The Renaissance of Silicon Valley Talent Management
As most of us know from media buzzwords and venture capital concentration per capita, Silicon Valley remains at the heart of the global tech movement.
The size and scope of the well-established technology companies and startup firms continue to grow exponentially alongside the complexity of the datasets they manage and interpret. As such, one of the current drivers of innovation is data, Big Data.
Aside from banter in industry and the scientific community about what “Big Data” actually means or does, all of us are inundated in real ways by it in one way or another. The current and future internet-of-things will continue to escalate the level of connection between all of our doodads and thingamajigs. All of this communication happening in real time between your thermostat, your fridge, and your night light will be enhanced by machine learning and respectively immense and potentially limitless datasets.
Your thermostat will learn how to be more efficient over time as it accumulates data from the environment. There is a name for this constant feedback system of communication and analysis, the predicted outcome compared with actual measured data (For example, your thermostat using an algorithm to predict what time to turn on the heater tomorrow based on your daily routine).
Sometime in 1948 or so Norbert Weiner borrowed the term “cybernetics,” taken from a Greek word κυβερνήτης (kybernētēs) meaning “steersman, governor, pilot, or rudder”. The steersman was the governance structure underlying the nature of reality which consisted of infinite probabilities and communication systems. According to Weiner, this communication system wasn’t composed only of doodads passing binary data back and forth to each other, it was based on human and living systems and included a model of machines and humans talking with each other as well. Do you own a personal computer? A smart phone? You communicate with the device from the moment you press the ON button.
Weiner’s book was published in 1950, decades before the first personal computer. He lauded the augmentation of society with automation technology that offered more opportunity for leisure and artistic pursuits, but also warned of us becoming controlled or dehumanized by becoming overly dependent on technology and losing site of what makes us uniquely human.
One area of industry that augments its toolset with the cybernetics of Big Data is traditionally referred to as Human Resources. Specifically, the very human role of recruiting and managing the training and retention of highly skilled technical workers that historically tended to rely on the cybernetic command and control system of human-to-human communication.
Talent Management in Tech
For most of industrialized history, the key workforce metric has been productivity. In Silicon Valley it is innovation. Lean and mean, concise and precise, taking calculated risks and pivoting on a dime at a moment’s notice is how things are here now, having learned from the bubble of excess and inefficiency of 1999. Innovators are those that create stuff like the latest and greatest app or Medium. Therefore, the workforce itself is a key component and driver of innovation and thereby the successful company. This workforce needs to not only be good but fast, flexible, appreciated and attended to.
Talented innovators want to create an impact on the world, and need us to remind them daily how they are in fact impacting the world. The worker, manager, and team needs data showing measurable progress, and we rely on analytics not only to know when to stay the course and when to pivot on our projects, but also in our workforce development and feedback.
Forget the yearly or even quarterly performance review, we now need real-time scorecards for all aspects of our business.
Managing People Using “Big Data”
People Analytics is not what I would call the new kid on the block (not the boy band from the 90’s), although it is now entering into mainstream consciousness in the business world . In fact Big Data is being used in HR departments now to track and evaluate performance data of employees in real-time, to get feedback from employees sooner and more often in order to retain talent; to help in deciding how, where, and what to train, and finally, in hiring the best talent based on predictive metrics .
Similar machine learning algorithms that brought us google search, spam filters, and self-driving cars may in the near future be used in predicting and categorizing performance, retention, and recruiting metrics(In fact, the author of this article is taking the MOOC course right now from Stanford Professor Andrew Ng on machine learning for that very purpose.). One thing that Big Data is critiqued for however, is that it might actually lose sight of the big picture while gathering and analyzing all that information in real time.
What about “Thick Data?”
Combing through huge datasets to quantitatively predict some behavior or other form of communication does not take into account the rich and “thick” information you get from a narrative. For example, a dataset with 200 participants where favorite ice cream flavor is used to predict favorite color doesn’t tell us much about what it means or why it matters.
However, just sitting and talking with a handful of people and simply asking them what their favorite color and ice cream flavor is and why for ten minutes each might provide you with more insight into why and for what utility favorite ice cream flavor predicts favorite color than your 200 participant survey. You might learn that blue is someone’s favorite color because they love cookie monster and now you have another possible answer as to why they like the blue cookie monster flavor. You have just unearthed a possible third variable, or another predictor to add to your model before crunching your regression model again.
You might be asking the question now, “this is well and good if I wanted consumer insights related to selling Cookie Monster ice cream, but what does this have to do with talent management or HR? Aren’t analytics related to performance tasks straight forward enough?” Let’s aim at an answer by taking a side-by-side look at Big (quantitative) versus Thick (qualitative) data, below.
This is where the interviewing skills and other qualitative research tools of the talent manager come into play. For all the behaviors Big Data and analytics can predict, they don’t do such a great job of understanding emotions, motivations, and intentions of humans. Talent is comprised of humans. This human skill cannot be supplanted. However, human intuition, training and lived experience can in fact be augmented much like Norbert Weiner suggested in his book on cybernetics. Let’s not forget that he also warned us of losing our humanity. People in the workplace have human issues, problems, and contain irrational and irreducible consciousness. And this augmentation of our human abilities with Big Data while not losing sight of our humanity is what I call the big picture.
The Big Picture
Big Data came on the scene rather quickly and will likely be part of the background music of our ecosystem much like the world wide web is now. However, part of the appeal of using data to drive business decisions has growing pains. The hype of the latest and greatest thing may prove to not be the panacea of profitability that some firms expected .
Much like internet companies were hyped before the first tech stock market bubble, the true utility and value of using Big Data to improve business will occur with time, and likely most useful among companies that use some form of evidence-based decision making already . One of those places where both Big, quantitative data and Thick, qualitative data will find a true symbiosis in improving the workplace is in Human Resources.
Yes, Big Data and People Analytics is now very real in HR. And if we are smart about it, we will measure more than just bottom line metrics like productivity was in the past and innovation is now. Quality of life, work-life balance, healthy social work environments, among other key evolving and multidimensional categories crucial to maintaining a happy and healthy workforce will continue to take center stage more often. We might just reinvent the way we do business, at least in the tech world where talent rules.
We can hope that innovations will continue to change even the names for our field of work that historically made humans sound like a commodity — from talent management to “people management,” human resources to “people operations,” from top talent wins the prize to everyone matters, from me to “us” . And some of the folks driving this bus of free market, socially and environmentally responsive capitalism will need to be flexible and able to engage in seemly disparate skills as part of the job description.
The Renaissance Wo[man]
People who have extensive knowledge in various and often disparate disciplines are called “polymaths”. πολυμαθής, or polymathēs, comes from the Greek meaning “having learned much.” People like Leonardo DaVinci or Hildegard of Bingen were experts in a variety of fields from science, art, music, medicine, and theology. In today’s incredibly specialized world, these so-called Renaissance men and women might have a somewhat difficult time finding a job, at least of the cookie-cutter corporate variety.
However, many tech companies have realized the benefit of multidisciplinary teams for quite some time. Moreover, individuals with cross disciplinary skills such as statistics, social sciences, computer science, mathematics, and engineering may be the workers of the future with the basic knowledge necessary to be flexible to the ever changing demands with emerging technology and ability to work in team environments of the future. Some of these new Renaissance men and women in the People environment in tech are psychologists.
People Matter: Psychologists in Talent Management
In the state of California, someone can use the title of Psychologist only if licensed by the board of psychology which also requires 3000 hours of supervised work. This requirement varies somewhat from state to state. The fields of licensure traditionally are clinical, school, and organizational psychology — the licensure exams in California are identical for anyone licensed as a psychologist.
The American Psychological Association has two main divisions that focus on organizational work such as talent management , institutional research, assessment, internal and external consulting. These are some of the oldest divisions of the APA, division 13 — consulting psychology, and division 14 — society of industrial and organizational psychology. The field of psychology definitely has its silos like most other disciplines, and clinical and organizational psychologists do receive specialized training, although they both need generalist skills in both fields relevant to passing board exams.
Psychologists trained in the clinical field often do find themselves working consulting and organizational business roles. The author of this article is one of those with a business and organizational temperament trained as a clinician, and also able to transfer those primary psychology skills from a focus on psychopathology of individuals to organizational excellence, optimal/peak human performance, research, and yes even some coding skills.
The Polymath / Data Artist as Chief People Officer
Norbert Weiner might have been somewhat of a polymath, a mathematical genius definitely and a pretty good writer. How would he have imagined the role of a 21st-century talent curator in a fast-paced tech environment? Big Data all day every day? What about those soft people skills necessary to participate in and manage teams? Both, of course, and more — perhaps like Queen Margrethe of Denmark, Galileo, Hildegard, and 50 cent .
A new role for the polymath in all locations where data exists is the Data Artist. The data artist can’t fit in a silo yet understands data. They intuitively see the big picture because of years of experience and having equally strong parallel processing right brains and serial processing left brains. They know machine learning in the form of Big Data and human learning in the form of Thick Data. They are the people now representing the talent of the company at the board level. This author is excited about the future for humans working in companies, maybe your next Chief People Officer?
About the Author
Daniel Pinedo was born and raised in Los Angeles. He currently is about to finish his dissertation in a doctoral program in Clinical Psychology at the multidisciplinary tech school Sofia University in Palo Alto, CA. He now specializes in organizational psychology and people management, with a background in addictions assessment and treatment. He has spent the majority of the last 20 years working for tech companies, having been lured away from college at a young age to work during the first internet tech boom.
He looks forward to working in tech again with affinities for quant and qual research methods and talent assessment. Daniel brings a people-centered, creative and right-left brain ambidexterity to work with him. He is trained as a yoga and meditation instructor, loves caring for rare and exotic plants, is a loving father, cook, has an insane and loving 18-year-old calico cat named Princess, and a fiancé named Marcia. These living and loving beings currently reside contentedly in the South Bay of the Los Angeles area.