The 6 Fundamental Skills Every Leader Should Practice

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There’s an old story about a tourist who asks a New Yorker how to get to the storied concert venue Carnegie Hall and is told, “Practice, practice, practice.”  Obviously, this is good advice if you want to become a world-class performer — but it’s also good advice if you want to become a top-notch leader.

Over the past year we have been writing the HBR Leader’s Handbook — a primer for aspiring leaders who want to take their careers to the next level. As part of our research for the book, we interviewed over 40 successful leaders of large corporations, startups, and non-profits to get their views about what it takes to become a leader. We also explored several decades of research on that subject published in HBR; and we reflected on our own experience in the area of leadership development.

Our research and experience have shown us that the best way to develop proficiency in leadership is not just through reading books and going to training courses, but even more through real experience and continual practice.

Take the case of Dominic Barton, who served as the Global Managing Director of McKinsey & Company from 2009-2018. In an interview with us, reflecting back on his own development as a leader, he didn’t cite education programs or books he had read, but rather described several “learn-by-doing” experiences that would shape his successful career.

As the office leader of McKinsey Korea, for example, he realized he had “a small playground to… try new stuff” — and against all advice of local colleagues to be cautious and follow cultural norms, started writing a provocative newspaper column that challenged traditional ways of working among local businesses as their markets continued to globalize. “I took a risk, and it helped put us on the map, as never before.” His tenure in Korea also taught him that he was better at some things than others: “My performance evaluator used to beat me up regularly during those days, because I was better at opening up new initiatives than bringing them to completion. When I later became head of McKinsey Asia, he helped me see that I had to hire a solid COO to work with me—which substantially increased my leadership effectiveness in that bigger role.”

Our research also pointed to six leadership skills where practice was particularly important. These are not mysterious and certainly aren’t new. However, the leaders we talked with emphasized that these fundamental skills really matter. Aspiring leaders should focus on practicing these essential basics:

Shape a vision that is exciting and challenging for your team (or division/unit/organization). Translate that vision into a clear strategy about what actions to take, and what not to do. Recruit, develop, and reward a team of great people to carry out the strategy. Focus on measurable results. Foster innovation and learning to sustain your team (or organization) and grow new leaders. Lead yourself — know yourself, improve yourself, and manage the appropriate balance in your own life.

No matter where you are in your career, you can find opportunities to practice these six skills. You’ll have varying degrees of success, which is normal. But by reflecting on your successes and failures at every step, and getting feedback from colleagues and mentors, you’ll keep making positive adjustments and find more opportunities to learn. Research by Francesca Gino and Bradley Staats published in HBR shows how important this reflection can be to your improvement: they found that workers were able to improve their own performance by 20% after spending 15 minutes at the end of each day writing reflections on what they did well, what they did wrong, and their lessons learned. Leaders often have a bias for action that keeps them from stepping back in this way — but it is the reflection on your practice that will help you improve.

Don’t wait for learning opportunities to be handed to you. Seek them out and volunteer to take them on.  And if you don’t see the opportunities in your own organization, find them outside your professional work in a community group, a non-profit, or a religious organization, which are often hungry for leaders to step in and step up. For example, Wharton’s Stew Friedman has described how one young manager who aspired to become a CEO joined a city-based community board, which allowed him to hone his leadership skills; three years later, he was on a formal succession track for CEO.

Eventually, as you progress, you’ll reach a level of capability in these areas such that you’ll start seeing results: you’ll successfully make things happen through the people who work for you on your team or in your division. As you succeed, these results will begin to build upon one another—you’ll oversee a new product that becomes a runaway hit or take charge of a transformational initiative that redefines a major market. More and more people will want to sign up and work with you. Clients or customers will ask for you by name. You’ll be invited to represent the company at major industry conferences. Whether you use this momentum to guide a new initiative or to start your own company, you’ll have begun to truly deliver major impact. You’ll have become a leader, capable of rallying an organization of people around a meaningful collective goal and delivering the results to reach it.

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Imagine if a single piece of legislation could effectively eliminate all U.S. corporate taxes, subsidize hundreds of millions of dollars in new corporate investment, increase the take-home pay of most U.S. employees, ease state and local budgets, and reduce the U.S. trade deficit — all without increasing the federal budget.

It sounds completely impossible, but it is not: All we have to do is put aside the moral and political debates about Obamacare and recognize our health care system for what it is: a burdensome and unnecessary tax on corporate America.

U.S. companies pay $327 billion in income taxes, but they pay $1.1 trillion — more than three times as much — in health insurance costs. No other OECD country imposes anything close to such a heavy “health care tax” on its businesses. Eliminating this tax by shifting all responsibility to the federal government under a single-payer system would create a massive economic stimulus, providing Democrats with the universal coverage they seek while offering corporate America a far greater stimulus than any proposed Republican tax cut.

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After all, when the 2017 Tax Cuts and Jobs Act (TCJA) lowered corporate tax rates by 40%, saving corporations an estimated $950 billion over a decade, it created an immediate economic stimulus that bolstered corporate earnings and pushed the stock market to record heights. Eliminating the corporate health care tax would free up more than a trillion dollars of corporate earnings every single year, a stimulus 10 times more powerful than the TCJA.

Transferring all responsibility for health care to the federal government would not only offset 100% of what companies now pay in income taxes, it would provide an additional $773 billion a year in immediate bottom-line corporate profits that would be available for new investment. (Depending on how companies use these funds, they may end up paying tax on their increased profits, but even so, the net increase in after-tax income would substantially exceed their total taxes.)

The magnitude of this stimulus is hard to comprehend. It is comparable in scale to the $835 billion emergency Troubled Asset Relief Program (TARP) bill signed in 2008 that helped the United States recover from the worst economic crisis in our lifetime — but it would put that much money into the economy every single year. In fact, such a health care stimulus bill would dwarf any previous economic stimulus effort in modern times.

Wouldn’t shifting responsibility for health care to the government simply add a trillion dollars to the government budget? Not according to economic studies and the experience of other countries. Studies have shown that a single payer plan would save over $900 billion a year, eliminating more than 80% of the costs now borne by employers.

Much of this saving would come from reducing redundancy and inefficiency in seven areas: unnecessary services ($210 billion), inefficient delivery of care ($130 billion), excess administrative costs ($190 billion), inflated health care prices ($105 billion), costs from the failure to pay for preventive care ($55 billion), and fraud ($75 billion), and running Medicare and Medicaid as separate programs ($136 billion). Other studies have suggested that billing and insurance-related administrative costs would fall by 70% or more. After all, private insurance overhead averages 12.1%, compared to 2.1% for single-payer Medicare. Countries with single payer systems spend an average of 8.5% of GDP on health care vs. 18% by the United States. If administrative overhead were to drop to the level in Canada’s single-payer system, that alone would save $400 billion. Add to this the potential increase in tax revenue that would come from the growth in corporate earnings and wages, and the entire cost of eliminating the corporate health care tax could be fully offset.

Unlike the TCJA, which the Office of Management and Budget (OMB) estimates will add the full amount of corporate savings to the federal deficit, a health care stimulus bill would offer far greater corporate savings with no net impact on the federal budget. Such a stimulus plan would not only increase corporate profits but would also strengthen our economy in multiple ways.

Wage growth. The economic recovery has not translated into wage gains for the average American worker. Worker productivity grew 72% in the 1973-2014 period while median pay rose only 9%. In real dollars, the median income of middle-class households declined from 2000 to 2014 by 4%. Although there has been modest improvement in recent years, one of the largest drags on wage growth has been the increase in health care costs, which have taken an ever-larger bite out of workers’ take-home pay. Ninety percent of CFOs polled agreed that reducing health care costs would enable them to increase wages. Shifting the entire cost of health care to the federal government would eliminate the employee contribution to employer health care plans and would immediately raise the take-home pay of 156 million U.S. employees by an average of $1,443 per year.

Consumer demand. The stimulus effect of a broadly distributed increase in take-home pay would be far greater than the effect of the TCJA. It is estimated that 60% of the benefit of the TCJA went to stockholders rather than to employees or new capital investment. This increase in wealth went overwhelmingly to the top quintile of households that own 92% of all stocks. Studies show that that wealthier households tend to save or invest the extra money that comes their way, dampening its impact on the overall economy.

A broad-based increase in wages for the average worker, however, immediately translates into increased consumption that has a much greater stimulating effect on the economy. The Congressional Budget Office estimates that a onetime increase of a dollar in income would result in 84 cents of increased consumption by those in the bottom third of income distribution and 57 cents by the middle third compared to only 30 cents of increased consumption by the upper third.

Balance of trade. The United States has recently imposed massive tariffs on foreign goods in an attempt to reduce the trade deficit. The health care tax, however, puts U.S. companies at an even greater global competitive disadvantage. For example, U.S. automobile manufacturers General Motors, Chrysler, and Ford estimate that health care costs add between $1,100 and $1,500 to the sticker price of every car sold. By contrast, Toyota’s financial statements indicate that health care is not a material cost that is even worth reporting.

Corporate leaders know this well: 93% of CFOs, in a recent survey, agreed that the high cost of health care in America gives foreign companies a competitive advantage. Harold McGraw III, CEO of the McGraw-Hill Companies and chairman of Business Roundtable, declared that “health care costs are one of the top cost pressures… hurting America’s ability to compete in global markets.” Add to that the impact of poor health on productivity for employees that do not have coverage. And, unlike the tariffs that hurt U.S. farmers and many domestic industries and also lead to retaliatory tariffs from our trading partners, eliminating the health care tax would simply level the global playing field without any negative consequences.

Easing state and local government budgets. State and local governments have been increasingly squeezed by growing health care costs. A Pew study found that state and local governments were spending 31% of their revenues on health care by 2012. And a “baseline” projection by the Brookings Institution found that, by 2034, the increased health care burden on state and local governments “is more than the entire amount that states and localities spend on police and prisons annually. And it is almost as large as spending by states and localities on highways and the judicial system combined.”

Without the federal government’s luxury of deficit spending, state and local governments have had to compensate for increasing health care costs by cutting spending in other critical areas. In many states, teacher salaries, school budgets, hiring and wages for police and fire departments, and numerous other essential services have already suffered, and the ability to continue, let alone expand, these services is in jeopardy. Relieving state and local governments of their health care burden would immediately free up billions of dollars that could be used for better schools, safer streets, and emergency services.

Universal single-payer healthcare would also save lives and reduce suffering for millions of people, a massive benefit not to be overlooked. And the idea of federal government providing single-payer universal coverage is already gaining popular support with a majority of Americans. But leaving aside issues of humanitarian concern or political popularity, the economic case alone justifies Congressional action. Repealing the corporate health care tax would be a massive economic stimulus that singlehandedly addresses many of the nation’s toughest economic challenges. It might well be the only economic stimulus that could satisfy both parties, boosting the stock market and corporate earnings while providing meaningful economic and health benefits at every income level across America.

If Congress moves to act on this idea, we can expect health care insurers and providers to lobby hard to protect their profits, since much of that $900 billion in savings will come out of their revenues. But there is no reason the health care sector, representing 18% of our economy, should be entitled to impose a tax on the other 82%, especially when that tax undermines our global competitiveness, undercuts wages, inflates our deficit, and compromises essential public services.

As the midterm elections draw closer and the economy tries to sustain the longest bull market in our history, politicians know well that an economic decline is the surest omen of a change in political power. Republican leaders have proposed a second round of tax cuts in an effort to further stimulate the economy, while progressive Democratic candidates are promoting the radical idea of “Medicare for all.” Each side is deeply entrenched in its own political ideology and utterly rejects the views of the opposing party. But there is a single solution that fulfills both parties’ most deeply held goals: repeal the corporate health care tax.

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Many managers of data science teams become managers because they were great individual contributors and not necessarily because they have the skills or training to lead a team. (I include myself in that group.) But management is a skill unto itself, and relying on your experience as a successful individual contributor is not enough to ensure that you are able to retain and develop great talent while delivering valuable learnings, products, and outcomes back to the organization. Great data scientists have career options and won’t abide bad managers for very long. If you want to retain great data scientists you’d better commit to being a great manager.

What does it take to become a great manager? Volumes have been written on that subject, of course, including from HBR. But in my experience, a few areas are particularly important for those who lead data science teams. Great management means caring about your team members, connecting their work to the business, and designing diverse, resilient, high-performing teams.

Build trust and be candid

Trust, authenticity, and loyalty are essential to good management. That’s particularly true in data science where confusion around the discipline and its role in the organization means the team manager is responsible for insulating team members from unreasonable requests and for explaining the team’s role to the rest of the organization. Your team needs to trust that you will have their back.

Having your employees’ back doesn’t mean blindly defending them at all costs. It means making sure they know that you value their contributions. The best way to do that is to make sure your team members have interesting projects to work on and that they’re not overburdened by projects with vague requirements or unrealistic timelines (which is all too common given the high demand for data scientists.)

To build trust over time, you should invest in candor. Data scientists are smart people who are trained in how to interrogate and handle information. Therefore, my heuristic is to be about 20% more direct and candid than you think you should be. Be transparent with the good and the bad during the entire process, from recruiting, to onboarding, to the day-to-day, to performance reviews, and when discussing the team’s, department’s and organization’s strategy. It’s painful but critical for success. The moment you start “being nice” to avoid a tough conversation, you and your team have begun to lose.

Finally, feedback should be consistent and bi-directional, and great data scientists will smell bullshit a mile away. If you say you’re a believer in candor but become defensive or (worse!) don’t actually act on feedback, then your best reports will want to leave.

Connect the work to the business

To get the most from a data scientist’s time, they need to have a clear understanding of what the business goal behind the project is. Anchoring your team’s work in the context of the broader organizational strategy is among the most important jobs a manager of data science has. Unfortunately, it’s not always easy to do.

Data science projects often start with a question from someone outside the team. But often the question that the person asks isn’t exactly what they actually want to know. A lot of managing data science involves discussing and fine-tuning questions from stakeholders to better understand the information they actually want and how it will be used. Don’t let questions or requests become projects for your team until you know exactly what the stakeholder wants to understand and how they’ll use it. Having very clear objectives for the data-related questions that come your way is one of the most important things you can provide for your team.

Of course, stakeholders can’t always answer these questions on their own. They might not have a clear idea of what a finished data science product would look like (or how they would apply it). To fill this gap, make sure members of the data science team are regularly invited to product and strategy meetings. This way they can be inputs into the creative process rather than merely responding to requests.

Design great teams

There are many professionals trying to break into the “sexiest profession of the 21st century” and so, as a data science manager, you’ll get lots of applications and will have to be picky. Take advantage of that to be picky in the right ways. Care about your hiring process.

One of the biggest areas where people fail as managers is in the tradeoff between the short- and the long-term. For instance, it’s easy to start thinking that you don’t have time to recruit. This is a huge mistake. If you don’t have the time to find great team members and to scrutinize your interview and onboarding processes to ensure that you have good ones in place, then you don’t have time to manage a new direct report. Creating a great hiring process will pay off in the long term.

What does a great hiring process look like? For one thing, it doesn’t just focus on technical skills. Social skills like empathy and communication are undervalued in data science and the disciplines from which data scientists usually emerge, but they’re critical for a team. Make this a part of your hiring (but not in a way that amounts to hiring just for ‘culture fit’ and reinforces your affinity and confirmation biases). Instead of focusing on whether you can get along with a candidate, ask yourself if there is a lens though which this person sees the world that expands the boundaries of the team’s knowledge sphere—and value that dimension as highly as you value other attributes such as technical ability and domain expertise. This is why it is important to prioritize diversity. That includes diversity of academic discipline and professional experience but also of lived experience and perspective.

A few areas in particular stand out as important for data science. First, don’t just hire senior people. Not only are they in high demand and expensive, but less experienced employees have the “luxury of ignorance” and can ask “dumb” questions. These questions are not actually dumb, of course, but are unencumbered by the usual assumptions that more experienced professionals stop being aware they are making. It’s not hard to become infatuated with a particular way of doing things and to forget to question whether a favored approach is still the best solution to a new task.

Second, data scientists come from a variety of academic backgrounds: computer science, physics, statistics, and many others. What matters most is having a creative mind coupled with first rate critical thinking skills. I have a team member who studied marine biology and this diversity of expertise has proven extremely valuable. (The ability to translate domain knowledge about how pods of dolphin behave in the wild can be surprisingly useful when modeling a fleet of robots.)

Third, it’s important to hire individuals whose strengths complement one another, rather than building a team that all excels in the same area. A “big picture” person, someone who can articulate stories with data, and a visualization wizard working together can collaborate to produce things none could independently. To take the most advantage of these complementary skills, it’s important to make sure that the team actually works as a team and collaborates. You want your team working with each other and not just alongside. Regularly requiring members to read each other’s code and reports and fostering team activities centered around technical discussions ensure that you get the most out of this sort of diversity.

Finally, it’s also important to build a team that reflects the people whose data you’re analyzing. This is the only way to ensure that you have a resilient team that will ask better questions and a have wider aperture of perspectives from which to ask these questions. This way, each individual’s blind spots are covered by another’s past experiences and skill set.

When to specialize

One final piece of advice: When a data science team is just starting out, everyone on it will “wear many hats” and do lots of different kinds of data science. That’s ok—it’s like when someone joins a startup. But as your team matures and proves its value, recognize that roles will become more defined and some activity will move to other teams (infrastructure, ops, etc.).

Having said this, I would caution against specializing too soon. Specialization only works when well-defined and clear requirements are available to offset the coordination delays and costs associated with multiple teams working together. “Full stack” data scientists are very hard to find, but it is possible to find smart and driven “partial stack” data scientists who can learn, with a little dedicated coaching, how to appropriately frame a problem, manage a small project, develop and train a model, integrate with APIs, and push to production.

If you’ve done your job right as manager, this evolution will proceed relatively smoothly. You’ll have been picky in your hiring and created a great team with a balanced skillset. Your employees will trust you, and they’ll understand how changes support the organization and its goals.

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