Monday, April 20, 2015

Principles of Great Performance

I enjoy having the opportunity to discuss and explore career options/goals with a number of people. I captured some of those learnings in my career decisions blog. But, sometimes the discussion is not at all about decisions, and is more focused on career progression. Said another way, people ask, "How do I get ahead in my career?"

I tend to think that getting ahead is a matter of 3 items:

1) Exceeding your business targets consistently. (whatever they may be)

2) Performing tasks beyond the scope of #1 (pro-actively) and exceeding expectations on those too.
3) Distinguishing yourself as an A-player, in any and all endeavors.

I think most people typically concern themselves with #1, ignoring the other two. That may be acceptable in the short term, but will likely catch up to someone that is interested in career growth, over time.

To help you think about personal development, I want to share my view of the principles of great performance. This is primarily relates to #3 above. You've seen many of these before. These are the kind of things that probably don't fit in a performance review per se, but they do directly relate to your ability to have an impact on any business. I don't take credit for penning all of these, but I have found them to be good guideposts for success. Lastly, no one can do all of these well, But then again, aspiring to achieve them is half the battle...

Principles of Great Performance

1) Study and read often. Be the smartest person in the room on your topic. Have a point of view, grounded in facts.

2) Be proactive, not reactive, in all matters.

3) Leverage your management and executive team for timely escalation of critical topics. On the other hand, don't cry wolf.

4) Differentiate yourself: Be responsive, Be decisive, Be passionate for the business, Have a bias for action, Understand details and be organized, Prioritize, Contribute to the overall team. Don't just go through the motions, Show a sense of urgency. Be an active participant.

5) Be on time and prepared for all interactions. Whether internal or external, be prepared and ready to contribute in a professional manner.

6) Demonstrate a capability for effective "straight talk," so that tough or controversial problems are dealt with directly, with the relevant party, rather than indirectly or not at all. The ability should be demonstrated inside and outside of your company.

7) Communicate swiftly and concisely. Expand into detail when prompted or when needed.
 Brevity in communication leads to greater impact.

8) Develop productive relationships across the company, leveraging them to solve problems at the lowest level possible in the organization.

9) Complete assigned tasks in a timely manner, without requiring reminders.

10) Produce grammatically well written documents, notes and presentations with simple and clear communication. Same applies to verbal communications. Strive for clarity of all messages.

11) Take risks, make mistakes, learn from them, and strive to not repeat them.

12) Accept accountability and responsibility for all actions (committed and omitted).

13) Measure everything important and know where you stand versus those measurements. Measure goals on a consistent and excuse-free basis.

14) Exude optimism and confidence, but not hubris.

15) Lead, don't manage.

16) Have an Edge, in everything you do. If you don't have the Edge, someone else does.

17) If you are a manager, it's not about 'You', it's about 'Them'.

18) Develop a superior external network.

19) Work with an enthusiasm unknown to mankind.

20) It's better to be a pirate, than join the Navy.

21) Plan, Prepare, Plan, Prepare...repeat.

22) Master the art of motivating others.

23) Be a contrarian, with facts to support your position.

24) Develop a core competence or skill. Become 'known' for it.

25) Panic is not a plan. Don’t panic. Just plan and then improvise.

26) Confidence is a learned skill. It’s developed through practice.

27) Avoid the ABC’s of personal and business decay: Arrogance, Bureaucracy, and complacency.

28) Share your expertise actively with modern social tools: blogging, twitter, connections, etc.

29) Make your family and personal relationships a priority, despite your career goals and the demands of the job.

Tuesday, March 24, 2015

Why Actuaries Will Become Data Scientists

Insurance today takes many forms: life insurance, health insurance, property insurance, casualty insurance (liability insurance for negligent acts), marine insurance, and catastrophe insurance (covering perils such as earthquakes, floods, windstorms, and terrorism). You name it, you can probably insure it. Prior to the insurance innovation that led to the varieties just mentioned, a branch of management science was established that served as the root of insurance: actuarial science. Actuaries are in the business of assessing risk and uncertainty. Said a different way, they value and assess financial impact, of a variety of risks. But that is much easier said than done. A variety of inputs provide the information an actuary needs for better decision-making.

The Institute and Faculty of Actuaries (IFoA) is the only professional organization in the United Kingdom dedicated to educating, regulating, and generally advocating for actuaries worldwide. If Ben Franklin is the father of insurance, Chris Lewin, associated with IFoA, is probably the original actuarial historian. He has published regularly on the topic and is well known in the community for his significant contributions. Lewin states, “An actuary looks at historical data, and then makes appropriate adjustments (subjective, of course).” One of the primary skills of an actuary, therefore, is to make estimates based on the best information available. Even if an actuary uses data to develop an informed judgment, that type of estimate does not seem sufficient in today’s era of big data. There is some- thing about modern-day insurance that has led the industry to believe that informed judgment is good enough. As the quantity and quality of data improves, it will be possible to calculate increasingly accurate estimates based directly on information, negating the need for human judgment and associated biases.


The data era has already begun to spark a new wave of innovation in insurance. Rich access to a variety of data assets, coupled with the ability to analyze and act, enables processes that were not previously possible. This will usher in the era of dynamic risk management and improved approaches for modeling catastrophe risk in the insurance industry. In the case of automobile insurance, the industry commonly refers to this type of insurance as Usage Based Insurance. There are two types of policies under this type of insurance — Pay-As-You-Drive (PAYD) and Pay How You Drive (PHYD). However, dynamic risk management can apply well beyond the scope of driving and automobile insurance.

Dynamic risk management is an accelerated form of actuarial science. Recall that actuarial science is about collecting all pertinent data, using models and expertise to factor risk, and then making a decision. Dynamic risk manage- ment entails real-time decision-making based on a stream of data. Let’s explore the two models with an example of car insurance for a 22-year-old female:

Actuarial insurance: Collect all the data available for the 22 year old — her driving history, vehicle type, location, criminal history, etc. Merge that data with demographic data for her age, gender, location, and work status. Leverage methods like probability, mortality, and compound interest to estimate benefits and obligations. Then, offer a policy to the woman, based on these factors.

Dynamic risk management: Install a sensor in her car and tell her to go about her normal life. Collect mileage, time of day she drives, how far she drives, acceleration/deceleration, and the locations that she drives to. When she is driving, monitor the motion of the vehicle. Said another way, this is an on-board monitor, constantly pricing her insurance policy based on her personal driving behavior. If she drives well, her next premium may be lower. The policy is tailor-made for her and is based on actual data, as opposed to estimates.

There is now increased momentum for dynamic risk-management. In March 2011, the European Court of Justice stated that “taking the gender of the insured individual into account as a risk factor in insurance contracts constitutes discrimination.” Since December 2012, insurers operating in Europe are no longer able to charge different premiums on the basis of an insured person’s gender. There are good reasons why insurers might want to use gender as a means of quantifying risk. Men under the age of 30 are almost twice as likely to be involved in a car accident as their female counterparts. Insurers also have empirical evidence to show that the claims they receive for young men are over three times as large as those for women.

Arguments around gender equality have rightly determined that it is unfair to blindly discriminate against young men. Furthermore this debate has highlighted the need for more appropriate metrics for forecasting risk rather than the blunt use of gender. This gap in the market calls for better models and dynamic risk management based on the actual driving ability of the individual. Unfortunately, in the meantime, we are all paying the price as car insurers have increased their premiums across the board.

Currently, many of the large insurance carriers offer some version of dynamic risk management, or pay-as-you-drive insurance for automobiles: Progressive, Allstate, State Farm, Travelers, Esurance, the Hartford, Safeco, and GMAC, to name a few. Most of the insurers market that premiums will cost 20 to 50 percent less for consumers who adopt this approach. The National Association of Insurance Commissioners estimates that 20 percent of insurance plans will have a dynamic approach, by 2018. For the moment, dynamic insurance for automobiles is less than one percent of the market.

Dynamic risk management can apply to any data-centric insurance process, whether a company is leveraging telematics or data points about a consumer in a lending scenario. In the Big Data era, dynamic risk management will become routine.

Insurers could make themselves more popular by recognizing that dynamic risk management could become a means for encouraging behavior change. Rather than offering non-negotiable premiums based on coarse models, the use of big data to assess individual risk would urge those customers to behave more responsibly. In this way, insurance could provide a price signal to nudge customers toward a lower-risk lifestyle. Insurers such as U.S.-based PruHealth have a healthy living rewards program, known as Vitality, which gives points for healthy activities such as regular gym attendance and not smoking. Points earned from the rewards program can also be redeemed for other lifestyle rewards such as cinema tickets or gift certificates.


Big data has the potential to create sophisticated risk models that are focused on individuals, extremely accurate, and capable of being updated in real- time. This is bad news for those hoping to use insurance as a means to justify excessive risk taking, but it is good news for those that want to be rewarded for managing risk more effectively. As more and more individuals opt for dynamic risk management, society will benefit from safer roads and smaller healthcare bills.

This post is adapted from the book, Big Data Revolution: What farmers, doctors, and insurance agents teach us about discovering big data patterns, Wiley, 2015. Find more on the web at

Tuesday, February 17, 2015

Even Doctors Will Be Data Scientists

We all know how it works. You walk into a doctor’s office complaining about some pain in your leg or otherwise. They take your temperature, get you on the scale, check your blood pressure, and perhaps even get out the rubber hammer. These measurements are simply snapshots at one particular instant in time and may be subject to error. This limited dataset fails to capture temporal variations or the many other important factors that are required to assess the patient’s health status. After reviewing the few measurements collected, the consultation between the patient and doctor begins. Baased on the rudimentary physical analysis, along with the discussion with the patient, the physician will assert the condition that they believe is present, followed by a recommended treatment.

This approach, which is common throughout the world, is much more based on instinct and gut feeling than a scientific approach to analyzing data. Accordingly, it seems that most decisions are made based on the opinion of the physician instead of a data-proven truth. This type of opinion-based medicine is a problem in both doctor-patient care and in medical research. This is a symptom of a lack of data, as well as years of training physicians to perform without complete data.

The data collected in a typical office visit is only a fraction of the data that could be collected if health were viewed as a data problem. And, if health were redefined as a data problem, physicians would likely need different skills to process and analyze the data.


Vinod Khosla is one of the most successful venture capitalists in the history of Silicon Valley. He was an original founder of Sun Microsystems, and has since gone on to finance a variety of start-up companies as a venture capitalist. While he is not a medical expert, he is a data expert. In his speech at Stanford Medicine X, Khosla highlights three major issues in medicine today:

1) Doctors are human: Doctors, like everyone else, have cognitive limitations. Some are naturally smarter than others or have deeper knowledge about a particular topic. The latter leads to biases in how they think, act, and prescribe. Most shockingly, Khosla cites that doctors often decide on a patient diagnosis in the first 30 seconds of the observation. Said another way, they base their diagnosis on a gut reaction to the symptoms that they can see or are described to them.

2) Opinions dominate medicine: Khosla asserts that medicine is much more based on opinion than data. He cites the Cleveland Clinic Doctors’ Review of Initial Diagnosis study, asserting that Cleveland Clinic doctors disagree with initial diagnoses 11 percent of the time. In 22 percent of cases, minor changes to treatment are recommended. And in a startling 18 percent of cases, major changes to treatment are recommended. As Khosla states, “This means it’s not medical science.”

3) Disagreement is common among physicians: Doctors disagree a lot. It’s so dramatic, that, Khosla states, “whether or not you have surgery is a function of whom you ask.”

Medicine is currently a process of trial and error, coupled with professional opinion.


The Data era in medicine will be defined by a shift from intuition and opinion to data. We can collect more data in a day now than we could in a year not too long ago. Collecting data and applying it to solve healthcare problems will transform the cost and effectiveness of medicine. The question is how quickly we can get there.

Medical schools must evolve as technology advances. Most advancement in medical schools, based on technology, have been focused on utilizing advanced tools and equipment, as opposed to addressing the core knowledge needed by a physician in the data era.

The curriculum for the first two years of medical school varies by school, but it is heavy on the sciences, the human body, and the human condition. This has been typical since the first medical schools in the 1200s. All this time, investment and history, yet the newly minted physician is unprepared for practicing in the data era.

The data era requires an augmentation in curriculum to include key skills required for data-based analysis:


*Data Analysis and Tools

The skills of physicians will necessarily evolve in the data era, and that has to begin in medical schools. This focus will expedite the move away from opinion-based medicine to a future that the ill prefer: prescriptions based on hardened data analysis.


This week, IBM is announcing a set of tools, technology, and processes to bring data science to the masses. Said another way, armed with IBM technology, everyone is a data scientist. We are democratizing the access to data in your organization.

Every organization sees Hadoop as providing an open-source, rapidly evolving platform that is capable of collecting and economically storing a large corpus of data, waiting to be tapped. Yet, most organizations are not yet fully realizing the value of Hadoop due to the lack of skilled data scientists and developers to extract valuable insight. IBM will make everyone a data scientist. We take the first steps this week by:

1) Introducing new modules for In-Hadoop analytics including SQL, Machine Learning, and R.

2) Confirming our commitment to open source with IBM BigInsights Open Platform with Apache Hadoop, to include new innovations like Apache Spark. We are excited to be a founding member of the Open Data Platform.

3) Rolling out expanded data science training for Machine Learning and Apache Spark via BigDataUniversity. Today, over 230,000 professionals and students are being trained at BigDataUniversity and we are on our way to 1 million trained.


We all look forward to how things will be in 15 years. You walk into a doctor’s office, and the physician immediately knows why you are there. In fact, she had discussed some data irregularities that she had spotted at your annual physical exam, six months prior. She doesn’t need to take your temperature, as she receives that data direct from your home every day. You also take your own blood pressure monthly and that is transmitted directly to your physician. Instead, the discussion immediately turns to the possible treatments, along with the probability of success with each one. Recent data from other patients with a similar history and physiology indicate that regular medication will solve the issue 95 percent of the time. With this quick diagnosis, involving no opinions, you are on your way after ten minutes, confident that the problem has been solved. This is medicine in the data era, administered by a physician steeped in mathematics and statistics. In the data era, even doctors become data scientists.

This post is adapted from my book, Big Data Revolution: What farmers, doctors, and insurance agents teach us about discovering big data patterns, Wiley, 2015. Find more on the web at

Sunday, January 25, 2015

Committing to One Percent

Dave Brailsford basked in the glow of the Olympics. The Great Britain cycling team just completed their participation at the 2012 Olympics in London, England, winning 70 percent of the medals in men’s cycling. Reporters probed with aggressive questioning, wanting to understand the silver bullet that led to this success. The irony: There was no silver bullet. In fact, it was the opposite of a silver bullet.

When the Great Britain cycling team, Team Sky, hired coach Dave Brailsford in 2010, the country had never won a Tour De France. In fact, the history of the sport in the country was filled with errors, mishaps, and minimal success. Historically, the team chased fads of success: new equipment, new uniforms, new techniques. But nothing changed the trajectory. Then, Dave Brailsford arrived.

Dave Brailsford fanatically talks about the aggregation of marginal gains. This concept means that by marginally improving each and every aspect of a process, the aggregation of those small gains will lead to large improvements. Brailsford’s goal was simple: one percent. He sought a one-percent improve- ment in every aspect of the cycling team.

Setting out to improve all aspects of a cycling team, the obvious places to start are in areas like nutrition, bike performance, and physical conditioning. After all, improving every meal by one percent promised a path to continued improvement. However, for Brailsford, those enhancements merely scratched the surface. He set out to improve every aspect by one percent. Not only sports massage, but the gels used for sports massage. Not only the bikes, but the grips on the bikes and, more specifically, the tackiness of the grips. He studied not only the physical conditioning, but also the sleep habits and, more specifically, the pillows used. He focused on every aspect: one-percent improvement. It’s that simple.

In 2012, a short two years after Brailsford joined the team, Great Britain won its first Tour De France. Shortly thereafter, the triumph at the Olympics in London occurred. The aggregation of the one-percent gains created superior outcomes.


Coincidentally, there is recent proof of the power of One Percent in the 'Deflategate' saga involving the New England Patriots. In this case, the One Percent improvement in the football led to exponentially differentiated results. See here for the details. For the record, I don't condone exploiting the One Percent outside of the rules or law.


You'll find this story and more like it in Big Data Revolution: What Farmers, Doctors, and Insurance Agents Teach Us About Discovering Big Data Patterns.

Wednesday, December 31, 2014

Thoughts for the New Year

While there is nothing magical about a single day in the year, the New Year does trigger a moment of reflection on personal continuous improvement. As the saying goes,

"Each day you can either get better or get worse. There's no staying the same. What will it be today?"

I read this post recently and thought it had a lot of good ideas. Three of the things that stuck with me are a) Being busy is a form of laziness, and b) Focus on effective instead of efficient (The difference is being outcome focused) and c) Planning what needs to be done (and setting aside time to do so).

With that context, and borrowing some ideas from the post, here are 5 goals for the New Year:

1) Each Friday, I'll write down the 3 outcomes that I must achieve for the upcoming week. Not tasks, but outcomes.

2) I'm going to set up an email rule to move all emails that I am cc'd on to a dedicated folder. I'll check that only once a day.

3) I'll reduce my number of information sources, but read more. I probably need to find some more challenging books.

4) For thoughts/direction that apply to many people, I'll reply via blog, instead of email.

5) I'll commit to three Pomodoro's a day.