Blogs

When the Going Gets Tough, Act the Part (HBR)

John Baldoni - Thu, 10/25/2018 - 07:54

When times are tough, people want to see their leaders act. Bold statements make headlines but actions provoke results. If an action is to be sustained, it must be reinforced by what followers do. In my new book, Lead By Example, 50 Ways Great Leaders Inspire Results, I discuss some of the ways leaders act for the benefit of the organization.

Manage by inclusion. It’s human nature to seek input from people we know best. Leaders are no different, but they have an obligation to seek out alternative points of view. Failure to do so leads to unilateral thinking, that is, everyone adopting the same point of view. That may be good for cheerleading squads, but it gets organizations into trouble. One way to avoid this trap is to make certain that people feel free (as well as safe) to voice opinions contrary to prevailing thought. That will only happen if the leader goes out of her way to seek alternate approaches.

Delegate for results. Pre-schoolers don’t mind being shown how to do something once or twice, but pretty soon they want to do it themselves. The same goes for those you lead. Give people directions but never tell them how to do something. Let them figure it out for themselves. Nowhere does this apply more than in setting goals. As a colleague of mine says, “Delegate for results, not tasks.” That is, tell people what needs to be done, but do not give them a to-do list.

Make a decision. If leaders truly are to propel the action, they must make decisions. Consultation with others, as discussed, is vital, and so too is deliberation. But sooner than later someone needs to pull the trigger when it comes to big decisions. Acting deliberately and decisively is essential to achieving intended results.

Another aspect of action is a leader’s behavior. How a person leads is often as important as what a leader does. The way a leader communicates, delegates, supervises and recognizes matters. People are not inclined to follow someone who is simply going through the motions; they want a leader who thinks about the impact of his decisions on others. This is especially true in times of crisis. Leaders accomplish little by themselves; they need the actions of others to succeed.

First posted on HBR.org on 10.15.2008

Categories: Blogs

How to Make Sure You’re Not Using Data Just to Justify Decisions You’ve Already Made

Harvard business - Thu, 10/25/2018 - 07:00
Alicia Llop/Getty Images

How can an organization can tell whether it’s actually letting data inform its decision making — or if it’s merely using superficial analyses to retroactively justify decisions it has already made?

Traditionally, organizations have used data analytics as a tool of retrospection, as a means of answering questions like, “Did this marketing campaign reach our desired audience?” or “Who were our highest-value customers over the last year?” or “Did engagement peak at regular intervals throughout the day or week?” These answers are typically built around metrics — or key performance indicators (KPIs) — like click-through rates, cost per impression, and gross rating points, which companies all-too-often decide on too late in the process.

These descriptive analytics — that is, analytics that measure what has already happened — are undeniably important. But they’re just a bit player in the far more sprawling drama that is data-driven decision making. Within organizations that are truly data-driven, KPIs aren’t arbitrarily plucked out of thin air, but are generated at the start of a decision-making process. More precisely, it’s not an organization’s KPIs, but the key business questions (KBQs) — of which KPIs are an extension that serve as the cornerstone of its success.

In their HBR article Big Data: The Management Revolution, Andrew McAfee and Erik Brynjolfsson arrived at a similar conclusion, writing, “Companies succeed in the big data era not simply because they have more or better data, but because they have leadership teams that set clear goals, define what success looks like, and ask the right questions.”

Insight Center

However, arriving at “the right questions” is easier said than done, as any investigation must extend beyond, “What do the data say?” At my agency, our KBQs emerge from a rigorous four-step process that forces us to leverage data throughout the planning phases of our marketing campaigns. Though its specific applicability may vary slightly from industry to industry, our process provides a highly actionable model for deploying data analytics in a proactive, transformational manner; one that guides your decision making instead of justifying it.

Step One: Define your purpose. At the start of every planning cycle, an organization should make a concerted effort to engage stakeholders from every corner of its business in a wide-ranging discussion aimed at defining the campaign’s purpose. This begins with methodically zeroing in on the challenge(s) you’re trying to solve. Are you trying to improve a customer satisfaction rating? Cultivate long-term loyalty among a specific subset of customers? Increase the number of products that ship from a certain warehouse?

Don’t hesitate to interrogate the status quo — and, when appropriate, dismantle it. A history of maximizing pageviews is not itself a compelling reason to set a renewed goal of maximizing pageviews. Take a step back, survey the landscape (both internal and external), and carefully consider whether you’ve defined your purpose in accordance with anything other than the force of habit.

Step Two: Immerse yourself in the data. Once an organization has identified its purpose, it should conduct a comprehensive survey of what it already knows to be true. This is the stage where an organization should answer, “What do the data say?” That said, it should do so with a distinctly forward-looking mindset. At this stage of the process, an organization should take little interest in evaluating — and even less in justifying — past decisions. The totality of its interest should rest with how its data can inform its understanding of what is likely to happen in the future.

Like the previous stage, stage two is highly collaborative. In pursuit of broad-based collaboration, an organization should democratize its data to the greatest extent possible, funneling it into the hands of experts and non-experts alike. Not everyone at your organization is going to have a PhD in mathematics or a professional background in data science, but this doesn’t preclude anyone from getting their hands dirty in your data — after all, one doesn’t need to understand how a tool works to appreciate and take advantage of its utility. Ensuring that stakeholders across your organization come to a mutual understanding not only of the facts, but of their importance, is critical to the success of the rest of the process.

Step Three: Generate key business questions. While the previous stage pushes an organization to the edge of its organizational knowledge, this stage sends it tumbling into the unknown. With a goal and a set of agreed upon assumptions in hand, the organization has everything it needs to start posing KBQs, or lines of inquiry that propel it from “What do we want to achieve?” to “What do we need to know in order to achieve it?”

Using the precise purpose-defining language it established during the initial stage, an organization should now challenge stakeholders to ask as many questions as they can think of, first individually, then as teams. Good questions, bad questions, self-evident questions, unrealistic questions — it matters not. The objective is quantity, not quality.

While no topic or line of inquiry should be off-limits, an organization could start with these:

  • Can we predict which customers are at the highest risk of switching to a competitor, and design programs to decrease that risk?
  • Can we predict which customers have the highest probability of trying and subsequently adopting our brand, and design cross-channel promotional strategies to reach them most effectively?
  • Can we identify the optimal price point for our brand in order to maximize growth at a certain level of profitability?
  • Can we rethink the way we communicate with our target customers across our portfolio of products by understanding the combinations of products that are most often purchased by the same customers?

In many cases, such unfettered inquisitiveness requires feigning a degree of ignorance; that is, pretending that you don’t know what you know or pretending that your data doesn’t exist. This can be something of a high-wire act, especially for organizations new to data analytics, but it pays immense dividends if executed properly. Creativity and innovation are central to this phase of KBQ generation, and hewing too closely to your existing data is a recipe for the opposite.

To a similar end, it can be valuable to take the KBQs you generate and “invert” them. Just as sketching an object upside down can help an artist more accurately reproduce its likeness, rewriting your KBQs in the negative can produce more “Aha!” moments than would otherwise arise. Consider the following hypothetical progression that a pharmaceutical company might go through:

Purpose: Increase medication adherence among patients who have been prescribed Drug X.

KBQ: Which outreach methods do non-adherent patients respond to most reliably?

Inverted KBQ: Which outreach methods do non-adherent patients not respond to?

This slight shift in perspective can be a game-changer. Like any activity dealing with human behavior, marketing is an inexact science, and the value of strategically constraining your efforts cannot be overstated. Uncertainty is far more palatable — and far less problematic — when you know precisely where it exists than when it pervades your entire operation. In business, known unknowns are preferable to unknown unknowns.

Step Four: Prioritize your key business questions. Only after an organization has compiled an exhaustive list of KBQs should it begin evaluating, critiquing, and prioritizing them. In practice, some KBQs are highly actionable but lack the clear potential for making a business impact, while others have the potential to revolutionize your business but are highly inactionable. Pipe dreams, curiosities, and incremental improvements are all situationally valuable, but focusing on the pursuit of high-value KBQs will ultimately drive meaningful results.

Transforming a defense mechanism into a change agent. It’s tempting to place data analytics at a discrete juncture in your operational processes, but the reality is that data is not something to be used periodically, nor within strict project-based silos.

To drive real results, an organization must use data analytics throughout its business cycle. Today’s descriptive analytics are the foundation of tomorrow’s KBQ-oriented planning processes, which in turn are the foundation for a forward-looking analytics brief that details how an organization is going to answer its high-value KBQs. It’s this cyclical, mutually-informing decision-making architecture that both accelerates organizational transformation and disrupts your fixation on the rear-view mirror.

As Nobel Prize-winning physicist Niels Bohr once quipped, “An expert is a man who has made all the mistakes which can be made in a very narrow field.” Nowhere is this truer than in business. A well-conceived data analytics program empowers organizations to redirect their focus from justifying past decisions to learning from past mistakes. The sooner organizations make this pivot, the sooner they will enjoy the benefits of truly data-driven decision making.

Categories: Blogs

The Motivating (and Demotivating) Effects of Learning Others’ Salaries

Harvard business - Thu, 10/25/2018 - 06:05
Dimitri Otis/Getty Images

Pay inequality is common in most workplaces. You get paid significantly more than your subordinates, your boss gets paid more than you, and your boss’s boss gets even more. In many large organizations, some employees can take home paychecks tens or hundreds of times more than others.

Whether you like it or not, your employees have wondered at some point about your salary — and their peers’. Should you be worried about that? Our recent research sheds light on this question, and our findings may surprise you.

We conducted an experiment with a sample of 2,060 employees from all rungs of a large commercial bank in Asia. The firm is quite representative of most companies around the world across some key dimensions, including its degree of pay inequality and non-disclosure policy around salary.

The first thing we looked at was manager salary. Through an online survey, employees had to guess the salaries of their managers. To make sure they had incentives to be truthful, we offered rewards for accurate guesses. The vast majority of respondents missed the mark by a significant margin (on average, employees tend to underestimate their manager’s salary by 14%). And this is where the action happens: by the flip of a virtual coin, we decided whether to “correct” a respondent’s estimate, by providing accurate information from the firm’s official salary records. So half of the respondents learned how much their boss truly earned — a salary higher than what they initially thought — while the other half did not.

Think about it this way: Let’s say there are two employees (similar in terms of level and experience) who think that their bosses get paid three times as much as them; but in reality, their boss gets paid five times as much. The flip of our coin randomizes which employee will learn that her boss actually gets paid five times more than she does, and which employee will not be corrected. Then we can compare the subsequent behavior of these two similar employees, to see how learning that your boss makes much more than you might affect your productivity.

To measure the behavior of these two groups of employees, we gathered daily timestamp, email, and sales data for the year following our survey. To our surprise, finding out that their managers got paid more seemed to make employees work harder than those who did not find out the true salary. Our estimates suggest that discovering that the boss’s salary is 10% higher than originally thought causes employees to spend 1.5% more hours in the office, send 1.3% more emails, and sell 1.1% more. (The higher the surprise, the larger the effect — finding out the boss earned 50% more led to effects five times larger.)

The evidence suggests that these effects were driven by aspirations. The effect of knowing manager salary was more substantial for employees who learned about the pay of managers who were only a few promotions away, whose shoes they could realistically aspire to fill. We find that, when the boss is fewer than five promotions away, for each 10% increase in the perceived salary of the boss, employees spend 4.3% more hours in the office, send 1.85% more emails, and sell 4.4% more. We also found that, after realizing that these managers get paid more, employees became more optimistic about the salaries they will earn themselves five years in the future. On the other hand, we found no effects on effort, output, or salary expectations when the employees learned about managers several promotions away (e.g., an analyst learning about C-suite salaries).

There is a caveat, though. While employees seemed perfectly capable of handling this vertical inequality, they did not handle horizontal inequality nearly as well.

In our experiment, we also asked employees to guess the average salary among their peers — that is, the other employees with the same position and title, from the same unit. Even though employees did better at guessing the salaries of their peers than that of their managers, most employees still guessed incorrectly. We flipped a second virtual coin to decide whether to “correct” their misperception about the peer salary.

We saw that finding out peers get paid more does have a negative effect on the employee’s effort and performance. Finding out that peers earn on average 10% more than initially thought caused employees to spend 9.4% fewer hours in the office, send 4.3% fewer emails, and sell 7.3% less.

This evidence suggests that it might not be wise to motivate individual employees through raises alone. If you increase the pay of one employee, that employee may work harder but the rest of the peer group could work less hard. You can avoid this by motivating employees through the prospect of a higher salary attached to a promotion. In other words, keep salaries compressed among employees in the same position, but offer them large raises when they get promoted to a higher position.

Our research raises the question: should you increase pay transparency at your company? Though surveys reveal most employees wish their employers were more transparent about salaries, the majority of firms maintain pay secrecy policies. But there is little evidence on how transparency affects the outcomes that managers care about. It is possible that managers choose pay secrecy because they think it is in their best interest when in fact it is not.

You may not need to worry too much if one of your employees catches wind of your salary. Employees in our study tended to underestimate the pay of their managers, and learning the actual amount led them to work harder. This degree of pay transparency seems to have given employees a sense of their earnings potential, driving up motivation. But we need further evidence to better understand how to best leverage transparency to promote productivity and employee satisfaction.

Of course, we must remember that salary information is sensitive, and thus there can be such a thing as too much transparency. For example, the majority of employees participating in our study were in favor of increasing transparency in an anonymous fashion, by reporting average salaries by position. However, when the same employees were asked about increasing transparency in a non-anonymous fashion, meaning their names and salaries would be shared, most of them opposed. And in a follow-up study, we found that most employees were willing to pay significant amounts in order to conceal their own salary from coworkers.

Many U.S. policies promoting pay transparency are mandating complete, non-anonymous salary transparency. For example, some states like California and New York publish online lists with the full names and salaries of every state employee. We think a wiser approach is what our study participants called for: transparency about average pay for a position, without disclosing individual salaries.

We encourage you to start experimenting with transparency at your company.  The first step is to figure out what your employees want. You can find out through anonymous surveys. Just mention some alternatives that you consider viable, and let them voice their preferences. For instance, do your employees feel informed about their salaries five years down the road? Would they want to find out the average pay two or three promotions ahead? Once you look at the survey results, you can decide what information to disclose and how. According to our findings, signals about the enticing paychecks waiting five years in the future is the push they need to be at their best.

Categories: Blogs

The Quality All Great Leaders Have in Common – and How to Cultivate It Within Yourself

Greatleaders hipbydan - Thu, 10/25/2018 - 06:00

Guest post from Christy Whitman
Above all other qualities, vision is the most essential to extraordinary leadership.   Throughout every era of time, and in in every imaginable industry, the most influential leaders have been those who innately understand that what has been in the past, as well as the circumstances that exist in the present, do not have the power to limit the potential of what can be created in the future.  Great leaders hold tenaciously to the reality they envision in their hearts — even in the shadow of previous failures, and even in the absence of tangible evidence that what they want is possible to achieve.  In other words, a great leader is someone who gives more credence to the vision that calls to them than they do to any voice of disbelief or doubt.  Most of the world is not living in a mindset of true leadership, but has instead fallen into the habit of simply reacting to whatever is going on around them.   And while it is very compelling to give our attention, our focus, and therefore our powerful, magnetic creative energy to those things that are not right now as we would like them to be, directing the precious gift of our attention in this manner nails our creative feet to the floor and keeps us from cooperating with our own desires.  Leadership requires us to launch ourselves out of the very human tendency to allow other people, external circumstances, and our own self-doubts to dictate what we believe we can accomplish, and therefore what we allow ourselves to envision.  Posing as the truth, these considerations are often camouflaged as legitimate concerns that go something like this: I don’t have the money. It’s not the right time. What will others think? If I go for my dream, I might fail. I should just be happy with the life I have.  Considerations like these may appear as formidable conditions over which we are powerless, but this is both an illusion and a critical error in thinking. The obstacle that stands in our way is not a money problem, a time problem or a people problem; it’s a vision problem. When we are focused only upon the current conditions of our lives, we deprive ourselves of our innate ability to create anything different.  We simply cannot give our attention to things that are other than we’d like them to be and create what we want at the same time.    In every moment, we are either doing one or the other. 
So for example, if you, as the leader of an organization, are focused upon the weakness or ineffectiveness of your team, you must understand that you are using your powerful creative energy to contradict rather than support your own desire to lead them to success.  But when on the other hand you go out of your way to notice and then deliberately appreciate each person and aspect of your business that is working well, your focus is aligned with your vision, and you are nurturing its growth and ultimate fruition through the power of your attention.   Some people believe that being a great leader requires discipline – and it most certainly does – but it’s not the “nose to the grindstone” effort and struggle that we’ve been taught is necessary for success.   The most important discipline that we as leaders can ever practice is that which takes place not in the realm of action, but in the quiet of our own minds.  It takes great discipline to identify a particular outcome and summon the intention to make it happen.  It takes discipline to focus on a desire with enough clarity that it begins to coalesce into a vision.  And it takes discipline to bring the energy of our most frequent and consistent thoughts, feelings, moods and expectations into alignment with the vision of what we do want, rather than chronically noticing the absence of it.  Once we have aligned ourselves with our own vision so completely that we are not simply willing to entertain any other possibility, we unlock the secret to magnetism, to charisma, and to seamlessly attracting those who want nothing more than to play their role in the play that we are orchestrating.
Right now in your own life, you are surrounded by conditions and considerations that may have you convinced that you are powerless to become the leader you desire to be, whether in your business or in your personal life.  You may believe that you’re too old, that the odds are stacked against you, or that everything you desire to accomplish has already been done before.  But however these thoughts, beliefs, and perceptions show up for you, it’s imperative that you begin to recognize them for what they really are.  They may be evidence of what has been, but they don’t need to limit your vision of what can become.  Once you understand that what you direct your energy toward is what you will ultimately begin to attract, you will reclaim the power to create your life on purpose rather than by default, and, by example, you will teach others how to do the same. 
Christy Whitman is a transformational leader and the New York Times bestselling author of The Art of Having It All and co-author of Taming Your Alpha Bitch. Here new book is called Quantum Success: 7 Essential Laws for a Thriving, Joyful, and Prosperous Relationship with Work and Money.  She has appeared on Today and The Morning Show, and her work has been featured in People, SeventeenWoman’s DayHollywood Life, and Teen Vogue, among others. As the CEO and founder of the Quantum Success Coaching Academy, Christy has helped thousands of people worldwide to achieve their goals through her empowerment seminars, speeches, coaching sessions, and products. She currently lives in Scottsdale, Arizona with her husband and their two boys.
Categories: Blogs

4 Steps for Writing the Perfect Learning Objective

Hr Bartender - Thu, 10/25/2018 - 02:57

One of the most essential (and challenging!) components to training design is creating the learning objective. If you design training (like I do), then you know it’s one of the first things that stakeholders ask for: “What’s the program objective?” And they don’t want a wimpy objective.

Wimpy objectives use what are considered to be weak verbs. Words such as know, learn, understand, and appreciateare examples of weak verbs. Here are a few examples of poorly written learning objectives:

  • Understand the four components of a learning objective.
  • Be able to describe the four components of a learning objective.
  • Our workshop will provide participants with the opportunity to learn the four components of a learning objective.

Please don’t hate on me for saying it. We all know it. And I’ll admit, upon occasion, I’ve used those weak verbs myself. But there’s a better way.

Now, before I share with you my rule for writing a solid learning objective, I want to have a quick side bar conversation about the term “learning objectives”. Often, we use the terms learning objective and learning outcome interchangeably. Again, I’ve been guilty of this myself. Traditionally, learning objectives are what participants can expect from the facilitator or trainer.

Learning outcomes are what participants are expected to know or be able to do by the end of the training session. They should be specific and measurable.

In today’s training world, I believe the program description is what participants can expect during the learning event and learning objectives are what participants expect to know by the end of training. So for the purposes of this post, we’re calling them learning objectives.

Back to the rule. I use what is called the A-B-C-D method for developing an objective.

A stands for audienceand is fairly self-explanatory. They are the participants.

B represents the behavioror the “thing” that participants need to know or do.

C is for condition, which is the support provided to the learner. It might be a book or job aid.

D refers to degreeor required efficiency level.

Here’s an example:

Given a complete copy of the manual on Instructional Systems Design, the participant should be able to accurately describe the four components on a learning objective without error when given at least three opportunities to do so.

In this example:

A (Audience) is “the participant”

B (Behavior) is “accurately describe the four components of a learning objective”

C (Condition) is “Given a complete copy of the manual on Instructional Systems Design”

D (Degree) is “without error when given at least three opportunities to do so.”

I find this four step process to be a thorough way in developing an objective. Just ask yourself the questions.

1) Who is the intended learner?

2) What do they need to know or do?

3) What kind of support will we provide? And lastly,

4) What is the degree of proficiency they need to have?

So, the next time you have to design training – whether it’s revamping the company’s orientation program or a quick 5-minute refresher for managers on conducting interviews – use the A-B-C-D method to come up with the learning objective. It will really focus your training content and improve your results.

P.S. I’m very excited to be facilitating a virtual seminar for the Society for Human Resource Management (SHRM) on L&D: Developing Organizational Talent. We’ll be talking about how to design learning initiatives. Details about the learning objectives can be found on the SHRM website – just follow the link above. I hope you can join us!

The post 4 Steps for Writing the Perfect Learning Objective appeared first on hr bartender.

Categories: Blogs

Is Retail Dying? Plus, How Are Companies Spending their Tax Cuts?

Harvard business - Wed, 10/24/2018 - 13:33

Youngme Moon, Mihir Desai, and Felix Oberholzer-Gee 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.

Download this podcast

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.

Categories: Blogs

Humble Leadership

Leadershipnow - Wed, 10/24/2018 - 09:55


IT NEVER HURTS TO BE REMINDED of the need for humility. We tend to fall back on transactional relationships and rule-based leadership. Edgar Schein and Peter Schein call this Level 1 based leadership.

What they advocate in Humble Leadership is moving to and developing an organizational culture based on Level 2 relationships. That is relationships that are intentionally personal, cooperative, and trusting.

Level 2 relationships come naturally with friends and family, but no so much at work. Level 2 relationships come about by seeing others as a whole person and not just as someone filling a role at the moment. It’s being able to say in actions and words, “I want to get to know you better so that we can trust each other in getting our jibs done better.”

In my own work, it’s not uncommon to see people who would tell you that relationships are important to them, shift into Level 1 transactional relationships with the people they are working with. Transactional relationships can somehow make us feel like we are serious and getting down to business by avoiding all of the relationship stuff. Not to mention, it’s just easier to avoid the hard work of developing trust and openness with another person or team. But it comes at a cost. As they describe it, Humble Leadership is about “building relationships that get the job done and that avoid the indifference, manipulation, or, worse, lying and concealing that so often arise in work relationships.”

Much of our work life occurs at Level 1 because the services, stores, hospitals, and businesses we deal with are organized bureaucratically to deal with us at that level. This is typically the source of our dissatisfaction with bureaucracies. We don’t like being treated so impersonally, especially at work.

Evolving the managerial culture from Level 1 to Level 2 is the defining task for Humble Leadership.” The authors suggest that to move from Level 1 to Level 2 relationships, we need to personize our relationships at work. By that, they mean getting to know them as a whole person by minimizing subordination so that we “emphasize collaboration, joint responsibility, and your own willingness to help them succeed.” One of the best ways to get that started is by learning together “because in that context the boss and the employee can give each other direct feedback and suggestions on how he work could be done better.” In a Level 2 workgroup Humble Leadership emerges by enabling whoever has pertinent information or expertise to speak up and improve whatever the group is seeking to accomplish.

The process of creating and maintaining Level 2 relationships requires a learning mindset, cooperative attitudes, and skills in interpersonal and group dynamics.
The Heroic leader will no longer be the leader with all of the answers, going it alone to forge a new future. The leader of the future will need to be humble, cooperative, and open. Leadership will be expressed as “we together.”

At the end of the book, there are exercises to help you shift from Level 1 relationships to Level 2 relationships.



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Of Related Interest:
  Leadership as Provocative Competence
  The Essentials of Theory U

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Categories: Blogs

How to Gracefully Exclude Coworkers from Meetings, Emails, and Projects

Harvard business - Wed, 10/24/2018 - 09:00
Here it is/Getty Images

You and about 20 of your coworkers are sitting around a crowded conference room table, discussing the details of some project. Some people are fighting for attention, trying to get a word in. Others won’t stop talking. Others have tuned the meeting out, retreating to their laptops or phones. At the end of the meeting, the only real outcome is the decision to schedule a follow-up meeting with a smaller group — a group that can actually make some decisions and execute on them.

Why does this happen? People hate to be excluded, so meeting organizers often invite anyone who might need to be involved to avoid hurt feelings. But the result is that most of the people in the meeting are just wasting time; some may literally not know why they’re there.

Whether it’s a meeting, an email thread, or a project team, people need to be excluded from time to time. Being selective frees people up to join more urgent engagements, get creative work done, and stay focused on their most important tasks. How, then, can leaders do this gracefully?

We recommend three steps.

Focus on key employees to protect them from overload. Most leaders try to pare down a meeting list or an email thread by looking for employees who clearly don’t need to be on it. But we suggest the opposite approach. Who is the valuable, collaborative employee you are most tempted to include? Now ask yourself: is she really necessary?

We pose this question because one of the foundational concepts to thoughtful exclusion is known as collaborative overload. The term was coined in a 2016 HBR cover story from leadership and psychology professors Rob Cross, Reb Rebele, and Adam Grant. Drawing on original research, they claimed that up to a third of collaborative efforts at work tend to come from just 3% to 5% of employees. These employees are often massively over-burdened and, in turn, at risk for burning out.

If the same small group of people get invited to every task force, every special project, every brainstorming meeting, there’s no way they can keep up with more valuable tasks. That’s why the first step to thoughtfully excluding people is to spot those employees at the greatest risk for collaborative overload, and then be incredibly selective about when to include them in meetings or other projects.

Address people’s natural social needs. The acts of excluding and being excluded are intensely emotional, even when people know they’re invited to too many meetings and resent getting too much email.

That’s because humans are social creatures; we naturally want to help those whom we consider close to us. The employees who suffer from collaborative overload take on such heavy burdens in part because they are compelled by these ancient impulses. It’s the same reason leaders over-include: They want others to feel like they belong.

The kind of exclusion that doesn’t trigger backlash or stymie productivity must address people’s varying social needs. If we look at who suffers from collaborative overload the most, we end up with two groups: employees who are too busy to be included in everything and employees who believe being over-included is a sign of prestige and status.

It’s up to leaders, therefore, to identify both groups and show them their time is better spent on projects with the highest return. Sample language might be variations on:

  1. “I know you’ve got a lot of important work on your agenda, and I’d like to keep you off of this upcoming project so that you can focus on what you’ve already got. What do you think?”
  2. “I’d like to take you off of this project, because someone else has a similar point of view. At the same time, you’d be able to add a ton of value to this other project because you bring a unique perspective. Would you be open to that?”
  3. “I noticed that a couple of deadlines have slipped recently and that’s pretty unusual for you. Are there meetings, projects, or other things on your calendar that are consuming time or energy, that we might be able to reallocate? We all have times where we need some breathing room. How can I help?”

When leaders approach exclusion with employees’ social brains in mind, they can be more thoughtful in how they frame their directive.

Set clear expectations. Exclusion only hurts when people expect to be included.

The neuroscience of expectations shows there’s a great cost to mismatched expectations. When the anterior cingulate cortex, a brain region heavily involved in expectation matching and processing social exclusion, detects an error, it kickstarts a process that drains huge amounts of cognitive energy. This happens every time we encounter something unexpected, like seeing a favorite restaurant closed or getting disinvited to a meeting we’d normally join. That’s because the brain wants to make sense of the situation; it expected one thing and got another. Leaders eager to get the most out of their team members, by redirecting their efforts to more valuable activities, must understand and appreciate this aspect of the brain’s behavior.

If you only need a small subset of people attending a meeting, communicate with the rest of the group to ensure each person understands why they are not needed. Laying this groundwork also helps mitigate what psychologists call “social threat.” Just as loud noises and scary images can feel physically threatening, humans are wired to avoid threats in social situations, whether it’s anxiety, uncertainty, or isolation.

Managing people’s expectations ahead of time can act as a buffer against people feeling these kinds of social threats. For instance, the brain craves certainty, and being explicit about meeting participants’ roles offers it. Most of us also crave fairness, which you can provide by being transparent about the reasons for someone’s exclusion. That way, people can be excluded without the sting of feeling excluded.

Thoughtful exclusion in action

Leaders are responsible for appreciating these fundamental, albeit fragile, nuances of perception. When the time comes to launch a new project or host a big meeting, they should make it perfectly clear who needs to be involved, who doesn’t, and the reasons why. This way, employees will better understand how their role fits into the team’s larger mission, and with knowledge of other people’s roles, they’ll know who is working on what.

Think back to that chaotic meeting with 20 people. Thoughtful exclusion pares down that meeting to a core team of six or seven. Since the project manager now thinks hard about whose skills and time are most valuable — and whose would be better served elsewhere — she graciously decides you (and a dozen other people) have more important things to work on. As a result, the project reaches the finish line earlier and those employees who were excluded make greater progress on their own work.

Scale that behavior throughout an organization, and you have more people making better use of their time, tackling projects where their contributions are known, not assumed, to add value.

Exclusion may earn a bad rap in a climate where leaders are admirably sensitive about others’ sense of belonging. And it’s important to remember that thoughtful exclusion is only possible with an appreciation of the benefits of diverse perspectives and inclusive decision-making. But in order to avoid the dreaded logjam of over-inclusion, the brain science makes it clear that, with the right approach, thoughtfully leaving people out could become one of the greatest managerial moves a leader makes.

Categories: Blogs

Auditing Algorithms for Bias

Harvard business - Wed, 10/24/2018 - 08:00
Laurence Dutton/Getty Images

In 1971, philosopher John Rawls proposed a thought experiment to understand the idea of fairness: the veil of ignorance.  What if, he asked, we could erase our brains so we had no memory of who we were — our race, our income level, our profession, anything that may influence our opinion?  Who would we protect, and who would we serve with our policies?

The veil of ignorance is a philosophical exercise for thinking about justice and society. But it can be applied to the burgeoning field of artificial intelligence (AI) as well. We laud AI outcomes as mathematical, programmatic, and perhaps, inherently better than emotion-laden human decisions. Can AI provide the veil of ignorance that would lead us to objective and ideal outcomes?

The answer so far has been disappointing. However objective we may intend our technology to be, it is ultimately influenced by the people who build it and the data that feeds it. Technologists do not define the objective functions behind AI independent of social context. Data is not objective, is it reflective of pre-existing social and cultural biases. In practice, AI can be a method of perpetuating bias, leading to unintended negative consequences and inequitable outcomes.

Today’s conversation about unintended consequences and fair outcomes is not new. Also in 1971, the U.S. Supreme Court established the notion of “disparate impact“ — the predominant legal theory used to review unintended discrimination. Specifically, the Griggs vs. Duke Power Company ruling stated that independent of intent, disparate and discriminatory outcomes for protected classes (in this case, with regard to hiring), were in violation of Title VII of the Civil Rights Act of 1964. Today, this ruling is widely used to evaluate hiring and housing decisions, and it is the legal basis for inquiry into the potential for AI discrimination. Specifically, it defines how to understand “unintended consequences“ and whether a decision process’s outcomes are fair. While regulation of AI is in early stages, fairness will be a key pillar of discerning adverse impact.

The field of AI ethics draws an interdisciplinary group of lawyers, philosophers, social scientists, programmers, and others. Influenced by this community, Accenture Applied Intelligence* has developed a fairness tool to understand and address bias in both the data and the algorithmic models that are at the core of AI systems.

How does the tool work?

Our tool measures disparate impact and corrects for predictive parity to achieve equal opportunity. The tool exposes potential disparate impact by investigating the data and model. The process integrates with the existing data science processes. Step 1 in the tool is used in the data investigation process. Step 2 and 3 occur after a model has been developed. In its current form, the fairness evaluation tool works for classification models, which are used, for example, to determine whether or not to grant a loan to an applicant. Classification models group people or items by similar characteristics. The tool helps a user determine whether this grouping occurs in an unfair manner, and provides methods of correction.

There are three steps to the tool:

  • The first part examines the data for the hidden influence of user-defined sensitive variables on other variables. The tool identifies and quantifies what impact each predictor variable has on the model’s output in order to identify which variables should be the focus of step 2 and 3. For example, a popular use of AI is in hiring and evaluating employees, but studies show that gender and race are related to salary and who is promoted. HR organizations could use the tool to ensure that variables like job roles and income are independent of peoples’ race and gender.
  • The second part of the tool investigates the distribution of model errors for the different classes of a sensitive variable. If there is a discernibly different pattern (visualized in the tool) of the error terms for men and women, this is an indication that the outcomes may be driven by gender. Our tool applies statistical distortion to fix the error term — that is, the error term becomes more homogeneous across the different groups. The degree of repair is determined by the user.
  • Finally, the tool examines the false positive rate across different groups and enforces a user-determined equal rate of false positives across all groups. False positives are one particular form of model error: instances where the model outcome said “yes” when the answer should have been “no.” For example, if a person was deemed a low credit risk, granted a loan, and then defaulted on that loan that would be a false positive. The model falsely predicted that the person had low credit risk.

In correcting for fairness, there may be a decline in the model’s accuracy, and the tool illustrates any change in accuracy that may result. Since the balance between accuracy and fairness is context-dependent, we rely on the user to determine the tradeoff. Depending on the context of the tool, it may be a higher priority to ensure equitable outcomes than to optimize accuracy.

One priority in developing this tool was to align with the agile innovation process competitive organizations use today. Therefore, our tool needed to be able to handle large amounts of data so it wouldn’t keep organizations from scaling proof-of-concept AI projects. It also needed to be easily understandable by the average user. And it needed to operate alongside existing data science workflows so the innovation process is not hindered.

Our tool does not simply dictate what is fair. Rather, it assesses and corrects bias within the parameters set by its users who ultimately need to define sensitive variables, error terms and false positive rates. Their decisions should be governed by an organization’s understanding of what we call Responsible AI — the basic principles that an organization will follow when implementing AI to build trust with its stakeholders, avert risks to their business, and contribute value to society.

The tool’s success depended not just on offering solutions to improve algorithms, but also on its ability to explain and understand the outcomes. It is meant to facilitate a larger conversation among data scientists and non-data scientists. By creating a tool that prioritizes human engagement over automation in human-machine collaboration, we aim to inspire the continuation of the fairness debate into actionable ethical practices in AI development. 

* An early prototype of the fairness tool was developed at a data study group at the Alan Turing Institute. Accenture thanks the institute and the participating academics for their role.

Categories: Blogs

End the Corporate Health Care Tax

Harvard business - Wed, 10/24/2018 - 07:00
Bjarte Rettedal/Getty Images

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.

Insight Center

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.

Categories: Blogs

Managing a Data Science Team

Harvard business - Wed, 10/24/2018 - 06:30
Ragnar Schmuck/Getty Images

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.

Categories: Blogs

The 6 Fundamental Skills Every Leader Should Practice

Harvard business - Wed, 10/24/2018 - 06:05
Thomas J Peterson/Getty Images

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.

Categories: Blogs

When Men Mentor Women

Harvard business - Tue, 10/23/2018 - 14:58

David Smith, associate professor of sociology at the U.S. Naval War College, and Brad Johnson, professor of psychology at the United States Naval Academy, argue that it is vital for more men to mentor women in the workplace. In the post-#MeToo world, some men have shied away from cross-gender relationships at work. But Smith and Johnson say these relationships offer big gains to mentees, mentors, and organizations. They offer their advice on how men can be thoughtful allies to the women they work with. They are the authors of Athena Rising: How and Why Men Should Mentor Women.

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Categories: Blogs

How the U.S. Can Rebuild Its Capacity to Innovate

Harvard business - Tue, 10/23/2018 - 09:00
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Many U.S. firms have long had a simple mantra: “Invent here, manufacture there.” But, increasingly, those same companies are now choosing to invent as well as manufacture abroad. From automotive to semiconductors to pharma to clean energy, America’s innovation centers have shifted east, offering growing evidence that the U.S. has lost what Harvard Business School’s Willy Shih calls the “industrial commons”: indispensable production skills and capabilities. It’s not just that virtually all consumer electronics are designed and made overseas. It’s that the U.S. has lost the underlying capacity to make products like flat-panel displays, cell phones, and laptops; nearly half of the foreign R&D centers established in China now belong to U.S.-based companies.

This isn’t just a lesson for the United States. It’s a lesson for countries around the world: Once manufacturing bids farewell, engineering and production know-how depart as well, and innovation activities eventually follow. We can trace how this happened in the U.S. by looking back to the original offshoring frenzy which started with consumer electronics in the 1960s. The invention of modern transistors, the adoption of standardized shipping containers, and the advent of low-cost assembly lines in East Asia lowered costs and created larger markets for televisions and radios, setting the stage for an Asian manufacturing powerhouse. By the time that substantial U.S. federal research investments enabled the invention of the magnetic storage drive, lithium-ion batteries, and liquid crystal display technologies that paved the way for the next generation of consumer electronics in the 1980s and 1990s, the U.S. had already ceded electronics manufacturing to Asia.

U.S. firms took offshoring a step further and began contracting design and product development activities overseas around the turn of the millennium when China joined the World Trade Organization and Asian producers started investing in major capacity improvements. That pattern has continued. In a recent survey of 369 manufacturers, researchers found that across a range of fields U.S. companies were deciding to move R&D to China to be closer to manufacturers, suppliers, and talent as well as to reap lower development costs and higher-growth markets.

We know from looking at strong economies around that world that a nation needs both R&D and manufacturing activities to maintain a healthy 21st Century industrial ecosystem. While America has continued leading the world in terms of investment in basic science research, it has lost the ability to do the kinds of process improvements that are essential for innovation. When it comes to manufacturing, the country has lost the capacity for “learning by doing.”

But it should be possible for the United States to reverse these developments. We have identified four principles with straightforward steps that policymakers, business leaders, and universities can take to restore innovation ecosystems.

1. Don’t fear picking winners. The United States invests an unrivaled $140 billion annually in federal R&D, and yet the U.S. annual trade deficit in advanced technology products alone stands around $100 billion. America’s problem? It isn’t seriously investing in turning good ideas from laboratories into manufacturable products. In too many cases, other countries are securing new industries by taking advantage of promising results from America’s federal research investments: maturing innovations that were seeded in U.S. basic research laboratories, manufacturing products, and exporting those products back to the United States.

The United States needs investment in “translational research.” This means investing in not only basic science, but also the design, engineering and manufacturing work that can turn a promising idea into a valuable product. Take the example of lithium-ion batteries. While U.S. federal research in the 1990s largely established the feasibility of the technology, U.S. battery companies including Duracell and Energizer opted out of volume manufacturing these new products—not because of domestic labor costs, but because of fears of high upfront investments, long development cycles, and a lack of access to consumers of rechargeable batteries. Countries in East Asia saw an opportunity for job creation and decided to help homegrown firms overcome these hurdles. They provided facilities, loans, and other assistance to establish domestic manufacturing in the field. It worked. Today, US firms have less than 2% of market share in the multi-billion automotive lithium-ion battery industry.

Japan spends about 7% of its government R&D budget on practical “translational research”—converting basic research into meaningful new manufactured goods and processes. Germany spends about 12%. South Korea spends roughly 30%. The U.S., in contrast, spends just 0.5 percent. Even with Japan’s smaller national budget, its total government spending on translational research amounts to about three-times what the U.S. spends. Germany’s translational investments amount to about six-times total U.S. investments. South Korea’s are approximately eight-times what the U.S. spends. Historically, Americans have been averse to translational investments for fear of “picking winners and losers.” But other free-market economies have been able to pick winners and make these investments in fair, unbiased ways that demonstrably boost competitiveness.

Rather than allowing promising R&D results to languish in labs or even be commercialized by foreign competitors, the U.S. should launch a “National Innovation Foundation” to invest in engineering and manufacturing R&D to mature emerging technologies and anchor their production onshore. Right now, there’s no single “focal point” for manufacturing-related R&D in the U.S. federal government. MForesight, a federally-funded independent consortium of academia and industry focused on the future of U.S. manufacturing, estimates that with about 5% of the $140 billion federal research budget, the U.S. could create such an institution and significantly increase the return-on-investment from taxpayer-funded research. This would simply bring the United States into line with the rest of the industrialized world. An estimated 50 countries now have government-backed innovation foundations or similar agencies devoted to turning discoveries and inventions into commercially-viable and socially-beneficial results.

2. Invest in hardware startups and scale-ups. According to a recent study, even when MIT-based hardware startups had access to the skills and financing needed for R&D and proof-of-concept work, they required additional capital, production capabilities, and lead customers that the U.S. simply couldn’t provide. The result: most still had to go to China or elsewhere to scale production up to commercial levels.

The problem lies with both the U.S. government and venture capital (VC). The U.S. government has a long history of strengthening innovation through a combination of R&D and strategic procurement (think both aviation and internet). Government purchase orders, for example, can help companies to raise needed capital (both investments and loans), initiate pilot production or scale production in the U.S., and catalyze private investment. In recent decades, however, the U.S. has generally decreased these types of investments, leaving startups and scale-ups to piece together their own funding. Over recent decades, VCs have overwhelmingly focused on software and biotech investments over “hardware” investments, closing additional doors to manufacturing innovations. It’s no wonder that so many promising manufacturing enterprises have to look abroad to simply get off the ground—let alone soar.

U.S. policymakers can correct this imbalance by building on existing resources to help innovative hardware startups and scale-ups succeed—particularly through domestic government procurement. Other countries—including OECD members like Australia, Sweden, France, and Germany, as well as China—use government procurement skillfully to foster innovation. For example, France used a combination of national public policy and procurement to build a world-class nuclear power industry. China has employed government procurement, strategic technology transfer, and domestic technology development to build its respected high-speed rail industry. Local and regional governments also use procurement to drive innovation. Consider how Barcelona, for example, systematically seeks innovative solutions from entrepreneurs: winning proposals receive guaranteed contracts, plus additional support like office space for their operations.

3. Mind the Mittelstand. Ask a German businessperson or policymaker about the secrets to the strength of their manufacturing sector, and they’re likely to mention the Mittelstand, their small and medium enterprises. For good reason: these firms are diverse, resilient, and geographically distributed engines of innovation. They’re defined by high levels of “buy-in” from owners, investors, managers, and employees. They’re an important basis of “bottom-up innovation.”

In this era, large multinational firms are essentially “systems integrators”—they depend on suppliers, mostly Small and Medium Manufacturers (SMMs), to provide most of the needed components in any product. While few SMMs entertain offshoring strategies, they do, increasingly, compete globally.

The loss of America’s industrial commons has led to the consolidation, weakening, or loss of many small suppliers. This can be corrected. In the United States, SMMs still amount to about 250,000 firms, or 98% of all manufacturing firms. By strengthening and supporting these firms, the U.S. could rebuild the backbone of its manufacturing sector. For example, America’s public sector could help by offering loan guarantees and technical assistance to SMMs to speed up the pace of adoption of new smart manufacturing technologies that are becoming essential for process improvements. Further, government could work to ensure that SMMs are taking advantage of existing opportunities and expanded programs to build awareness about procurement opportunities, emerging domestic and export market opportunities, and new technologies. SMMs can play crucial roles in innovation by engaging in partnerships with universities and other laboratories to help mature technologies. Finally, there’s a straightforward way to help SMMs boost their own expertise: The U.S. could launch a program of industry fellowships to pay recent engineering and business retirees to help SMMs as well as to “coach” next generation of manufacturing start-ups, business incubators, and technology accelerators.

4. Power to the people. The U.S.’s manufacturing innovation decline has traced a similar decline in practical engineering talent. While American high schools typically require students to dissect a frog, few require students to disassemble a power tool. Exposure to real-world engineering is a crucial and cost-effective way to build interest in manufacturing careers—through either four-year engineering degrees or vocational training. Germany’s dual vocational training systems, which pairs apprenticeship with practical classroom learning, has long been a global gold standard. More recently, China has made major investments in talent to address the exponential growth of its manufacturing sector.

Around the world, educated people are the one single indispensable ingredient for innovation. This starts with elementary education and early opportunities to cultivate the necessary creative mindset—think Maker Faires and FIRST Robotics. At higher levels, the public sector can address the need for talent by boosting the availability of graduate fellowships for qualified students. Industry can also work with local technical schools to customize classroom training and experiential learning programs—particularly in areas of identified talent needs.

The common denominator to all these strategies is patience. From the examples above we can see that real innovation takes time. We understand that this is difficult. With the overwhelming pressures of quarterly profit reporting and short-term election cycles, it’s hard for business leaders and policy makers to focus on long-term strategies for strengthening innovation ecosystems. But history does show us: with a foresighted, sustained, cross-sectoral strategy it is possible to both invent and manufacture at home. Strong economies depend on it.

Categories: Blogs

How Lilly Is Getting More Women into Leadership Positions

Harvard business - Tue, 10/23/2018 - 08:00
Jenner Image/Getty Images

Much has been written about the troubling lack of women in leadership roles generally and in health care in particular. At Lilly, we have tackled this problem head-on. Our approaches, we think, can be helpful to other companies working to address this imbalance.

In 2015, we conducted a workforce analysis that revealed a significant shortage of women in leadership at our company. Overall, our global workforce was 47%  female — and 53% of entry-level employees were women. But at higher levels, the percentage dropped off sharply, plummeting to 20% at the top. While this number was comparable to the percentage of women executives at Fortune 500 healthcare companies in 2015, it was not a number we were proud of. Companies that have gender-diverse leadership deliver better financial performance than companies that do not – so addressing the gap was not just the right thing to do; it made business sense as well.

But before we could solve the problem, we had to understand it. So we embarked on an in-depth study of our own employees, based on a proprietary, multi-faceted process we use for market research. We engaged an outside firm to conduct the study to ensure independence and anonymity.

Insight Center

We surveyed high-potential women and men in the U.S. and asked highly personal questions we had never asked before, recording their stories. Our objective was to better understand how the experiences of women working at Lilly differed from those of men — and more specifically, to identify and remove barriers to career growth so we could increase the representation of women in leadership.

Here are some of the lessons we learned:

Get buy-in from the outset. Our management team was invested in this undertaking from the beginning. Dave Ricks, who was then president of one of our largest business units and who became CEO in January 2017, commissioned the research and supported it throughout the process. He and other senior leaders gathered for two days with human resources and the Lilly Women’s Network, one of our employee resource groups, to brainstorm solutions to barriers uncovered in the research. This was not “just” an HR issue — it was a business issue. We went all-in.

Do your homework. Only through rigorous internal research – asking the right questions, listening to the answers and crystallizing the results – can a company expect to understand its own workforce. We started with a business problem: If women comprised nearly half of our workforce, why were we seeing such a drop-off in senior management? Leaders (mostly male) hypothesized that many women were less ambitious than men, or weren’t capable of ascending to the highest levels of leadership. Traditional engagement surveys do not go deep enough to show whether either point is actually true. Not surprisingly, the surveys showed, neither is based in reality.

Understand your research. Numbers are just numbers unless translated into insights that can be put to use. The research showed that women are just as ambitious as men and equally likely to seek growth opportunities. But many women did not feel supported or recognized for their work. High-potential women start their careers at Lilly excited to take on more responsibility, despite relatively few women role models at the top, especially for non-white women. As they advance, our data showed that some women wrestled with how to fit in and move ahead in a culture that, as with most companies, was dominated by men. They reported encountering biases (conscious and unconscious), gender stereotypes, and talent-management practices that undermined their ambitions. For example, the women reported experiences in which “relationship capital” — whom you know and trust — was an important but unspoken factor in decisions about promotions. Study results showed that Lilly women were more likely to focus on doing the work itself than on networking, and therefore sometimes missed opportunities for promotions despite strong performance.

Be an open book. Accountability is key, so we acted transparently. We held our own feet to the fire by sharing our findings with leaders and employees in 2016. Two years later, we’re seeing women becoming more vocal and more influential. I’m here at the table, they’re saying, and I want to be heard.

Commit to change. Understanding the causes of our gender imbalance was a start, but we next needed to use the findings to create interventions and culture change. For example, we initiated training and deployed instructors to help managers lead more inclusively by valuing differences, recognizing and overcoming bias, fostering a speak-up culture—and we held them accountable for results. More than 2,000 managers, senior directors and vice presidents globally have participated so far. We are revamping our talent-management processes to minimize unconscious and conscious biases in our hiring, management and promotion practices. We’ve set a goal to increase the number of women in management by four percentage points within two years, and we are close to attaining that goal.

Where we are now

Already, we’re seeing progress and are now taking the same approach with racial and ethnic minority populations in the organization. From 2016 to the end of 2017, the number of women leaders at Lilly globally rose from 38% to 41%, and the number of women who report directly to our CEO climbed from 31% to 43%. Last year, women at Lilly accounted for 61%  promotions to senior director and above in the U.S., compared to 54% in 2016. Half of the unit presidents in our pharma business are now women.

As we increase the number of women in our leadership ranks, we become better positioned to increase the diversity of our clinical trials, boost innovation, and authentically and responsibly market our medicines. We have become more deliberate, for example, about having women lead the marketing efforts for drugs that treat diseases that disproportionately affect women. The development and marketing teams for our new medicine to treat metastatic breast cancer were led by women and made up almost entirely of women, for example.

While many companies are trying to build more gender-diverse leadership teams and workforces, progress remains slow. We knew it would remain slow at Lilly, too, unless we took a different approach. So we sought to do something difficult: to understand and address our blind spots. Only then could we hope to grow our pipeline of potential women leaders.

Categories: Blogs

Your Data Literacy Depends on Understanding the Types of Data and How They’re Captured

Harvard business - Tue, 10/23/2018 - 07:37
Marco Guidi/EyeEm/Getty Images

The ability to understand and communicate about data is an increasingly important skill for the 21st-century citizen, for three reasons. First, data science and AI are affecting many industries globally, from healthcare and government to agriculture and finance. Second, much of the news is reported through the lenses of data and predictive models. And third, so much of our personal data is being used to define how we interact with the world.

When so much data is informing decisions across so many industries, you need to have a basic understanding of the data ecosystem in order to be part of the conversation. On top of this, the industry that you work in will more likely than not see the impact of data analytics. Even if you yourself don’t work directly with data, having this form of literacy will allow you to ask the right questions and be part of the conversation at work.

To take just one striking example, imagine if there had been a discussion around how to interpret probabilistic models in the run up to the 2016 U.S. presidential election. FiveThirtyEight, the data journalism publication, gave Clinton a 71.4% chance of winning and Trump a 28.6% chance. As Allen Downey, Professor of Computer Science at Olin College, points out, fewer people would have been shocked by the result had they been reminded that, Trump winning, according to FiveThirtyEight’s model, was a bit more likely than flipping two coins and getting two heads – hardly something that’s impossible to imagine.

What we talk about when we talk about data

The data-related concepts non-technical people need to understand fall into five buckets: (i) data generation, collection and storage, (ii) what data looks and feels like to data scientists and analysts, (iii) statistics intuition and common statistical pitfalls, (iv) model building, machine learning and AI, and (v) the ethics of data, big and small.

Insight Center

The first four buckets roughly correspond to key steps in the data science hierarchy of needs, as recently proposed by Monica Rogati. Although it has not yet been formally incorporated into data science workflows, I have added data ethics as the fifth key concept because ethics needs to be part of any conversation about data. So many people’s lives, after all, are increasingly affected by the data they produce and the algorithms that use them. This article will focus the first two; I’ll leave the other three for a future article.

How data is generated, collected and stored

Every time you engage with the Internet, whether via web browser or mobile app, your activity is detected and most often stored. To get a feel for some of what your basic web browser can detect, check out Clickclickclick.click, a project that opens a window into the extent of passive data collection online. If you are more adventurous, you can install data selfie, which “collect[s] the same information you provide to Facebook, while still respecting your privacy.”

The collection of data isn’t relegated to merely the world of laptop, smartphone and tablet interactions but the far wider Internet of Things (IoT), a catch-all for traditionally dumb objects, such as radios and lights, that can be smartified by connecting them to the Internet, along with any other data-collecting devices, such as fitness trackers, Amazon Echo and self-driving cars.

All the collected data is stored in what we colloquially refer to as “the cloud” and it’s important to clarify what’s meant by this term. Firstly, data in cloud storage exists in physical space, just like on a computer or an external hard drive. The difference for the user is that the space it exists in is elsewhere, generally on server farms and data centers owned and operated by multinationals, and you usually access it over the Internet. Cloud storage providers occur in two types, public and private. Public cloud services such as Amazon, Microsoft and Google are responsible for data management and maintenance, whereas the responsibility for data in private clouds remains that of the company. Facebook, for example, has its own private cloud.

It is essential to recognize that cloud services store data in physical space, and the data may be subject to the laws of the country where the data is located. This year’s General Data Protection Regulation (GDPR) in the EU impacts user data privacy and consent around personal data. Another pressing question is security and we need to have a more public and comprehensible conversation around data security in the cloud.

The feel of data

Data scientists mostly encounter data in one of three forms: (i) tabular data (that is, data in a table, like a spreadsheet), (ii) image data or (iii) unstructured data, such as natural language text or html code, which makes up the majority of the world’s data.

Tabular data. The most common type for a data scientist to use is tabular data, which is analogous to a spreadsheet. In Robert Chang’s article on “Using Machine Learning to Predict Value of Homes On Airbnb,” he shows a sample of the data, which appears in a table in which each row is a particular property and each column a particular feature of properties, such as host city, average nightly price and 1-year revenue. (Note that data are rarely delivered directly from the user to tabular data; data engineering is an essential step to make data ready for such an analysis.)

Such data is used to train, or teach, machine learning models to predict Lifetime Values (LTV) of properties, that is, how much revenue they will bring in over the course of the relationship.

Image data. Image data is data that consists of, well, images. Many of the successes of deep learning, have occurred in the realm of image classification. The ability to diagnose disease from imaging data, such as diagnosing cancerous tissue from combined PET and CT scans, and the ability of self-driving cars to detect and classify objects in their field-of-vision are two of many use cases of image data. To work with image data, a data scientist will convert an image into a grid (or matrix) of red-green-blue pixel values or numbers and use these matrices as inputs to their predictive models.

Unstructured data. Unstructured data is, as one might guess, data that isn’t organized in either of the above manners. Part of the data scientist’s job is to structure such unstructured data so it may be analyzed. Natural language, or text, provides the clearest example. One common method of turning textual data into structured data is to represent it as word counts, so that “the cat chased the mouse” becomes “(cat,1),(chased,1),(mouse,1),(the,2)”. This is called a bag-of-words model, and allows us to compare texts, to compute distances between them, and to combine them into clusters. Bag-of-words performs surprisingly well for many practical applications, especially considering that it doesn’t distinguish “build bridges not walls” from “build walls not bridges.” Part of the game here is to turn textual data into numbers that we can feed into predictive models, and the principle is very similar between bag-of-words and more sophisticated methods. Such methods allow for sentiment analysis (“is a text positive, negative or neutral?”) and text classification (“is a given article news, entertainment or sport?”), among many others. For a recent example of text classification, check out Cloudera Fast Forward Labs’ prototype Newsie.

These are just two of the five steps to working with data, but they’re essential starting points for data literacy. When you’re dealing with data, think about how the data was collected and what kind of data it is. That will help you understand its meaning, how much to trust it, and how much work needs to be done to convert it into a useful form.

Categories: Blogs

Why Privacy Regulations Don’t Always Do What They’re Meant To

Harvard business - Tue, 10/23/2018 - 06:05
Maartje Van Caspel/EyeEm/Getty Images

First, California passed major privacy legislation in June. Then in late September, the Trump administration published official principles for a single national privacy standard. Not to be left out, House Democrats previewed their own Internet “Bill of Rights” earlier this month.

Sweeping privacy regulations, in short, are likely coming to the United States. That should be welcome news, given the sad, arguably nonexistent state of our modern right to privacy. But there are serious dangers in any new move to regulate data. Such regulations could backfire — for example, by entrenching already dominant technology companies or by failing to help consumers actually control the data we generate (presumably the major goal of any new legislation).

That’s where Brent Ozar comes in.

Ozar runs a small technology consulting company in California that provides training and troubleshooting for a database management system called Microsoft SQL Server. With a team of four people, Ozar’s company is by all means modest in scope, but it has a small international client base. Or at least it did, until European regulators in May began to enforce a privacy law called the General Data Protection Regulation (GDPR), can carry fines of up to 4% of global revenue.

A few months before the GDPR began to be enforced, Ozar announced that it had forced his company to, in his words, “stop selling stuff to Europe.” As a consumer, Ozar wrote, he loved the regulations; but as a business, he simply couldn’t afford the costs of compliance or the risks of getting it wrong.

And Ozar wasn’t alone. Even larger international organizations like the Los Angeles Times and the Chicago Tribune — along with over 1,000 other news outlets — simply blocked any user accessing their sites with a European IP address rather than confront the costs of the GDPR.

So why should this story play a central role in the push to enact new privacy regulations here in the United States?

Because Ozar illustrates how privacy regulations come with huge costs. Privacy laws are, from one perspective, a transaction cost imposed on all our interactions with digital technologies. Sometimes those costs are minimal. But sometimes those costs can be prohibitive.

Privacy regulations, in short, can be dangerous.

So how can we minimize these dangers?

First, as regulators become more serious about enacting new privacy laws in the United States, they will be tempted to implement generic, broad-based regulations rather than to enshrine specific prescriptions in law. Even though in the fast-moving world of technology, it’s always easier to write general rules than more explicit recommendations, they should avoid this temptation wherever possible.

Overly broad regulations that treat all organizations equally can end up encouraging “data monopolies” — where only a few companies can make use of all our data. Some organizations will have the resources to comply with complex, highly ambiguous laws; others (like Ozar’s) will not.

This means that the regulatory burden on data should be tiered so that the costs of compliance are not equal across unequal organizations. California’s Consumer Privacy Act confronts this problem directly by opting out specific business segments such as many smaller organizations. The costs of compliance for any new regulation must not give additional advantages to the already-dominant tech companies of the world.

Second, and relatedly, a few organizations are increasingly in charge of much of our data, which presents a huge danger both to our privacy and to technological innovation. Any new privacy regulation must actively incentivize organizations that are smaller to share or pool data so that they can compete with larger data-driven organizations.

One possible solution to this problem is by encouraging the use of what are called privacy enhancing technologies, or PETs, such as differential privacy, homomorphic encryption, federated learning, and more. PETs, long championed by privacy advocates, help balance the tradeoff between the utility of data on the one hand and its privacy and security on the other.

Last, user consent — the idea of users actively consenting to the collection of their data at a given point in time — can no longer play a central role in protecting our privacy. This has long been a dominant aspect of major privacy frameworks (think of all the “I Accept” buttons you’ve clicked to enter a website). But in the age of big data and machine learning, we simply cannot know the value of the information we give up at the point of collection.

The entire value of machine learning lies in its ability to detect patterns at scale. At any given time, the cost to our privacy of giving up small amounts of data is minimal; over time, however, that cost can become enormous. The famous case of Target knowing a teenager was pregnant before her family did, based simply on her shopping habits, is one among many such examples.

As a result, we cannot assume that we are ever fully informed about the privacy we’re giving up at any single point in time. Consumers must be able to exercise rights over their data long after it’s been collected, and those rights should include restricting how it’s being used.

Unless ours laws can adapt to new digital technologies correctly — unless they can calibrate the balance between the cost of the compliance burden and the value of privacy rights they seek to uphold — we run some very real risks. We can all too easily implement new laws that fail to preserve our privacy while also hindering the use of new technology, and both at the same time.

Categories: Blogs

Candidate Experience: The Importance of Developing a Strategy

Hr Bartender - Tue, 10/23/2018 - 02:57

(Editor’s Note: Today’s post is brought to you by our friends at Criteria Corp, a leading provider of pre-employment testing services. They’ve recently relaunched their customer interface, HireSelect. It’s been completely reworked to help organizations hire faster and smarter. Get a demo when you have a chance. Enjoy the article!) 

We’ve talked before about the need for organizations to have a defined candidate experience. But what does that mean? The “candidate experience” includes all of the touchpoints that a candidate experiences from the time they discover the company until they learn whether they’ve been hired. It’s important to note that the candidate experience includes more than just the company. Every outside organization that the company partners with (i.e. background check companies, pre-employment testing organizations) is part of the experience.

A bad candidate experience can hurt a company’s brand and bottom-line. According to a 2016 Talent Board survey, 41 percent of candidates who received a negative experience indicated that they intended to stop buying products and services from the company. Conversely, a positive candidate experience can benefit the organization in building a strong talent pipeline.

Obviously, it makes sense to have a positive candidate experience. I don’t know that anyone is intentionally trying to create a negative experience for candidates. The challenge is trying to create a candidate experience where individuals feel positively about the company, even when they don’t get the job.

The 4 C’s to Developing a Candidate Experience Strategy

To really have a positive and lasting impact with candidates, it’s going to take a strategy. The candidate experience isn’t simply another HR program, because what happens during the hiring process has a direct link to the employee experience (we will talk more about the employee experience another day.) Everything is related. For the candidate experience, I like to think of the strategy as having four key components which I’m going to call the 4 C’s (current, clear, communicative, and connecting).

1. Current: What I mean by current is being reflective of today’s business world. When I purchase something, very little comes with the    product in terms of instructions. How to assemble or activate the item is intuitive.

The same is necessary for the candidate experience. While getting hired is a process, make the process easy for candidates to understand and follow. For instance, if your competitive set is using mobile to accept applications, then it’s possible you’re missing out by not doing the same. So, use technology where it makes sense and brings the most advantage.

2. Clear: A couple of months ago, I wrote a post about the “8 Things Job Seekers Want from Recruiters”. One of the biggest things candidates mentioned was honesty – about the job and the company. Candidates understand that companies aren’t perfect. They do want to know both the good and the not-so-great about a future employer.

Consider putting together a “day in the life” video and posting it on your career portal. It can help employees learn about the company and the jobs available. It can show off some of the company culture.

3. Communicative: This will be no surprise to anyone, but candidates want to know where they are in the process. They deserve to be treated with respect. Even if they’re no longer being considered.

Tell applicants when their application has been received. Communicate with candidates when they’re scheduled for video or panel interviews, so they can prepare. The same applies to when candidates are going to complete an assessment. Tell them in advance so they’re not caught off guard.

4. Connecting: By connecting, I mean letting candidates “connect” with the company. First by creating online talent networks so individuals can hear about job openings. Then once they apply and are called for an interview, connecting can mean touring the office, meeting future co-workers, and getting a chance to see what it would be like to work there.

Another aspect to connecting is allowing candidates to stay connected with the hiring manager and recruiter via email and even on social media platforms like LinkedIn. It’s possible that if the candidate isn’t selected for this opportunity, they might be perfect for another one.

The Candidate Experience Lasts Beyond the Interview

Organizations need to realize that the candidate experience will stay with a person long after the interview. So, make it a good one by creating a strategy. But keep in mind that the candidate experience is only one part of an organization’s overall talent acquisition strategy. It’s equally important to have a sourcing strategy as well as a selection philosophy.

If you’re looking for more ways to step up your recruiting game, I hope you’ll join me and the Criteria Corp team for a webinar on “Standing Out in a Candidate’s Market: 5 Recruiting Strategies for Success”. We’re going to add to this conversation and discuss additional steps employers can take to attract and hire the best talent. The webinar is scheduled for Wednesday, November 7, 2018 at 10a Pacific / 1p Eastern. And if you can’t make the live event, sign up anyway and get the recording. Look forward to seeing you then!

The post Candidate Experience: The Importance of Developing a Strategy appeared first on hr bartender.

Categories: Blogs

The Art of Claiming Credit

Harvard business - Mon, 10/22/2018 - 14:48

From the Women at Work podcast:
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Have you ever offered up an idea in a meeting and been ignored — but then, 10 minutes later, a man repeated the idea and everyone called it brilliant? Or have you ever worked hard on a team project and been left off the thank-you email?

If we aren’t thoughtful about how we present our ideas at work, we risk not being heard or, worse, missing out on the credit we’re due. Research shows that women get less credit when we work in groups with men. So, it’s important for us to be strategic with our suggestions and insights.

We talk with two experts on workplace dynamics and difficult conversations. First, Amy Jen Su covers how to artfully share your contributions. Next, Amy Gallo tells us how to call out credit stealers.

Guests:

Amy Jen Su is a managing partner and a cofounder of Paravis Partners, an executive coaching and leadership development firm.

Amy Gallo is a contributing editor at Harvard Business Review. She’s the author of the HBR Guide to Dealing with Conflict.

Resources:

● “Research: Men Get Credit for Voicing Ideas, but Not Problems. Women Don’t Get Credit for Either,” by Sean Martin

● “Proof That Women Get Less Credit for Teamwork,” by Nicole Torres

● “Research: Junior Female Scientists Aren’t Getting the Credit They Deserve,” by Marc J. Lerchenmueller and Olav Sorenson

● “How to Respond When Someone Takes Credit for Your Work,” by Amy Gallo

Fill out our survey about workplace experiences.

Email us here: womenatwork@hbr.org

Our theme music is Matt Hill’s “City In Motion,” provided by Audio Network.

Categories: Blogs

Clarity First

Leadershipnow - Mon, 10/22/2018 - 10:07


TO BE CLEAR, we live in a VUCA (volatile, uncertain, complex and ambiguous) world. In most cases, it is all man-made, but it is our reality. To be clearer, while our environment may be ambiguous, our organizations should never be. Ambiguity will always be with us and must be dealt with constructively.

Ambiguity can create forward momentum, or it can stop us in our tracks unable to move at all. If ambiguity is pervasive throughout an organization, it will fail.

Great leaders work with it and use it to their advantage. And the advantages are many. Ambiguity is a part of leadership. It’s where the risks are and where the future lies. Like stress, some is good.

The trick is to know what you must bring clarity to. Disorganization is not ambiguity. Confusion is not ambiguity. They are created by a lack of clarity. A lack of clarity is death to an organization.

While author Karen Martin would not seem to agree with what I just said, it is precisely because we live in a VUCA world that her book Clarity First becomes so essential. It is the fact of ambiguity that makes clarity so important. When clarity exists as a value, individuals and the organizations they work for operate in a way that places a premium on clarity and rewards the people who seek it. In that environment leaders and team members pursue clarity in their daily activities, and cultivate an expectation of clarity throughout the organization.
Ambiguity may exist in the world around us, but we should never be ambiguous about our purpose, our priorities, our process, our performance, our problems, or our communication. In each of these areas, we must be clear. Beginning in chapter 2, Martin delves into a practical discussion on how to bring clarity to each.

Purpose
This is the foundation of all organization (and personal) clarity. Purpose is knowing why you do what you do. As Maritn puts it, “What problem does your product solve?” She takes you three steps to discover your purpose: What do you do? What problem are you solving by doing it? and Why do you do it? A clear purpose makes clarity around priorities, processes, performance, decision-making, and communication possible and enables everyone in the organization in the how of their work.

Priorities
We all think we have priorities, but we probably have too many priorities. Martin divides priorities into two types. First are those priorities relating to the work we do every day. The second type refers to issues that are outside of the normal course of business—special projects, rollouts, strategic initiatives. The key here is that “priorities included on a strategy deployment plan are framed in problem terms—as gaps to be closed—not a predetermined solution…. Most companies frame priorities as actions to be taken, things to be done, changes to be made, and so on. A problem orientation injects clarity into the process, because everyone can see for each priority what the starting point is and where the organization wants to go. There is no room for pet projects or fuzzy ‘solutions’ unconnected to a corresponding problem.”

Process
Many organizations “limp along with ambiguous, undocumented, wasteful, and poorly managed processes.” She adds, “Ambiguity about the specific steps needed to deliver outstanding value is the largest contributor to poor customer experience, runaway costs, and potentially dangerous mistakes.” Internal relationships, job descriptions, and decision-making authority should be clear.

Performance
To effectively run an organization you need to know where you are. You need data of some kind. The first step of course is to define what you need to know and then determine where you can find it. Once collected and understood, “make sure that what you measure does not move leaders and teams to take actions that work against the broader interests of the organization.”

Problem-Solving
A problem occurs when we discover that we are not where we want to be. There is a gap that needs to be closed. Clarity requires that we know exactly what that gap is. Problems don’t go away unless you are fixing the real problem. Too often we jump in before we have taken the time to understand what we are dealing with. Martin provides a question-based process called CLEAR problem solving to help you to dig deeper into the issue you are facing. When your purpose is clear, problem-solving becomes much easier—at all levels.

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Of Related Interest:
  The Clarity Principle
  8 Reasons to Seek Out Ambiguity
  Leading Clarity

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