Curiosity-Driven Data Science

HBR.org

Data science can enable wholly new and innovative capabilities that can completely differentiate a company. But those innovative capabilities aren’t so much designed or envisioned as they are discovered and revealed through curiosity-driven tinkering by the data scientists. So, before you jump on the data science bandwagon, think less about how data science will support and execute your plans and think more about how to create an environment to empower your data scientists to come up with things you never dreamed of.

First, some context. I am the Chief Algorithms Officer at Stitch Fix, an online personalized styling service with 2.7 million clients in the U.S. and plans to enter the U.K. next year. The novelty of our service affords us exclusive and unprecedented data with nearly ideal conditions to learn from it. We have more than 100 data scientists that power algorithmic capabilities used throughout the company. We have algorithms for recommender systems, merchandise buying, inventory management, relationship management, logistics, operations — we even have algorithms for designing clothes! Each provides material and measurable returns, enabling us to better serve our clients, while providing a protective barrier against competition. Yet, virtually none of these capabilities were asked for by executives, product managers, or domain experts — and not even by a data science manager (and certainly not by me). Instead, they were born out of curiosity and extracurricular tinkering by data scientists.

Data scientists are a curious bunch, especially the good ones. They work towards clear goals and they are focused on and accountable for achieving certain performance metrics. But they are also easily distracted, in a good way. In the course of doing their work they stumble on various patterns, phenomenon, and anomalies that are unearthed during their data sleuthing. This goads the data scientist’s curiosity: “Is there a better way that we can characterize a client’s style?” “If we modeled clothing fit as a distance measure could we improve client feedback?”  “Can successful features from existing styles be re-combined to create better ones?”  To answer these questions, the data scientist turns to the historical data and starts tinkering. They don’t ask permission. In some cases, explanations can be found quickly, in only a few hours or so. Other times, it takes longer because each answer evokes new questions and hypotheses, leading to more testing and learning.

Are they wasting their time? No. Not only does data science enable rapid exploration, it’s relatively easier to measure the value of that exploration, compared to other domains. Statistical measures like AUC, RMSE, and R-squared quantify the amount of predictive power the data scientist’s exploration is adding. The combination of these measures and a knowledge of the business context allows the data scientist to assess the viability and potential impact of a solution that leverages their new insights. If there is no “there” there, they stop. But when there is compelling evidence and big potential, the data scientist moves on to more rigorous methods like randomized controlled trials or A/B Testing, which can provide evidence of causal impact. They want to see how their new algorithm performs in real life, so they expose it to a small sample of clients in an experiment. They’re already confident it will improve the client experience and business metrics, but they need to know by how much. If the experiment yields a big enough gain, they’ll roll it out to all clients. In some cases, it may require additional work to build a robust capability around the new insights. This will almost surely go beyond what can be considered “side work” and they’ll need to collaborate with others for engineering and process changes.

The key here is that no one asked the data scientist to come up with these innovations. They saw an unexplained phenomenon, had a hunch, and started tinkering. They didn’t have to ask permission to explore because it’s relatively cheap to allow them to do so. Had they asked permission, managers and stakeholders probably would have said ‘no’.

These two things, low cost exploration and the ability to measure the results, set data science apart from other business functions. Sure, other departments are curious too:  “I wonder if clients would respond better to this this type of creative?” a marketer might ask.  “Would a new user interface be more intuitive?” a product manager inquires. But those questions can’t be answered with historical data. Exploring those ideas requires actually building something, which will be costly. And justifying the cost is often difficult since there’s no evidence that suggests the ideas will work. With its’ low-cost exploration and risk-reducing evidence, data science makes it possible to try more things, leading to more innovation.

Sounds great, right? It is! But you can’t just declare as an organization that “we’ll do this too.” This is a very different way of doing things. You need to create an environment in which it can thrive.

First, you have to position data science as its own entity. Don’t bury it under another department like marketing, product, finance, etc. Instead, make it its own department, reporting to the CEO. In some cases, the data science team will need to collaborate with other departments to provide solutions. But it will do so as equal partners, not as a support staff that merely executes on what is asked of them. Instead of positioning data science as a supportive team in service to other departments, make it responsible for business goals. Then, hold it accountable to hitting those goals — but let the data scientists come up with the solutions.

Next, you need to equip the data scientists with all the technical resources they need to be autonomous. They’ll need full access to data as well as the compute resources to process their explorations. Requiring them to ask permission or request resources will impose a cost and less exploration will occur. My recommendation is to leverage a cloud architecture where the compute resources are elastic and nearly infinite.

The data scientists will need to have the skills to provision their own processors and conduct their own exploration. They will have to be great generalists. Most companies divide their data scientists into teams of specialists — say, Modelers, Machine Learning Engineers, Data Engineers, Causal Inference Analysts, etc. – in order to get more focus. But this will require more people to be involved to pursue any exploration. Coordinating multiple people gets expensive quickly. Instead, leverage “full-stack data scientists” with the skills to do all the functions. This lowers the cost of trying things, as a single tinkering initiative may require each of the data science functions I mentioned. Of course, data scientists can’t be experts in everything. So, you’ll need to provide a data platform that can help abstract them from the intricacies of distributed processing, auto-scaling, etc. This way the data scientist focuses more on driving business value through testing and learning, and less on technology.

Finally, you need a culture that will support a steady process of learning and experimentation. This means the entire company must have common values for things like learning by doing, being comfortable with ambiguity, balancing long-and short-term returns. These values need to be shared across the entire organization as they cannot survive in isolation.

But before you jump in and implement this at your company, be aware that it will be hard if not impossible to implement at an older company. I’m not sure it could have worked, even at Stitch Fix, if we hadn’t enabled data science to be autonomous from the very the beginning. I’ve been at Stitch Fix for six and a half years and, with a seat at the executive table, data science never had to be “inserted” into the organization. Rather, data science was native to us in the formative years, and hence, the necessary ways-of-working are more natural to us.

This is not to say data science is destined for failure at older, more mature companies, though it is certainly harder than starting from scratch. Some companies have been able to pull off miraculous changes. And it’s too important not to try. The benefits of this model are substantial, and for any company that wants data science to be a competitive advantage, it’s worth considering whether this approach can work for you.

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Most people would like to have a job, a boss, and a workplace they can engage with, as well as work that gives them a sense of purpose. This aspiration is embodied by a famous Steve Jobs statement: “Your work is going to fill a large part of your life, and the only way to be truly satisfied is to do what you believe is great work. And the only way to do great work is to love what you do.” In line, a recent report by the Conference Board shows that 96% of employees actively try to maintain a high level of engagement, even if many of them struggle to succeed.

In a similar vein, the scientific evidence suggests quite clearly that few things are more critical to an organization’s success than having an engaged workforce. When employees are engaged, they display high levels of enthusiasm, energy, and motivation, which translates into higher levels of job performance, creativity, and productivity. This means not only higher revenues and profits for organizations, but also higher levels of well-being for employees. In contrast, low engagement results in burnout, higher levels of turnover, and counterproductive work behaviors such as bullying, harassment, and fraud.

It is therefore not surprising that a great deal of research has been devoted to identifying the key determinants of engagement. Why is it that some people are more engaged — excited, moved, energized by their jobs — than others? Traditionally, this research has focused on the contextual or external drivers of engagement, such as the characteristics of the job, the culture of the organization, or the quality of its leaders.  And although there is no universal formula to engage employees, it is generally true that people will feel more enthusiastic about their jobs when they are empowered to achieve something meaningful beyond their expectations, feel connected to others, and when they work in an environment — and for someone — that is fair, ethical, and rewarding, as opposed to a constant source of stress.

But despite the importance of these contextual drivers of engagement, how people feel about their job, boss, and workplace may also vary as a function of people’s own character traits. Indeed, even before organizations started talking about the need to “engage employees,” many managers appeared to regard motivation as something individuals brought with them to work — a characteristic of people they hired. This is why two individuals may have very different levels of engagement even when their job situation is nearly identical (e.g., they work for the same company, team, and boss), and why there is always demand for employees who display consistent levels of ambition, energy, and dedication, irrespective of the situation they are in.

This raises an obvious yet rarely discussed question, namely: how much of engagement is actually just personality? A recent meta-analysis provides some much needed data-driven answers. In this impressive study which synthesized data from 114 independent surveys of employees, comprising almost 45,000 participants from a wide range of countries — and mostly published academic studies, which met the standards for publication in peer-reviewed journals — the researchers set out to estimate the degree to which people differed in engagement because of their character traits. To illustrate this point, imagine that a friend tells you that she hates her job. Depending on how well you know her, you might question if her views are a genuine reflection of her dreadful job, or if they just reflect your friend’s glass-half-empty personality. Or think of when you read a Tripadvisor, Amazon, or IMDB review of a hotel/product/movie: to what degree does that review convey information about the object being rated, versus the person reviewing it? Even intuitively, it is clear that reviews are generally a mix of both, the rater and object being rated, and this could also apply to people’s evaluations of their work and careers.

Although the authors examined only the impact of personality on engagement — without considering the known contextual influences on it — their results were rather staggering: almost 50% of the variability in engagement could be predicted by people’s personality. In particular four traits: positive affect, proactivity, conscientiousness, and extroversion. In combination, these traits represent some of the core ingredients of emotional intelligence and resilience. Put another way, those who are positive, optimistic, hard-working, and outgoing tend to show more engagement at work. They are more likely to show up with energy and enthusiasm for what they do.

So if you want an engaged workforce, perhaps your best bet is to hire people who have an “engagable” personality?  The recent study we reviewed suggests that doing so will actually boost your engagement levels (as measured by surveys) more than any intervention designed to improve leadership, or to craft the perfect job for people. However, while this may look like an attractive position to take for managers, particularly if they wish to make engagement an employee problem, there are four important caveats to consider:

For starters, being more resilient to bad or incompetent management may be helpful for individual employee well-being, but it can be damaging for the wider performance of the organization. Frustrated employees are often a warning sign of broader managerial and leadership issues which need to be addressed. If leaders turn employee optimism and resilience into a key hiring criterion, then it becomes much harder to spot and fix leadership or cultural issues using employee feedback signals. It is a bit like a restaurant owner saying: “Instead of serving better food, or improving the service, I will boost my reviews by ensuring that my diners have lower standards!” While that may boost customers’ satisfaction ratings, it will certainly not raise the quality of the restaurant. Put another way, surrounding yourself with people who are more likely to give you positive and optimistic feedback does not actually make you more competent at your job.

Second, as data from the study clearly show, at least half of engagement still comes from contextual factors about the employees’ work — issues or experiences that are common across employees in an organization. So while one employee’s opinion might be heavily biased by the personality of that individual, a collective of views (like those often captured in organizational surveys) are more representative of the shared issues and challenges that people face at work. This is important because organizations are not really just a collection of individuals — they are coordinated groups with shared identity, norms, and purpose. Engagement therefore represents the “cultural value-add” an organization provides to its people at work — shaping their energy, behaviors, and attitudes over and above their personal preferences and styles. Ignoring this point isn’t an effective strategy for good leadership, and would cause you to leave valuable performance drivers out of your decision making.

Third, the most creative people in your organization are probably more cynical, skeptical, and harder to please than the rest. Many innovators also have problems with authority and a predisposition to challenge the status quo. This makes them more likely to complain about bad management and inefficiency issues, and makes them potentially more likely to disengage. Marginalizing or screening out these people might seem like a quick win for engagement, but in most organizations these people are a significant source of creative energy and entrepreneurship, which is more difficult to get from people who are naturally happy with how things are. To some extent, all innovation is the result of people who are unhappy with the status quo — who seek ways to change it. And even if hiring people with engageable personalities does boost organization performance levels (and decreased undesirable outcomes), there are big fairness and ethical implications when it comes to excluding people who are generally harder to please and less enthusiastic in general, especially if they are just as competent at their jobs as any others.

Last, anything of value is typically the result of team rather than individual performance, and great teams are not made of people who are identical to each other, but of individuals who complement each other. If you want cognitive diversity — variety in thinking, feeling, and acting — then you will need people with different personalities. That means combinations of personalities to fit a variety of team roles — having some individuals who are naturally proactive, extroverted, and positive, working together with some who are maybe the exact opposite. The implication of this is clear: if your strategy for “engaging” your workforce is to hire people who are all the same — in that they are more engageable — you will end up with low cognitive diversity, which is even more problematic for performance and productivity than having low demographic diversity (though we think both types of diversity should be pursued).

So, if you want to truly understand engagement in your organization then you need to look at both who your people are and what they think about their work. In other words, more calibration to employee personality is needed. For example, managers leading teams of people who are generally harsh or negative could benefit from seeing engagement data through the lens of personality, helping them to target issues that are genuinely impacting team performance.

And this then also opens up a new opportunity: to think about how engagement data could also be used to encourage employees to better understand their own views about work, giving them more flexibility to take personal ownership and find ways to thrive. In a recent study, researchers found that 40% of managers identified emotional intelligence and self-awareness as the most important factors influencing whether an employee takes responsibility for their own engagement. If we can combine what we know about engagement with what we know about personality, then we can help each person more effectively navigate their organizational reality — leading to better, more effective organizations for all.

Business.com
4 Tips to Motivate Your Employees in 2019

It doesn't have to be all boredom and monotony at your job. Korn Ferry found that 33 percent of professionals plan on looking for a new job in the upcoming year due to boredom and the need for new challenges in their present job. It's difficult to work for the same company when you don't feel motivated.

But with the right mindset and attitude, you could create a workplace that is not only fun to be in but also filled with hard workers who boost your business and create a positive environment.

Nowadays, businesses are becoming more laid-back with their strategies surrounding company culture, working remotely and implementing more fun into the equation. Employees are no longer willing to sacrifice a 9-to-5 life every day until they die; they're looking for a better way to live and make money, and they want to have fun while doing it. That's why it's so important to keep employees motivated and excited about the work they do.

If you're looking for fresh ways to motivate your employees this upcoming year, try these four tips.

1. Lead by example.

In a survey conducted by Accounting Principals and Ajilon, 32.5 percent of employees said the reason they left their job was due to a bad boss or manager. You don't want to be part of that statistic.

If you want good workers, you have to show them that respectable principles are what you – and your company – are all about. It's not enough to tell them you expect X, Y and Z when you don't implement those core values to yourself. It's hypocrisy at its finest and far too many bosses don't count themselves in this equation.

Predictive Index found that roughly 80 percent of workers agree that a quality boss possesses a good work ethic, is honest and has confidence.

If you want your business staffed by hard workers, be a hard worker. If you want honest feedback, be honest with yourself when you get constructive criticism. Be open and upfront with your employees. If you want respect and fairness, show that to each of your workers. Don't single anyone out or pick favorites. People will pick up on it instantly, and it will lead to a bitter, negative environment.

2. Transform the workspace culture.

A Randstad survey found that 58 percent of professionals would leave their job because of negative office politics, as this affects many aspects of work culture: the amount of work put in, what kind of attitude employees have and employee retention.

It's your job as an employer to make your employees feel safe, cared about and welcome while they're at the company. Motivate the people who work for you by telling them they are heard, that they have autonomy and that they're a valuable asset to the business moving forward. Express gratitude for their hard work and let them know you don't take them for granted.

3. Respect their capabilities.

If you want productive, intelligent, hard-working employees, treat them like adults.

There are far too many employers who micromanage their employees to death, looking over their shoulder, not believing in them to complete tasks within deadlines and talking down their abilities. Comparably conducted a study that found that 39 percent of employees said being a micromanager was the worst trait a boss could have. If you're trying to drive your professional team away, this is the fastest way to do it.

Don't parent your employee – partner up with them. Treat them as equals because that's exactly what they are. You may be their boss, but the work they do for your company is invaluable and you can't afford to lose them.

Think of different ways you can show them you've put your full trust into their capabilities. To start, ask them directly for suggestions on how they could feel more respected and appreciated. Figure out ways to compromise with their wants and needs and meet them in the middle.

More companies are allowing their workers to work from home, allowing for a better-balanced work/home lifestyle. A study by FlexJobs found that more than 76 percent of workers would be more loyal to their companies if they offered flexible work options.

4. Give them a voice.

Your work environment should be one filled with trust, honesty and transparency.

If your employees feel that what they say or think doesn't matter, they're going to believe their work doesn't either. This is the easiest way to lose faithful people who add value to your company.

Only 12 percent of businesses are happy with current levels of employee engagement according to a study by CBI. This could be for a multitude of reasons.

In order to combat this, make sure each person's voice is heard. Leave out a suggestion box for tips or have a separate meeting time where you discuss issues with the business. What's going great with the business? What could be done better and how? Let everyone pitch in on what they think could be done for better results.

Get to know your employees one on one as well. There's never a one-size-fits-all situation anywhere you go. By asking your employees questions about themselves, you can figure out what aspects of the job they care most about and what areas they're more likely to excel in.

Over to you

Getting to know the people who work for you will build a relationship of empathy, trust, security and understanding. Lead by example by treating them how you'd like to be treated, and trust that they're adults who can do their job efficiently and successfully.

Anything can be resolved with a little patience and communication, and your employees will stay motivated and happy because of it.

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