Courage and Mental Toughness

different risk tolerances

My awesome wife and I respond differently to uncertainty.  This artist brilliantly illustrates our two reactions in the picture.  I easily juggle multiple plans.  My wife looks longer before she commits, but she does not look back.

We’re a great team.  In uncertainty, I grab opportunity and nimbly dodge risks.  She avoids taking foolish risks and perseveres to make opportunity into reality.  We love, trust, and encourage one another as we seek wisdom.

Wisdom enables us to adopt a measured, thoughtful, appropriate response to uncertainty.  Seeking wisdom takes courage.  Over the past week, I’ve been thinking a lot about courage.  Here is my working definition:

Courage is the worldview that strengthens us so we face the danger, fear, and sudden, unexpected changes we encounter in life, in activities, or in our surroundings with poise, confidence, and resolution.

Courage is not stupidity.  Rather, it is the fear-killer that prepares my mind for action so that I can do what I know I must do, despite physical, moral, or social opposition.

Do we need courage to win?  I think so.  Our world is certainly uncertain.  We know that storms will come.  Courage helps us ride out storms without losing our love of the sea.  Without courage, we choose to stay on the sidelines and lose the chance to win the victor’s prize.

Can we measure courage?  Possibly.  How about mental toughness?  Quantitative analysis of mental toughness is increasingly measurable.  Mentally tough individuals act according to their convictions despite opposition.

Mental toughness doesn’t mean inflexible rigidity.  Quite the opposite.  It measures flexibility and responsiveness as much as strength and resiliency.  Mental toughness means developing internal and external consistency before, during, and after the moment of truth.

If we need courage to win and if we can measure courage by mental toughness, then we have a victometric.  When the pressure is on, who consistently shows courage and mental toughness?  Ceteris paribus, they have an edge.

Bonus: this 3 page article has helped me bring these principles into my everyday work.  The Pursuit of Courage, Judgment, and Luck


InMaps from LinkedInLabs – Data Scientists in action

“It ain’t what you know, it’s who you know.”  Agree?  Disagree?  

However nuanced your response, I believe we all know that relationships matter.  We are creatures that love to communicate.  We need others.  We don’t generally achieve greatness in isolation.  

Systems are dynamic, purposeful orchestrations of people, processes, and platforms.  Systems are usually  multi-dimensional beasties that don’t like having their picture taken.  We illustrate systems using “views” that emphasize its different aspects.  Like the blind men and the elephant, we can tend to characterize the system according to the view that we most directly use.  A set of views enable us to build up a wholistic understanding of the system by looking at it from different perspectives.  

The organizational chart is a hugely important view of your system.  Your organizational chart focuses on the People view of the system and highlights authority and influence structures.  As a time-tested and well-loved view, the organizational chart works very well in an established hierarchical context.  

But, what if your relational network doesn’t fit nicely into the abstract boxes and lines of an organizational chart?  What if you have relationships that cross professional, corporate, and physical boundaries?  How do you find a view of your system of relationships?  How do you find people with influence?  How do you keep important relationships from dropping off the map?

My answer (right now) is InMaps.

InMaps is provided by the LinkedIn Analytics team. You can check out the official video, brought to you by DJ Patil, LinkedIn’s chief scientist (one of the creators of the data scientist role) and published on the LinkedIn Analytics blog.   InMaps provides a powerful illustration of the People view.  

InMaps visualizes all of the people that you know, connecting them to all of the people that you mutually know.  This creates your relational map.  As LinkedIn calls it, this is your professional universe.  

I use this view of my relationships to:

1. Monitor change in my professional network
2. Understand the “people” environment
3. Focus networking and team-building
4. Identify and navigate opportunities
5. Keep in touch with old friends

Here is a recent screenshot of my map:
I can click on anyone in this map, and view the network of people connections that we share.  In this example, I have clicked on my brother, David.  
David is a hugely talented graphic artist and web designer.  Check out his work and get to know him at SMRVL.  
As you can see, David and I have a lot of overlap within the blue network.  We share a few family members in my purple network.  Other than that, David doesn’t know too many folks in my other networks.  
David was kind enough to share his map with me.  (One major — though necessary — limitation is that you can only see your own map.)  Here is his full map.  

Here’s what David says about his map.  “I like it because it’s a brilliant at-a-glance snapshot of my web of relationships. I can, at a glance, see where I have relational density … the two big masses on my map are both social networks.”

This looks great.  But in order to get anywhere, we need to know how to read this map.  Here are some questions that I ask my map in order to find meaningful stories.

1. How many “lobes” do I have?  InMaps will automatically color-code clusters that it thinks are distinct networks.  The more good-sized lobes that I have on my map, the more valuable I think my network is.  For me, this highlights a relational victometric – Number of Significant Bridged Networks.  I want to bridge 3 or more significant professional networks in order to minimize risk as an independent contractor.  Connections to multiple networks represent opportunities to make things happen.

2. Who are the “foci”?  Who are the people who are well-connected within communities?  They are the people who know people.  When I’m seeking to get better connected with a group, I’ll try to start with these gregarious types.  

3. Who are the “edges”?  Who are the people who seem to be minimally connected?  Here, I might read more closely.  If the person is someone whom I know to be outgoing, they may connect me with new networks to explore.  Many people simply don’t use LinkedIn that much.  The loosely connected person may be someone who would be open to learning more about what their tools can do for them.

How do you get your own map?  Building your map is easy.  If you have a LinkedIn account, go to http://inmaps.linkedinlabs.com/ and they’ll walk you through the steps from there.  You’ll need to decide how you want to share access to your data.  I chose to share for a month, and have just continued to allow access.
A couple of extra factoids:
  • Originally, InMaps needed you to have at least 50 connections in order to build your map.  I’m not sure if that’s still true.  If it is and you have less than 50 people in your relational network, get busy!
  • InMaps are updated every Sunday.  If you add to your network, don’t expect them to show up until sometime on Monday.  I usually capture a screenshot every week.  I dump these into PowerPoint.  As I play through the slides chronologically, I can more easily highlight changes and realignments in my network.
  • On the splash screen, hit “Next Map” in the lower-right corner to see different shapes of networks.
  • InMaps uses Hadoop/Pig , Ruby , VoldemortJava and Processing for the heavy backend maps calculation and image processing.
  • For more, check out the very informative InMaps FAQ.



Am I a data scientist?

I love LinkedIn.  LinkedIn gives me the power to explore the “long tail” of professional disciplines.  It’s amazing how many different job titles and descriptions you can find on LinkedIn.  Here is an example of the titular diversity that I find when browsing my professional network:

  • Captain, Ayanox LLC
  • Motorcycle Mechanic
  • Chief Innovation Officer
  • Chief Scientist
  • Arborist Representative
  • Director of Product Management
  • Forensic Consultant
  • Display Coordinator – Anthropologie
  • Software Craftsman
  • Home Manager
  • Driver Service Provider – UPS
  • Super Hero
Data Scientist is a job title has created some buzz recently.  Buzz usually indicates that there is a need that is not being met, currently.  The Wall Street Journal’s Marketwatch reported a month ago that only 1 out of 3 companies are making effective use of the data they have — worldwide.  Consider how much data is being created automatically by the “internet of things”, and you have an epidemic of ignorance.  Data science seeks to transform this ignorance into insight.
This post by Chris Taylor (3 hours old at time of authoring) suggests that the Data Scientist is the Career of the Future. Since this is the Systems-Illustrated blog, I must plug the solid EMC infographic that makes up the majority of the Chris’ post. Kudos!
DJ Patil, the guy who coined the term “data scientist” while he was working at LinkedIn, has written a great (free!) little eBook on Building Data Science Teams that explains who a data scientist is and what a data scientist does for a business.  Great read!  
In a nutshell, a data scientist may:
  • Instrument the tools that collect and cleanse data
  • Investigate the data to find patterns and stories
  • Illustrate the stories so that they can be shared
According to Mr. Patil, a data scientist is characterized by:
  • Technical expertise: the best data scientists typically have deep expertise in some scientific discipline.
  • Curiosity: a desire to go beneath the surface and discover and distill a problem down into a very clear set of hypotheses that can be tested.
  • Storytelling: the ability to use data to tell a story and to be able to communicate it effectively.
  • Cleverness: the ability to look at a problem in different, creative ways.
My strengths are certainly weighted toward curiosity, storytelling, and cleverness.  I would consider my expertise in the systems engineering discipline to be deep, but not necessarily scientific.  That said, my entire professional career has been built upon real-world problem solving using a disciplined variation of the scientific method.  So, maybe I’m closer than I think.
Most people distinguish between a Business Intelligence analyst and a Data Scientist.  What am I?  How does one quantitatively assess this question and make a data-driven decision about whether to pursue the career of the future?
  • Education: 31% of data scientists have a Master’s degree.  I have a Master of Science in Systems Engineering from the Johns Hopkins University.  Only 12% of Business Intelligence (BI) professionals have a post-baccalaureate degree.  Edge: Data Scientist. 
  • Major: 10% of data scientists studied Business as a major, compared with 37% of Business Intelligence (BI) professionals.  I have studied Business Administration (AA), Management Science & Statistics (BS), and Systems Engineering (MS).  Slight Edge: Data Scientist.
  • Comfort with Incomplete Data: Big Data-oriented Data Scientists feel comfortable working with incomplete datasets, and enjoy the challenge of cleansing and exploring data.  This totally resonates.  Recently, I have been very happily snorkeling through years of usage data for my client’s data warehouse in order to understand the impact of a recent upgrade and how we can drive user adoption.  I am pushing the envelope for my team and have had to work through several defects in the data in order to get to the point where I believe the stories it is telling me.  Strong Edge: Data Scientist.
  • Involvement across the Decision & Data Lifecycle: 30-40% of data scientists have significant involvement in the entire process of acquiring data, parsing data, filtering data, mining data, applying algorithms to data, visualizing data, storytelling with data, dynamically interacting with data, and making business decisions based on data.  Again, my recent experience with user adoption data includes all of these.  I am very excited that we are driving forward in some truly mission-oriented directions with greater clarity and confidence bolstered by my analysis.  Edge: Data Scientist.
I think I could evolve a bit and successfully perform the job of a data scientist.  It would certainly be an exciting challenge.  That said, I’m not sure the name “data scientist” truly captures the essence of decision science.  Data science emphasizes the rigorous discipline and analytic techniques.  These are necessary.  
However, I feel that what I do is closer to medicine than science.  Medicine is the science and art of healing (Wikipedia, Medicine).  Medicine directly applies life science to real-world wellness challenges.  
I find myself most excited and fulfilled when I am able to observe an organizational risk or opportunity, diagnose the problem using insights from data that might have been ignored or overlooked, tell the story, and watch the organization change into something that is stronger and more healthy.  
Data Physician, Change Agent, or (my favorite) Opportunity Navigator seems to come a little closer to describing the role that I find myself playing.  As more organizations become aware of the role that their data plays in their health, I believe that we will see an increasing need for caring, creative, analytical professionals who can transform lifetimes of experience into moments of truth for our clients.  Bring on the data scientists!

A new personal victometric – Return on Luck (ROL)

Good To Great (Jim Collins) has profoundly influenced my life.  The Hedgehog Concept has focused the way I view my role as a husband and father, the way that I measure my success or failure in life, and the way that I approach each day with its new uncertainty.  In many ways, it has become a controlling model for me.  I have described this in detail previously (The Meaning of Life – Illustrated).  

I’m also a huge Uncertainty junkie.  One of my favorite and most illuminating reads from 2011 was The Black Swan (Nassim Nicholas Taleb).  When pursuing my Master of Science in Systems Engineering, I was intrigued by the fact that almost every class had a significant segment of time devoted to Risk Management.  Reading The Failure of Risk Management and How to Measure Anything by Douglas Hubbard fueled my growing passion data-driven decision-making and the role of quantitative analytics in everyday decisions.
Imagine my unbounded delight when Amazon.com informed me that the latest book from Jim Collins was on the subject of Uncertainty, Chaos, and Luck!!!  I voraciously read Great by Choice from cover-to-cover today.  I was not disappointed.  In typical fashion, Collins reduces an almost obscene amount of raw data (read the book to see how much) to a few pithy concepts.  
One of these is the concept of Return on Luck (ROL).  Return on Luck (ROL) is simply the return (gain or loss) that you are able to create in a luck situation (good or bad luck).  Put another way, it is the value that you create from a Black Swan … an Unknown Unknown.
In my ongoing quest for ways to ensure that I don’t waste my life, I’m always looking for things that I can use as personal victometrics.  Last time, I defined victometrics as measurable elements that objectively define victory or defeat in a competitive scenario. You could also call them “victory points”.  Return on Luck (ROL) is a good one to add to my personal scoreboard. 

Why?  Well, a quick review of this past year reveals that when I make decisions designed to increase my Return on Luck (ROL), I find that I choose wisely before the storm, increase the chance of survival during the storm, and gain proper perspective after the storm.  

Storms have come in this past year.  My job changed overnight in Spring 2011.  My personal relationships changed almost overnight in Summer 2011.  A work deliverable had a very visible an unexpected visit from Murphy in Fall 2011, despite due precautions.  In all of these situations, I have emerged more optimistic and excited about what is coming next.  

I know that storms will come.  I just don’t know all of the unimportant stuff like what storm will come when or where and how.  Seeking to increase my Return on Luck helps create sea-worthy life-systems that are designed to weather the inevitable storms.  

I believe that there is something in this understanding of uncertainty that is fundamental to that thing I call the American Dream.  I believe that it is possible to build something truly great (although my goal differs from most people’s definition of greatness).  I am seeking to hear “Well Done” from the only One whose opinion really matters to me.  I am seeking to excellently steward the time, talent, trust, and treasure that has been entrusted to me. 

I believe that America has been the land of the Free and the home of the Brave and a land of Opportunity because it has been built by people who didn’t depend on luck, but made the most of what luck they were given by Providence.  I believe America’s future greatness will depend upon her citizens investing their own empirical creativity, productive paranoia, and fanatic discipline to achieve their most selfless ambitions.

I don’t think I can say it better than Jim Collins and Morten Hansen do in their Epilogue (quoted):

“We sense a dangerous disease infecting our modern culture and eroding hope: an increasingly prevalent view that greatness owes more to circumstance, even luck, than to action and discipline – that what happens to us matters more than what we do … taken as an entire philosophy, applied more broadly to human endeavor, it’s a deeply debilitating life perspective, one that we can’t imagine wanting to teach young people.

Do we really believe that our actions count for little, that those who create something great are merely lucky, that our circumstances imprison us?  Do we want to build a society and culture that encourage us to believe that we aren’t responsible for our choices and accountable for our performance?”

No and no.