Privilege and responsibility: reflections on a career

With so much global change and innovation going on, some readers seek understanding to make sense of it all. I try to meet that need with my blogs and articles. However based on reflections of my childhood followed by a forty year working career, I write this piece about my feelings and emotions resulting from having had the fortune and opportunity to make contributions. I have been privileged to have had many and diverse opportunities to learn about managing organizations, but it comes with responsibilities.

Privilege

It has been a privilege to have worked for excellent and prominent organizations including FMC Corporation; the management consulting arms of Deloitte, KPMG, and EDS (now part of HP); and with SAS. SAS has been an inspiration to me because of its core values and its ability to help its customers anticipate opportunities, empower action, and drive impact. It does this with a strong commitment to customers, skilled employees, and robust technologies.  

It has been a privilege to have had career experiences implementing enterprise performance management (EPM) and business analytics systems. I have been fortunate to have worked with so many talented colleagues. Life is fun when one’s work is interesting. Given my quantitative nature, the emergence of business analytics is a thrill ride.

It has been a privilege to mentor younger co-workers and employees of other organizations. Having a true mentor is a pleasurable experience. I myself had a mentor in the now deceased Bob Bonsack who I worked for at Deloitte consulting. All of my hardcover books I have authored have been formally dedicated to him.

It has been a privilege to travel so often on business trips to many international countries and cities in every continent and to meet so many professionals, many of whom have become more than acquaintances – they are friends.

It has been a privilege to present talks and seminars at conferences, to be interviewed by the media, and to write published books, articles, and blogs. It is motivating to receive feedback from listeners and readers who say my points were spot-on and relevant to them. It is a further privilege to be sought for my ideas, opinions, and guidance.  

It has been a privilege to have been personally trained on enterprise performance management methods by the luminary Dr. Robert S. Kaplan of the Harvard Business School and Dr. David Norton.

It has been a privilege to have been selected as an exclusive author and on advisory boards for organizations like www.cfo.com , www.businessfinance.com, www.informs.org, www.iianalytics.com, www.information-management.com , and www.smartdatacollective.com , and to be on the advisory board with Dr. Kaplan for the International Monetary Fund.  

It has been a privilege to have been raised by hard working and loving parents, who operated a classic mom and pop delicatessen in Chicago; and to have a close-knit extended Greek-American family. It has been a privilege to be married to my wife Pam and now enjoy having two grandsons (so far).

It has been a privilege to have received financial scholarships and attended outstanding universities: Cornell University for an industrial engineering and operations research degree, and Northwestern University’s Kellogg School of Management for an MBA. My education provided a strong foundation to continue learning – which I do to this very day.

It has been a privilege to be a member of teams starting with high school and university sports, including being elected as captain of my football team at Cornell; and continuing with participation on teams as a consultant and with my employers. There is always shared pleasure in accomplishing things with others.

It has been a privilege to have my 1970 junior year university statistics course project accepted in the National Baseball Hall of Fame in Cooperstown, New York as the oldest computer baseball game. The reaction of my friends I tell this to almost always begins with their saying “Wow!”

 

Responsibility

While being fortunate – perhaps lucky – to have experienced these privileges, responsibilities come with privilege.

Responsibility involves working hard and always giving your pursuits your best effort. It leads one to make contributions that make a positive difference for others.

Responsibility has demands to represent your employer or organizations you are associated with and to be viewed with integrity.

Responsibility means treating with respect customers, partners, co-workers, the media, and all others one interacts with.

Responsibility requires one to set a good example to younger people who look to you and are developing their own career knowledge and values.

Responsibility demands one to use one’s talents to their fullest potential.

Responsibility involves giving back.

 

Musings on leadership

There are many flavors on leadership and hundreds of books about the topic. One type of leaders are those who are at the top of an organization chart. My flavor has been thought leadership of managerial methods for organizational improvement and transformation. Each type of leadership involves responsibilities and ideally a pursuit of excellence.

Leadership means trying to do what one believes are the right things, making bold choices, and accepting the consequences.

(I wrote this based on listening to a speech at a sports conference by Kirk Cousins, a quarterback of the Michigan State University football team. It made me think about privileges I have been given and responsibilities.)

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Analytics blogger – Journalist or personal diary?

There are several bloggers like me who write about the topics of analytics and enterprise risk and performance management (ERM / EPM). What are our writing styles?

Some do a deep dive into the details of equations and algorithms of analytics or of the various methodologies like strategic balanced scorecards and customer profitability analysis. Some write about current news such as happenings with Facebook, Apple, or other companies. Some write help advice articles with tips on how to be more effective and successful applying these techniques.

What is my writing style?

I have been blogging weekly for over five years. I also write monthly columns for Information-Management.com (now eight years), SmartData Collective.com, INFORMS.org, the Institute of International Analytics.com, and Business Finance.com. These pieces are regularly re-posted at AllAnalytics.com, KPI Library.com, and the EPM Channel.com. When I contemplate on my writings I cannot determine if I write like a journalist or as if I am sharing with readers my personal diary. What is the difference?

A journalist writes as a news reporter. Think Lois Lane, Clark Kent, or Tom Friedman. In contrast, a diary writes about personal thoughts, feelings, and perspectives, and it is intended for limited circulation amongst friends and relatives.

I sense that my writing style is a hybrid combination – what I call a diary-ist. Sometimes my blogs and articles refer to books authored on various topics, and sometimes they are a personal reflection of my thoughts. I prefer the latter – writing essays like a diary.

Write for your target readers and followers

I have been very fortunate to have had a 40+ year career implementing and observing applications of analytics and performance improvement methods and systems. My earlier writings were educational. Examples included explaining how to construct a balanced scorecard with key performance indicators (KPIs), how to design a managerial accounting system to measure customer profitability and value, and how to reform a broken budgeting process with predictive analytics.

My later writings have been more motivational with attempts to be inspirational. I write for followers who are struggling to get buy-in and overcoming the human nature resistance to change. I try to inject humor. My motive is to accelerate the adoption rate of analytics, ERM, and EPM which are now proven. Technology is no longer the barrier. Peoples' behavior and an organization’s culture are the obstacles.

A close friend mentioned to me that I write like Mark Twain and Will Rogers. I write for the common worker. Examples are my pieces “We’re Down Here,”   "Geeks are Chic",  and “More Spocky, Less Rocky.”

My article “True Confessions – My Struggle with Two Loves” sums up my current writing style. I write as a diary that I choose to share with friends. I write for the advancement of understanding and the adoption rate of methods to provide employees, managers, and executives with better information to gain insights and foresights for better decisions. I write to help others with organizational transformation. I love my work, and it has loved me back.

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Analytics and Big Data – Press pause on the Stairmaster

Our lives have become hectic. We are working harder and longer. We talk about life balance, but for so many of us we continue to have imbalance. Every once in a while we need to step back, press the “pause” button on the Stairmaster exercise machine, take some deep breaths, and reflect on  just what the heck is going on. I’d like to reflect with you my take on what is driving the accelerating interest in analytics and Big Data.
 

The two pyramids – (1) executive power and (2) information technology
In the middle fifteen years of my now forty year work career, I was a management consultant with Deloitte, KPMG, and Electronic Data System (EDS, now part of HP). In our slide presentations we always had two types of compulsory slide pictures: a pyramid with multiple layers and a four quadrant grid. Both provided an over-simplified but effective way of communicating ideas. My explanation for the fast emerging interest in analytics and Big Data can be explained with these two pyramids:
 

Executive power and influence  pyramid – The savvy executives are realizing they must now delegate and distribute decision rights deeper down into their organization to empowered managers and employees. This is because with the exponentially growing mountain of data, both structured (numbers) and unstructured (text), and a speed-up and volatile world, they can no longer hoard decisions at the C-suite level. The executives are at the top of a pyramid slide labeled “types of decisions.” Their decision types are strategic ones. As examples, what is our organization’s mission? What products and services should we offer to maximize value to our constituents? What altered strategic direction should we navigate our organization toward?   

In contrast, at the lower levels of the pyramid are operational decisions that should be made by employees who ideally have had the strategy communicated to them by the executives (via a strategy map, scorecard, and dashboards). With expanding Big Data, the base of this pyramid is widening, and executives are realizing it is futile for them to be able to explore, investigate, and comprehend this massive treasure trove of data. This why the role of analysts (think “data scientist”) is emerging as being mission-critical. Executives cannot do it all. They must now delegate decision making, and provide analytical tools and capabilities for decisioning to their workforce.

Information technology (IT) pyramid – This pyramid is chronological from the past at the bottom to today at the top. At the bottom, say around the 1950s, are the initial IT applications of basic and once time-consuming efforts of payroll and purchasing. Cutting bank checks. Then in the 1960s came invoicing, bookkeeping, and financial accounting IT applications. Next came the waves of customer relationship management (CRM), enterprise resource planning (ERP), and supply chain management systems. Next higher pyramid layers involved information management and data warehouse technologies to enable the managerial systems. Then came the evolution of business intelligence highlighted with query-and-search drill-down capabilities. Next came business analytics, with a huge horsepower lift from high performance analytic (HPA). I would argue that we have now reached the peak of this pyramid where executives, sitting at the top, can nimbly navigate the execution of their dynamic and constantly adjusting strategy with agility. Poor execution of a well-formulated strategy is a major frustration and source of downfall of executives. This pyramid peak represents a GPS-like capability for executives at the helm to use metrics, performance measures and indicators, enterprise performance management (EPM) methodologies, and motivational methods to gain insights and drive the behavior of employees, customers, suppliers, and partners.

 

Where do we go from here?

Are there new yet constructed layers at the top of these two pyramids? I would argue no. Technology is no longer the impediment to driving improvement. It is proven. The obstacle is now people and behavioral change management. Increasing skills with analytics. Overcoming resistance to change. The two pyramids have reached their peak.

The issues now involve strengthening the layers and making them more efficient and effective. The new higher weight at the top of the pyramids needs a strong foundation, and this involves an organization’s culture and leadership style.

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Analytics in a “city that works”

This is a difficult blog for me to write. Why? Because I grew up in Chicago that is known for being the “city that works” which means rarely being dysfunctional. However this past week I was on a business trip in Chicago’s rival New York City and guess what? NYC seems to work too! I believe this is due in part to casual users applying analytics.

New York City. Chaos or coordination?
My purpose for being in NYC was to present a seminar for the American Society of CPAs (AICPA) on “Financial Planning and Analysis (FP&A) and Business Analytics.” Analysts must have been applying analytics because to my delight my trip went smoothly. Here are some examples:

Flight departure. I travel a lot on business and hence periodically benefit from receiving flight upgrades to business class. But how do the airlines select who gets upgraded? The ticket price? Loyalty program status (gold and platinum)? Longevity of loyalty status? None of us know the weightings of their algorithm, but you can be assured the airlines have been optimizing their equations to influence retaining their most profitable they want to keep re-booking flights with them.

Hotel check-in. Since I travel a lot I also benefit with hotel room upgrades. It worked this time. The hotel chain knew my preferences, and my upgrade was a perfect match.

Office building elevators. You may not think that analytics plays much of a role with elevators, but the ones in the AICPA building are “smart elevators.” You do not press the “up” button and enter the next elevator door to open. Instead you key in your destination floor number, and it replies with which specific elevator to wait by to enter when it arrives. The algorithm dynamically optimizes in real time the minimum time that anyone will be serviced to their selected floor.

City walking tours. During my NYC visit I took time for guided walking tours to Greenwich Village and Brooklyn Heights using a website. It was personalized. The tour guide provided their e-address and frequently communicated to those registered the maximum number that could attend the probabilities of cancellations for no shows and cancellations for those wait-listed.

Subway train intervals. Summer is peak tourist season in NYC, and many use the subway. I did. The trains were packed full, but not over-packed. How did they know the number of cars per train and interval times to schedule? It is with analytics. The historical entry turnstyle data provides them volume information by day and time of day to forecast the needed capacity requirements.

Female statues. Here is a problem. One of the tour guides noted that of the hundreds of monument statues in NYC, only five are of females. Title IX in college sports funding for women is solving the male and female imbalance. NYC should use analytics to increase the number of women statues and their best locations.

Cities that work
Our lives are more hectic today than when we grew up as kids. When cities and their services work, it reduces all of our stress and living more enjoyable. Praise analytics!

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Analytics admittance. Adults unaccompanied by minors.

 

The Motion Picture Association of America's film rating system rates a film's content suitability for variuos audiences. For example, A PG-13 rating requires children age 13 or under to be accompanied by a parent or adult guardian if some content is deemed inappropriate.

Let's consider a similar rating system for using analytics. I think children should have restrictions on when parents or adults can engage on what children are analyzing.

Children and curiosity 

When I was young I loved math. I was quantitative and analyical. Growing up in the 1950s, we did not have PlayStations, Nintendos, or Xboxes – no video games at all. We did not have 150 channels on cable or satellite TV or video movies or DVDs. We had no surround sound or CDs, no smart phones, no personal computers and no Internet. But we did have our imaginations, and we made up games. One game I played was dice baseball.

With my dice baseball game the roll of the dice led to a batter making hits or outs. I played full seasons for teams and maintained records. A result is I learned a lot about probabilities and statistics. I learned how to accumulate numbers and compute batting averages and team statistics. When adults poked their head in to see what I was doing, although their observations and suggestions were well intended, they confused me. I preferred to make up my own methods.

Was I being short-minded? Did I not want them to enter my world of play?

Analytics for adults

Today I sense experienced analysts are somewhat like children. And that is a good thing. What experienced analysts want is easy and flexible access to data and the ability to manipulate it. They too have curiosity and imagination. Experienced analysts typically do not plow through data like searching for a diamond in a coal mine or flogging the data until it confesses with the truth. Rather they hypothesize that two or more variables are somehow related and there is some pattern or insight to be discovered in how the variables behave.

Eventually managers and employee teams, the “adults” in this scenario, should get involved with seeing and understanding what that the analyst is investigating. But analysts should be allowed their play time to explore. That provides them the time to let their curiosity and imagination go full steam ahead.

Postscript: Baseball Hall of Fame

A few years ago during my junior year in 1970 at Cornell University my classmate Pete Watzka and I  converted my childhood dice baseball game into a computer program for an operations research game theory course. I used the “random number generator function (1-100)” to do what the dice did when I was a kid. I submitted my computer code (done on big IBM card decks) to James Gates, the librarian at the USA’s National Baseball Hall of Fame in Cooperstown, New York – and voila. It was accepted as the oldest computer baseball game

Unaccompanied analysts can produce results.

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Why Will Analytics Be the Next Competitive Edge?

 

Bob Thompson, the CEO and founder of the popular website CustomerThink.com has published an e-book, Profiting Big Data - 10 BIG Ideas, compiling ten articles from authors. I am honored to have my article as Chapter 1. You can download the e-book for free by clicking on the book title link in this paragraph. Below is a slightly expanded version of my article.

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Analytics is becoming a competitive edge for organizations. Once "nice-to-have," applying analytics is now becoming mission-critical.

An August 6, 2009 New York Times article titled, "For Today's Graduate, Just One Word: Statistics" reminds me of the famous quote of advice to Dustin Hoffman's character in his breakthrough movie The Graduate. It occurs when a self-rightrous Los Angeles businessman takes aside the baby-faved Benjamin Braddock, played by Hoffmans, and declares, "I just want to say one word to you - just one word - plastics." Perhaps a remake of this movie will be made and updated with the word analytics substituted for plastics

This spotlight on statistics is apparently relevant, because the article in that week's top three e-mailed articles as tracked by the New York Times. The article cites an examplr of a Google employee who "uses stastical analysis of mounds of data to come with ways to improve (Google's) search engine." It describes the employee as "an Internet-age statistician, one of many who are changing the image of the profession as a place for dronish number nerds. They are finding themselves increasingly in demand - and even cool."

 

Analytics – just a skill, or a profession?

The use of analytics that include statistics is a skill that is gaining mainstream value due to the increasingly thinner margin for decision error. There’s a requirement to gain insights and inferences from the treasure chest of raw transactional data that so many organizations have now stored (and are continuing to store) in a digital format.  Organizations are drowning in data but starving for information. The article states:

“In field after field, computing and the Web are creating new realms of data to explore – sensor signals, surveillance tapes, social network chatter, public records and more. And the digital data surge only promises to accelerate, rising fivefold by 2012, according to a projection by IDC, an IT research firm. … Yet data is merely the raw material of knowledge. We’re rapidly entering a world where everything can be monitored and measured, but the big problem is going to be the ability of humans to use, analyze and make sense of the data. … (Analysts) use powerful computers and sophisticated mathematical models to hunt for meaningful patterns and insights in vast troves of data. The applications are as diverse as improving Internet search and online advertising, culling gene sequencing information for cancer research and analyzing sensor and location data to optimize the handling of food shipments.”

The application of analytics is becoming mainstream, but will senior executives realize it?

How do executives and managers mature in applying accepted methods?

Here is an observation on how managers mature in applying progressive managerial methods. Roughly 50 years ago CEOs hired accountants to do the financial analysis of a company, because this was too complex for them to fully grasp. Today, all CEOs and mainstream businesspeople know what price-earnings (PE) ratios and cash flow statements are and that they are essential to interpreting a business’ financial health.  They would not survive or get the job without this knowledge.

20 years ago CEOs of companies did not have computers on their desks. They did not have the time or skill to operate these complex machines and applications, so they had their secretaries and other staff do this for them. Today you will become obsolete if you don’t at least personally possess multiple electronic devices such as laptops, smart phones and PDAs to have the information you need at your fingertips.

Business analytics are the next wave

Today many business people don’t really know what predictive modeling, forecasting, design of experiments or mathematical optimization mean or do, but over the next 10 years, use of these powerful techniques will have to become mainstream, just as financial analysis and computers have, if businesses want to thrive in a highly competitive and regulated marketplace. Executives, managers and employee teams who do not understand, interpret and leverage these assets will be challenged to survive.

When we look at what kids are learning in school, that is certainly true.  We were all taught mean, mode, range, and probability theory in our first-year university statistical analytics course. Today children have already learned these in the third grade! They are taught these methods in a very practical way. If you had x dimes, y quarters and z nickels in your pocket, what is the chance of you pulling a dime from your pocket?  Learning about range, mode, median, interpolation and extrapolation follow in short succession. We are already seeing the impact of this with Gen Y/Echo boomers who are getting ready to enter the work force – they are used to having easy access to information and are highly self-sufficient in understanding its utility.  The next generation after that will not have any fear of analytics or look toward an "expert” to do the math.

There is always risk when decisions are made based on intuition, gut feel, flawed and misleading data or politics. Increasingly, the primary source of attaining a competitive advantage will be an organization’s competence in mastering all flavors of analytics. If your management team is analytics-impaired, then your organization is at risk. Analytics is arguably the next wave for organizations to successfully compete and optimize the use of their resources, assets and trading partners.

Substantial benefits are realized from applying a systematic exploration of quantitative relationships among performance management factors. When the primary factors that drive an organization’s success are measured, closely monitored and predicted, that organization is in a much better situation to adjust in advance and mitigate risks. That is, if a company is able to know – not just guess – which nonfinancial performance variables directly influence financial results, then it has a leg up on its competitors.

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Board of directors’ dashboards – Navigation or naiveté?

Have you ever wondered about how well boards of directors do their job? I have. And I do not have a good answer. But I was stimulated by an article written by Donald Delves, President of The Delves Group, titled “Dashboards for Boards.”

Ever since the Enron meltdown and numerous other companies with governance problems, my perception is that being a board member is no longer a ceremonious job where you simply show up for a board meeting and receive a nice paycheck. I believe that boards are now much more activists in defending the interests of shareholders and investors.

By accepting this perception, I presumed boards have their act fully together. But Delve’s article introduced some doubt with me.

 

Confusion between scorecards and dashboards

Delves’ observation is that younger board members are more shrewd and comfortable with using and deploying information, and they desire access to deeper and more robust information to perform business analytics. However, he states “Truly enlightening dashboards are still a rarity.” To complicate matters, there is confusion about what the difference is between a balanced scorecard and a dashboard. There is similar confusion differentiating key performance indicators (KPIs) from normal and routine measures that we can refer to as just performance indicators (PIs). Both types of measures are important, but they serve different purposes. The adjective “key” of a KPI is the operative term.

When an organization proudly proclaims they have three hundred KPIs, one must ask them the question, “How can they all be a K?” To use a radio analogy, KPIs are what distinguish the signal from the noise – the measures of progress toward strategy execution. As a negative result of this confusion, organizations are including an excessive amount of PIs in their balanced scorecard that should be restricted only to KPIs.

The difference between a scorecard and dashboards comes from the context in how they are applied. Here are some guidelines and definitions for understanding the differences:

  • Scorecards monitor progress toward accomplishing strategic objectives. A scorecard displays periodic snapshots of performance associated with an organization’s strategic objectives and plans. It measures organizational activity at a summary level against pre-defined targets to see if performance is within acceptable ranges and favorable or unfavorable relative to the targets. 
  • Dashboards monitor and measure processes. A dashboard, however, is operational and reports information typically more frequently than scorecards and usually with measures. Each dashboard displays PIs which are reported with little regard to their relationship to other dashboard measures. Dashboard measures do not directly reflect the context of strategic objectives.

In summary, a scorecard serves as a feedback mechanism to allow everyone in the organization, from front-line workers up to the executive team and board directors, to answer the question: “How are we doing on what is important?” More importantly, the scorecard should facilitate analysis to also know why. The idea is not to just monitor the dials but to move the dials.

 

Board members and business intelligence and analytics

Delves writes this: “Board members do not have to limit themselves to the 30,000 ft. view of the company. Vast amounts of data can be assembled on a regular basis to provide meaningful insight quickly. Given the size and highly complex nature of so many companies, board members have a responsibility to dig deep, be curious, and satisfy that baby boomer urge for the truth.”

But this raises the issue of how easily and flexible is the access to the data and the ability to manipulate it. This is the same conundrum of experienced business analysts. Board members need much more than “drill-down” capabilities. So do managers and employees of the companies that board members provide oversight for.

The problem is many organizations have disparate data sources, “dirty” data quality, and allegedly effective data warehouses. Until these obstacles are fixed, dashboards for boards will remain an elusive goal.

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Analytics is like a dentist. It’s all in the inside.

My cousin Chris is a dentist. We are the same age, and we lived together when we were both in graduate school at Northwestern University. Chris was in dental school, and I was earning my MBA. Chris once said to me that dentistry is all on the inside. Of course he meant his work is inside his patients’ mouths, and he did not mean that people are not responsible for their own dental hygiene. But his quote has always remained with me. In thinking about it, it can apply to analysts and analytics as well.

 

Big Data and analytics

There is much interest in Big Data. Just Google the phrase and you will see what I mean. Organizations are drowning in data but starving for information. The difference is that raw and transactional data is only a starting point. It is the “inside.” The problem and opportunity is to convert this treasure trove of data into information – to get it “outside” of the daunting and formidable black hole of raw data. That gets to the heart of analytics. It is a translation process to convert data into something that is meaningful for insights, better decisions, and ultimately actions that lead to organizational improvement.

An increasingly new term is the “data scientist.” Another one that I like is the “data investigator.” The high popularity of the American crime drama television series, CSI: Crime Scene Investigation, is indisputable. The ratings for the original Las Vegas show and its spin-offs are proof. In each episode criminal investigators rely on physical evidence to solve murders. Since these shows are so popular, why do we not see similar zeal by organizations to sift through the mountains of data available to them to solve problems and seek opportunities? Business intelligence (BI) and analytics software technology is quickly emerging

 

Data investigators have an analytical mindset

Some believe that BI and business analytics provide a game changing competitive edge. They realize that the commonly accepted competitive strategies heralded by the strategy guru and Harvard Business School Professor Michael Porter are now vulnerable. Porter’s three main and generic strategies are low cost leadership, product or service differentiation, and segmented customer focus. But today, all three types of strategies are defenseless against competing and enterprising companies that can quickly lower their cost, imitate a supplier, or invade a supplier’s market space. Advocates of BI and analytics believe that the only lasting and sustainable competitive advantage comes from achieving competency by its employees with these investigative methodologies.

The investigators today in business and government are analysts of all types. And today everyone can add value to their organization with an analytical mindset. Experienced analysts rely on exploration. They require easy and flexible access to data and the ability to manipulate it. They want more than data mining. And they do not want the IT function to be an obstacle and prevent them accessing and manipulating the data.

BI and business analytics are becoming the forensic science, abbreviated as forensics, for organizational improvement.  Forensics was originally the term for applying scientific methods to answer questions of interest to a legal system typically in relation to a crime or a civil action. But today forensics applies to solving an organization’s problems, exploring its opportunities, and balancing its risk appetite with its risk exposure.

If people love the CSI television series, they may likely love BI and analytics too! I know my cousin loves being a dentist. He has done it for almost 40 years. Given the flexible access to data, analysts and all managers and employees can also see what is in the “inside.”

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Intelligent people but stupid choices – try using analytics!

My previous blog on June 5 was about horse racing’s prestigious Triple Crown race and its final leg, the Belmont Stakes. I posted my blog before the favored horse, I’ll Have Another, was scratched due to a leg injury. For gamblers who would likely to have bet on I’ll Have Another for the Belmont, maybe this saved them some money. Why? I will get to that in a moment

To set up my answering the “Why?” let’s first discuss decision making. I am fascinated about how and why poor decisions are made. A writer on this topic that I follow is Michael J. Mauboussin, chief investment strategist at Legg Mason Capital Management. In an article he wrote in The Futurist (March-April, 2010) he said, “Smart people make poor decisions because the mental software that we humans inherited from our ancestors isn’t designed to cope with the complexity of modern day problems and systems. In short, smart people, like everyone else, face two major obstacles to making good decisions. The first obstacle is the brain, which evolved over millions of years to make decisions unlike what we face in modern life. The second obstacle is the growing complexity of the world in which we live.”

 

Decisions by guessing versus with analytics

Others have also written about this. In the book Thinking, Fast and Slow by Dan Kahneman, recipient of the Nobel Prize in Economic Sciences for his seminal work in psychology that challenged the rational model of judgment and decision making, Kahneman explains the two systems that drive the way we think. System 1 is fast, intuitive, and emotional; System 2 is slower, more deliberative, and more logical. System 1 is largely unconscious and it makes snap judgments based upon our memory of similar events and our emotions. System 2 is painfully slow, and is the process by which we consciously check facts and think carefully and rationally.

A problem Kahneman points out is that System 2 thinking (slow) is easily distracted and hard to engage and that System 1 thinking (fast) is wrong as often as it is right. System 1 thinking is easily swayed by our emotions. As an example, he describes an observation that people buy more cans of soup in a grocery store when there is a sign on the display that says "Limit 12 per customer." People miss the opportunity to analyze.

 

Why I’ll Have Another would probably have lost the Triple Crown.

Mauboussin wrote a blog this week for the Harvard Business Review titled “The Business Lessons of the Belmont Stakes.” I have done some editing of his points. He writes:

“It's easy to think about I'll Have Another's chances to win the Belmont using the System 1 (fast) thinking. He won the Triple Crown's first two races in impressive fashion. And handicappers certainly like his chances (the betting odds suggest a 50%-60% probability that he'll outrun the other 11 horses in the race). System 1 thinking sees mostly upside.

System 2 thinking (slow) paints a more pessimistic picture. Consider that of the 30 horses in a position to win the Triple Crown in the last 130 years, only 11 have succeeded. That's about a 40% rate. But it gets worse. Prior to 1950, eight of the nine horses that tried, triumphed. Since 1950, only 3 of 21 have managed the feat, and none have done so since 1978. A success rate of less that 15% is not encouraging.

Perhaps I'll Have Another is a really special horse, you may be thinking, a once-in-a-generation speedster. Well, we can quantify that with something called a Beyer Speed Figure, a measure of a horse's performance adjusted for track conditions. All you really need to know for this purpose is that higher speed figures belong to faster horses.

Here are the speed figures for the Kentucky Derby and Preakness combined for the last seven Triple Crown aspirants, all of which failed, along with I'll Have Another:

Silver Charm — 233
Smarty Jones — 225
Funny Cide — 223
War Emblem — 223
Real Quiet — 218
Charismatic — 215
I'll Have Another — 210
Big Brown — 209

I'll Have Another looks pretty lead-hoofed. Big Brown, the only horse that appears worse, was eased coming down the homestretch in the Preakness, paring a few points off of his speed figure. And he went on to finish dead last in the Belmont in 2008.”

 

The case for analytics

OK. One example of a horse race revealing how a better decision would have been made via using analytics may not be sufficient. But can you recall any decisions made by your managers or executives that were based more on their intuition, gut feel or political positioning rather than on fact-based information and analysis? If not, you are lucky to work with such competent people. My “guess” is most of you can recall one or more decision blunders. Intelligent people but stupid choices.

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Horse racing’s Triple Crown – just like business analysts*

The Kentucky Derby and Preakness horseraces, the first and second legs of the USA’s prestigious Triple Crown races, have been run. The winner of both races, I’ll Have Another, will be trying to win horseracing’s famous Triple Crown by winning the Belmont, but he will need to again outrun the final stretch Bodemeister, the favorite of the first two Triple Crown race legs.

It made me think that thoroughbred racehorses and business analytic and performance management project leaders have similarities depending on which type they are. (This metaphor is also applicable to professional careers.) There are three types of racehorses: starters, stalkers and deep closers. How are business analytics and enterprise performance management methodologies project managers similar?

Starter racehorses directly break to lead from the starting gate. This year’s Bodemeister is a good example. This type of horse does not normally win races because their early energy burst takes a toll. Similarly, some project managers, for example of a balanced scorecard project, try to move too fast for the organization. The obstacles that slow the adoption rate for business analytics and enterprise performance management methodologies are not technical – they are social. This type of project manager, often ambitious young ones, does not patiently earn buy-in from their organization. Consequently they are likely to come up short of a fully successful implementation of the fully integrated analytics-based enterprise performance management framework.

Stalker racehorses run a few lengths behind the starters until near the end of the race before turning up their speed to the finish line. This year’s I’ll Have Another is a good example. They often win as I’ll Have Another has now proven twice. Similarly, this type of project manager who paces them self are often successful. They carefully watch what lies ahead of them and how others are reacting to changing conditions. Which horse is changing lanes? Which manager is changing allegiances? 

Deep closer racehorses run near the back. After about half way through the race they begin to advance forward weaving through the horses ahead with momentum to pass the somewhat surprised leaders just before the finish line. The 2009 long-shot Kentucky Derby winner, Mind That Bird, ran as a deep closer and just missed winning the 2009 Preakness, the second jewel of the horseracing’s famous Triple Crown. And Zenyatta, the famed filly that always began at the very back of the pack, won every race except for her very last one at the Breeders Cup – and would have one if the race was just a few more yards.

 

I personally like the deep closer project manager (and career person too). They do take a risk by lying low and being somewhat out of sight, but they understand the finish line is at the end of the race – not in the middle of it. These types of project managers know the virtue of patience. While ahead of them during the race there is much “jockeying” for position, their goal is ultimate success – the fulfillment of helping their organization complete the full vision of the combined business analytics and enterprise performance management framework that I passionately write about.

Each of these three types can win. I do not know which type of racehorse wins relatively more than the others. Personally I like deep closers. They are exciting to watch, and when they win you sense they had the perspective of how races and organizations work.

 

* This blog is an edited and revised version of my May, 2011 blog titled “Horse Racing’s Triple Crown – Just like Business Analysts.”

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