Here is an excerpt:

The Gift of Endless Memory, a 60 Minutes story originally broadcast on December, 19, 2010, introduced viewers to emerging research on superior autobiographical memory – the extraordinary capacity to recall specific events from one’s personal past. The story featured five of the six people recognized by researchers as having this superlative level of memory, including actress and author Marilu Henner…

I would have liked to have learned much more about how each group member actively uses their memory to their benefit. How does each person effectively manage what amounts to a vast personal “database” of highly detailed memories, each one as vivid as any other, regardless of the passage of time?

Please read the entire article here:

The Gift of Memory – and the Gift of Perspective by Mike Urbonas

Source: HikingArtist.com (Flickr CC)

In a recent thought-provoking TDWI article, David Champagne informed readers of The Rise of Data Science: a discipline of emulating the scientific method when analyzing data, in a conscious and laudable effort to ensure objectivity and avoid poor analytical practices.  Having just recently blogged on the Texas Sharpshooter Fallacy, a type of flawed analytical logic business intelligence users might fall into, David Champagne’s article caught my attention.

From David Champagne’s article:

Back in the “good old days,” data was the stuff generated by scientific experiments. Remember the scientific method? First you ask a question, then you construct a hypothesis, and you design an experiment. You run your experiment, collect and analyze the data, and draw conclusions. Finally, you communicate your results and let other people throw rocks at them.

Nowadays, thanks largely to all of the newer tools and techniques available for handling ever-larger sets of data, we often start with the data, build models around the data, run the models, and see what happens.  This is less like science and more like panning for gold…Perhaps the term “data scientist” reflects a desire to see data analysis return to its scientific roots…

Barry Devlin, in his business-focused commentary on David Champagne’s article, noted the worlds of science and business have rather different goals and visions, which I interpreted as data science might offer limited benefit to business managers.  But perhaps the best practices of data scientists have a lot more in common with those of business managers after all, in light of some commentary I came across on effective business decision-making.  That commentary gave high praise to the manager who utilizes the scientific method in the decision-making process. The author was not a technologist, but rather: Peter Drucker, the father of modern business management.

Revisiting Peter Drucker’s writings on effective decision-making process will show surprising similarities to the best practices of data science, and yield beneficial insights for business managers seeking to make more effective, data-informed decisions.

Barry Devlin wrote that the worlds of science and business work differently with data. For example,  business is concerned with improving the bottom line while science seeks “real and eternal truths.”  This is true, but scientists also never stop evaluating and improving on “truths.” The scientific process of seeking “real truth” and not accepting conventional wisdom is very similar indeed to the business decision-making process. From Peter Drucker:

(E)xecutives who make effective decisions know that one does not start with facts. One starts with opinions. These are, of course, nothing but untested hypotheses and, as such, worthless unless tested against reality…

People inevitably start out with an opinion; to ask them to search for the facts first is even undesirable. They will simply do what everyone is far too prone to do anyhow: look for the facts that fit the conclusion they have already reached. And no one has ever failed to find the facts he is looking for…

The only rigorous method…is based on the clear recognition that opinions come first…Then no one can fail to see that we start out with untested hypotheses – in decision-making as in science the only starting point.  We know what to do with hypotheses – one does not argue them; one tests them. One finds out which hypotheses are tenable, and therefore worthy of serious consideration… (Source: The Essential Drucker , Peter Drucker, p. 252).

The process of testing opinions, aka hypotheses, will of course involve analyzing data – a process for which Peter Drucker again calls for a scientist-like inquisitiveness: “The effective decision-maker assumes the traditional measurement is not the right measurement…The best way to find the appropriate measurement is to…look for “feedback,” (Drucker, p. 253), which Drucker describes as “organized information” that is ”built around direct exposure to reality” – better known today as a performance metric. Finding the right performance metric(s) is “a risk-taking judgement,” Drucker says.  And the best way to mitigate that risk is by using metrics with some actively proven experiential value.

Finally, the evaluation stage of scientific method - “letting people throw rocks” at your conclusions, as David Champagne wrote – is again very similar to Drucker’s advice to business leaders, urging them to “create dissension and disagreement rather than consensus” (p. 254):

The effective decision-maker…organizes disagreement…It gives him alternatives so that he can choose and make a decision, but also so that he is not lost in the fog when his decision proves deficient…And it forces the imagination – his own and that of his associates…[The effective decision-maker] starts out with the commitment to find out why people disagree. (p. 256)

The best practices of data scientists and business decision-makers seem to overlap heavily.  Thankfully, though, the business manager does not need to actually become a data scientist.  Doing so would require adding to the business manager’s functional expertise two additional copious skill sets of the data scientist: extensive mathematics/statistical capabilities, and “hacking skills” – the ability to find and retrieve data from disparate sources on one’s own. David Champagne writes, “Finding and retrieving data sometimes requires the skills of a burglar” (emphasis added).   This is cringe-worthy terminology to CIOs already working hard to avoid shadow systems and the multiple versions of the truth they might bring.

That said, Barry Devlin makes a succinct and strong case for the data warehouse as a leading source, if not the only source, of trusted data, not to mention trusted performance metrics, that business decision-makers can use with no need for “hacking skills”:

This is where a data warehouse comes in.  Of course, only a small proportion of the data can (or should) go through the warehouse.  But the value of the warehouse is in the fact that the data it contains has already been reconciled and integrated to an accepted level of consistency and historical accuracy for the organization.

An effective data warehouse will serve business managers as a laboratory of sorts, enabling decision-makers to test opinions/hypotheses by asking good questions and getting good answers: data that is “true” (accurate) along with proven performance metrics.

Business executives and managers will do well to adopt Peter Drucker’s best practices of decision-making, which closely follow the best practices of data scientists analyzing and interpreting data.  Doing so will help lead towards better, data-informed decisions, and away from managing based on irrelevant measurements or “looking for the facts that fit the conclusion.” – in short, acting like a data scientist and not a ‘mad scientist.’

Here are the three most popular blog entries of 2010, with a new year’s resolution to write many more in the new year!  Please enjoy.

UPDATE:  Moments after tweeting my resolution to blog more often in 2011, I see I am being held accountable (!) by WordPress’ PostADay / PostAWeek Challenge.  OK, WordPress, count me in … for the PostAWeek, that is!

The most popular post overall during 2010 was actually a 2009 post: 

Poor Communication can Scuttle Effective BI, Your Personal Brand, and a Simple Bus Ride 

Top 3 most popular posts added in 2010:

1.  Not All Interruption Marketing is Bad 

2.  Play the Product Marketing Game Like a Chess Grandmaster

3.  Animal Metaphor Farm: Don’t be a “Gorilla” or “Eagle” in Business … Be a Crow

A Texas cowboy fires several rounds at a barn.  Looking at the holes riddled across the barn wall, he observes there are lots of holes clustered together.  He excitedly paints a bulls-eye centered over the biggest cluster of holes and proudly shows it off as proof he is a sharpshooter.

Artist: Mahbsol - appearing on: youarenotsosmart.com

This old joke is the namesake for a type of illogic known as the Texas Sharpshooter Fallacy: a false assumption that the existence of clusters of information – be it bullet holes or sales data – must be the result of a particular direct cause.

Such illogic can be deliberately fraudulent like the cowboy’s, or at least disingenuous.  Examples of the Texas Sharpshooter Fallacy can be seen in public health investigations, politics (a politician might accuse an opponent of a “pattern” of poor actions), and pseudoscience.  I especially like YoureNotSoSmart.com’s David McRaney’s description: “If hindsight bias and confirmation bias had a baby, it would be the Texas Sharpshooter Fallacy.”

But a Texas Sharpshooter Fallacy can also be the result of the mistaken assignment of meaning to what is in fact a small subset of all related data.   It then occurred to me: virtually every business intelligence tool has a feature to highlight a portion of a graph to focus only on a particular data cluster, enabling every BI end-user to “draw a bulls-eye” of their own!  The risk of jumping to false conclusions based on such selective data selection can be very high.

So how can business intelligence users avoid unwittingly creating their own Texas Sharpshooter Fallacies, and help debunk those put forth by others in the organization?

The problem occurs when a BI end-user draws a conclusion that is based on that isolated data cluster.  Suppose a sales manager using a BI tool highlights a cluster of data  points, all with a noticeably low gross margin compared to the other monthly sales.  The sales manager then sees that those ten data points – all sales with low gross margins – are all deals closed by Joe Bloggs.

Is Joe Bloggs giving away lavish pricing concessions to customers?  Well, if the sales manager can’t stand Joe Bloggs, has poor managerial, interpersonal, analytical skills, or all of the above, this data cluster may be all the “proof” (s)he needs to “have a word” with Joe, or subvert Joe’s efforts by grousing over his lousy dealmaking skills with others in the hall…you get the picture.  Again quoting David McRaney, “You commit the Texas Sharpshooter Fallacy when you need a pattern to provide meaning, to console you, to lay blame.”

Of course, the sales manager does not have ”proof” Joe Bloggs is “giving away margin” any more than that cluster of bullet holes is “proof” the cowboy is a sharpshooter.  What the sales manager does have is a hypothesis - defined succinctly by Wikipedia as “a proposed (repeat, proposed) explanation for an observable phenomenon.”  That data cluster of low margin sales should be the beginning of the sales manager’s line of inquiry, not the conclusion.

A wise user of business intelligence tools will proceed like a scientific researcher, who will begin with a hypothesis and then try to disprove it.  Here are two suggestions to do so effectively:

• Use the word “ALL” a lot.  Seriously, asking new questions of the data with liberal use of the word “all” is wise; for example, how do Joe Bloggs’ ten low margin sales compare with “all” of his deals for the month (both in terms of currency and quantity); ”all” sales of the same products by “all” sales reps; “all” sales to those customer(s), etc.  Thinking and asking questions of the data in terms of “all” will effectively erase the cowboy’s bulls-eye target, so to speak, by focusing on relevant data in its entirety and not the original data cluster in isolation, which may well prove statistically insignificant in light of “all” related data.
• Time is on your side.  The longer time series of data you can utilize in your line of inquiry, the better.  Here’s a real world example: A college finance and budget department recognized a common assumption on campus that administrative spending rates were higher than those of faculty.  The budget and reporting director produced a trending analysis of campus-wide spending over fifteen(!) years, convincingly showing that growth rates in spending were the same between faculty and administration.  This diminished the conventional wisdom and led to fact-focused planning and prioritization discussions and enhanced trust and communication between departments.  It is possible that a look at a small subset of this trend analysis might have yielded “proof” of disproportionate growth in administrative spending.

If a co-worker makes an assertion based on some isolated data set, try judiciously asking questions to determine if these lines of inquiry have taken place; if not, consider offering to do so, thereby avoiding potentially large fallout resulting from a small, misinterpreted subset of data.

One final thought: A highly effective business intelligence implementation will make it easy, even intuitive, for the end-user to compare a data cluster with “all” related data and readily observe the significance, if any, of a selected data cluster.  Now there’s some fancy data sharpshooting!

If you liked this post, you may also like:

Business Users Can Learn a Lot from Data Scientists

For Success, Grit Beats Intelligence! Or: Use Business Intelligence Software to Achieve Grit Goals

Business Intelligence is a Diagnostic Tool, but is the “Patient” Willing to Listen?

Source: Rick Steves Press Room

I am a longtime fan of Rick Steves, host of the long-running PBS television series Rick Steves Europe and author of an extensive series of European travel guides. His London guide helped me make the most of limited free time while traveling to the old world on business.  I hope to return sooner rather than later!

Always keeping an eye out for interesting business analogies, I suggest Rick Steves and highly user-friendly BI tools actually have a lot in common.   For openers, Rick Steves has a knack of making European travel as non-intimidating as possible for new travelers; very user-friendly BI tools help eliminate the ‘intimidation factor’ non-technical personnel might feel when trying to use an analytical tool. 

I think the analogy between Rick Steves’ brand of travel and user-friendly BI tools goes much deeper, after reading Rick Steves’ unique travel philosophy…  

Over the years I have found BI author and consultant Neil Raden to be a welcome voice of common sense, from advocating simplicity for successful BI, insisting on avoiding hype, to a healthy disdain for BI buzzwords (“I’ve reluctantly come to the conclusion that ‘analytics’ means anything the speaker/writer/vendor/analyst wants it to mean,” Raden said in a recent droll tweet).

Photo: ThisIsIt2 - Flickr CC

Somehow, until recently, I had overlooked a particular Neil Raden BeyeNETWORK blog entry which really struck a chord.  The title speaks for itself: You Cannot Fix a Broken Company by Measuring How Broken it is.  Neil Raden’s post goes to the heart of what Business Intelligence is, and what it is not.  From Neil’s post:

The BI industry has sort of casually sent the message that BI makes companies better. I’ve seen this in presentations, webinars, seminars, books and blogs from vendors, practitioners and analysts. But the question is, once you expose something that needs attention, what next? As a consultant and implementer of data warehousing and BI for many years, I never really came up with a good answer…

And here is an especially important comment:

Even when we did everything we set out to do [in a BI/DW consulting engagement], when approaching management about the next phase of the operation, to help the client start addressing the problems with employee morale, high turnover, inventory snafus, poor customer service, etc., the response was usually something like, “Neil, aren’t you the data warehouse guy? Shouldn’t we get McKinsey in here to work on that?”

I empathize with Neil and this ‘You’re just the techie’ brush-off, but management’s response to Neil’s inquiry raises much more serious concern: the first response by management to address serious business problems was to bring in outside consultants…?  Was the accidental point of the response that the company’s executives perceive they cannot effectively address, on their own, the issues identified by Neil Raden’s BI solution?

One more quote from Neil’s post:

In short, if you’re involved with BI, you may have as good or even better insight into what is going on in the company, but you clearly lack the portfolio to do anything about it…

After reading Neil’s observations, a medical analogy came to mind.  Like a medical device designed to identify illness and disease, BI is also a diagnostic tool that can identify performance “illnesses” in an organization, but cannot in itself be a ”cure.” (This is an incomplete analogy, of course, as BI can help identify ‘good’ things too, like uncovering positive new business trends, etc., but it matches the experience shared by Neil Raden).

Extending the BI/medical diagnostic tool analogy:  Many years ago I spoke with the founder of a start-up medical device company.   He told me that in order for a medical diagnostic system to be successful, a course of treatment has to also exist.  For example, he explained that (at the time) there was no course of treatment to reverse the effects of osteoporosis; therefore, existing medical devices to measure bone density were of relatively nominal value or benefit.  In other words, once the data (i.e., substantial bone density loss) is uncovered, there was no corrective action for the patient to take.  (Today, thankfully, this is no longer the case.)

The good news for practitioners and developers of BI/DW technology is there are indeed “courses of treatment” to reverse the effects of low employee morale, high turnover, inventory snafus, poor customer service, etc.  These courses of treatment, however, require strong leadership dedicated to rolling up their sleeves, and actively use BI tools as part of a culture of data-informed decision-making.

In contrast, if management is so hands-off that it calls in management consultants to, in effect, ”sub out” addressing pervasive business problems, no amount of BI may be enough to effect authentic change.  It’s a bit like a doctor showing a chain-smoking patient an x-ray of his damaged lungs, and yet the patient, disengaged from the very process of maintaining his own good health, is not persuaded to quit smoking.

I’d like to point to the University of Maryland, Baltimore County (I very much enjoyed working with UMBC, a customer of a former employer) as an excellent example of an organization embracing that essential culture of data-informed decision-making.  This culture is actively advocated from the top, by the University’s President, Dr. Freeman Hrabowski.  It’s no coincidence in a recent University Business article that President Hrabowski emphasizes the importance of a “culture of critical thinking” and the need for a “deliberate effort” to engage in conversations and problem-solving with others, while fellow leaders at UMBC describe an excitement on campus over a new iStrategy financial management solution!

The bottom line is BI can inform management, like a diagnostic tool, but management in turn has to believe in the efficacy of the “courses of treatment” – that is, buying into and publicly committing to a culture of data-informed decision-making, and, for that matter, a managerial culture that does not regard practicing good business health as somehow being someone else’s responsibility. 

Source: Lifehacker.com

I just read a clever tip on the Lifehacker website on how to easily reuse sturdy electronic device boxes for shipping or storage: turn the box inside out.  Many electronic device boxes use no glue or tape at all, making the process very easy.  You end up with a like-new, “fresh” box ready for easy labeling for shipping, storage use, etc.

In fact, a shipping box company has taken the inside-out box idea further with a reusable box design that turns inside out, by enabling easy removal and reattachment one of the glued box edges.

This tip suggested to me a business metaphor: Think outside the box by turning it inside-out! After all, that’s one thing that takes place when implementing a business intelligence system: data and insights previously not visible become available for many, if not all, people in the organization to see.  That can be regarded as a boon or a liability depending on one’s perspective.  Indeed, a significant hindrance to business intelligence acceptance is the perceived loss of control over the data, and therefore perceived risk of judgment and  reprisal, of one’s department, region, product line, etc.  In her book Successful Business Intelligence, Cindi Howson quoted a senior executive who said, “Some departments don’t like [their] data being exposed…others may see they are not doing a good job…” (p. 159).

Maureen Clary wrote an excellent article for the BeyeNETWORK on proactively addressing people problems that might derail a business intelligence initiative.

Personal issues are usually the drivers behind resistance. Concerns about job security, visibility, competition, personal risk, inadequate skills and fear of failure can create a reaction to change. The resistance to change is almost always caused by personal/individual concerns. Business intelligence leaders must be central to understanding and mitigating the individual resistance related to these organizational changes…Research shows that people problems are the most commonly cited reason for project failures. Experience indicates that people problems are also a dilemma for the changes associated with business intelligence initiatives.

Maureen Clary proposes the use of the ADKAR model to successfully manage the “people dimension of change” a business intelligence system will bring.  I encourage you to read the article for a solid overview of the ADKAR model and its utilization within a BI project.  I will cite here the “D”  of ADKAR – Desire – which appears to me to be the most important of the five change model elements for a BI project:

Desire or motivation refers to the way an organization’s system of rewards and punishments either encourages or discourages behaviors…Motivation is important at both an individual level and to overall organizational success. Rewards and recognition can help compensate for the inherent uncertainty associated with any type of complex change, including business intelligence…Rewards should be structured so that individuals benefit when the organization is successful and the organization benefits when the individual is successful. Otherwise, neither the organization nor the individuals will be aligned toward achieving the results.

Successfully aligning of individual interests with overall organizational interests will require senior management to openly embrace a new culture of data-informed decision making that unabashedly ”confronts the brutal facts” of the organization (as Cindi Howson quotes Jim Collins’ Good to Great), and does so even-handedly across the organization. No sacred cows!  Plus, the fear of “killing the messenger” must be taken off the table.  In other words, managers and workers must have the confidence to call attention to business problems identified by BI data (even in their own areas of responsibility).

By openly embracing and cultivating such a new business culture, the organization can experience a like-new, “fresh” start with its new business intelligence system, successfully “turning the box inside out.”

Photo by Zevotron (Flickr CC)

I have written before about the risks of misusing business intelligence software and misinterpreting business intelligence data results.  To help avoid these and many other business risks, organizations should encourage employees to be “intelligently disobedient.”

This term is from a great article by The Leader as a Mensch (click for my review) author Bruna Martinuzzi which cleverly presents this highly beneficial trait:

I once worked for a technology company that encouraged employees to practice what they called “Intelligent Disobedience.” The concept originates from Seeing Eye dogs: while dogs must learn to obey the commands of a blind person, they must also know when they need to disobey commands that can put the owner in harm’s way, such as when a car is approaching.

Intelligent disobedience is not about setting out to be disagreeable or arbitrarily disobeying rules for its own sake. Rather, it is about using your judgment to decide when, for example, an established rule actually hinders your organization, rather than helps it…the antonym of intelligent disobedience is blind conformity.

That blind conformity, all too rampant in weakly-run organizations with executives preoccupied with loyalty, can rear its head any number of ways, including using business intelligence/business performance management software to “do the wrong things right.”  Blind conformity can also result in incuriously sticking to conventional wisdom within product marketing or product management, resulting in, for example, a preoccupation with competitors instead of actively differentiating your products from the market.

Bruna Martinuzzi offers a number of ideas to encourage cultivating an environment of intelligent disobedience, several of which are directly applicable to the wise interpreter of business intelligence data, as well as product marketers and product managers, including the following…

Quoted from “Intelligent Disobedience” by Bruna Martunizzi:

Consider the benefits of decentralizing some of the decision-making in your unit. If you are used to making all the decisions, allow those closest to the customer the flexibility to make appropriate decisions on the spot, as for example, to right a wrong, even if the decision is contrary to some established rule of the organization. This places the value where it should be — on customer satisfaction rather than on lockstep adherence to the process — but it also places value on team members by giving them the authority to bend the rules when necessary.

Don’t surround yourself with yes-men. Ponder the words of Barry Rand of Xerox, quoted in Colin Powell’s A Leadership Primer: “. . . if you have a yes-man working for you, one of you is redundant.”

Help your people distinguish between fact and conjecture. Conjecture can be influenced by mental scripts which don’t have a bearing on current reality. Be the voice in the room that calls others’ attention to this possibility, and help everyone pause so that they can analyze inferences and conjectures that may or may not be valid.

Be aware of mind traps that lead to blind conformity. Mind traps act as mental straight-jackets, preventing you from thinking creatively and rationally. These include, for example, the “herd instinct”, i.e. relying on the fact that “everybody else is doing it.” Here is a compiled list of the ten most common thinking traps.

Read Bruna Martinuzzi’s complete post.

It’s no earth-shattering statement, nor is it a flattering commentary on our society: ads featuring attractive, provocative women have a proven track record selling everything from cars to beer to men’s fragrances to hokey get rich quick real estate plans, advertised on hilarious late night TV ads in the 80′s.

But can it sell Business Intelligence software?

Should it?

There is a video ad that seems to think so, which I stumbled upon via a fellow WordPress blogsite “Integrate the Clouds.”

The ad in question is here (SFW):

“I know what you’re thinking,” says as the attractive model as she dives in a pool. Well, as a husband and father of two teenage daughters, I was thinking about the SAP BusinessObjects ad marketing team’s assumption that IT decision makers are all men (and that this ad is the right way to market to them).

In that light, the ad is a sobering reminder of how few women choose to pursue computer science degrees and careers in technology. Indeed, only about 9% of all CIOs are female. 

That said, why would any company risk alientating 9% of its market? Carve the BI/DW market up based on verticals, namely higher education, with women occupying about 25% of CIO posts, and suddenly this ad is risking irking a quarter of that particular vertical.

You were supposed to give the bus driver the number of your bus stop near your car.  Running late, I rushed to catch my departing flight and didn’t make note of the number, but I knew where I was in relation to the entire lot.

“Excuse me,” I said to the bus driver, “but I don’t have my bus stop number. Can you just drop me off at whatever stop is near to the far right corner of the lot?”

“What’s the number?” grunted the bus driver.

“I don’t have the number.  But I know my car is near the far right corner of the lot from where we are right now.”

A slightly louder grunt this time: “What’s the number?”

Huh…?! After one more similarly circular exchange I said, “Sir, any stop near the far corner of the lot will be just fine…”

One of my compadres from the trade show mercifully interjected with a stop number he knew was somewhat close to my car. The bus driver, now given “the number,” did silently acquiesce to stop there, his eyes forward. Note that there was no language barrier or misunderstanding. The driver could also hear me just fine. But he was simply locked into his own way of thinking to a degree beyond the pale.

The way a person communicates is a major part of their reputation, and therefore, their personal brand.  I also suggest the vast majority of communication problems are caused by the personal baggage we bring to the table when communicating, known in sociological terms as conditional confirmation bias.  

Specifically, a person might pay attention to those bits of a conversation that verify their beliefs about the matter at hand, and ignore other information that doesn’t fit that pre-existing bias. That bus driver was certainly was not communicating with me; for all I know, his confirmation bias may have been, who does this guy think he is for not knowing his bus stop number…?!

There is an excellent book I am now reading of relevance to this topic: Know What You Don’t Know: How Great Leaders Prevent Problems Before They Happen by Michael Roberto.  A book for anyone involved in management, marketing communication, or business intelligence/data warehousing, Michael Roberto’s book addresses human flaws in how we interpret, communicate and act upon information.  Michael Roberto explores why failures to discover informational insight and/or act upon it happen.  He draws illuminating examples from business, healthcare and world events (including 9/11). Michael Roberto also highlights the active behaviors and actions needed to effectively communicate and see the forest through the trees of information to prevent crises before they happen.  I look forward to sharing more when I finish this book.

I recently read that vast majority of all organizations in a recent survey reported very little benefit from Business Intelligence systems, if ever!  While many such BI failures may have been due to various technical problems, I have to wonder: are such lopsided statistics really all due to the Business Intelligence tools and Data Warehousing solutions themselves, or, as Michael Roberto suggests, might it be due to the corporate cultures that discourage asking “politically unpopular” questions based on data analytics, or pooh-pooh “gut feel” when little or no supporting data exists (yet)…? As the saying goes, a bad carpenter blames his tools!

It’s easy to recognize the bus driver’s rudeness and poor communication skills. The much harder question that demands an honest answer is to ask ourselves what personal baggage have we brought to a series of communications that caused misinterpretation of information, inappropriate actions, or worse?  Did we fail to communicate, filter information to match our personal biases, and act in our own way at least a little like that bus driver?