities and move forward in taking your business to the next level.
I've started and grown six hyper-growth tech businesses – including two that have IPO'd – where I grew up in Ames, Iowa. And if you think that sounds unbelievable today, you should have heard the responses when I told people I was building another company in a small town in the Midwest a decade ago.
At that time, I heard a lot of jokes about farmers and flyover country, but the truth was, venture capitalists largely stuck to the coasts. My company didn't get much interest from professional investors until we'd been operating for several years and had nearly $100 million in revenue.
But things have changed. My current company, Vertex, is just one year old and 57 people strong, and I get at least five unsolicited emails a week from some of the biggest investors in the world. Interest from venture capitals in the Midwest and other nontraditional tech environments is spiking, and some of the most innovative and promising businesses are developing far away from the usual tech hubs.
But the best way to foster tech success in your region is to build cutting-edge tech companies and attract smart people to the area to help you do it. At Vertex, 20 percent of our workforce has been recruited from out of state. We've attracted people from Silicon Valley, Boston, Chicago and Houston with the promise of working with bleeding-edge tech and a product that will be an industry game-changer. Relocating, I hope, will be great for their careers, but also for Iowa's tech ecosystem.
At my past business, Workiva, I recruited a talented employee from Microsoft. He picked up and moved with his young family from Seattle to Iowa. Six months later, he asked to meet for a coffee. "Dan," he told me, "I am so glad that you convinced me to come here. The people here are nice. The cost of living is low. There are so many things to do. Seattle's a great place, but Ames is the best-kept secret in the country."
I was happy his family got to take advantage of everything Iowa has to offer – the great quality of life and excellent schools – but highly skilled and compensated people like this engineer discovering our region is also great for our community.
Growing the next generation of entrepreneurs
Not only does building businesses attract intellectual capital to the area, it also builds it among native workers, giving employees from both far away and the next town over a chance to see up close how a hyper-growth business is built. There's cross-pollination of ideas between newcomers and locals, and a new generation of leadership learns the ropes of how to create and grow an early-stage business.
My hope is that building my businesses in my hometown and being conscious about hiring employees who want to put down roots here will help people learn enough about scaling a business to do it themselves one day, right here in Iowa.
I remain involved in mentoring and investing in early-stage businesses, but when I really look at it, the best way to foster tech success in a nontraditional tech environment is to get out and build a tech company. The startup bug is contagious, and if you gather great talent and keep them close, some of them are sure to catch it.
When Twitter launched in March 2006, the earth did not move. Its founders and a few early funders were excited about the technology, but the microblogging site was not the immediate blockbuster you might imagine it was, given that it now has more than 300 million users and has become a wildly influential marketing tool for businesses, nonprofits, and even politicians. Rather, Twitter crept along in its early months, growing slowly.
So, what happened to transform it from another also-ran into one of the largest communication platforms in the world?
Twitter seems on the surface to be the kind of technology that journalist Malcolm Gladwell and Wharton School marketing professor Jonah Berger refer to as “contagious.”1 To jump-start Twitter’s growth, its founders decided to promote it at a South by Southwest (SXSW) Interactive conference in 2007, where it was a big hit. From there, people assume it rapidly spread across the United States through the internet, thanks to social contacts connected by what network researchers call “weak ties” and “long bridges.”2 Two years later, in 2009, Twitter adoptions were catapulted into a global orbit when a major opinion leader, Oprah Winfrey, sent her first tweet on her talk show.
That narrative is easy to grasp and compelling. It gives startups, and the people who invest in them, a road map for success. Unfortunately, it is also inaccurate, and the road map leads to a dead end.
In a very interesting study, Twitter’s actual growth pattern was revealed to be surprisingly geographic.3 Friends and neighbors adopted the technology from one another in much the same way people join a PTA fund-raiser or get excited about a candidate for town office. Twitter didn’t spread virally across the internet; it spread locally, like a grassroots social movement.
Although that explanation of Twitter’s success is less sensational than the usual “going viral” story, it is far more useful for understanding how social networks promote behavioral change. And it corresponds with a growing body of research that describes behavioral change as a complex contagion, which needs reinforcing ties and wide bridges to spread. We’ll explore those concepts here. They are key elements in a diffusion playbook for companies attempting to launch innovations and facilitate both customer and employee adoption.
Contagions: Simple Versus Complex
Let’s begin by discussing the essential but often-unrecognized distinction between two kinds of contagions: simple and complex.4 Simple (or viral) contagions, such as the transmission of the flu or measles, spread through a single activated contact. Complex contagions, such as the adoption of new behaviors, require multiple sources of exposure.
Even though there may be only one person in your network who has the flu, if that person sneezes on you, you are likely to catch it. If you in turn sneeze on others, they can also become infected, and so on. The germs move fast. At no point does anyone need to be persuaded to get sick.
Like the flu, most information spreads via simple contagion. If you learn the score of today’s playoff game, you can easily repeat it at a party. Anyone who hears you also learns the score and can just as easily repeat this information to others. News propagates effortlessly through a network.
That’s not the case for technological innovations and practices — or, really, anything involving meaningful behavioral change — because adoption often involves financial, psychological, or reputational risks. At least four psychological mechanisms help explain why a complex contagion requires multiple sources of reinforcement:
Strategic complementarity: The more people who adopt an innovation or a behavior, the more its value increases. Even free and/or easy-to-adopt technologies, like Twitter and Facebook (and phones and fax machines), take time and exposure to spread, since their value increases with the number of users you know.5Credibility: The more people who adopt a behavior, and the more we trust them, the more believable it is that the behavior is worth the cost or risk of adoption. Credibility matters a great deal when individuals or organizations decide whether to invest in expensive new technologies, for instance.
Legitimacy: The more people who adopt a behavior, the greater the expectation that others will approve of the decision to adopt and the lower the risk of embarrassment or sanction. Think fashion trends.
Emotional contagion: The more people who adopt a behavior, the more excited other people get about adopting it. This is the mechanism at work in a workshop where participants reinforce one another’s enthusiasm about learning a new practice.
At the heart of all four mechanisms is a need for social confirmation from more than one person. That’s something we all tend to seek in “adoption” decisions, such as investing in a new technology or market or selecting a partner for a new venture, because the stakes are high and we want to mitigate risk. Whereas multiple exposures to the same individual may be sufficient for simple contagions to spread, multiple sources of exposure are needed to transmit complex contagions. If we know many people who can vouch for the new technology or business partner we’re considering, we’ll feel much better about diving in.
Social Ties: Weak Versus Strong
Once we acknowledge that behavioral change is a complex contagion, we must also reconsider the conventional wisdom regarding weak and strong ties in social networks.
The distinction between weak and strong ties, introduced by Mark Granovetter in the 1970s, is powerful and clear: Your casual acquaintances — the people you meet at a conference, in an Uber car, or on a cruise — are your weak ties. They are random connections that link you to new people. They are your outer social circle. Conversely, your close friends and family are your trusted strong ties. They make up your inner social circle.
Granovetter found that strong ties are not a great way to spread a new idea.6 Why? Because they all know one another, so there’s redundancy. Even if your message is sticky and popular, if it spreads only through strong ties, it keeps bouncing around the community of people who already know about it.
Granovetter identified weak ties as the solution to this frustrating problem. They connect you to people and ideas that you would never discover through your strong ties. They are the best people to enlist in your promotional campaigns precisely because you do not know them very well. And they connect you to strangers — people you don’t know at all and, most likely, never will. They give your idea reach by creating an invisible link from your network to parties to which you have no direct access.
Indeed, the power of influencers like Oprah Winfrey comes from the fact that they have so many weak ties in their social networks. Their messages reach hundreds of social circles, exposing an idea not only to many people but also to many kinds of people. That exposure is the essence of viral diffusion.
For simple contagions, weak ties are all you need. But while Granovetter argued that “whatever is to be diffused” will spread most effectively through weak ties, we cannot generalize from the spread of simple contagions to the diffusion of complex contagions.7
As it turns out, you need redundant ties to get people to adopt new behaviors, and most executives are not aware of that. They are more likely to subscribe to viral theories of innovation diffusion, following the lead of Gladwell, who wrote, “Ideas can be contagious in exactly the same way that a virus is.”8
In fact, the more complex a contagion, the more its diffusion depends on social confirmation from multiple sources. For the same reasons that Granovetter said strong ties inhibit simple contagions like information sharing, they facilitate complex contagions like innovation adoption.
Bridges: Narrow Versus Wide
The trust that is inherent to strong ties is not the only advantage they offer for spreading a complex contagion. Their most important feature is the reinforcement that results from multiple sources of exposure. That’s why a nuanced understanding of bridges is a key to understanding diffusion.9
Ever since Granovetter’s pioneering work on diffusion, connections between distant parts of a population have been called bridges. We typically gauge their value by their length (the distance spanned by the bridge), and we think of long bridges as pathways for weak ties to do their viral work. They spread simple contagions through reach, not redundancy. For instance, in a company where the members of the engineering team do not have any direct contact with the members of the sales team, let’s say one engineer, Jacob, goes out of his way to connect with one of the sales associates, Rashid. The tie between Jacob and Rashid is the only tie between the two groups, and because this tie connects two distinct parts of a social network, it acts as a bridge between them, without which there would be no direct communication. A bridge, in other words, is a long-distance tie.
The more enterprising Jacob is, the more of these ties he can seek out. He can create bridges to the manufacturing, design, and marketing groups. By doing so, Jacob will provide a great service to the organization because these bridges will accelerate the spread of new information. Not incidentally, because he has so many long-distance ties across the organization, he also positions himself as a very important actor in the organizational economy of information brokering.
But we also can measure bridges by their width (the number of ties they contain). What if, instead of forming new ties to so many different departments, Jacob instead introduced Rashid to some of his engineering colleagues, and Rashid, in turn, set up a few meetings to create connections between the other sales associates and some of Jacob’s friends in engineering? These interactions would establish new pathways of communication between engineering and sales. From Jacob’s point of view, some of his structural advantage has been lost. He’s no longer the sole broker for information flow between engineering and the other groups. The bridge between the engineering and sales teams has become much wider, and it now comprises several close ties instead of one long-distance tie between Jacob and Rashid.
Why would Jacob trade his unique brokerage position at the intersection of engineering and several other departments to create reinforcing ties between engineering and sales? Doesn’t having fewer long-distance connections slow down the diffusion process for simple contagions? It does, but for complex contagions, long-distance ties are precisely the problem. A signal that travels across a narrow bridge arrives alone, without any social reinforcement. In other words, narrow bridges do not create useful pathways for complex contagions to diffuse.
In addition to not helping, narrow bridges can hurt diffusion. Efforts to create more-efficient pathways to accelerate information flow between weak, distant ties can inadvertently erode the social reinforcement that is necessary to maintain behavioral influence. For instance, suppose everyone in the engineering group follows Jacob’s enterprising lead of creating brokerage ties across the organization — and in the process, they neglect their “in-group” ties to members of engineering in favor of cultivating “out-group” ties to members of other divisions, such as accounting and customer service. As these social entrepreneurs do more networking across the organization, they’ll maintain fewer connections inside engineering. The ironic result: An initiative to spread a new innovation within their team may fail because the engineers have become so focused on their weak ties that they have minimal connections left in their own group. This has implications for any setting in which creating networks for speedy information diffusion may undercut the goals of spreading a behavioral innovation — for instance, when you’re trying to grow group solidarity, spread complex technical knowledge, or diffuse new cultural norms in an organization.10
Narrow bridges are where the viral story of Twitter’s success goes wrong: Just because one person you know uses Twitter, that will not necessarily convince you to use it, too. Learning about it quickly through that single contact won’t spur adoption. It needs to be worth your time. The reason we use Twitter is because lots of other people are using it. Without them, Twitter is useless. Wide bridges made Twitter a success.
And not just Twitter. In the past few years, researchers have found that Facebook and Skype also spread through complex contagion — for the same reason as Twitter.11 They are all worth adopting only if many of the people you want to interact with have also adopted them. Particularly during early diffusion, social reinforcement through wide bridges is essential. Redundancy, not reach, is the mantra for diffusing complex contagions.
Spreading Innovations in and Across Organizations
The story of Jacob the engineer also illustrates sociologist Ronald Burt’s concept of structural holes, which is the source of what is perhaps the most influential application of network theory to organizations.12 Burt defines a structural hole as a gap between two diverse social clusters that prevents access to nonredundant information.
The strategic benefits for individual brokers who bridge structural holes are enormous. They have exclusive access to new information. And they are more likely to be included in new opportunities, because their visibility is increased by the diversity of their contacts. These benefits beget more benefits: For instance, brokers’ access to novel information makes them more attractive ties for other people looking to establish brokerage connections.
In turn, those benefits can translate into organizational value. Without brokers, information would fail to diffuse beyond established clusters. Bridges that span structural holes have thus been argued to play an essential role in promoting cultural exchange and knowledge transfer within and across companies.13
But brokers are less valuable for promoting innovation adoption and other behavioral changes in organizations. They are unlikely to transmit practices or norms that require social reinforcement, for three critical reasons.
First, an individual broker is not necessarily trustworthy. That person can exploit both sides for his or her advantage, and both sides know that the broker is the only link between the two otherwise disconnected groups. This may not have any significant consequences for simple information sharing, but for the spread of a new business practice or adoption of a costly new tool, the sincerity and trustworthiness of the messenger can be just as important as the message.14
With wide bridges, however, individuals on both sides of the bridge have multiple contacts in common. Thus, the potential for reputation effects at both ends puts constraints on the actions of the bridge members.15 Careless or exploitative behavior by a bridging individual is likely to be detected and therefore less likely to happen. Wide bridges between groups increase the trustworthiness of messages coming from other parts of an organization.
Second, one group’s innovative new technology or practice is not necessarily useful to another group. Even if a broker has good intentions, the interests and goals of an innovating group may feel too different from those of a receiving group to merit adopting the change.16
But a wide bridge between groups can smooth adoption. If multiple members of a receiving group share contacts in common with members of an innovating group, the credibility of the change increases. For instance, if one team in an organization has multiple contacts with colleagues on another team that has adopted a new kind of project management software, that allows them to observe how easily the members of the innovating group work together to use the new software and how effective it is for improving their performance. These reinforcing exposures increase the likelihood that the receiving group will be willing to coordinate on adopting it.17
Third, a single broker between two groups is a fragile bridge. Indeed, the power that an individual gains from holding this structural position is due in part to the costs an organization will face if he or she leaves. Redundancy eliminates this advantage. Wide bridges endure even as individuals come and go.
The advantages of wide bridges over brokerage ties are especially relevant to partnerships between organizations. The wider the bridges are between organizations, the more reliable and enduring these relationships are likely to be, and the more influence they will have over each organization’s culture and the adoption of innovative practices.18
When we realize how often the dream of virality does not take network context into account, it becomes easier to understand why so many innovation initiatives and change efforts fail. To appreciate the context of a contagion is to appreciate how susceptible new-behavior adoption is to both countervailing influences and positive reinforcement. If we cultivate that understanding and rely on strong ties and wide bridges to spread innovations and pursue behavior change, we can dramatically improve the success of our diffusion efforts.
Before making such a profound career change, the most important factor to consider is your risk tolerance. Entrepreneurship is inherently a risky proposition that may lead to uncharted situations with immense risks. If you are not willing to take such risks, it is unlikely for you to be able to cope with the uncertainties of entrepreneurship. - Kamyar Shah, World Consulting Group
Originally published at https://www.forbes.com/sites/forbescoachescouncil/2018/11/06/think-youre-ready-to-quit-your-day-job-11-entrepreneurs-offer-their-advice/#6b4b62125aca
Becoming aleaderis not something that occurs quickly or easily. The most effective way to be recognized as aleaderis to help others become leaders. That servant leadership will inherently translate into authority, credibility and, eventually, the recognition of being aleader. - Kamyar Shah, World Consulting Group
Originally published at https://www.forbes.com/sites/forbescoachescouncil/2018/11/06/managing-more-experienced-colleagues-13-ways-to-establish-yourself-as-a-leader/#31be2cc13a66
The one question that almost always trips candidates is: What else should I have asked you about? This is usually asked at the end of the interview. From experience, most applicants are either shocked or outright speechless. Those who are able to quickly and calmly point to some of their strengths that were not brought up during the interview will leave the interviewer with a lasting impact. -Kamyar Shah,World Consulting Group
Originally published: https://www.forbes.com/sites/forbescoachescouncil/2018/11/06/15-off-the-wall-interview-questions-you-should-know-how-to-answer/#42a07f3478d5
Season 2 – Episode 4
Can Netflix Keep Winning? And Why People Are Fleeing Latin America
Youngme, Mihir, and Felix debate whether Netflix’s success is sustainable, before trying to wrap their heads around the unthinkably high murder rate in Latin America. They also share their After Hours picks for the week.Read more
Season 2 – Episode 3
How Bad is Airline Service, Really? And Other Customer Service Complaints
Youngme and Mihir welcome their colleague Ryan Buell to discuss whether airlines deserve their reputation for terrible customer service. They also share other customer service pet peeves, as well as their personal “Customer Experience Picks.”Read more
Season 2 – Episode 2
Is Retail Dying? Plus, How Are Companies Spending their Tax Cuts?
Youngme, Mihir, and Felix discuss whether the “retailpocalypse” is real, try to figure out how companies are spending their Trump tax cuts, debate whether share buybacks are a good thing or a bad thing, and offer their picks for the week.Read more
Season 2 – Episode 1
Debating Minimum Wage, and Reflections on a Year of #MeToo
Youngme, Mihir, and Felix are back with Season 2 of After Hours! In this episode, they debate whether the federal minimum wage should be raised, offer their personal reflections on a year of the #MeToo movement, and share their picks for the week.Read more
Season 1 – Episode 20
New Media and Predictive Policing
In this episode, hosts Felix Oberholzer-Gee and Mihir Desai explore the prospects for media outlets like Vice and Buzzfeed, discuss their thoughts on predictive policing, and offer their After Hours picks for the week.Read more
In this light-hearted episode taped a couple of weeks ago, Youngme, Mihir and Felix discuss the #eatclean movement, their most/least favorite food trends, and offer their After Hours picks for the week.Read more
Season 1 – Episode 16
Is the Job of the Presidency Too Big? Plus, Vaping Among Teens
In this episode, Youngme, Felix and Mihir debate whether the job of the presidency is too big even for the most competent of executives; discuss whether the vaping trend among teenagers should have us worried; and offer their After Hours picks for the week.Read more
Season 1 – Episode 15
Brainstorming Gun Control Ideas, and the Affordable Housing Dilemma
In this episode, Youngme, Mihir and Felix brainstorm out-of-the-box ideas for gun control; discuss whether cities like Boston should be trying to attract companies like Amazon despite the affordable housing crunch created; and offer their After Hours picks for the week.Read more
In this special episode, Youngme asks three teenagers (including her own son) a set of 10 random questions that address everything from how they think about social media, to bullying in high schools, to how optimistic they are about the future.Read more
Season 1 – Episode 13
Antitrust and Big Tech, and Is Corporate Lobbying A Good or Bad Thing?
In this episode, Youngme, Mihir and Felix discuss antitrust and whether we should be concerned about the size of the big tech companies; debate the propriety of corporate lobbying; and offer their After Hours Picks for the week.Read more
Season 1 – Episode 12
Why Management Practice Matters
In this episode, Mihir sits down with HBS economist Rafaella Sadun, who has dug deep into why and how management practices matter with award-winning large-sample empirical work. Rafaella discusses the problems and promise of family ownership, why Americans do IT better, the secrets of her productive partnership and how she came to economics, and her recommendation for a biography of a pioneering female economist.Read more
Season 1 – Episode 11
The Rise of Voice Assistants like Amazon Echo, and How to Punish Wells Fargo
In this episode, Youngme, Felix, and Mihir discuss why voice assistants like Amazon Echo and Google Home are all the rage; debate how to punish Wells Fargo for criminal wrongdoing; and offer their After Hours picks for the week.Read more
Season 1 – Episode 10
The Future of Newspapers, and Debating Big Tech
In this episode, Youngme, Felix, and Mihir discuss whether there’s a market for a Netflix for News; debate the future of newspapers like The New York Times; argue about which Big Tech company (Apple, Alphabet, Amazon, Facebook) is most and least vulnerable; and offer their After Hours picks for the week.Read more
Season 1 – Episode 9
Why They Do It: White Collar Criminals
Youngme Moon interviews Eugene Soltes, who talks about “Why They Do It: Inside the Mind of the White Collar Criminal.” Among other things, Eugene discusses his unique relationship with Bernie Madoff, the motivations behind white collar crime, how firms can prevent such crimes from occurring, and his most memorable conversations with criminals he has interviewed. Eugene also shares an After Hours recommendation. Read more
Season 1 – Episode 8
The Gender Wage Gap, and Debating the Benefits of Retail Medicine
In this episode, Youngme, Felix, and Mihir debate what it would take to close the gender wage gap; discuss whether retailers like Walmart and CVS entering the medical care space is good or bad for consumers; and share their After Hours picks for the week. Read more
Season 1 – Episode 7
Zuckerberg Faces Congress, and the Plight of the Post Office
In this episode, Youngme, Felix, and Mihir give their quick takes on Mark Zuckerberg’s appearance before Congress; discuss the plight of the U.S. Post Office; and share their picks for the week.Read more
In this episode, Youngme talks to Professor Frances Frei, who was hired by Uber last year to help rebuild a broken culture. Frances describes how toxic the culture at Uber actually was and how she dealt with difficult work scenarios. She also talks about what should be done about “bad people doing bad things” and the link between strategy and culture. Plus, she gives a behind-the-scenes look at preparing for her TED talk.Read more
In this episode, Youngme talks to her friend and colleague, Professor Mike Norton, about how to spend money to create happiness. Mike’s tips include spending money on experiences rather than on “stuff,” “buying time,” and investing in others. Youngme adds a few tips of her own.Read more
Season 1 – Episode 2
Making Sense of Online Reviews, Our Best/Worst Service Experiences, plus Debating the Future of Spotify
In this episode, Youngme, Mihir, and Felix try to make sense of online reviews, dish about their best/worst service experiences, and offer their thoughts on Spotify. Plus, their After Hours recommendations for the week.Read more
Season 1 – Episode 1
The #NeverAgain movement, Facebook and the Russian Influence Campaign, Should Pornography Be Banned, and Oscar Picks
In this pilot episode, Youngme, Mihir, and Felix discuss the NRA and whether the #NeverAgain movement has a chance; disagree about Facebook and the Russian influence campaign; and debate the idea of banning pornography. Plus, they offer their After Hours recommendations for the week and reveal what movies they’re rooting for (and against) at this year’s Oscars.Read more
HBR Presents is a network of podcasts curated by HBR editors, bringing you the best business ideas from the leading minds in management. The views and opinions expressed are solely those of the authors and do not necessarily reflect the official policy or position of Harvard Business Review or its affiliates.
About the Hosts
Youngme Moon is the Donald K. David Professor of Business Administration at Harvard Business School. She sits on the Board of Directors of Unilever, Rakuten, and Warby Parker. She is the author of the bestseller, Different.
Mihir A. Desai is the Mizuho Financial Group Professor of Finance at Harvard Business School and a Professor of Law at Harvard Law School. His research has been cited in The Economist, BusinessWeek, and The New York Times. He is the author of The Wisdom of Finance.
Felix Oberholzer-Gee is the Andreas Andresen Professor of Business Administration in the Strategy Unit at Harvard Business School. His work has been profiled by media outlets around the world, including The New York Times, The Financial Times, Le Figaro, Neue Zürcher Zeitung, and The Straits Times.
In 2012, HBR dubbed data scientist “the sexiest job of the 21st century”. It is also, arguably, the vaguest. To hire the right people for the right roles, it’s important to distinguish between different types of data scientist. There are plenty of different distinctions that one can draw, of course, and any attempt to group data scientists into different buckets is by necessity an oversimplification. Nonetheless, I find it helpful to distinguish between the deliverables they create. One type of data scientist creates output for humans to consume, in the form of product and strategy recommendations. They are decision scientists. The other creates output for machines to consume like models, training data, and algorithms. They are modeling scientists.
Data science for humans: the consumers of the output are decision makers like executives, product managers, designers, or clinicians. They want to draw conclusions from data in order to make decisions such as which content to license, which sales lead to follow, which medicine is less likely to cause an allergic reaction, which webpage design will lead to more engagement or more purchases, which marketing email will yield higher revenue, or which specific part of a product user experience is suboptimal and needs attention. These data scientists design, define, and implement metrics, run and interpret experiments, create dashboards, draw causal inferences, and generate recommendations from modeling and measurement.
Data science for machines: here the consumers of the output are computers which consume data in the form of training data, models, and algorithms. Examples of the work products of these data scientists are: recommendation systems which recommend what shirt a customer might like or what medicine a physician should consider prescribing based on a designed optimization function, such as optimizing for customer clicks or for minimizing readmission rates to the hospital. Depending on the engineering background of these data scientists, these work products are either deployed directly to the production system, or if they are prototypes they are handed off to software engineers to help implement, optimize and scale them.
The elusive full stack data scientists do exist, though they are hard to find. In most organizations, it makes sense for data scientists to specialize into one type or another. But data scientist are curious creatures who thrive from being able to creatively dabble; there are benefits to giving them flexibility to work on projects that touch both “types” – both for them and for the organization. (The sidebar offers more detail on how the two types of data scientists differ not only in their skills and the work they do, but in whom they partner with and their measures of success.)
Decision Scientist vs. Modeling Scientist
Who consumes the output?
Decision scientist: Humans.
Modeling scientist: Machines.
What is the output?
Decision scientist: Dashboards, presentations, memos, new metrics, predictive models to inform decision-making, opportunity analysis to determine what to invest in or prioritize, reports on the results of experiments including recommendations.
Modeling scientist: Models, training data, algorithms.
What are the measures of success?
Decision scientist: Improved decision-making in the organization.
Modeling scientist: Direct improvements in the product or business from the code developed and shipped.
What are some examples?
Decision scientist: Which content to license, which sales lead to follow, which medicine is less likely to cause an allergic reaction, which webpage design will lead to more engagement or more purchases, which marketing email will yield higher revenue, which specific part of a product user experience is suboptimal and needs attention.
Modeling scientist: recommendation systems that recommend what shirt a customer might like or what medicine should be prescribed based on a designed optimization function such as optimizing for customer clicks, or for minimizing return rates to the clinic.
What skills are required?
Decision scientist: Statistics, experimentation, analytical thinking, communication and collaborations skills to work with both technical and non-technical partners, knowledge of both scripting and query languages (e.g. Python, R, SQL), and ideally also formal computer science background.
Modeling scientist: Computer science, machine learning, production-grade coding skills, strong communication to work with both technical and non-technical partners
Who are their main partners on the job?
Decision scientist: Decision makers (executives, business leaders, product managers), data engineers, software engineers responsible for the applications generating data.
Modeling scientist: backend engineers, product managers (to determine what to optimize for), other modeling-scientist colleagues who share techniques, decision scientists on what features to consider and datasets to use.
A more detailed look at data roles
In larger and more sophisticated data operations, more fine-grained roles are necessary. Here are five key areas that contribute to data science operations. In small organizations, one person will do several of these things. In slightly bigger teams, each of these may be a role staffed by one or more individuals. In larger operations, each may be a team unto itself. These roles cover the creation, maintenance, and use of data, and are in addition to the data scientists described above (decision scientists and modeling scientists).
Data infrastructure: data ingestion, availability, operations, access, and running environments to support workflows of data scientists. e.g. running Kafka and a Hadoop cluster
Data engineering: determination of data schemas needed to support measurement and modeling needs, and data cleansing, aggregation, ETL, dataset management
Data quality and data governance: tools, processes, guidelines to ensure data is correct, gated and monitored, documented, standardized. This includes tools for data lineage and data security.
Data analytics engineering: enabling data scientists focused on analytics to scale via analytics applications for internal use, e.g. analytics software libraries, productizing workflows, and analytic microservices.
Data product manager: creating products for internal customers to use within their workflow, to enable incorporation of measurement created by data scientists. Examples include: a portal to read out results of A/B tests, a failure analysis tool, or a dashboard that enables self serve data and root cause diagnosing of changes to metrics or model performance.
Who to hire
So which kind of data scientist should you be recruiting? To answer that question, first decide what stage you are in with your data operation, and second ask how vital data is to your product. If you’re a small organization just starting off and hiring your first data scientist, try to hire someone who can span as many of these roles as possible — the elusive full stack data scientist. If you’re larger or farther along in your data operation, the answer will depend more on how essential data is to your product. If your product is going to depend on machine learning from inception, you’ll need machine learning expertise in your first hire, or your first leader. If, by contrast, you’re looking to identify product opportunities or to improve general decision-making throughout the organization, you’ll need someone more trained in decision science, descriptive and predictive analytics, and statistics, and someone who can translate how to use data across the leadership team and to non-technical partners.
Finally, if you don’t have internal data in a format that is consumable or reasonable, you will need a data scientist with a strong enough engineering or computer science background that they can work with engineers to guide what data must be captured and how, before they can start their work.
How to organize
Much has already been written about how data science functions should be organized. Perhaps the most important point is that if data science is a strategic differentiator for the organization, the head of the data science unit should ideally report into the CEO. If this is not possible, they should at least report into someone who understands data strategy and is willing to invest to give it what it needs. Data science has its own skillset, workflow, tooling, integration processes, culture; if it is critical to the organization it is best to not bury it under a part of the organization with a different culture.
The other big question is whether and how to embed data science into the different business lines. There are three basic models: centralized in one data science team, distributed throughout the business lines, or a hybrid between the two where you have a centralized team reporting into one head, but physically co-locate and embed teams of data scientists into business units long term. Unless your data operation includes several hundreds of employees, it’s pretty clear at this point that the hybrid model is most effective. (If you reach this scale, a fully distributed model can make sense, but very few companies work this way.)
In the hybrid model, the centralization in reporting structure enables data scientists to have career progression and growth in a ladder specialized for data scientists, to grow with and be assessed against their peers, and to facilitate and ensure that best practices will be shared across them since they are not each in their own silos. (Establishing this peer group is key; data scientists are curious creatures that want to grow and learn from each other.) Due to the reporting structure, it also enables the leader to more easily promote internal mobility across business groups; this cross-pollination across the company is usually a large benefit.
At the same time, embedding within business groups enables data scientists to establish themselves as domain experts in their business group, and develop a rapport with business partners as an essential long-term part of the team. This partnership will provide the data scientists with rich business context, enabling them to have maximal impact by truly understanding and guiding what business priorities should be addressed using data, and how.
What data scientists need to succeed
Although different kinds of data scientists may have different specialties or duties, there are a few things they all need to succeed. They need business partners who can help them integrate into the core business line and product line. They need data partners — such as software application engineers and data infrastructure engineers — who help ensure the necessary foundational data instrumentation and data feeds are correct, complete, and accessible. And they need leaders willing to invest in the foundations necessary for their work, including data quality, data management, data visualization and access platforms, and a culture of expecting data to be part of the process of business and product development. Key to this is allotting appropriate (and often underestimated) time within the development process for data and measurement. Far too often, product and software teams think of data and measurement as something they can quickly “add on” at the end.
A final piece of advice for those hiring data scientists: Look for people who are in love with solving problems, not with specific solutions or methods, and for people who are incredibly collaborative. No matter what kind of data scientist you are hiring, to be successful they need to be able to work alongside a vast variety of other job functions — from engineers to product managers to marketers to executive teams. Finally, look for people who have high integrity. As a society, we have a social responsibility to use data for good, and with respect. Data scientists hold the responsibility for data stewardship inside and outside the organization in which they work.
In recent years, much has been written about how the Blockchain is poised to transform traditional industries such as banking, real estate, and healthcare. More recently, it has gained attention as a way to finance new ventures, through what is known as an Initial Coin Offering (ICO). Less noticed, though, is ICOs appear almost antithetical to the standard approach to financing a risky venture.
In fact, ICOs have upended the conventional pattern of staged experimentation and fundraising. Blockchain startups raised over $5 billion in 2017 through ICOs and over $12 billion through the first three quarters of 2018. The average amount of capital raised by a Blockchain project through an ICO in 2017 was $13 million; through the third quarter of 2018 it was $25 million. These ICOs are nearly always held when a project is at an immature stage of development akin to a seed stage startup — when it is testing hypotheses around its consumer value proposition and forming a founding team.
Blockbuster capital raises will always occur in unusual situations (e.g., EOS raising over $4 billion in their ICO and Telegram raising nearly $2 billion in a private financing), but if the average amount of capital raised by a Blockchain project is 10-20x that of a normal startup at an equivalent stage of development, while the failure rate remains roughly similar to staged financing startups, then either investors are foolishly leaping into a dangerous bubble or there are more profound differences in early stage financing at play.
3 Defenses of Large ICOs
Some observers have pointed out that blockchain projects may have an inherent incentive and strategic reason to be more aggressive in raising capital earlier in the experimentation process. Those benefits fall into three categories:
To jumpstart network effects that provide a first-mover advantage:
Many of the projects being built using blockchain technology are “protocols” that govern the interactions between users in a decentralized autonomous network. In this framing, the native tokens issued through the ICO are the means through which users transact between a decentralized network of participants without the need for any central organization or platform.
Just as with other such platforms or marketplaces that connect users, the value of the decentralized network is a function of the users who choose to transact using the given protocol. By making the tokens issued through the ICO widely available and liquid (and by using the cash raised to finance further development of activity on the network), projects can rapidly channel developer attention towards their protocols. For example, Sia is a decentralized storage platform on the blockchain, leveraging underutilized hard drive capacity around the world. The more hosts offer up their storage capacity, the more users will be attracted. The more users that come online to store files, the more hosts will be attracted. If the users and the hosts are both owners of the Sia tokens, which appreciate with greater usage of the network, they have an even greater incentive to see the network grow. This so-called “token network effect” creates a positive feedback loop, making it more valuable to be transacting using a given protocol when many others are also transacting through it.
To generate publicity that allows them to solicit broad feedback on their beta product
The publicity around the upcoming launch of an ICO that plans to raise several tens or even hundreds of million dollars is a related way to drive developer interest and engagement. This focused attention from developers has the added benefit of crowdsourcing feedback on the beta version of the project. When the decentralized exchange protocol 0x raised $24 million in their ICO in the middle of 2017, a few months after releasing an early-stage version of the software, it created an enormous amount of developer attention. By completing its ICO shortly after going live with its over-the-counter (OTC) platform for exchange tokens, developers and investors were attracted to testing out the protocol. Today, 0x is widely considered one of the leading decentralized exchange protocols with numerous other applications built on top of its platform.
To create a decentralized governance structure that is inherently beneficial to the nature of the project
Blockchain projects that can achieve a fully decentralized architecture and governance are inherently more valuable because they are more resistant to attacks and collusion. As Ethereum founder Vitalik Buterin notes, “Once you adopt a richer economic model…decentralization becomes more important.” But achieving decentralization requires a meaningful investment in capital in order to attract a distributed network of users and network managers that maintain the decentralized ledgers (or nodes). A larger injection of upfront capital is more likely to create the incentives for autonomous agents to participate in the creation of the blockchain network, thereby making the network that much more valuable.
The Downside to Large ICOs
In some cases, these benefits are real. However, there are very real potential downsides to a large, public fundraising through an ICO. To understand the downsides and why they’re important, though, it helps to understand why staged venture-capital financing has been so successful in the first place.
One of the fundamental elements of commercializing new ventures is the high failure rate they face. Failures are not necessarily due to bad execution; it is just that most new ideas fail, a few become incredibly successful and it is virtually impossible to know which outcome it will be without undertaking the hard work to develop and commercialize an idea. Indeed, over 60% of startups backed by venture capitalists fail and evidence points to the most successful VCs having bigger “hits” as opposed to fewer failures.
A solution to this challenge is multi-stage financing, which allows entrepreneurs and investors to learn about the ultimate viability of an idea through a sequence of investments over time. Multi-stage financing is usually seen as benefiting the investors: It allows them to commit only a fraction of the money upfront, preserving the option to abandon the investment if the idea does not pan out, but allowing them to reinvest if things continue to go well.
What is often less appreciated is that this methodology is equally valuable for entrepreneurs. For the entrepreneur, the earliest money invested into a venture, which is raised when uncertainty is highest, is the most expensive. By raising only a small amount of money initially and de-risking the venture through a series of structured experiments, entrepreneurs who succeed raise subsequent capital at higher prices and are able to retain a higher share of the venture they have built — never mind avoid wasting years of their lives fruitlessly pursuing bad ideas.
This approach to structured experimentation from the entrepreneur’s perspective, popularized by Eric Ries’ The Lean Startup with concrete steps for how to de-risk the venture in the most capital efficient way, has been widely embraced as the gold standard for how to approach the commercialization of radical new ideas. From Boston to Beijing to Bangalore, entrepreneurs and investors rattle off the importance of designing focused experiments to test hypotheses in a capital-efficient fashion in order to achieve product-market fit. Moreover, as the cost of experimentation has fallen in software (due to the cloud, open source tools, reusable code components and global distribution platforms), hardware (due to rapid prototyping, 3D printing, improved design and modeling software) and biotech (due to technological advances in gene sequencing and editing) and across-the board increases in computational power, modeling tools and big data techniques, so has there been a massive explosion of experimentation in a broad range of industries.
How ICOs Constrain
ICOs substantially limit the benefits associated with such staged experimentation, for three reasons:
One of the benefits of blockchain technology is that it is immune to centralized parties making changes of their own accord. But this also implies that the software protocol at the time of the ICO needs to embed — as much as the project’s creators can — the set of rules that will govern the protocol forever.
It is hard for the project’s creators to fully anticipate the technological and incentive issues that will arise from a given protocol, and being able to learn from the way in which users engage can have a consequential effect on the ultimate usability and quality of the platform. An ICO “bakes in” the protocol early in the life of the project and makes it hard to adjust architecture to enhance performance and capabilities.
ICOs cede control of decision making to the community. In the early stages of a venture, centralization can be very powerful as it allows for speed, focus and collaborative effort towards one direction. Centralized decisions can be valuable when testing a particular idea and deciding when to abandon, pivot or double down on the effort. Once the project has an ICO, governance becomes decentralized, slowing down decision-making and reducing flexibility.
While some entrepreneurs believe that selling tokens is different from selling equity in that it is “non-dilutive” — they don’t give up stock in the company — there remains substantial risk that the protocol will not succeed. When you’re raising money, there is no free lunch. As the markets become more sophisticated, the price at which the ICO happens will reflect this risk and the price of the token will appreciate as the risk is mitigated over time. Selling tokens early therefore has implications for the amount of value that is captured by the entrepreneur who creates the protocol — potentially leaving substantial “value on the table” for raising capital when the risk is so high. This dynamic is no different from the dilution cost faced by an entrepreneur raising a substantial money at the earliest stages as opposed to raising a small amount of this expensive capita, de-risking, and raising further funding once the odds of success have improved. For example, Ethereum’s original crowdsale in the summer of 2014 raised $18 million. Today, Ethereum’s market capitalization is $24 billion.
In addition to these constraints on experimentation, there is a another cost to ICOs:
Exposing Strategic Roadmap to Competition
Many early stage ventures start off in “stealth mode” to prevent their idea from being widely accessible and among the reasons firms have taken advantage of the abundance of growth capital to remain private much longer (e.g., Uber, Airbnb, WeWork) is that it allows them to only selectively disclose confidential information that can be important for strategic reasons to not be available to competitors. An ICO exposes a startups strategic roadmap and, in many cases, actual software code to the public, allowing competitors to learn and adopt elements of it into their own protocols.
In summary, while there are particular benefits of ‘going public’ early through an ICO, there are also a number of potential costs. Entrepreneurs, investors, and managers need to understand the full implications and risks of having a large ICO early and seek ways to mitigate unintended consequences while taking advantage of the inherent benefits. For example:
Complete a few rounds of more traditional staged equity financings in advance of an ICO.
Wait to expose the actual software code and detailed design as long as possible.
Use a subset of your community to enhance feedback and speed up experimental cycle time (e.g., private briefings of product road maps).
Begin with a more centralized governance structure (e.g., NEO choosing to launch with seven consensus nodes, growing to over 1000 over time) and then migrate the governance structure to a more decentralized one over time.
Consider the ICO more conceptually equivalent to the firm’s “IPO” — executed at the moment in time when the idea has become mature and is ready to be widely held and governed in a more decentralized manner.
Disclosure: One of the authors, Ramana Nanda, is a board director at Dunya Labs, a blockchain startup. The other, Jeffrey Bussgang, is an investor in and board member of numerous blockchain startups as part of his role as a general partner at Flybridge Capital Partners.
The potential for improving the quality of healthcare has never been greater. Advances in data analytics give us the ability to look at large populations and precisely segment their needs and new technologies such as tele-medicine give us the capabilities to deliver customized experiences at scale.
But the most powerful drivers of change are not necessarily technological; radical improvements increasingly also come from applying new innovation methodologies like design thinking that focus on developing a deep understanding of patient experiences and invite patients and partners into co-creation processes.
These methodologies free us from cognitive blinders. Healthcare professionals often see the patient experience through the lens of their own expertise. They come with a theory about what needs changing, which they assume will improve the system. That can be helpful, but by not looking at the experience from the patient’s own perspective, they may well not recognize where the system has lost its relevance to patients’ needs.
In order to bridge the gap between what patients need and what the system offers, healthcare professionals must begin by setting their expertise aside. This creates the conditions in which key stakeholders can explore new strategies together. The co-creation journey involves seven steps:
Step 1: Find the future in the present.
We begin by developing insights into today’s experiences. Exploratory research using design thinking’s ethnographic tool kit helps define the jobs that key stakeholders, patients, caregivers and partners, want or need done.
A partnership between the Business Innovation Factory and the Children’s Health System of Texas provides a good example. As its first step in addressing a decline in children’s health in North Texas, Children’s identified a number of families to study, working through what BIF called “trusted agents” such as pastors and neighbors.
The agents interviewed the patients and their families to gain a deeper understanding of patients’ lives and to gauge their “say-do” divide (the difference between what people say they will do and what they actually do). The team used journaling, journey mapping, shadowing and collage making to increase patients’ ability to reflect on their own perceptions and experiences. The following conclusions emerged:
If Children’s Health wanted to improve kids’ health, it needed to focus on families, not just the kids.
What families wanted was a better life, not better health. If parents needed to feed their kids fast food to get to work on time, they would do so.
Families also wanted to feel in control of their health journey. This was difficult in a system where things were done to and for people, not with them.
Families listened to those they knew and trusted: teachers, pastors, YMCA staff, and other families who had been through similar experiences.
Step 2: Identify opportunity spaces
The findings emerging from step 1 translate into a set of opportunity spaces, promising areas in which to look for new solutions. At Children’s, each such space posed a different question:
How might Children’s create more convenient sources of care? Because the emergency department was often seen as a family’s most convenient source of care, making alternatives more convenient was another opportunity space, involving solutions built on leveraging trusted information sources within the community and on improvements in the attractiveness of nonemergency care.
How might Children’s make children more responsible? Because it is difficult for kids to see the link between their health and the choices they make, nudging them towards awareness and accountability was critical, suggesting solutions that made healthy goals more meaningful to children and provide frequent real-time feedback.
How might Children’s deliver care beyond the child? Because families can play such a critical role in children’s health, moving from a place that ignored the whole context of a child’s environment to one of acknowledging and treating root causes and built a family network that was a positive influence was key (suggesting solutions that equipped children with life skills to make healthier choices as they grow).
How might Children’s inspire, guide, and support other change agents? Because families cannot always be relied on to make and encourage good choices, reaching beyond them offered a third opportunity space, suggesting solutions that provide mentors and offer children opportunities to share their stories and get positive reinforcement.
Helping the staff at Children’s Health understand and own these opportunity spaces was critical. By listening to the children and their families telling the story of their experiences, staff could move from judging these families to co-imagining possibilities.
Step 3: Identify organizational capability gaps
Once they understood the main features of the future they wanted to create, the Children’s team began identifying, unbundling, and realigning their capabilities in order to get there. Capabilities are made up of people, processes, and technologies.
Once a key capability was identified, staff could engage in conversation about how to use that capability differently, which allowed the owners of that capability an opportunity to identify with the new future. For example, Children’s has a strong care management capability, which is largely comprised of a team of people who help patients manage their medical care, medications, etc. As Children’s began to imagine a well-being model that emphasized patient agency, it began to imagine how might it repurpose that team to focus less on managing care and more on activating agency.
It allocated a portion of the team’s time to serving as “coaches” and a new protocol was developed to help the team understand the differences in the role that they would be playing. In an agile and experimental process, the team participated in reimagining this new role, critiqued it and iterated on it, helping them feel that they were leading change rather than being subject to it.
Step 4: Test critical assumptions
Before an organization actually applies a new strategy, it must test the critical assumptions underlying it. To do this, the Children’s Health team designed, with patients, two programs.
The first was called “Your Best You” and involved self-discovery and education for self-knowledge through a six-week summer camp that aimed to activate kids’ sense of self by marrying hip hop education and Design Thinking. This helped the kids to figure out who they were, what they wanted to do in this world, and who could help them achieve their goals.
The second program (“What’s Cookin’, Dallas?”) engaged family members in curating a food and nutritional experience for other families in their communities. This measured people’s sense of connection and belonging, as well as their sense of agency and control.
Step 5: Co-create the new model with key partners
The opportunity spaces Children’s identified pointed toward a transformational business model that was wellbeing (versus sickness) centered, citizen (versus physician) driven, prevention (versus intervention) focused, partnership based, and community supported.
In four opening sessions, the team identified the key institutions, resources, and people who might offer valuable local knowledge for designing the new business model. They then invited these partners to a participatory design studio focused on a single question: How might we design a new system that connects convenient clinical care with self-managed well-being?
The new healthcare delivery model that Children’s came up with from these processes consists of a series of twelve activities, from generating family awareness of the child’s needs and the available resources, through to the creation of a wellbeing plan, and culminating in sharing and comparing treatment experiences. For each stage they identified the people who needed to be involved, the medium of the meeting (face-to-face/phone/e-mail/online), and the goals of the interaction, both functional and emotional. For instance, in assessing the barriers getting in the way of health needs the professionals interacting with the children and families would have a functional goal of raising the children’s and families’ understanding of those issues and an emotional goal of making sure that the children and family felt heard.
Step 6: Find sustainable funding for continued experimentation
In a world still dominated by fee for service, it is often a challenge to sustainably fund new business models. Children’s identified a way to combine private and public sources of funding. It could use resources from its licensed insurance company (funded by the savings from enrollees’ utilizing less expensive medical care) coupled with funding from the Texas Medicaid Section 1115 Waiver program, plus philanthropy and grants. This package would give them five years to pilot the new approach.
Children’s recruited 15 families for 16 weeks to engage in a change process centered on family meals where families met the supportive coaches who would act as their “navigators” to access the wide range of community services that could improve children’s wellbeing. Families did an exercise where they were asked what the one thing was that they wanted to address. Navigators contracted with relevant agencies to deliver this service (for example, providing a gym) and checked in frequently to assess and guide progress.
Step 7: Measure progress
Children’s developed a metric of family wellbeing, based on five key dimensions: family members’ sense of control over their healthcare, their understanding of their wellness goals, their sense of self, the quality of their access to information and knowledge, and the quality of the community support system. The test was administered both before and after the pilot, as was the family’s adherence to the model. At the end of the program these metrics were then correlated to observed changes in health management behavior (for example, compliance with prescriptions).
Children’s observed that the pilot engaged people in their wellbeing. With a greater sense of control in their lives, people also started taking greater control in their health management by, for example, regulating their blood pressure and following through on smoking cessation programs. Following the pilot, the program was rolled out as a core health offering through its HMO. The program is currently being rolled out with other populations.
New strategies that offer dramatic increases in value creation for stakeholders, and are executable within the constraints of today’s reality, emerge most readily from the kind of bottom-up, patient-centered approach that the Children’s story illustrates. This approach involves combining a deep understanding of the realities of patients’ lives with a critical assessment of organizational delivery capabilities to create a real conversation about marrying the two.
Recently the Chief HR Officer for a healthcare firm asked us to identify the best new framework for leadership that she could use to train and develop a cadre of high potentials. The challenge, she said, was that these managers were highly proficient in their own disciplines such as finance, marketing, research, clinical care, and insurance reimbursement — and had demonstrated that they could manage people in these areas — but she needed them to be “bigger” leaders. What, she asked us, did the newest thinking about leadership development say they needed to learn to lead multiple functions, or influence whole segments of the organization, particularly in the rapidly changing world of healthcare?
Explicit in our HR officer’s question was her assumption that the newest thinking on leadership development must contain something essential. After all, there are hundreds of books written about leadership every year, adding to the thousands of titles already available on Amazon. There also are new assessment tools based on advancements in brain science, emotional intelligence, and relational modeling; new computer aided algorithms for decision-making; virtual reality simulations; and a host of new experiential programs, online courses, and university certifications. With such a flurry of developments, there must be some useful new ways to think about leadership.
The reality, however, is somewhat different. Yes, the leadership development industry is thriving, and yes there are a lot of new and interesting ideas, some of which may prove to be helpful. But despite many changes in our context — as organizations have become more democratic and networked, for example — in its fundamentals leadership has not changed over the years. It is still about mobilizing people in an organization around common goals to achieve impact, at scale.
This tried and true perspective on leadership was reinforced for us during the past year as we researched and wrote the HBR Leader’s Handbook. We interviewed over forty successful leaders from a variety of organizations (corporate, non-profit, startup), across different industries. We then reviewed several decades worth of articles from the Harvard Business Review to understand the recurring messages from academics and practitioners about what leaders should do. Our conclusion from this research, and from our own years of experience as leadership and organizational advisors, was that the best leaders with the most outsize impact almost always deploy these six classic, fundamental practices:
uniting people around an exciting, aspirational vision;
building a strategy for achieving the vision by making choices about what to do and what not to do;
attracting and developing the best possible talent to implement the strategy;
relentlessly focusing on results in the context of the strategy;
creating ongoing innovation that will help reinvent the vision and strategy; and
“leading yourself”: knowing and growing yourself so that you can most effectively lead others and carry out these practices.
Sure, sometimes the starting point is different, or one of the six areas requires more heavy lifting than another, or the sequence of activities varies. And yes, leaders go about these practices in different ways depending on their personalities and their situations. But the same handful of practices are always present.
For example, when Seraina Macia (one of the leaders we interviewed) joined XL Insurance in 2010 to head their North American Property and Casualty unit, it was a stable, but slow-growth business. As she learned about the numbers, the organization, and the markets, Macia envisioned that the unit could be transformed into a much faster-growing and more profitable company with a wider range of product offerings. Bringing her team together around this vision, and sharpening it with their help, which is the first fundamental practice, became the focus of her early days with XL.
To translate that vision into action, Macia then challenged her team to triple the level of premiums, without sacrificing underwriting quality, in three years — and asked each of them to quickly develop a strategy for how to make that happen in their product areas, and how to best use underwriting and the other support functions to do it. She then worked with each manager to help them craft these strategies, making choices about how to deploy resources, where to focus, and how fast to proceed. This is the essence of the second core practice that we heard about in our research.
When some of Macia’s team members struggled to come up with thoughtful strategies, or couldn’t move quickly into action, she gave them tough feedback, pushed them beyond their comfort zones, gave them developmental help as needed, and in some cases replaced them or moved them to other positions. These actions were all in the service of building the best team to implement the strategy, which is practice number three.
This stronger team was then able to respond to Macia’s unrelenting drive for results by quickly testing new ideas, engaging local brokers, expanding target markets, and a host of other specific action-steps, all of which were aimed at focusing on results, which is the fourth practice. As results came in, Macia encouraged the team, to reassess their plans, learn from their experiences, innovate, and continually improve, which exemplifies the fifth practice, innovation. For instance, some of the teams experimented with sending underwriters out to the field to work with brokers so that they would send them business that was more likely to be underwritten by XL, a complete departure from past practices, and one that turned out to be key to the unit’s success.
While taking these actions, Macia also was learning about her own leadership, what worked and what she needed to do differently. Gradually she learned how best to allocate her time, how to build support from other parts of the company, what metrics were most useful, and how to make faster decisions about people, all of which is part of the leading yourself practice.
Most importantly, by putting all six of these practices together, Macia succeeded in doubling the level of profitable premiums in two years and (after she left for another job) seeing her successor reach the original goal of tripling the business the year after.
To move their organizations to the next level, all of the leaders we talked with deployed these practices — practices that are supported by numerous studies and articles, many of them far from new. And even though these leaders were operating in different industries, geographies, and with new technologies and structures, they were still dealing with people who needed to work together to achieve a common goal, which is what leadership has always been about. So when it’s time to think about developing bigger leaders—as our HR executive wanted to do—we believe the secret is not to look for a new framework, but rather to help leaders master the tried and true practices that already exist.
Youngme Moon, Mihir Desai, and Felix Oberholzer-Gee debate whether Netflix’s success is sustainable, before trying to wrap their heads around the unthinkably high murder rate in Latin America. They also share their After Hours picks for the week.
HBR Presents is a network of podcasts curated by HBR editors, bringing you the best business ideas from the leading minds in management. The views and opinions expressed are solely those of the authors and do not necessarily reflect the official policy or position of Harvard Business Review or its affiliates.