Category Archives: Effectiveness


Knowledge adds certainty to our doing things right and doing the right things.   We are knowledge workers after all,  so we should strive to become really good at learning and how it can make us more knowledgeable. 

Learning is the information economy’s production function.  Unlike the capital or land intensive production functions of other economies, this one requires primary inputs of data and information along with a healthy dose of human motivation, curiosity and time.  People learn by doing, reading, writing, listening and observing.  And as Drucker suggested, we should know which works best for us. 

When we learn, we apply our existing knowledge to extract relevance and purpose from data.  Data are just facts about things and our knowledge helps assemble these facts in a way that helps us know more.

Machine learning works in much the same way.  In supervised machine learning, a data scientist uses her domain-specific knowledge to extract a set of features from tons of data representing an observed phenomenon.  Each row of features is associated to a desired output and an algorithm generates a model to optimally map the two.   The model is then capable of predicting the output value for a new row of features.  The more  rows come in, the more this new data can be used to retrain the model to make it more accurate.  More accuracy means knowledge is gained and the model has learned. 

Machine Learning’s Complexity Problem

I’ve been experimenting with machine learning lately.   For someone who started writing code in the early 90’s and witnessed firsthand the explosion of the web and all the software engineering practices that evolved from it, I find amazing how machine learning  flips traditional software engineering on its head.

Traditional software engineering taught us to divide and conquer, minimize coupling, maximize cohesion while artfully abstracting concepts in the problem domain to produce functional and maintainable code in the solution domain.  Our favorite static code analysis tools helped keep our code (and its complexity) in check.

Similarly, traditional software architecture taught us to worry less about code complexity and more about architectural complexity for it had farther reaching consequences. Architectural complexity had the potential to negatively impact teams, businesses and customers alike, not to mention all phases of the software development lifecycle.

Yes, this was the good ol’ world of traditional software engineering.

And machine learning flips this world on its head.  Instead of writing code,  the engineering team collects tons of input and output data that characterize the problem at hand.  Instead of carving component boundaries on concepts artfully abstracted from the problem domain,  engineers experiment with mathematics to unearth boundaries from the data directly.

And this is where machine learning’s complexity problem begins.  Training data sets rarely derive from a single cohesive set.  They instead depend on a number of  other data sets and algorithms.     Although the final training data set may be neatly organized as a large table of features and targets, the number of underlying data dependencies required to support this can be quite dramatic.

Traditional software engineering became really good at refactoring away dependencies in static code and system architectures in order to tame the complexity beast, the challenge now is to do the same for data dependencies in machine learning systems.

In conclusion, the paper “Machine Learning: The High Interest Credit Card of Technical Debt” summarized this and a number of other ML complexity challenges nicely:

No inputs are ever really independent. We refer to this here as the CACE principle: Changing Anything Changes Everything.”

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AI 3.0

AI, Machine Learning (ML) and Deep Learning (DL) are all the hype these days, and for good reason. By now we know progress in AI accelerated over the past decade thanks to a convergence of factors including Big Data and compute power. And results are impressive as a recent Economist article highlights:

In February 2015 DeepMind published a paper in Nature describing a reinforcement-learning system capable of learning to play 49 classic Atari video games, using just the on-screen pixels and the game score as inputs, with its output connected to a virtual controller. The system learned to play them all from scratch and achieved human-level performance or better in 29 of them.

Over the next two years, many businesses will continue ramping up their ML/DL initiatives with the hope of improving every aspect of their business performance. These companies will follow a path similar to Major League Baseball’s pursuit of sabermetrics, or Wall Street’s appetite for algorithmic trading.

I think at some point in 2018, the latest wave of AI hype will peak and begin receding thereafter. Ongoing issues with model accuracy, as well as high costs required to operate less-than-stellar model performance will be two of the primary reasons behind this. I also believe decision-makers will feel increasingly vulnerable as AI effectively detaches them from understanding and refining the theories underlying their business performance.

This will usher in a new period of enlightenment  where companies adjust their be-all-end-all expectations of AI in favor of empowering their people to effectively coexist with AI.  This will be good news for workers too as Tyler Cowen suggests in Average is Over:

As intelligent-analysis machines become more powerful and more commonplace, the most obvious and direct beneficiaries will be the humans who are adept at working with computers and with related devices for communications and information processing. If a laborer can augment the value of a major tech improvement by even a small bit, she will likely earn well. This imbalance in technological growth will have some surprising implications. The key questions will be: Are you good at working with intelligent machines or not? If the answer is yes, then your wage and labor market prospects are likely to be cheery. If the answer is no, but you have an unusual ability to spot, recruit, and direct those who work well with computers, then the contemporary world will make you rich.


Simple is that ‘horse that left the barn’ but remains in your line of sight.  Chase her down and the problem is solved.  Apply best practices in horse management to ensure it doesn’t happen again.

Complicated is trickier.  It’s that feeling of being ‘caught between a rock and a hard place.’  You’re aware of being unaware of how to get out. Nevertheless, you are confident that good survival practices will help navigate you out of this mess soon enough.

Complexity grows each second you ‘grab the bull by the horns.’  Your best bet is to try things, getting a sense for what works and repeat. If you succeed in taming the wild beast, remember to reflect on your experience, teasing out useful knowledge that will help you repeat this success in the future.


We hear these idioms everyday because we encounter these types of problems everyday. Understanding the category of problem we are solving is the first step towards effectively solving it.

The really interesting part is to get better at ordering complex problems, thereby diminishing their complexity, or increasing the order of complicated ones so they become simpler.



A recent New York Times article suggests that Rome is falling apart.  This may come as no surprise considering similar articles have suggested the same over the past decade.  It is nonetheless strange news for a city and country that are blessed with fourty-eight million tourist to its stunning countrysides, beautiful cities, and cultural treasures each year.

My wife and I moved to Rome in 2009.   I spent many summers in Italy as a child, but it is by living here that I realized the city is dazzling almost entirely through the preservation and promotion of its past success. This has the net effect of pushing aside the practical everyday needs of Romans.

In Thomas Friedman’s much talked about book, The World is Flat, a comparison is made to cities as collaborative platforms for social and economic progress.  As an IT professional, I can tell you that a technology platform’s value hinges on what it offers being fit-for-purpose and how it offers this being fit-for-use.  Rome is prioritizing the preservation of storied relics over the renewal of everyday services.  This makes the city a better fit for the purposes of its visitors than those of its residents.

Similarly, no resident here will resist the notion that Rome is increasingly unfit-for-use.  There are many complex reasons for this. A video that went viral last week may have a simple one.  In it, bus driver Christian Rosso attributes the recent chaos in the city’s public transportation system to the large quantity of city buses parked in the garage awaiting maintenance.  In other words, they are unfit for use and this has exhausted the patience of Rome’s visitors and residents alike.

My point here is not to fuel the nytimes article and its ensuing firestorm.  The fact of the matter is that Rome is one of the nicest cities you’ll ever visit.  However, If the city is to become more valuable to current and future generations of tourists and residents, the mayor and his team need to propose services that satisfy the changing needs of its 21st century residents.  They need to equally ensure these services work and can be relied upon throughout the year by residents and non-residents alike.

Value is an atomic all or nothing proposition.  Uncovering it requires the wisdom and leadership to understand purpose, as well as the knowledge and management to ensure its uninterrupted availability.

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A thought hit me the other day which I will briefly share with you in this post.  Read through today’s popular management journals and magazines and you’ll find numerous references to culture and its unique ability to influence quality of work and organizational performance.  Take for instance  Clayton Christensen’s brilliant portrayal in the widely popular article “How will you measure your life?“:

 “Culture, in compelling but unspoken ways, dictates the proven, acceptable methods by which members of the group address recurrent problems. And culture defines the priority given to different types of problems.   It can be a powerful management tool.​”

What hasn’t been clear, at least to me, are the characteristics of culture in achieving this influence.

If you agree with Clayton –  that culture is a mechanism by which individuals prioritize and select ways to tackle recurring problems, then consider that this mechanism is inherently instinctive, not unlike the seemingly innate behaviors that characterize an individual’s unique talents.   So while culture and talent are conceptually different (e.g. the former underpinned by values, the latter by genetics), they both appear to promote instinctive and recurrent behaviors.  It is these same behaviors that can have a huge influence (i.e. positive or negative) on quality and performance.[1]

*** Notes ***

[1] In their book, First Break All the Rules, Marcus Buckingham and Curt Coffman suggest that a focus on talent offers the advantages of a strengths-based hiring approach.  One of these advantages is employee engagement, and as Tom Rath, Author of StrengthsFinder 2.0, points out, “People who have the opportunity to focus on their strengths every day are six times as likely to be engaged in their jobs and more than three times as likely to report having and excellent quality life in general”.

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Some food for thought on product or service quality.  Deming defined it in relation to the value offered to the customer. Drucker had a similar customer-centric view when he said  “Quality is not what the supplier put in, but what the customer gets out and is willing to pay for”.  (Note: Deming did define a manufacturing centric view of quality in his effort divided by cost equation.)

Moving past traditional management science circles, I like Robert Pirsig’s philosophy on quality from his classic book, Zen and the Art of Motorcycle Maintenance.   Here, Pirsig presents quality not as a thing, but “as an event” – representing a path to discovery of the “right facts” between the creator and her creation.   When you apply his definition to knowledge work it begs the question – do we understand how quality is affected by the relationship between a worker and the tools and materials with which she works?  Consider the elevated joy and satisfaction an individual derives from programming in Ruby vs. Visual Basic, for example.  Returning to the definition proposed by both Deming and Drucker, it’s easy to imagine how Pirsig’s interpretation of quality is the event that leads to creation of customer value.

So there you have it, two perspectives on quality, one is customer centric, the other is manufacturing centric, both highly dependent on one another for the reasons Seth Godin presents in his quality of design vs. quality of manufacture post.

Can we therefore agree that in knowledge work, more important than our collective understanding of the characteristics that constitute ‘high-quality’ is the understanding of the subtle factors that allow these characteristics to emerge?

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“Slip the Jab”

Fan’s of Sylvester Stallone’s Rocky series may recognize the expression “Slip the Jab”.  During the fifth sequel, Stallone’s character, Rocky Balboa, returns to his Philadelphia origins, and location of the gym willed to his son by his late trainer Mickey Goldmill.  After entering the abandoned, dusty gym, Rocky is overcome with emotions as he flashes back to his gym training days with Mickey insisting “Slip the jab, Rock, slip the jab!”.

Rockey and Mickey in Rocky V

During this flashback, Mickey offers Rocky remarkably wise lessons on life.  These lessons carry with them a curious applicability to knowledge work,  which is the subject of this post.

1. “Slip the jab”

Mickey’s insistance that Rocky “slip the jab” refers to a common practice in boxing whereby a boxer learns avoid incoming punches, while also quickly regrouping in order to seize the vulnerability resulting from the missed punch.

A knowledge worker requires similar preemptive and reflexive abilities in order to look ahead, avoid oncoming industry, organizational, or career perils, while simultaneously positioning herself for success once the peril subsides.

“Slipping the jab” for a knowledge worker allows her to operate as the CEO of her professional life.  To do so effectively, she should borrow from leadership models such as Peter Drucker’s Effective Executive, or career management techniques such as Charles Handy’s Sigmoid Curve.

2. “Mesmerize”

“Mesmerize!  See that bum in front of you, see yourself do right and you do right”.  What a wise set of words from Mickey as he instructs Rocky to the benefits of looking ahead and envisioning the result during his shadow boxing session.

Effectiveness is wisdom, and wisdom requires prediction.  What better way for a knowledge worker to boost his effectiveness than to envision the scenarios that may unfold in his project, while also imagining the best possible ways he can respond.

An example of this predictive component can be found  in some software development practices.  Consider test-driven development, whereby a programmer “envisions” his future implementation by first establishing the boundaries for success.

3. “Motavisation”

“The fact that you’re here and doing as well as your doing gives me the, what do they call it  – motavisation – to continue on.” Here Mickey opens up with Rocky, revealing just how important his relationship with the promising young fighter truly is (while succumbing in his struggles to correctly pronounce the word).

Motivation has become a key lever in management’s quest to build  high-performance knowledge worker teams.   Daniel Pink’s Drive offers a simplistic but helpful understanding to the components of this Motivation, as does Fredrick Herzberg’s Two Factor Theory.

But the real essence of a knowledge worker’s Motivation is implied in Mickey’s words.  Think about it – Rocky’s career is doing well, Mickey is his trainer, and so he has every reason to believe he is being effective as a trainer.  Effectiveness brings motivation as is the case with Mickey.  A highly-motivated Mickey will only increase Rocky’s chance to be a successful boxer.

The same applies to knowledge work.  Staying motivated requires an individual increase the chances her efforts will lead to the desired effect.  Aligning work with strengths offers one such way for an individual, as does a strengths-based hiring approach for organizations.

4. “Nature’s smarter than people think”

“People die when they don’t want to live anymore, and nature is smarter than people think”.

Not only is nature smarter than people think, as Mickey suggests, but there’s a growing pervasiveness to incorporate the principles of Evolutionary theory and Complexity Science into management and engineering disciplines to prove it.  Just look at the recent successes of adaptive approaches to management and software development, for example.

5. “Outside the ring”

Later in Rocky’s flashback, Mickey is heard saying “When I leave you, you’ll not only know how to fight but you’ll know how to take care of yourself outside the ring”.

The idea of improving not just one aspect of an individual’s life, but larger aspects is not unlike principles we see in software development and/or manufacturing.  Consider, for example, the “See the whole” principle which is a cornerstone of Lean software development and Continuous improvement.   In order for Rocky to remain a champion fighter for a long time, Mickey realizes he’ll need to ensure Rocky’s success outside the ring as well.

This fits the continuous improvement mantra.  Sustaining and leveraging the improvements in knowledge worker processes requires improving their dependent aspects as well.

6. “Angel on your shoulders”

Finally, towards the end of Rocky’s flashback, Mickey is seen removing his most favorite possession, a cufflink given to him by Rocky Marciano.  He offers this as a gift to Rocky suggesting it will serve as “an angel on your shoulders”, while also suggesting when Rocky feels himself going down “the little angel will scream at you saying: get up you son of a bitch cause Mickey loves you”.

Whether we’re talking about mentors, coaches, retrospectives or daily stand up meetings, to name a few, the key point is to establish necessary feedback channels in order to help individuals and teams adjust early and often.


I hope you enjoyed this post.  For any questions or comments please email


How vs. Why

Here is an interesting parallel between the Data, Information, Knowledge, Wisdom pyramid and the knowledge worker roles and responsibilities defined by Peter Drucker.  Depending on what you read, there exists a tendency to refer to Knowledge as “doing things right”, which happens to fit Drucker’s classic definition of “efficiency”.  On the same token, there’s also a tendency to see Wisdom as “doing the right things”, which also neatly fits Drucker’s definition of “effectiveness”.

DIKW  Pyramid

So from Drucker we know that management represents efficiency, leadership represents effectiveness, executives need to be leaders, and all knowledge workers need to think and act like executives.

This leaves us with a curious relationship between [Knowledge, Management, Efficiency]  vs. [Wisdom, Leadership, Effectiveness]. Description is at the heart of the former, which defined work in the 20th century.  Prediction, on the other hand, is at the heart of the latter, and it will define work in this 21st century.


Best (mal)Practices?

What if I tried to sell you on the notion of “best practices” as just a bunch of superfluous hogwash?  You know, the kind of waste another best practice – Lean’s “Eliminate Waste” principle, attempts to eradicate.  I’d try hard to convince you of the uselessness of pair-programming, ineffectiveness of test-driven development, or the wastefulness of the more appropriately named Sick Sigma. “You’re just wasting time and money”, I would plead.

You might try to convince me otherwise by showing how it’s clearly possible for a best practice, like SWOT, in helping a naturally deliberate person find his new career path (read part 1 and part 2 first), or how there’s not a lack for imagination in applying Theory of Constraints to electronic trading.  Heck, you could even remind me of my own past success with Charles Handy’s Sigmoid Curve, or the undeniable boost in software quality brought by test-driven development.

Backpedaling, I would formulate my own rebuttal, including convincing and equally dizzying material from David Snowden on best practices in complex adaptive systems.  “Those examples worked because the system was ordered!”, I’d bark.

I had the pleasure of listening to David Snowden speak on the issue of effectiveness in Complex Adaptive Systems.   He suggests to lay off best practices, particularly in knowledge management when applied to complex domains.  To understand why, simply imagine what comes of trying ‘to fit the square peg to a round hole’.   A best practice represents a codification of knowledge, and “knowledge cannot be entirely codified”.  He instead advocates using approaches which promote the discovery of shared context:

“…shared context is vital to knowledge exchange, and such context always involves some human trusted validation.  This is not to say that codification of material in advance of need is not advantageous, but the effective reference is nearly always human.” – David Snowden

Returning to our discussion, the lightbulb finally goes off for the both of us.  “To boost effectiveness in complex domains, practices need to be adaptive and promote continuous feedback, the software industry must have known this all along when they moved away from predictive practices towards adaptive ones like Agile”, I conclude.  To which you respond,  “yes, but even David Snowden suggests there’s still plenty of value to glean from a best practice.”


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