Do you run a meritocracy? Does your company promote only the best and brightest? Is your hiring process color- and gender-blind?
If you said yes to any of the above, the chances are that your company is none of those things. Why? Because our biases towards hiring white men over women and people of color are documented, unconscious and most prevalent in those who deny they have them.
Don’t get upset, though; this certainly doesn’t mean you’re a sexist or a racist. It just means that you’re human and living in the 21st century. But just because a nondiverse leadership team isn’t your fault, this doesn’t mean it’s not your problem.
Your biases and the biases of your company’s current leadership may be quickly backing you into a corner when it comes to leadership development for the next wave of leaders who will move the company forward.
But just because a nondiverse leadership team isn’t your fault, this doesn’t mean it’s not your problem.
In a previous post, I described how women are the canaries in the coal mine for corporate leadership development. As women are leaving corporate America to start new businesses at one-and-a-half times the rate of men, these birds are flying out the entrance of your mine, and the Millennials (who share similar values) may soon begin to follow them. They’re leaving companies like yours right and left for entrepreneurial endeavors in search of meaningful work and quality of life. And they’re taking their talent with them.
You need to recognize that this isn’t because you’re bad (you’re not necessarily a sexist, remember?) or because they’re going off to have kids (65% of mothers work, as compared to 63% of all men). In fact, the glass ceiling isn’t something that’s being “done to” women anymore. It’s the impact of leadership bias, which generally makes women believe (often correctly) that the only way up is to play a game they can’t win and aren’t particularly interested in to begin with.
So what can you do to combat your own unconscious bias, and the bias of those making hiring decisions?
First, accept that bias exists. This isn’t going to toss you into a lawsuit, it’s going to help you seek and find employee practices that reward true talent instead of unconscious bias, which may have you rewarding underperforming men as much as 63% of the time.
Third, begin a conversation with your employees (of both sexes). This will help understand what’s important to them and how your leadership culture can evolve to share those values and make your company a place where the truly talented want to climb the ladder.
This isn’t rocket science, it’s simply a sincere effort to counter-act the unconscious tendencies we all have to reinforce our culture’s stereotype of a “good leader.” It can be done. You can do it.
The good news is that, based on what the research says about the number of companies doing a good job at this (barely 20%), if you do a decent job at it, you’ll be a more competitive employer and attract a more talented and resilient workforce.
It can be done. You can do it.
So, my questions is, why wouldn’t you confront your bias to reduce employee turnover, increase employee engagement, and establish a true meritocracy?
Generation Z’s arrival in the workforce means some changes are on the horizon for recruiters. This cohort, born roughly from the mid-90s to approximately 2010, will be entering the workforce in four short years, and you can bet recruiters and employers are already paying close attention to them.
This past fall, the first group of Gen Z youth began entering university. As Boomers continue to work well past traditional retirement age, four or five years from now, we’ll have an American workplace comprised of five generations.
Marketers and researchers have been obsessed with Millennials for over a decade; they are the most studied generation in history, and at 80 million strong they are an economic force to be reckoned with. HR pros have also been focused on all things related to attracting, motivating, mentoring, and retaining Millennials and now, once Gen Z is part of the workforce, recruiters will have to shift gears and also learn to work with this new, lesser-known generation. What are the important points they’ll need to know?
In general, the Generation Z cohort tends to be comprised of self-starters who have a strong desire to be autonomous. 63% of them report that they want colleges to teach them about being an entrepreneur.
42% expect to be self-employed later in life, and this percentage was higher among minorities.
Despite the high cost of higher education, 81% of Generation Z members surveyed believe going to college is extremely important.
Generation Z has a lot of anxiety around debt, not only student loan debt, and they report they are very interested in being well-educated about finances.
Interpersonal interaction is highly important to Gen Z; just as Millennials before them, communicating via technology, including social media, is far less valuable to them than face-to-face communication.
Of course Gen Z is still very young, and their opinions as they relate to future employment may well change. For example, reality is that only 6.6% of the American workforce is self-employed, making it likely that only a small percentage of those expecting to be self-employed will be as well. The future in that respect is uncertain, and this group has a lot of learning to do and experiences yet ahead of them. However, when it comes to recruiting them, here are some things that might be helpful.
Generation Z is constantly connected
Like Millennials, Gen Z is a cohort of digital natives; they have had technology and the many forms of communication that affords since birth. They are used to instant access to information and, like their older Gen Y counterparts, they are continually processing information. Like Millennials, they prefer to solve their own problems, and will turn to YouTube or other video platforms for tutorials and to troubleshoot before asking for help. They also place great value on the reviews of their peers.
For recruiters, that means being ready to communicate on a wide variety of platforms on a continual basis. In order to recruit the top talent, you will have to be as connected as they are. You’ll need to keep up with their preferred networks, which will likely always be changing, and you’ll need to be transparent about what you want, as this generation is just as skeptical of marketing as the previous one.
Flexible schedules will continue to grow in importance
With the growth of part-time and contract workers, Gen Z will more than likely assume the same attitude their Millennial predecessors did when it comes to career expectations; they will not expect to remain with the same company for more than a few years. Flexible schedules will be a big part of their world as they move farther away from the traditional 9-to-5 job structure as work becomes more about life and less about work, and they’ll likely take on a variety of part time roles.
This preference for flexible work schedules means that business will happen outside of traditional work hours, and recruiters’ own work hours will, therefore, have to be just as flexible as their Gen Z targets’ schedule are. Companies will also have to examine what are in many cases decades old policies on acceptable work hours and business norms as they seek to not only attract, but to hire and retain this workforce with wholly different preferences than the ones that came before them. In many instances this is already happening, but I believe we will see this continue to evolve in the coming years.
So how will this impact their behavior and desires as job candidates? Most of them are the product of Gen X parents, and stability will likely be very important to them. They may be both hard-working and fiscally savvy.
Sparks & Honey, in their much quoted slideshare on Gen Z, puts the number of high-schooler students who felt pressured by their parents to get jobs at 55 percent. Income and earning your keep are likely to be a big motivation for GenZ. Due to the recession, they also share the experience of living in multi-generational households, which may help considerably as they navigate a workplace comprised of several generations.
We don’t have all the answers
With its youngest members not yet in double digits, Gen Z is still maturing. There is obviously still a lot that we don’t know. This generation may have the opposite experience from the Millennials before them, where the older members experienced the booming economy, with some even getting a career foothold, before the collapse in 2008. Gen Z’s younger members may get to see a resurgent economy as they make their way out of college. Those younger members are still forming their personalities and views of the world; we would be presumptuous to think we have all of the answers already.
Generational analysis is part research, but also part theory testing. What we do know is that this second generation of digital natives, with its adaption of technology and comfort with the fast-paced changing world, will leave its mark on the American workforce as it makes its way in. As a result, everything about HR will change, in a big way. I wrote a post for my Forbes column recently where I said, “To recruit in this environment is like being part wizard, part astronaut, part diplomat, part guidance counselor,” and that’s very true.
As someone who loves change, I believe there has never been a more exciting time to be immersed in both the HR and the technology space. How do you feel about what’s on the horizon as it relates to the future of work and the impending arrival of Generation Z? I’d love to hear your thoughts.
The Digitalist Magazine is your online destination for everything you need to know to lead your enterprise’s digital transformation.
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About Meghan M. Biro
Meghan Biro is talent management and HR tech brand strategist, analyst, digital catalyst, author and speaker. I am the founder and CEO of TalentCulture and host of the #WorkTrends live podcast and Twitter Chat. Over my career, I have worked with early-stage ventures and global brands like Microsoft, IBM and Google, helping them recruit and empower stellar talent. I have been a guest on numerous radio shows and online forums, and has been a featured speaker at global conferences. I am the co-author of The Character-Based Leader: Instigating a Revolution of Leadership One Person at a Time, and a regular contributor at Forbes, Huffington Post, Entrepreneur and several other media outlets. I also serve on advisory boards for leading HR and technology brands.
Although executives, analysts, and experts regularly try to predict where business is headed, the pace of innovation continues to exceed our expectations and imagination – especially when it comes to the world of work. Not only is technology impacting how we work and interact with each other, it’s transforming what we actually do for work.
Consider this: 2 billion jobs that exist today will disappear by 2030, according to futurist Thomas Frey. 2 billion. That’s roughly 50% of all of jobs worldwide. Cathy N. Davidson, Duke University professor, backed up this prediction in her book Now You See It, noting that 65% of children entering grade school this year will assume careers that don’t yet exist.
How can you possibly plan for a future workforce in jobs we can’t today know? And how can we develop talent when we don’t what our business will need not just in a few years, but even in a few months from now?
The future of talent acquisition relies on a broad footprint enabled by technology
The dynamic of workforce mix is changing. Employees no longer fit neatly into a box, nor should they. Salaried employees. Hourly employees. Contingent employees. These categories are more fluid than ever.
As digital businesses like Uber and Airbnb have shown, the understanding of “employee” is being redefined to include people who are not employed in the traditional sense or necessarily found on the company payroll. Rather, they are customers – on the other side of the seller-buyer relationship.
This new approach does not come without risk. Once the salary-wage relationship is removed from the employer-employee equation, the degree of employee loyalty and affinity seen in the past will slowly deteriorate. This forces CHROs to adjust how to relate to their existing workforce, and as important, their future employees and the people who influence them.
To create an employer brand that is more fluid and differentiated, CHROs should consider four things:
1. Your employer brand matters whether you’re actively recruiting or not.
Your employer brand needs to be an interaction that happens consistently – whether or not you are looking for new talent to join your team at the moment. And while the brand is not the sole purview of HR, HR is in the best position to shepherd it.
2. Expand your footprint to attract the best – before they’re even in the workforce.
In our age of social media, people follow brands they admire. But here’s a secret: This also brings an opportunity for following high-performing professionals within or outside the industry as well as students of all ages who are mastering valuable skills.
As I look at my two school-aged boys, I see firsthand how their new generation – Gen Z – will create their own definition of work and career fulfillment. Pretty soon, new graduates will be less concerned about job titles and more interested in working for companies with whom they feel an affinity. And increasingly, these interactions begin long before a job search.
3. Master the science of data – no PhD required.
How many of us groan when terms like “data science” and “number crunching” get mentioned? Today’s technology is taking away the fear factor; analysing data is becoming more intuitive and delivering more valuable insights. And increasingly, the machines are doing it for us, melting processes along the way.
4. Engage before Day 1.
HR today has the tools to become less about process and more about employee engagement. Onboarding is a perfect example of how, and why it matters.
Typically, onboarding has been about providing the physical things a new employee needs to start working: security badge, laptop, desk assignment, setup of a 401k account, and payroll deductions to name a just a few. None of this generally happens until the person walks through the door on Day 1.
Now we have the ability to make onboarding a social interaction, allowing a new employee the opportunity to be engaged before they even start. HR can provide the ability for new employees to connect with their manager, along with peers who can help them better understand and navigate the organisation, and potential mentors who can help them become successful – reducing the traditional ramp up process that can take months or longer.
In today’s digital economy, it’s less about the job and more about the talent. How are you preparing?
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About Mike Ettling
Mike Ettling is the President of SAP SuccessFactors. He is an inspirational, visionary and highly dynamic leader with a wealth of leadership expertise, genuine business acumen, and an exemplary record driving multi-million dollar sales, marketing initiatives and transformation in a global context.
We humans make sense of the world by looking for patterns, filtering them through what we think we already know, and making decisions accordingly. When we talk about handing decisions off to artificial intelligence (AI), we expect it to do the same, only better.
Machine learning does, in fact, have the potential to be a tremendous force for good. Humans are hindered by both their unconscious assumptions and their simple inability to process huge amounts of information. AI, on the other hand, can be taught to filter irrelevancies out of the decision-making process, pluck the most suitable candidates from a haystack of résumés, and guide us based on what it calculates is objectively best rather than simply what we’ve done in the past.
In other words, AI has the potential to help us avoid bias in hiring, operations, customer service, and the broader business and social communities—and doing so makes good business sense. For one thing, even the most unintentional discrimination can cost a company significantly, in both money and brand equity. The mere fact of having to defend against an accusation of bias can linger long after the issue itself is settled.
Beyond managing risk related to legal and regulatory issues, though, there’s a broader argument for tackling bias: in a relentlessly competitive and global economy, no organization can afford to shut itself off from broader input, more varied experiences, a wider range of talent, and larger potential markets.
That said, the algorithms that drive AI don’t reveal pure, objective truth just because they’re mathematical. Humans must tell AI what they consider suitable, teach it which information is relevant, and indicate that the outcomes they consider best—ethically, legally, and, of course, financially—are those that are free from bias, conscious or otherwise. That’s the only way AI can help us create systems that are fair, more productive, and ultimately better for both business and the broader society.
Bias: Bad for Business
When people talk about AI and machine learning, they usually mean algorithms that learn over time as they process large data sets. Organizations that have gathered vast amounts of data can use these algorithms to apply sophisticated mathematical modeling techniques to see if the results can predict future outcomes, such as fluctuations in the price of materials or traffic flows around a port facility. Computers are ideally suited to processing these massive data volumes to reveal patterns and interactions that might help organizations get ahead of their competitors. As we gather more types and sources of data with which to train increasingly complex algorithms, interest in AI will become even more intense.
Using AI for automated decision making is becoming more common, at least for simple tasks, such as recommending additional products at the point of sale based on a customer’s current and past purchases. The hope is that AI will be able to take on the process of making increasingly sophisticated decisions, such as suggesting entirely new markets where a company could be profitable, or finding the most qualified candidates for jobs by helping HR look beyond the expected demographics.
As AI takes on these increasingly complex decisions, it can help reduce bias, conscious or otherwise. By exposing a bias, algorithms allow us to lessen the impact of that bias on our decisions and actions. They enable us to make decisions that reflect objective data instead of untested assumptions; they reveal imbalances; and they alert people to their cognitive blind spots so they can make more accurate, unbiased decisions.
Imagine, for example, a major company that realizes that its past hiring practices were biased against women and that would benefit from having more women in its management pipeline. AI can help the company analyze its past job postings for gender-biased language, which might have discouraged some applicants. Future postings could be more gender neutral, increasing the number of female applicants who get past the initial screenings.
AI can also support people in making less-biased decisions. For example, a company is considering two candidates for an influential management position: one man and one woman. The final hiring decision lies with a hiring manager who, when they learn that the female candidate has a small child at home, assumes that she would prefer a part-time schedule.
That assumption may be well intentioned, but it runs counter to the outcome the company is looking for. An AI could apply corrective pressure by reminding the hiring manager that all qualifications being equal, the female candidate is an objectively good choice who meets the company’s criteria. The hope is that the hiring manager will realize their unfounded assumption and remove it from their decision-making process.
At the same time, by tracking the pattern of hiring decisions this manager makes, the AI could alert them—and other people in HR—that the company still has some remaining hidden biases against female candidates to address.
Look for Where Bias Already Exists
In other words, if we want AI to counter the effects of a biased world, we have to begin by acknowledging that the world is biased. And that starts in a surprisingly low-tech spot: identifying any biases baked into your own organization’s current processes. From there, you can determine how to address those biases and improve outcomes.
There are many scenarios where humans can collaborate with AI to prevent or even reverse bias, says Jason Baldridge, a former associate professor of computational linguistics at the University of Texas at Austin and now co-founder of People Pattern, a startup for predictive demographics using social media analytics. In the highly regulated financial services industry, for example, Baldridge says banks are required to ensure that their algorithmic choices are not based on input variables that correlate with protected demographic variables (like race and gender). The banks also have to prove to regulators that their mathematical models don’t focus on patterns that disfavor specific demographic groups, he says. What’s more, they have to allow outside data scientists to assess their models for code or data that might have a discriminatory effect. As a result, banks are more evenhanded in their lending.
Code Is Only Human
The reason for these checks and balances is clear: the algorithms that drive AI are built by humans, and humans choose the data with which to shape and train the resulting models. Because humans are prone to bias, we have to be careful that we are neither simply confirming existing biases nor introducing new ones when we develop AI models and feed them data.
“From the perspective of a business leader who wants to do the right thing, it’s a design question,” says Cathy O’Neil, whose best-selling book Weapons of Math Destruction was long-listed for the 2016 National Book Award. “You wouldn’t let your company design a car and send it out in the world without knowing whether it’s safe. You have to design it with safety standards in mind,” she says. “By the same token, algorithms have to be designed with fairness and legality in mind, with standards that are understandable to everyone, from the business leader to the people being scored.” (To learn more from O’Neil about transparency in algorithms, read Thinkers in this issue.)
Don’t Do What You’ve Always Done
To eliminate bias, you must first make sure that the data you’re using to train the algorithm is itself free of bias, or, rather, that the algorithm can recognize bias in that data and bring the bias to a human’s attention.
SAP has been working on an initiative that tackles this issue directly by spotting and categorizing gendered terminology in old job postings. Nothing as overt as No women need apply, which everyone knows is discriminatory, but phrases like outspoken and aggressively pursuing opportunities, which are proven to attract male job applicants and repel female applicants, and words like caring and flexible, which do the opposite.
Once humans categorize this language and feed it into an algorithm, the AI can learn to flag words that imply bias and suggest gender-neutral alternatives. Unfortunately, this de-biasing process currently requires too much human intervention to scale easily, but as the amount of available de-biased data grows, this will become far less of a limitation in developing AI for HR.
Similarly, companies should look for specificity in how their algorithms search for new talent. According to O’Neil, there’s no one-size-fits-all definition of the best engineer; there’s only the best engineer for a particular role or project at a particular time. That’s the needle in the haystack that AI is well suited to find.
Look Beyond the Obvious
AI could be invaluable in radically reducing deliberate and unconscious discrimination in the workplace. However, the more data your company analyzes, the more likely it is that you will deal with stereotypes, O’Neil says. If you’re looking for math professors, for example, and you load your hiring algorithm with all the data you can find about math professors, your algorithm may give a lower score to a black female candidate living in Harlem simply because there are fewer black female mathematicians in your data set. But if that candidate has a PhD in math from Cornell, and if you’ve trained your AI to prioritize that criterion, the algorithm will bump her up the list of candidates rather than summarily ruling out a potentially high-value hire on the spurious basis of race and gender.
To further improve the odds that AI will be useful, companies have to go beyond spotting relationships between data and the outcomes they care about. It doesn’t take sophisticated predictive modeling to determine, for example, that women are disproportionately likely to jump off the corporate ladder at the halfway point because they’re struggling with work/life balance.
Many companies find it all too easy to conclude that women simply aren’t qualified for middle management. However, a company committed to smart talent management will instead ask what it is about these positions that makes them incompatible with women’s lives. It will then explore what it can change so that it doesn’t lose talent and institutional knowledge that will cost the company far more to replace than to retain.
That company may even apply a second layer of machine learning that looks at its own suggestions and makes further recommendations: “It looks like you’re trying to do X, so consider doing Y,” where X might be promoting more women, making the workforce more ethnically diverse, or improving retention statistics, and Y is redefining job responsibilities with greater flexibility, hosting recruiting events in communities of color, or redesigning benefits packages based on what similar companies offer.
Context Matters—and Context Changes
Even though AI learns—and maybe because it learns—it can never be considered “set it and forget it” technology. To remain both accurate and relevant, it has to be continually trained to account for changes in the market, your company’s needs, and the data itself.
Sources for language analysis, for example, tend to be biased toward standard American English, so if you’re building models to analyze social media posts or conversational language input, Baldridge says, you have to make a deliberate effort to include and correct for slang and nonstandard dialects. Standard English applies the word sick to someone having health problems, but it’s also a popular slang term for something good or impressive, which could lead to an awkward experience if someone confuses the two meanings, to say the least. Correcting for that, or adding more rules to the algorithm, such as “The word sick appears in proximity to positive emoji,” takes human oversight.
Moving Forward with AI
Today, AI excels at making biased data obvious, but that isn’t the same as eliminating it. It’s up to human beings to pay attention to the existence of bias and enlist AI to help avoid it. That goes beyond simply implementing AI to insisting that it meet benchmarks for positive impact. The business benefits of taking this step are—or soon will be—obvious.
In IDC FutureScapes’ webcast “Worldwide Big Data, Business Analytics, and Cognitive Software 2017 Predictions,” research director David Schubmehl predicted that by 2020 perceived bias and lack of evidentiary transparency in cognitive/AI solutions will create an activist backlash movement, with up to 10% of users backing away from the technology. However, Schubmehl also speculated that consumer and enterprise users of machine learning will be far more likely to trust AI’s recommendations and decisions if they understand how those recommendations and decisions are made. That means knowing what goes into the algorithms, how they arrive at their conclusions, and whether they deliver desired outcomes that are also legally and ethically fair.
Clearly, organizations that can address this concern explicitly will have a competitive advantage, but simply stating their commitment to using AI for good may not be enough. They also may wish to support academic efforts to research AI and bias, such as the annual Fairness, Accountability, and Transparency in Machine Learning (FATML) workshop, which was held for the third time in November 2016.
O’Neil, who blogs about data science and founded the Lede Program for Data Journalism, an intensive certification program at Columbia University, is going one step further. She is attempting to create an entirely new industry dedicated to auditing and monitoring algorithms to ensure that they not only reveal bias but actively eliminate it. She proposes the formation of groups of data scientists that evaluate supply chains for signs of forced labor, connect children at risk of abuse with resources to support their families, or alert people through a smartphone app when their credit scores are used to evaluate eligibility for something other than a loan.
As we begin to entrust AI with more complex and consequential decisions, organizations may also want to be proactive about ensuring that their algorithms do good—so that their companies can use AI to do well. D!
Travis McDonough has always been looking for a competitive edge. As an amateur athlete “on the small side,” he sought other ways—exercise, nutrition, strategy—to get ahead.
Today McDonough is the of CEO of Kinduct, a provider of cloud-based software that analyzes data from wearables, electronic medical records, computer vision solutions, and more to assess and make recommendations about physical human performance. Kinduct provides 100 professional sports organizations, including the five major sports leagues in North America, with intelligence to make decisions about their athletes and training programs.
Digital Fills a Gap
A chiropractor by training, McDonough owned and operated a network of sports rehabilitation clinics, where he found that patients retained only a fraction of what they were instructed to do through text or conversation. “As we treated athletes, we realized there was a gaping hole in the industry for technology [to fill],” he says.
McDonough first launched a company to create 3D videos designed to help his athlete patients better understand their injuries and the resulting therapy. The videos, delivered by text or e-mail, would illustrate what happens inside the human body when it experiences whiplash, for example.
“We quickly realized we couldn’t just be a content company and push information without understanding more about the athlete,” he says. Athletes and their trainers collected a massive amount of individual health and performance data that was available to be tapped from electronic medical records, wearable devices, and computer vision-based tracking systems that measure and record information such as how fast an athlete is running or jumping. “We needed to be agnostic and aggressive consumers of all kinds of data sources in order to push more targeted programs to our clients,” he says. So McDonough recruited his brother’s brother-in-law (vice president of product, Dave Anderson) to develop software to make sense of it all.
Innovate a Better Athlete
The software is suited for healthcare and military applications: the Canadian Armed Forces uses it to deliver exercise, wellness, and nutrition programs to its troops. But McDonough knew that the world of professional sports would provide his most eager customers.
“The sports world is willing to embrace innovation more quickly than other markets, like healthcare, that are slower-moving. And that’s where our passion lives. Many of us are sports fanatics and have been athletes,” says McDonough of the company’s 70 employees. Kinduct’s first customers were National Hockey League (NHL) teams, followed in short order by the National Basketball Association (NBA).
For its professional sports clients, Kinduct has uncovered more than 100 novel correlations. Most are closely guarded secrets, but several have become public. The company found, for example, that when a basketball player’s sleep falls below a certain threshold, there is a strong correlation with reduced free throw percentages two days later. That discovery led one NBA team (McDonough won’t say which) to focus on getting players to bed on time and making travel schedule changes to enable the requisite rest.
Kinduct software also found correlations for hockey teams. It demonstrated to a leading hockey team that better grip strength was likely to lead to harder and faster shots on goal. Moreover, when the system ingested three years of historical computer vision information, it found that a player’s ability to slow down dramatically affects the chances of soft tissue injuries, which are costly to professional sports teams and athletes. The software can send an alert when it spots a trend that could predict the possibility of such an injury.
We’re in this to go big. That means carrying a burn rate, hiring aggressively, and investing in research.
The software “will never replace the experts in the trenches,” says McDonough. “But we are able to arm coaches and trainers with the intelligence necessary to make more informed decisions. Technology will never replace the power of a good relationship.”
Think a Few Plays Ahead
Kinduct is based in McDonough’s hometown of Halifax, Nova Scotia, which boasts five universities, strong government subsidies, a low cost of living, and, for Kinducts’s predominantly U.S.-based customers, a favorable currency exchange rate. Despite these advantages, Halifax isn’t widely known for its digital innovators. “We’ve got a huge chip on our shoulder,” says McDonough. “We want to prove that we’re just as capable of becoming a global success as companies elsewhere,” such as Silicon Valley or London.
Nevertheless, McDonough spends significant time in Silicon Valley meeting with investors and looking at potential U.S. expansion (Kinduct closed a US$9 million Series A investment led by Intel Capital in October). “There’s a huge benefit to growing in Nova Scotia,” he says, “but we also need to be in the epicenter of the tech space.”
McDonough has big ideas for Kinduct’s future, thanks to the explosion of health- and fitness-tracking devices. “We can pull all the data in and, when we see a negative pattern, provide the user with the exact roadmap they need to follow to improve their condition or performance,” he says. “That’s equally as useful to a professional football player or an Olympic athlete as it is to someone recovering from a knee replacement or living with type 2 diabetes.”
Kinduct has 16 projects underway to measure the impact of the platform in helping individuals manage conditions like peripheral vascular disease and cognitive decline. “We want to show how the platform can empower and engage patients,” says McDonough.
Go Big or Go Home
Meanwhile, however, McDonough intends “to dominate the sports space. That is our bubble wrap of credibility, and we can leverage that to do other things.”
Focus was never a strong suit for McDonough, who struggled with dyslexia and ADD as a kid. “Thank God for sport, which helped to channel my energy,” he says. But that wandering mind, he says, has also been an asset. “Like a lot of ADD sufferers, I have a lot of imagination,” he says. For balance, he’s hired a leadership team that keeps him grounded, and he has assembled a board of experienced business and technology leaders. “They have the institutional knowledge in how to scale,” he says.
McDonough is blunt: right now, he’d rather be innovative than profitable. “We’re in this to go big. That means carrying a burn rate, hiring aggressively, and investing in research,” he says. “We’re lucky enough to be in locker rooms with these teams and close to some of the best in the business in terms of medicine and training and data science. That’s helping us to produce our future roadmap.” D!