Canaries in the Coal Mine

How your leadership development programs can serve as more than an introduction to corporate life

Most corporate leadership development programs serve one purpose: attract top students and acclimate them into the processes and constructs of your existing hierarchy, career progression, and process.  It’s not a bad model if you want to maintain the firm’s status quo with perfection:  pull a bunch of type A kids into a room and give them a roadmap for “success”.  They’re going corner every turn perfectly.

But it’s no secret that large corporations are facing increased competition from smaller, more nimble companies that adopt new internal processes and operations continuously.  Perpetuating corporate status quo, in other words, is a losing strategy.  Codifying your “Business As Usual” with your newest, most flexible employees wastes an amazing opportunity to bring new thinking into the firm and spot problems that may seem the norm to veterans.

I’ll cover how large companies can structure their corporate development programs to leverage the creative minds of young employees soon, but for now I want to highlight a relatively low-cost, high reward vehicle for insight into your company.  New employees can be your canaries in the coal mine – they may not understand the history, nuance, and complexity of “why things are the way they are”, but they can sniff out the consequences of bad practices pretty quickly.

Take for example, junior investment bankers.

Tenure in the most prized position (for compulsive finance majors) of investment bank “Analyst” is dropping steadily.  The most commonly held grief is sheer time investment, mounting up to around 90 hours a week for a first year employee.  In response, many banks have introduced policies that limit working on weekends or offering a guaranteed work reprieve every month.

But are HR partners addressing the core conflict?  Every junior banker is prepared for weeks from hell, in fact, being able to take the heat is a point of pride (assuming they keep getting their paycheck).  So if they’re prepared to sacrifice their first years out of college to make a lot of money and get great exposure and experience in the field – what’s the problem?

While they’re getting great exposure to clients and executives it isn’t the sort of challenging analytic work they imagined walking in the door.  Managing Directors, Chairmen, and anyone else with a potential client uses the “Analyst” ranks to push out deck after deck of the same verbiage, comps, and graphs regardless of client or context.  Turning comments on endless iterations of documents used as excuses for meetings with clients are the sort of unnecessary processes that serve as symptoms for a broken business model. 

More lean investment banks like Evercore have gained considerable market share for their ability to provide relevant and meaningful insight. Clients have left “bulge bracket” advisors for more niche expertise in smaller outfits. The market has spoken against the larger banks who are known for offering either “never in a million year” scenarios or boilerplate financials. Throwing copy to get in the door has marginal returns.

In sum, junior investment bankers are leaving their well paid, high exposure jobs because they know their work isn’t as meaningful or insightful for themselves or the consumer. They leave for jobs at smaller funds, start-ups, or higher-impact roles at other companies with a hope for using more of their brain.

The recent college grad may not be able to pinpoint why a dangerous practice exists – much like the canary has no idea why rocks seep poisonous gas – but they’re much more sensitive to slights of internal policy, process inefficiencies, gaps in workplace technology, and dissonance between outputs and market demand.  Middle management tends to absorb and rationalize these small issues and thus cloud the symptoms of fundamental problems.  VPs in banking don’t tell their bosses that the decks they provide lack innovative analysis. 

Core problems arise on the front line of execution – which is conveniently where your corporate development program resides.  Grabbing unadulterated perspectives on your business model might involve speaking with your newest cohort of 22 year old college grads.  They don’t have the biases and crystallized interests that come with years of climbing the ladder.  Given a safe outlet where their views are not punished or stored on record – they could be more than willing to open up about what they really see.

“My job is just to comply”

I was being THAT guy in the cab.  After changing destinations a few times chasing the elusive blue dot that represented my friend in the park, I finally had the decency to apologize to the driver.  He chuckled in an understanding way “My job is just to comply.”

The response was in jest, but it really struck me.  Should anyone’s job description be so limiting?  Computers have command lines, people shouldn’t.

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Aside from the moral implications of world reliant upon dictated service, let’s look at the economic consequences.  When you assert a task on someone it implies that your idea of that person’s productivity is better than their own.

In some cases this is totally appropriate; a more senior employee or manager may have the experience necessary to give people challenging tasks that help them grow.  But if we underestimate of the talents of others, or worse – continually put our immediate needs over their goals, we leave a lot of potential on the table.  That lost opportunity exponentiates when we make these mistakes for entire segments of the population across entire industries:  transportation and hospitality to name a few.  The time and energy poured into driving a car could be used to teach a new language at a public school or host a restauranteur at a local farm.

Firms that understand the efficiency of using technology to free human labor from jobs best left to machines are organizations that understand half the battle of “Digital Era” economics.  Google has made heavy investments in transportation automation with it’s huge effort into driverless cars and landmark stake in Uber.  Tech innovation alone has costs though; It hurts for those caught in the transition.

Thus, the other half of progress will come in finding and deploying the unique strengths of those who have been pushed into a box for so long.  Once employees move “out of the weeds”, firms will have to organize around human strengths in order to capture the full value of improvement in technology.  Firms can cut costs with automation alone, but will severely lack in competitive innovation and market connection without the crucial, complementary inclusion of human networks.


The (Statistical) Importance of Diversity

We often hear diversity as a requisite for modern business but why should it be more than a social norm?  Because it’s one of the best ways to build an organization that takes both a holistic AND agile approach in decision making.

Let’s take an imaginary interaction between Maria and Volkswagen.

Maria is in the market for a new car and heads to her local VW dealership to browse the new models.  She wants a safe, sporty drivewith storage space in case she decides to move across town, but nothing too flashy.  Oh, and it has to have the latest iPhone sync capability so she can jam on the road.  But today she’s in a hurry; it’s one o’clock, there’s a client call at two, and traffic was pretty bad.  The dealer has one chance to make a good impression.

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So he absorbs what he can with Maria’s vague requirements and picks up øor a segment of what she was trying to communicate.  Ømight be the bare minimal – a safe, sporty drive.  

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The salesman is also busy and has been hard pressed to sell more cars – sitting down with Maria to painstakingly examine every model’s pros and cons won’t fit his schedule and she may not be interested in that level of detail anyway. So, he calculates an offer with the first product that came to his mind when he heard sporty – the Bug.   

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Maria mentioned the other specs too, but the salesman misses these details because he’s biased to attach importance to some data points over others.  It’s worked in past sales, so why dig deeper?

Now this may not seem like a big deal at the moment.  But what happens if this sort of bias or gap occurs at scale, i.e. becomes systemic?  What if everyone at Volkswagen is looking at ø and responding around that finding, but not incorporating other sources of information?  

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If Volkswagen salesmen continue pairing women to Bugs, they might miss a better match (and higher value proposal) with other products.  A Passat coupe, for example, has more features, better storage, and maybe a higher safety rating, and still fits the desire for a sporty drive.  

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The true danger occurs when employees don’t know these blind spots exist.  Statisticians call this scenario a Type II error.  A false confidence prevails in your beliefs about the market and your understanding of consumers.  Since you weren’t including women’s opinions of the Passat into your model, it would be impossible to find you were off target.  The data points to the Bug, but that average understanding is flawed, it’s not the true mean!

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Incorporating a more diverse set of data would help the salesmen understand a more complete picture of market preferences and respond accordingly.  In statistics terminology, they would have a more representative sample of the (sub)population(s) which would include previously omitted, but relevant, information into their analysis.  The result would reflect a broader and more accurate view of possible solutions to provide consumers.

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But even this model is far too simplified.  In the digital era with thousands of touch-points across geographies and media there is an inherent mandate to embrace higher orders of complexity. One data point at the “new” understanding of “reality” isn’t enough – there is no longer a single, planned equilibrium in the market.

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Software could automate the parsing of different online communities or transaction histories, but ultimately the creative process of building and offering products for these clusters will rely upon human empathy and ingenuity.  In order to capture the variance of market characteristics, a firm needs a wide range of backgrounds in it ranks.  Diversity should expand beyond gender and race to encompass a broader spectrum of demographics including income, geography, and values. 

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An organization with a deep  portfolio of perspectives will innovate at scale  by engaging these groups with far more efficiency and accuracy than an org with a singular POV.  In the 21st century when market sentiment is changing at the drop of a viral video or a trending blog, business can be won or lost depending on how that information is absorbed by the organization.   But it’s hard to absorb new ideas and dynamic information if you only have one type of person working for you.

Diversity is more than a nice thought, it’s the new survival tool.

Levels of Analysis for Social Business

Every junior year political science major has probably heard the classic “levels of analysis” lecture in their international relations courses.  I’ve probably heard it six or seven times as a recent graduate.  But only after a few weeks of post-grad retrospect have I realized the applicability of this political science tool to the emerging world of e-commerce and “social business” systems.

The “levels” framework was designed to guide the research of political phenomena into digestible, organized sectors of study:  individual political actors, institutions within a given nation, a nation’s general dynamics, regional environments, and most generally, the international system at large.

The intent of the structure was to create spaces where researchers, regardless of theoretical perspective, could explore ideas on the same wavelength and avoid asynchronous debate.  Compiling research within and across these levels allows for scalability in meta-data research.

Applying this framework to digital and social business could help conceptualize the increasingly systems-based study of economic behavior in dynamic environments like e-commerce.  The application of “levels of analysis” to the world of digitalized social business might look something like this:


The approach allows us to map studies and insights within the broader system.  Mapping each analysis in respect to its place in the “big picture” allows us to understand the insight and its relevance to the organizational environment.

Rationally Bounded

Herbert Simon coined the phrase “bounded rationality” to describe why organizations and individuals often make decisions that are irrational or incomplete.  Without going into too much detail, the concept of “bounded rationality” was born out of Simon’s understanding that people are limited in their ability to collect, process, interpret, and assess the importance of information.  Thus, if we cannot properly analyze information, we cannot make complete rational decisions.  If we are forced to simplify concepts and expected outcomes, we are forced to settle for our understanding of reality and make a decision based off that perspective.  Our rationality is thereby limited, or bounded, to our scope of understanding the information.

So how does this abstract concept fold into today’s discussion on technology?  Firms are accumulating data at an unprecedented rate and eager to tap into insights via advanced analytics.  Something to the extent of 667 exabytes of data, or 1.7 million Libraries of Congress, are currently traveling over the globe’s telecommunications networks.  Value like individualized transaction information, geographic preferences, purchasing patterns, and medical histories open a large window of opportunity for firms.  Some estimates put the “big data” market capitalization at around $150 billion.  And that number doesn’t even include the value created by insights born from analysis.

Algorithms and predictive models have replaced some human subjectivity from the traditional model of decision making, but this replacement has been skewed toward mundane and robotic tasks like airport ticket kiosks or switchboard operators.  In fact, the vast amount of data that is now available at our fingertips is only forcing us to make more decisions.  In a way, technology has enabled the human employee to focus on higher value-added work.  And these roles carry more weight than employees have previously been entrusted with.

Sure, a call center can automate simple decisions like giving better leads to more successful agents or an online insurance company can create quotes in seconds.  But what about the back-end?  Ultimately, it is human behavior which will dictate where those decisions are automated and, more importantly, how that automation will be formulated.  At the front end?  Instead of having attendants at the ticket counter, airlines may find opportunities in using those employees to create travel plans and itineraries for prospective customers transforming their role from clerical to sales oriented.

But this presents a new challenge for organizations in the coming decades.  If we shift human actors from the mundane and repetitive, to more complex roles that rely upon human judgment and interaction, then how can we measure and analyze commensurate behavior?  Instead of relying upon correlations and patterns of naturally occurring data like we do now, how can we look at substantive metrics of individual level decision making?

IBM and McKinsey have published a lot of great articles on this subject in a blooming field known as “Social Business”.  Business leaders have begun to acknowledge the augmented role of individuals and their ability to collaborate, empathize, and realize business opportunities outside of their rigid job descriptions.  The workforce of the future will be far more cybernetic and synergistic creating value from multiple points of expertise and perspective.  Companies who do not acknowledge these forces will be left behind, and therefore it is vital for enterprises to begin partnerships with industry leaders to build an institutional awareness and comfortability with this new level of analysis.

The analysis of human behavior using our current mathematical models has been a difficult task.  Ask any social scientist who has attempted to use empirical methodology in their research and they will likely agree with the notion that human behavior is not explicitly rational and therefore isn’t necessarily a linear process.  The question then becomes – “what methodology of data analysis for human outcomes makes sense?” – and the answer won’t be regression.