Dan Erwin

A Coach's Nightmare: When My Career Theory Is Wrong. Dead Wrong.

As a research-based consultant, I’m used to getting my ideas tweaked by new research. After all, I think of myself as a fairly flexible, open guy, so a small adjustment to my theories and ideas is usually illuminating. But unknown to Harvard’s Rosabeth Kanter, yesterday she took a Shillelagh, a big club to one of my big ideas. Her blog, Why running a family doesn’t help you run a business, knocked me back on my heels. And I yelled a silent Aaargh!, knowing full well that she was quite correct and I was quite wrong. So there, I admitted it Prof Kanter.

Kanter lays out her case with panache, acknowledging the conventional wisdom first. Here’s her argument:

In theory, those years of family work at home could look great on a resume. Consider the skills required: setting priorities, training others, organizing complex logistics and schedules, and using interpersonal sensitivity to handle difficult people problems. Indeed, some advocates have argued that the time women with advanced degrees spend out of the workplace managing a family is valuable experience for managing back in the paid work world.

I agree it is valuable experience — if the paid job one returns to involves managing a handful of people who are vulnerable and can't leave. Otherwise, the operating skills for family manager are nothing like the qualifications for workplace professionals.

Her reasoning and evidence that follows are flawless—and quite correct.

Suffice it to say, that I’ve taught the very opposite and spent time preaching to the choir of empty nest women wanting back into the workforce.

To err is human
Yet I’m certain that most of us go through life assuming and even insisting that we are right about everything. And that includes stuff from the origins of the universe to how to load the dishwasher. That’s why Moliere’s comment is spot on: It infuriates me to be wrong when I know I’m right.

But I’ve figured out why, primarily, I’ve wanted so badly to be right. It was all about my allegiances and the narratives I’d built around them.

WYSIATI
In his fascinating book, The Black Swan, Nassim Taleb introduces the idea of narrative fallacy to describe how flawed stories of the past shape our view of the world—and our expectations for the future. I have three daughters, all of whom were raised to be professionals just like their mother. Inevitably, as a father of smart daughters, I experienced male chauvinists who drove me nuts. After all, every father wants his best for his daughters, and their achievement was always tops in my priority list.

So, for instance, when my youngest was a high school freshman, I found myself in a teacher conference that raised my ire. Her male math teacher rather pointedly said, “Kris does very well in math, especially for a girl.” Knowing that her husband was about to become slightly volatile, my wife kicked me under the table, got my eye and essentially said (nonverbally) “cool it.” But you get the point. And so in later life, after having kids, they went back into the workforce rather successfully. Clearly, because of my family allegiances I got caught up in the narrative fallacy about mothers going back to work and began to preach it.

The narrative fallacy
The narrative fallacy is a decision error that most of us make largely because we are gripped by the illusion of understanding. As Daniel Kahneman explains them, they arise inevitably from our continuous attempt to make sense of the world. And inevitably also, the explanatory stories that people find compelling are simple: are concrete rather than abstract; assign a larger role to talent, stupidity , and intentions than to luck; and focus on a few striking events that happened rather than on the countless events that failed to happen.

Taleb clarifies the issue even more, suggesting that we humans constantly fool ourselves by constructing flimsy accounts of the past and believing they are true. The core of the illusion is that we believe we understand the past. That implies we should also know the future, but in fact we understand the past less than we believe we do. Thus WYSIATI: what you see is all there is. . . takes over in spades.

Resolving the narrative fallacy
I seriously doubt that any one of us will ever fully block our narrative fallacies, but there are a number of strategies we can take to limit their power over our lives.

The beginning place is a lot of stupid mindsets, rejected and reframed intelligently here by Daniel Kahneman:

• Our thinking is designed to jump to conclusions with little evidence.
• The world is far less predictable than we believe.
• Luck plays a much larger role than we believe.
• Because of WYSIATI, only the evidence at hand is questionable.
• The amount of evidence and its quality don’t count for much because poor evidence can make a very good story.
• Considering how very little we know about most situations, the confidence we have in our beliefs is preposterous.
• The notion that we’re essentially rational is no more than an economist’s pipe dream.

Blocking errors like the narrative fallacy is simple in principle: recognize the signs that you are in a cognitive minefield, slow down, and engage in controlled thinking that emphasizes facts, statistics and thinking about your thinking.

It’s a lot harder than we think. But much of the best decision making and learning consists of challenging old ideas and assumptions.

So, I should have asked what made me think that running a family helps in runnning a business? Why do I think that? What evidence supports that reasoning? What’s the quality of the data? How else can I look at the success of a few empty nesters returning to the workforce? And especially, what evidence rejects that reasoning?

Well. . . you get the point. So thanks, Rosabeth Kanter. I guess.

Flickr photo: ashishvosharma99

Big Data: The All-Important "Why" and "How"

Nearly 10,000 have visited my postings of two blogs on Big Data on a couple sites since January 17. There’s no question that big data is “hot” and that the business landscape is shaped by data more than ever before. Google’s Eric Schmidt tells us the world creates 5 exabytes of data every two days. That’s really big data. (FYI: Exabyte—Cisco says annual global IP traffic will reach two-thirds of a zettabyte or 667 exabytes in 2013).   But the real big data question is not how much, but so what?  

Recently, the Economist Intelligence Unit, with support from SAS Software, set out to answer that question.  They surveyed 752 senior executives from a broad range of sectors and countries. They also conducted in-depth interviews with 17 executives, consultants and specialists regarded as data pioneers. Fifty-seven percent (57%) of the respondents had been working with big data for at least three years, and nearly one-half of the respondents said that their companies are well ahead of their peer firms and have a defined data strategy.

What’s significant about the results is that they are reflective of a growing consensus in the field of big data. The consensus research also confirms my blogs on the big data IT fumble and the data scientist as “sexiest career.” The summary highlights of the research are as follows:

The link between effective use of big data and financial performance is strong. Top performing companies, the so-called strategy data managers, process data more rapidly, see the rewards across all business disciplines, place a higher premium on data than their peers, collect more data from everywhere and use data more broadly across the business.

The survey suggests that there is a correlation between big data and financial performance. But a chief problem with all survey research is the omission or de-emphasis of other factors. The firms surveyed tended to be successful firms prior to the use of big data. It’s probable that the big data leverages other success factors.

Still, it’s arguable that big data is a highly significant competitive advantage. At this point in its development, firms without that expertise are liable to be left behind.

Well-defined data strategy is always the top priority, not collecting and processing information. One of the intriguing research bits is that 46% of executives from companies that significantly outperform their peers financially have a “well-defined” data strategy. Inevitably and always, the strategy should be based on key business priorities. The role of the data strategy, then, is to identify the problems the firm wants to solve. But the data component serves those priorities and, therefore, is developed afterwards.

Strategy is key: big data merely supports strategy.

Talent matters as much as technology. Research finds that executives need to ensure that analytic thinking is not confined to the It department. Especially valuable is a sidebar, “What’s in an algorithm?” detailing training that works with employees with little or no knowledge of data science, showing employees how to create a data plan that results in a series of business recommendations.  Yet, a close reading of the language of the research conclusion indicates the need for still more clarity on this issue.

As I emphasized in my blog, Big data: the IT fumble, the uses of big data emphasize that IT and data science are very different animals. Any observant employee is aware that when it comes to data or even the use of the term, data, the ship is listing way over to the side of IT. The lack of professional data scientists for the discipline makes the problem even more serious. Data scientists will need to understand the business as well as cognitive and behavioral sciences. One principal at Deloitte Consulting recommends that companies may need to search academic departments or consider sharing a data scientist. So it can’t be emphasized enough that people are at the heart of the discipline, not data.

The biggest gains from big data impact are in customer-facing areas. The report recommends that social media and web-tracking technologies are especially useful for collecting customer data. Loyalty cards and user-generated web content have already led to significant changes in retail and entertainment. Of course, consumer products companies have long used customer data supplied by information and measurement companies like Nielson. Thus, marketing and sales groups have built-in experience and history of using customer generated data, making analogies to web-data an easier reach for those firms.

Evidence of the use and advance of data is everywhere. As the report indicates, an ecosystem of data-focused companies is springing up and creating new businesses. Still further, analysts at the McKinsey Global Institute estimate that just one sector—the US healthcare industry—could create US$300bn in value annually if it used big data to drive efficiency and quality.

The “why” of big data is fairly obvious. It’s the “how” that creates and will continue to create frustration, while at the same time creating significant business success. That’s typical of an evolving discipline.

Flickr photo: by Laura ATL

You'd Think the Military-Industrial Complex Would At Least Take a Nap

After more than 10 years of war and nearly 6,000 American deaths in Iraq and Afghanistan, plus untold civilian deaths, you’d think the military-industrial complex would take a breather. But no, they’re already back at it. But before I explain, I was shocked, to learn a couple days ago that I’d better define the term. A member of the younger generation asked me, “What’s the military-industrial complex?”

I assume that the same lack of information is shared by others in the business community. Still, it’s the unstated subtext of much of the machinations of the media. So here goes with that answer. The term, military-industrial “complex,” was nicely coined and defined by a military man: General, and then President Dwight Eisenhower. In his farewell address he argued that interlocking and self-serving relationships among the military, industry and the legislature, driven by the defense industry’s search for profits, would warp foreign policy. But he didn’t stop there. He also worried that too much state control of the private sector would result in economic stagnation, that continual preparations for war were inconsistent with our history, and that it was bad for our “spiritual health.”

Eisenhower farewell
"This conjunction of an immense military establishment and a large arms industry is new in the American experience. The total influence – economic, political, even spiritual – is felt in every city, every Statehouse, every office of the Federal government. We recognize the imperative need for this development. Yet we must not fail to comprehend its grave implications. Our toil, resources and livelihood are all involved; so is the very structure of our society. "

Some of the evidence
The fact that the “complex” is back at it, even though America is exiting Afghanistan, is evidenced by a number of reports.

First, the war hawks are lining up against the candidacy of Chuck Hagel. No question but what Hagel lines up with Obama, viewing war as corrupting. The hawks argue that Hagel won’t support Israel and that he would make America “a weaker country.” But the real problem is that Hagel doesn’t bow to the military establishment or Netanyahu, and knows, as a Vietnam vet, that it’s important to question the military and its use--and that a different world order is demanded.  

Second, Panetta and Joint Chiefs Chairman General Martin Dempsey warn that the hiring freeze and deep cuts at the Pentagon are a “perfect storm” that would leave the military with a worst-case outcome as a “hollow force.” Dempsey argued that even though he would do their best to protect the readiness of units, “how do you protect the war-fighter, those involved in Afghanistan, those areas that are critical to our national defense.” Nothing, of course was said about the well-known fact that US defense expenditures were larger than the total of the next 26 countries combined, 25 of whom are our allies. Thankfully, Obama, although boxed in by his generals to escalate the war in Afghanistan, has now outmaneuvered them, calling their bluff and ordering a drawdown.

Third, in response to the attempt at gun control, the gun industry is using contests, games and semiautomatics to recruit children. The point is that these young “peer ambassadors” should help introduce guns to their friends. The NRA argues that the research is supportive of the need for guns to “protect” us . . . from the government(?). Since the country has nearly as many guns as people, every American can now be his own policeman. Hmmmm.

Fourth, the reinforcement of gun rights continues in never-ending fashion on the tube, the cable and the media at large. No one any longer questions the interpretation of the 2^nd Amendment. The consequence is that a fundamentally erroneous interpretation by the “originalists” has become doctrine and calcified into pulpit-pounding dogma, much to the delight of its adherents.

Were Eisenhower alive, he’d be aghast at our militarily-created debts and the still expanding military-industrial complex. Eisenhower was more aware than we know. Originally, he defined the complex as the military-industrial-legislative complex but his advisors warned again that truth. Put John McCain, Lindsey Graham, and Buck McKeon, along with a few hundred other legislators into the mix, and you know why without strong leadership we’re beating a dead horse about a death-giving issue.

In a recent article on the militarization of America, Aaron O’Connell of the US Naval Academy got it right when he wrote that Eisenhower would certainly be critical of the “insidious penetration of our minds” by video game companies and television networks, the news media and the partisan pundits. With so little knowledge of what Eisenhower called the “lingering sadness of war” and the “certain agony of the battlefield,” they have done as much as anyone to turn the hard work of national security into the crass business of politics and entertain.

Flickr photo: David Swift Photography

Big Data: The IT Fumble

If you read my recent blog, True data about big data, in which I proposed that Zero Dark Thirty’s lead character, Maya was a near perfect example of a data scientist, you’ll understand today’s blog title. The unstated subtext of that blog was that it’s unwise to turn big data over to the IT group. Sure, you’ll need them around, but their discipline differs significantly from what’s needed. And. . . I’ve found plenty of IT execs who understand that reality.

I argued the case from my experience and knowledge about the thinking styles and knowledge base of IT and other business functions. My cognitive approach is one way to go at the issue. But Marchand and Peppard arrive at the same conclusion from still a different tack in their October HBR article, entitled Why IT Fumbles Analytics. They got right to the crux of their understanding in the first paragraph:

In their quest to extract insights from the massive amounts of data now available from internal and external sources, many companies are spending heavily on IT tools and hiring data scientists. Yet most are struggling to achieve a worthwhile return. That’s because they treat their big data and analytic projects the same way they treat all IT projects, not realizing that the two are completely different animals.

What this implies is that rather than emphasizing information in the big data, information is viewed not as a resource that makes possible the design and implementation of more IT systems, but, rather as something that people themselves make valuable in their discipline and for their customers. To go back to the CIA analyst in Kathryn Bigelow’s film, Maya’s task as the most knowledgeable analyst was to question the validity and usefulness of the data. Inevitably, that’s a difficult task because she had to be able to ask “what” the data means, “why” the data is in such form and why and how it’s taking such a shape before she could get around to “what next?” Her questions were constantly iterated for years. What more data do I, as knowledgeable CIA agent, need? What conclusions can I draw thus far, and is this enough data to make a decision? Then, as I emphasized strongly, she had to sell her conclusions.

Strategic approach to big data
Without focused theory development, the data scientist will be overwhelmed by the amount of data available. All the IT processing power in the world won’t help. Maya’s theory, of course, focused upon a strategy to locate Osama Bin Laden. Her fundamental hypothesis was that if she could locate his primary couriers, she locate him. The film began with torture tactics for gathering courier identities, and, essentially stayed on that fascinating and difficult track for nearly two hours before actually finding the terrorist. Then, she faced the intriguing task of selling her conclusions to the State Department, an issue that was just as difficult, perhaps, as locating the terrorist. In sum, the first steps after creating a hypothesis are acquiring, harmonizing and mining the data sources. Of course, in the iterations of this process, you will inevitably find new, exciting insights. That also implies, as David Meer has written, that as you gather these insights, it will be important to be open to new approaches and to challenge sacred cows.

If you’ve ever done a major hypothesis-driven, research thesis, the strategic approach to big data is exactly the same iterative process as the Zero Dark Thirty portrayed. The distinction between my two analogies (Maya and hypothesis driven thesis) and the “big data approach” is that once the hypothesis is in place, IT tools are necessary to mine the data.  

At the heart of the initiative
It can’t be emphasized strongly enough that people are at the heart of the initiative, not IT tools, big data initiatives or the IT group. Marchand and Peppard are devastating in their critique of the IT logic:

The IT approach assumes. . . that giving managers more high quality information more rapidly will improve their decisions and help them solve problems and gain valuable insights. That is a fallacy. It ignores the fact that managers might discard information no matter how good it is, that they have various biases, and that they might not have the cognitive ability to use information effectively.  

As I have argued previously, most IT professionals come from computer science, engineering and math backgrounds resulting in a focus on technology and the use of different cognitive approaches rather than what’s necessary. Thus, a different animal will be needed to create and make the best use of information. I have no doubt that Marchand and Peppard are correct in their recommendation of the use of cognitive and behavioral scientists. But that’s a personnel expense only the largest of firms are currently willing to fund. People who have a deep and rich understanding of the business are absolutely necessary for big data success. And then, gradually, as business understands the opportunities provided by big data, they will need to add the cognitive and behavioral scientists who. . . understand how people perceive problems, use information, and analyze data in developing solutions, ideas, and knowledge.

The evidence is already clear that data-driven decisions tend to be better decisions. Thus, I’ve attempted to lay out a big-picture of the fundamental processes to make advanced analytics work for a company. The process—simplified--is to get the right hypothesis from the appropriate people, mine the data iteratively, create the new business information, sell it to the relevant executives and implement.

Flickr photo: UWWW ResNet

What's Good About Bad Bosses?

One of the ironies of work relationships is that we tend to gain more in-depth lessons from bad bosses than good ones. The endless list of bad bosses includes the following: assholes, control freaks, time wasters, poor communicators and etc. At one time or other nearly all of us have to deal with a bad boss. And most of us spend time trying to avoid them, rather than learn from them. Recently, however, I was reminded by Adam Phillips, a smart Freudian psychoanalyst, of the fact that bad bosses offer a significant good:

People become real to us by frustrating us. If they don’t frustrate us they are merely figures of fantasy.

That’s not to say that we don’t learn from good bosses. We all love to work for a boss who’s excellent at managing senior execs and resources, keeping his organization’s objectives and strategy in focus and who gives guidance to the team, both as a group and one-on-one. A boss of that sort is of inestimable value.  I’ve learned plenty from such bosses.

Candidly, however, I learned the most from a poor boss. He was the nicest guy, everybody’s friend and always effusive with praise. . . the very epitome of likability. I read all the classic traits of likability into his person: friendliness, warmth, helpfulness, sincerity, trustworthiness and, of course, morality. But after a long period of time, his likability began to wear thin. And that’s where the real learning began to show its messy face.

"Nice, but incompetent"
The particular form of bad boss I faced was the “nice, but incompetent, guy.” Systematic research excuses my difficulty and reveals that warmth (or likability) dominates our insights early on. Indeed, likability even disguises a person’s lack of intelligence, skill, creativity and efficiency—the very definition of competency. And, initially, I was seduced.

The research 
The research shows that warmth or “nice-guyness” carries a lot more weight. That makes sense to an evolutionary psychologist because another person's intent for good or bad is a lot more important to survival than whether or not a person has the smarts to act on his intentions. Not only do we make nice-guy judgments about someone first, we also make those judgments very rapidly. Furthermore, those judgments do not readily disintegrate. We tend to hold them tightly, blinding us to damaging incompetence.

According to Wharton’s Michael Useem, the research on incompetent bosses is deadly. It shows that bad bosses sap motivation, kill productivity, and can make you want to run from the job screaming. And, yeah, that was my experience.

Certainly my boss wasn’t a demeaning asshole. But if I’d understood earlier on that, in spite of his friendliness, he was significantly incompetent, I’d have treated him far differently. Recognizing the political implications of his power, I’d have been less transparent.  I would have spent time educating him on a few issues and learned to be very discreet on others. I would also have built an even stronger network outside his range.

But my understanding of managerial incompetence, disguised by likability grew by leaps and bounds. Like anyone else, I like nice guys. But now, I very quickly look beyond their friendliness to their competence. Distinguishing between likability and competence is exceptionally clarifying. It helps us make more effective decisions regarding our relationship and our career. And it also saves us a lot of time.

Flickr photo: gunnisal

True Data About Big Data

The data scientist may well be the “sexiest job of the 21^st century” say Davenport and Patil in a recent HBR article. They define the data scientist as an information technologist who can“coax treasure out of messy, unstructured data.” But that too easily leads us to think in IT categories.

Davenport and Patil are correct that the data scientist may well be the “sexiest job of the 21^st century,” but they need a different, more visual way to understand the profession.

Jessica Chastain’s Maya, the CIA’s pro in Zero Dark Thirty, is not an obvious data person, but she’s a near perfect model of the data scientist. The movie narrative refers to the origination and development of data, but focuses primarily on data interpretation and organizational communication. The movie is a piece of luck for understanding a “hot” profession.

History of the career
Data science as a career is not new and it is not fundamentally digital. The CIA has been developing and using data scientists since WWII. The true big data professionals can come from a current, existing career model.

The competencies
The competencies of data scientist can be framed into three buckets: big data, interpretation and communication. That’s a single, somewhat hard science (data acquisition and development), and two soft sciences.

The first bucket, big data, takes a number of forms today: digital troves, major enterprise systems, including the nanotechnologies and even gene therapies. Though the “old” CIA operatives used different data tools than today’s CIA, the problems and underlying processes of data origination and development are all analogous. Today’s digital tools are enablers, facilitators, accelerators and magnifiers of human capability. They don’t replace the human component that was so obvious in the early CIA professionals. In their excitement over the digital tools, many stumble over “data” and forget about the human component, much to their detriment.

So I remind you that machines don’t make the essential, important connections among data and they don’t create information. That’s the job of humans. And that’s what the software architect, Grady Booch, had in mind when he uttered that famous phrase about fools and tools: A fool with a tool is still a fool. This blog, at the least, is a warning not to get sidetracked by a vision about ties between big data and information technology. It was such failure that led to Target’s faux pas of sending baby coupons to a teenager who hadn’t yet told her parents she was pregnant. And it was the same failure that brought about a 1,000 point crash of the Dow Jones in May 2010, and that also brought the financial crisis when hedge funds failed all over the world.

That second bucket, interpretation, is about theorizing and inferential and associative thinking, a major problem in nearly every academic discipline, from the humanities, to the social sciences and the biological and physical sciences. It’s exemplary, for example, that my friends in statistics say that drawing inferences is profoundly plagued with error—and that you need graduate work and a supervisory experience to be able to do at least a half-assed job. It’s just as obvious in social psychology, rhetoric and even physics.

Furthermore, interpretation will require a knowledge base about a given subject such as health care or consumer products to succeed in associative thinking. A labor economist, for example, has to draw inferences and conclusions from his knowledge of the labor context as well as from the economic data. Ever wonder why economists disagree so much? It’s rarely about data origination and development, but almost inevitably about the inferences, associations and conclusions drawn from the data. A social scientist has to draw his conclusions from the various, almost infinitesimal forms of social data applied to human behavior. Thus, as a social scientist and executive coach, I’m always warning managers about this problem. My warnings usually surface nicely as “How else can you interpret John’s behavior?” And I force them to dig—warning them that even when we’re through, our interpretation of the motivational data may be quite wrong. In short, the gap between data and interpretation is a huge black hole not readily demystified.

Interpretation is unique not only because of its thinking requirements, but also because of the required understanding of a given context. Inferential thinking is a really tough discipline and intelligent execs won’t want the conclusions of a mere info technologist impacting the company’s business development, marketing, strategy or sales.

The third bucket, communication, may well be the most difficult and most misunderstood of the three competencies. Candidly, it’s the Achilles heel of far too many business people. It shouldn’t be a surprise that there’s a lot of data (here goes my interpretation) suggesting that as a result of our digital infatuation, communication competencies are getting worse, not better. There’s no doubt that there is a vast scope for data scientists to boost productivity in health and education if only those sectors were more open to change. There’s extensive data with solid policy implications if you could get legislators to accept and act upon the conclusions. When you watch Zero Dark Thirty, observe the hoops that Maya had to go through to get governmental executives to believe in her conclusions. Her campaign of communication hit pothole after pothole before she had success. The technological demands of interpersonal and organizational communication are at a level rarely experienced in human history.

So pay close attention to a correct caveat added by Davenport and Patil’s article in which they write that though hacking seems to be having its heyday, this may be less true in five years’ time, when many more people will have the title “data scientist” on their business cards. More enduring will be the need for data scientists to communicate in language that all their stakeholders understand—and to demonstrate the special skills involved in storytelling with data, whether verbally, visually, or—ideally—both. So yeah, the gap between interpretation and communication is problematic. And frustratingly, the relationship between communication and organizational buy-in is still another huge black hole.

The education of a data scientist
It’ll take smarts, well-rounded education and experience to develop the competencies. No two-year wonder from the local tech school can muster the kind of education needed. Thus the CIA, which has a storied data science history dating back to WWII, is looking for the following types of people:

• potential data scientists with background in economic, mathematical and modeling methodologies
• economists who can track foreign financial activities
• intelligence collectors who can provide colleagues the data to understand international and cultural behaviors and actions
• medical and health analysts (physicians) who analyze and assess global health issues
• military analysts who stay on top of threats from foreign military and technical developments
• psychiatric analysts who can tap their expertise to study the health of foreign officials and assess the psychological and social factors that influence world events

The reason that the CIA has such a diversity of need is that each discipline has its own substance, but especially its own way of thinking. IT people, for example, tend to be very concrete, linear thinkers. That’s what IT wants and needs. Marketing people tend to be concrete, but random, creative thinkers. Economists tend to be abstract, but fairly sequential thinkers. And the military, in contrast, is filled with people who are concrete and sequential but not especially creative. One reason General Petraeus, for example, had so much success and got so much attention is that his thinking styles are atypical for the military, making possible creative strategies for the management of Middle Eastern conflict.

The data scientist, as its own discipline, requires a highly adaptable person with superb skills in analysis, concrete and sequential thinking, yet also oriented to the big-picture abstract and creatively random patterns. Even more importantly, the profession requires phenomenal interpersonal and presentational communication skills. Creativity and innovation go nowhere without the ability to sell and get buy-in.

Davenport and Patil got it right: the data scientist is the sexiest job of the 21^stcentury. But visualize Maya if you really want to understand the profession—and its glamor.

Relevant reading:

• Thomas H. Davenport and D.J. Patil, DataScientist: The Sexiest Job of the 21^stCentury. Harvard Business Review, October 2012.
• For the perils of big data: Emanuel Derman, Models.Behaving.Badly.: Why Confusing Illusion with Reality Can Lead to Disaster, on Wall Street and in Life. (New York: Free Press), 2011.
• Many academic disciplines address inferential thinking, but this is one of the best: Nisbett and Ross, Human Inference: Strategies and Shortcomings of Social Judgment.(Englewood Cliffs: Prentice-Hall), 1980.
• Communication and rhetorical writers have failed to keep up with the needs of technology and organizational life. Sadly, even the new book by Groysberg and Slind, Talk, Inc. (Cambridge: Harvard Business School Publishing, 2012) fails to do little more than put traditional communication categories into the work setting. It lacks an understanding of how language works, the use of forms, or an understanding of how to create rhetorical impacts upon individual mindsets, all needed for today’s technologies.

Flickr photo: The next list

Five Myths About the Brain

Here’s a book that kills off all the brain mythology, shows us how to build a “super brain,” addresses Alzheimer’s and speaks truth to Stupid, all in one fell swoop. Rudy Tanzi and Deepak Chopra’s new book, Super Brain, belongs on every desk. Tanzi, Harvard’s Professor of Neurology and director of the Genetics and Aging Unit at Mass General is joined by the well-known Chopra in a smart manual for relating to your brain in a new way.

The prevailing wisdom is that an individual’s intelligence, his or her brainpower, is a stable, static attribute that cannot grow or be developed. It’s all a matter of genetics and heredity—a roll of the dice.  And that, we are told, is the key reason that people plateau in their jobs and professions, and lose out to the more intelligent. NONSENSE!!   

Tanzi and Chopra challenge conventional wisdom and a lot more with their new book. Challenging five major brain myths, they point out how the new, new neuroscience offers a phenomenal world for both our health and business organizations. Although the first myth is only indirectly related to business, the rest have fairly direct relationships with productivity, development and career success. Here then, are the five major myths, along with personal implications growing out of the research.

Myth 1. The injured brain cannot heal itself. Odd that although science has known for several hundred years that nerves outside the brain could regenerate themselves, scientists could not believe that nerves (neurons) inside the brain could regenerate. In the 1770s, Cruikshank found that peripheral nerves could regenerate. If you cut your finger and lose a sense of feeling, you know that the numbness will go away. Nerve regeneration has taken place. We now know as a result of battlefield injuries that the brain can remap and remodel its connections. Not as robustly as peripheral nerves, but still it’s very much in the realm of possibility. Tanzi has found that such neuroplasticity (the regeneration of nerves) can be triggered even in early Alzheimer’s destruction of brain cells.

Myth 2. The brain’s hardwiring cannot be changed. Today’s research reveals that though the brain has circuitry, the bottom line is that it has no “wires”; the circuits are made of living tissue. Brains are incredibly resilient. Neuroplasticity makes development of thoughts, emotions and actions possible in any direction. Extensive research supports rejects the notion of hardwiring. Stroke patients, for example, are not stuck with the damage caused by a broken blood vessel or clot. Indeed, neighboring cells can compensate, maintaining neural circuits.

The idea of hardwiring is used both negatively and positively in business. Negatively, managers may refer to a person as hardwired and unable to change or adapt to differing needs. Positively, the term is used to refer to the expertise of an individual. A manager may say, for example, that John’s analytical skills are hardwired and he can be counted on for excellent problem solving. All hardwiring can be changed. It’s just a matter of learning.

Myth 3. Aging in the brain is inevitable and irreversible. The conventional knowledge is that you can’t teach an old dog new tricks. Systematic research puts a new look on aging. As the authors clearly state, no single aspect of aging is inevitable. For any function, mental or physical, you can find people who improve over time. There are ninety-year-old stockbrokers who conduct complex transactions with memories that have improved over time.

Getting lazy and apathetic about learning inevitably results in adhering to the norm. But fortunately, conscious choices can be made. And. . . you can decide to follow a learning curve upward.

The research on adult learning finds that most professionals stop significant learning about six years into their career. That means if you’re not learning and growing, you’ve got a learning problem. Not an aging problem.

Myth 4. The millions of brain cells lost every day cannot be replaced. Instead, we now know that several thousand new nerve cells are born in the brain every day, particularly in the area of the short-term memory. Physical exercise and stimulating environments stimulate the growth of new cells. We can safely discard the myth of brain cell loss. Even alcohol use only kills a minimal number of cells.

Positively, this is a major argument for regular project, job and even career change. Most professionals have their job well in hand within two to three years. At that point, smart professionals move on, learn and grow. Death is staying in the same job long-term. 

Myth 5. The primitive emotions of fear, anger, jealousy, etc., overrule the rational brain. Undoubtedly instinctive reactions such as fear and desire are instinctively programmed from the womb. Some psychologists find the argument that certain people are programmed to become antisocial, criminals, rage-aholics, just as others are programmed for anxiety, depression and schizophrenia.

But those arguments overlook the fact that the brain is multi-dimensional. The experiences which will dominate are neither automatic nor genetically programmed. A proclivity for one experience or another is not determinative. And we don’t need to be ruled by negative experiences or negative emotions.

Workers, for example, who are debilitated by feedback can learn to manage feedback for their own good. Even Freud believed that civilization depends on our ability to override primitive urges and drives so that higher values can prevail. As Tanzi and Chopra argue so well, freedom of choice is not prohibited by preset programming.

There’s a lot of the victim in many professionals, often tied to these myths. But that’s a nutty, destructive approach to reality. Much of our perceived limitations have been shown to be wrong-headed. And now there’s systematic, thorough scientific research detailing those myths. And exploding them.

Deepak Chopra and Rudolph Tanzi, Super Brain. (Harmony Books: New York), 2012.

Flickr photo: biology corner 

 

 

Adam Lanza: Evil or Just Crazy?

Those of us in business, who sometimes work on the edge of abnormal psychology, say coaches and HR staff—occasionally find ourselves trying to figure out whether a certain exec is narcissistic or just a plain asshole.  Of course, the fact is that the exec may be both or neither. But whatever we decide, it should determine whether we attempt to “fix” the exec, or just terminate him. (Executive narcissists, more often than not, are male.)

The massacre of innocents at Sandy Hook Elementary School, however, demands a far more difficult assessment. Was Adam Lanza evil, or just crazy? Most of us lack the background to make that call. So I was delighted (delighted may not be the best term, but I’m intrigued that someone finally addressed the issue) to see that Martin Seligman made the call for me in today’s Washington Post, detailed the distinctions between crazy and evil and also made the requisite recommendations for dealing with such folk. (The article is a “keeper” for future reference. It masterfully defines and distinguishes between crazy and evil.)

Seligman comes to the decision with impeccable credentials: former president of the American Psychological association, professor of psychology at the University of Pennsylvania, and author of the eminently useful book “What You Can Change and What You Can’t.” Though the Post article failed to mention, Seligman is also the primary go-to researcher and teacher in the field of “Positive Psychology.”

Just crazy
As Seligman writes, “crazy” refers to a person with delusions, hallucinations and bizarre beliefs. So Will McAvoy, in Sorkin’s Newsroom, refers to certain Tea Party members with their bizarre beliefs—much to my amusement--as “crazy.” And then elucidates their belief structure. More psychologically significant, crazy people are labeled with such technical terms as “insane,” “psychotic” or “schizophrenic.” Obviously, “crazy” sometimes lacks precision, and is applied in a lot of different ways in both our popular and scientific culture. But it’s clearly pejorative.

Plainly evil
“Evil” is not a term widely used other than in religious circles. However, I was surprised to notice that David Brooks referred to both sinfulness and evil in a column “When the good do bad.” But Seligman, wise soul that he is, thankfully takes on “evil.” He points out that “evil” is as ancient a label as “crazy.”  

Its hallmark is a narrow moral circle in which other people are objects of moral indifference or hatred, people deemed not to deserve to live. In this usage, the label evil is not mysterious nor derived from a belief in “the devil.” Rather, it is clarifying; it denotes people inclined to be violent and to put many other people at risk.

We know evil when we see it: “mean,” “violent,” “full of hate,” “selfish,” “grandiose,” “without a conscience” and “bullying” all signal evil. Whatever mental illness he may have had, Adam Lanza died and, most likely, lived at the extreme end of evil.

Crazy and evil, like narcissistic and asshole, are separable: a person can be crazy, evil, neither or both. What’s clear, though, is that the American public and policymakers need to do their homework before they decide on restraining or rehabilitating people. And, as Seligman says, the past record and future promise are dismal. And, frankly, the policy makers need to get on with that research and those decisions, otherwise. . . .

FYI: Since modern science has shield away from “classifying and unpacking craziness,” you won’t find the distinction between evil and crazy in the DSM-IV (Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV). Nor, for that matter, will you find a clear-cut diagnosis of the NRA’s Wayne LaPierre, but I won’t have difficulty labeling him and his ilk.

Martin E. P. Seligman, Evil vs. crazy: What’s in the minds of mass murderers?  The Washington Post, January 4, 2013.

Flickr photo: God's world USA 

Expletives from the Edge of the "Cliff"

I used to have a middle-class, religious allergy to expletives, but gradually I’ve lost it. There are a number of reasons: expletives can be liberating, they can work well, they get another’s attention, and they have a fascinating history. Yeah, I know. Some would interpret the return to colorful expletives as indicative of the demise of civility. I suppose I could blame my dad—one of those earthy Southern Baptists. But I don’t take expletives too seriously. Although the almost inane use of the F---- word in some movies is boring. Still, in a flush of verbiage, I recently clarified a number of personal expletives in an often-read blog.

 

So I was amused by John Boehner’s insult of Harry Reid, reported in both Politico and the Washington Post.  As Politico first noted, House Speaker John Boehner couldn’t hold back when he spotted Senate Majority Leader Harry Reid in the White House lobby last Friday.

It was only a few days before the nation would go over the fiscal cliff, no bipartisan agreement was in sight, and Reid had just publicly accused Boehner of running a “dictatorship” in the House and caring more about holding on his gavel than striking a deal.

“Go f— yourself,” Boehner sniped as he pointed his finger at Reid, according to multiple sources present.

Reid, a bit startled, replied: “What are you talking about?”

Boehner repeated: “Go f— yourself.”

Alexandra Petri commented in her Post article regarding the same episode reported that she missed the euphemistic contortions of pre-internet life that would result in a statement, like, “The speaker of the House told the Senate majority leader that he hoped he would take more pleasure than usual in his own company.”

Of course, political insults have always been with us. My favorite is one of the canonical examples Petri refers to: “Mr. Wilkes, I don’t know whether you’ll die on the gallows or of the pox!”  “That depends, my lord, on whether I embrace your principles your principles or your mistress.”

Then too, the not so holy scripture is rife with insults. The Old Testament prophets were scathing—at least in the original Hebrew. And Paul the apostle on occasion used the term me genito which translates easily into “hell no.” And when he attempts even more colorful scatology, the expurgated version translates “shit” into a milder “dung.” My Kentucky grandmother, who used to reward my cute ways with “you little shit,” would enjoy that.

Enjoy!