Twitter, Meaningful Conversations, and #FollowFriday

/

In social media, one of the most common features of social networking services is allowing users to share brief status updates.  Twitter is currently built on only this feature and uses status updates (referred to as tweets) that are limited to a maximum of 140 characters, which creates a rather pithy platform that many people argue is incompatible with meaningful communication.

Although I use Twitter for a variety of reasons, one of them is sharing quotes that I find thought-provoking.  For example:

 

This George Santayana quote was shared by James Geary, whom I follow on Twitter because he uses his account to provide the “recommended daily dose of aphorisms.”  My re-tweet (i.e., “forwarding” of another user’s status update) triggered the following meaningful conversation with Augusto Albeghi, the founder of StraySoft who is known as @Stray__Cat on Twitter:

 

Now of course, I realize that what exactly constitutes a “meaningful conversation” is debatable regardless of the format.

Therefore, let me first provide my definition, which is comprised of the following three simple requirements:

  1. At least two people discussing a topic, which is of interest to all parties involved
  2. Allowing all parties involved to have an equal chance to speak (or otherwise share their thoughts)
  3. Attentively listening to the current speaker—as opposed to merely waiting for your turn to speak

Next, let’s examine why Twitter’s format can be somewhat advantageous to satisfying these requirements:

  1. Although many (if not most) tweets are not necessarily attempting to start a conversation, at the very least they do provide a possible topic for any interested parties
  2. Everyone involved has an equal chance to speak, but time lags and multiple simultaneous speakers can occur, which in all fairness can happen in any other format
  3. Tweets provide somewhat of a running transcript (again, time lags can occur) for the conversation, making it easier to “listen” to the other speaker (or speakers)

Now, let’s address the most common objection to Twitter being used as a conversation medium:

“How can you have a meaningful conversation when constrained to only 140 characters at a time?”

I admit to being a long-winded talker or, as a favorite (canceled) television show would say, “conversationally anal-retentive.”  In the past (slightly less now), I was also known for e-mail messages even Leo Tolstoy would declare to be far too long.

However, I wholeheartedly agree with Jennifer Blanchard, who explained how Twitter makes you a better writer.  When forced to be concise, you have to focus on exactly what you want to say, using as few words as possible.

I call this reduction of your message to its bare essence—the power of pith.  In order to engage in truly meaning conversations, this is a required skill we all must master, and not just for tweeting—but Twitter does provide a great practice environment.

 

At least that’s my 140 characters worth on this common debate—well okay, it’s more like my 5,000 characters worth.

 

Great folks to follow on Twitter

Since this blog post was published on a Friday, which for Twitter users like me means it’s FollowFriday, I would like to conclude by providing a brief list of some great folks to follow on Twitter. 

Although by no means a comprehensive list, and listed in no particular order whatsoever, here are some great tweeps, and especially if you are interested in Data Quality, Data Governance, Master Data Management, and Business Intelligence:

 

PLEASE NOTE: No offense is intended to any of my tweeps not listed above.  However, if you feel that I have made a glaring omission of an obviously Twitterific Tweep, then please feel free to post a comment below and add them to the list.  Thanks!

I hope that everyone has a great FollowFriday and an even greater weekend.  See you all around the Twittersphere.

 

Related Posts

Wordless Wednesday: June 16, 2010

Data Rock Stars: The Rolling Forecasts

The Fellowship of #FollowFriday

Social Karma (Part 7)

The Wisdom of the Social Media Crowd

The Twitter Clockwork is NOT Orange

Video: Twitter #FollowFriday – January 15, 2010

Video: Twitter Search Tutorial

Live-Tweeting: Data Governance

Brevity is the Soul of Social Media

If you tweet away, I will follow

Tweet 2001: A Social Media Odyssey

MacGyver: Data Governance and Duct Tape

/

One of my favorite 1980s television shows was MacGyver, which starred Richard Dean Anderson as an extremely intelligent and endlessly resourceful secret agent, known for his practical application of scientific knowledge and inventive use of common items.

While I was thinking about the role of both data stewards and data cleansing within a successful data governance program, the two things that immediately came to mind were MacGyver, and the other equally versatile metaphor for versatility—duct tape. 

I decided to combine these two excellent metaphors by envisioning MacGyver as a data steward and duct tape as data cleansing.

 

Data Steward: The MacGyver of Data Governance

Since “always prepared for adventure” was one of the show’s taglines, I think MacGyver would make an excellent data steward.

The fact that the activities associated with the role can vary greatly, almost qualifies “data steward” as a MacGyverism.  Your particular circumstances, and especially the unique corporate culture of your organization, will determine the responsibilities of your data stewardship function, but the general principles of data stewardship, as defined by Jill Dyché, include the following:

  • Stewardship is the practice of managing or looking after the well being of something.
  • Data is an asset owned by the enterprise.
  • Data stewards do not necessarily “own” the data assigned to them.
  • Data stewards care for data assets on behalf of the enterprise.

Just like MacGyver’s trusted sidekick—his Swiss Army knife—the most common trait of a data steward may be versatility. 

I am not suggesting that a data steward is a jack of all trades, but master of none.  However, a data steward often has a rather HedgeFoxian personality, thereby possessing the versatility necessary to integrate disparate disciplines into practical solutions.

In her excellent article Data Stewardship Strategy, Jill Dyché outlined six tried-and-true techniques that can help you avoid some common mistakes and successfully establish a data stewardship function within your organization.  The second technique provides a few examples of typical data stewardship activities, which often include assessing and correcting data quality issues.

 

Data Cleansing: The Duct Tape of Data Quality

About poor data quality, MacGyver says, “if I had some duct tape, I could fix that.”  (Okay—so he says that about everything.)

Data cleansing is the duct tape of data quality.

Proactive defect prevention is highly recommended, even though it is impossible to truly prevent every problem before it happens, because the more control enforced where data originates, the better the overall quality will be for enterprise information. 

However, when poor data quality negatively impacts decision-critical information, the organization may legitimately prioritize a reactive short-term response—where the only remediation will be finding and fixing the immediate problems. 

Of course, remediation limited to data cleansing alone will neither identify nor address the burning root cause of those problems. 

Effectively balancing the demands of a triage mentality with a best practice of implementing defect prevention wherever possible, will often create a very challenging situation for data stewards to contend with on a daily basis.  However, like MacGyver says:

“When it comes down to me against a situation, I don’t like the situation to win.”

Therefore, although comprehensive data remediation will require combining reactive and proactive approaches to data quality, data stewards need to always keep plenty of duct tape on hand (i.e., put data cleansing tools to good use whenever necessary).

 

The Data Governance Foundation

In the television series, MacGyver eventually left the clandestine service and went to work for the Phoenix Foundation. 

Similarly, in the world of data quality, many data stewards don’t formally receive that specific title until they go to work helping to establish your organization’s overall Data Governance Foundation.

Although it may be what the function is initially known for, as Jill Dyché explains, “data stewardship is bigger than data quality.”

“Data stewards establish themselves as adept at executing new data governance policies and consequently, vital to ongoing information management, they become ambassadors on data’s behalf, proselytizing the concept of data as a corporate asset.”

Of course, you must remember that many of the specifics of the data stewardship function will be determined by your unique corporate culture and where your organization currently is in terms of its overall data governance maturity.

Although not an easy mission to undertake, the evolving role of a data steward is of vital importance to data governance.

The primary focus of data governance is the strategic alignment of people throughout the organization through the definition, and enforcement, of policies in relation to data access, data sharing, data quality, and effective data usage, all for the purposes of supporting critical business decisions and enabling optimal business performance. 

I know that sounds like a daunting challenge (and it definitely is) but always remember the wise words of Angus MacGyver:

“Brace yourself.  This could be fun.”

Related Posts

The Prince of Data Governance

Jack Bauer and Enforcing Data Governance Policies

The Circle of Quality

A Tale of Two Q’s

Live-Tweeting: Data Governance

 

Follow OCDQ

If you enjoyed this blog post, then please subscribe to OCDQ via my RSS feed, my E-mail updates, or Google Reader.

You can also follow OCDQ on Twitter, fan the Facebook page for OCDQ, and connect with me on LinkedIn.


Wednesday Word: June 23, 2010

/

Wednesday Word is an OCDQ regular segment intended to provide an occasional alternative to my Wordless Wednesday posts.  Wednesday Word provides a word (or words) of the day, including both my definition and an example of recommended usage.

 

Referential Narcissisity

Definition – When referential integrity is enforced, a relational database table’s foreign key columns must only contain data values from their parent table’s primary key column, but referential narcissisity occurs when a table’s foreign key columns refuse to acknowledge data values from their alleged parent table—especially when the parent table was created by another DBA.

Example – The following scene is set on the eighth floor of the Nemesis Corporation, where within the vast cubicle farm of the data architecture group, Bob, a Business Analyst struggling with an ad hoc report, seeks the assistance of Doug, a Senior DBA.

Bob: “Excuse me, Doug.  I don’t mean to bother you, I know you are a very busy and important man, but I am trying to join the Sales Transaction table to the Customer Master table using Customer Key, and my queries always return zero rows.”

Doug: “That is because although Doug created the Sales Transaction table, the Customer Master table was created by Craig.  Doug’s tables do not acknowledge any foreign key relationships with Craig’s tables.  Doug is superior to Craig in every way.  Doug’s Kung Fu is the best—and until Craig publicly acknowledges this, your joins will not return any rows.”

Bob: “Uh, why do you keep referring to yourself in the third person?”

Doug: “Doug is bored with this conversation now.  Be gone from my sight, lowly business analyst.  You should be happy that Doug even acknowledged your presence at all.” 

 

Related Posts

Wednesday Word: June 9, 2010 – C.O.E.R.C.E.

Wednesday Word: April 28, 2010 – Antidisillusionmentarianism

Wednesday Word: April 21, 2010 – Enterpricification

Wednesday Word: April 7, 2010 – Vendor Asskisstic

The Balancing Act of Awareness

/

This is my sixth blog post tagged Karma since I promised to discuss it directly and indirectly on my blog throughout the year after declaring KARMA my theme word for 2010 back on the first day of January—surprisingly now almost six months ago.

Lately I have been contemplating the importance of awareness, and far more specifically, the constant challenge involved in maintaining the balance between our self-awareness and our awareness of others.

The three sections below are each prefaced by a chapter from Witter Bynner’s “American poetic” translation of the Tao Te Ching.  I certainly do not wish to offend anyone’s religious sensibilities—I am using these references in a philosophical and secular sense.

Since I also try to balance my philosophy between Eastern and Western influences, Lao Tzu won’t be the only “old master” cited.

Additionally, please note that the masculine language (e.g., “he” and “man”) used in the selected quotes below is a by-product of the age of the original texts (e.g., the Tao Te Ching is over 2,500 years old).  Therefore, absolutely no gender bias is intended.

 

Self-Awareness

“Nothing can bring you peace but yourself.”  Ralph Waldo Emerson wrote this sentence in the closing lines of his wonderful essay on Self-Reliance, which is one of my all-time favorites even though I first read it over 25 years ago.  My favorite passage is:

“What I must do is all that concerns me, not what the people think.  This rule, equally arduous in actual and in intellectual life, may serve for the whole distinction between greatness and meanness.  It is the harder because you will always find those who think they know what is your duty better than you know it.  It is easy in the world to live after the world’s opinion; it is easy in solitude to live after our own; but the great man is he who in the midst of the crowd keeps with perfect sweetness the independence of solitude.”

Emerson’s belief in the primacy of the individual was certainly not an anti-social sentiment.

Emerson believed society is best served whenever individuals possess a healthy sense of self and a well-grounded self-confidence, both of which can only be achieved if we truly come to know who we are on our own terms.

Writing more than 150 years later, and in one of my all-time favorite non-fiction books, The 7 Habits of Highly Effective People, Stephen Covey explains the importance of first achieving independence through self-mastery before successful interdependence with others is possible.  “Interdependence is a choice only independent people can make,” Covey explained.  “Dependent people cannot choose to become interdependent.  They don’t have the character to do it; they don’t own enough of themselves.”

“Private victories precede public victories,” wrote Covey, explaining that the private victories of independence are the essence of our character growth, and provide the prerequisite foundation necessary for the public victories of interdependence.

Of course, the reality is that self-awareness and independence cannot be developed only during our moments of solitude.

We must interact with others even before we have achieved self-mastery.  Furthermore, self-mastery is a continuous process.  Although self-awareness is essential for effectively interacting with others, it provides no guarantee for social success.

However, as William Shakespeare taught us by way of the character Polonius in Hamlet:

“This above all—to thine own self be true;
And it must follow, as the night the day,
Thou canst not then be false to any man.”

Other-Awareness

Empathy, which is central to our awareness of others (i.e., other-awareness), is often confused with sympathy.

Sympathy is an agreement of feeling that we express by providing support or showing compassion for the suffering of others.  Empathy is an identification with the emotions, thoughts, or perspectives expressed by others.

The key difference is found between the words agreement and identification.

Sympathy is the ability to relate oneself to others.  Empathy is the ability to see the self in others—not your self, but the unique self within each individual.  Sympathy is about trying to comfort others.  Empathy is about trying to understand others.

“Empathy is not sympathy,” explains Covey.  “Sympathy is a form of agreement, a form of judgment.  And it is sometimes the more appropriate response.  But people often feed on sympathy.  It makes them dependent.  The essence of empathy is not that you agree with someone; it’s that you fully, deeply, understand that person, emotionally as well as intellectually.”

Although both sympathy and empathy are important, empathy is more crucial for other-awareness.

We often simply act sympathetic when in the presence of others.  Therefore, sympathy is sometimes all too easy to feign and can easily remain superficial.  Empathy is less ostentatious, but can exert a far more powerfully positive influence over others.

In the words of Roy Schafer, who emphasized the role of narrative (i.e., the interpretation of our life stories) in psychoanalysis:

“Empathy involves the inner experience of sharing in and comprehending the momentary psychological state of another person.”

Balanced Awareness

Although it is easy to be aware of only our good qualities, while at the same time, only be aware of the bad qualities of others, these convenient blind spots in our awareness can also become our greatest teachers. 

Borrowing the wise words of Socrates, which thankfully were recorded for us by Plato:

“The unexamined life is not worth living.”

Examining our awareness, and shifting its focus when appropriate between self-awareness and other-awareness truly requires a delicate balancing act. 

When we become preoccupied with self-awareness, our consideration for others suffers.  Likewise, if we become too focused on other-awareness, we can neglect our own basic needs.

Aristotle wrote about such challenges using what he called the Golden Mean, which is usually simplified into the sage advice:

“Moderation in all things.” 

Obviously, there will be times when self-awareness must be our priority, and other times when it must become other-awareness. 

I believe that there is no such thing as achieving a perfect balance, but if we remain true to our own character, then hopefully a consistency will flow freely throughout all of our behaviors, our actions, and our communication and collaboration with others.

 

Related Posts

The Importance of Envelopes

The Point of View Paradox

The Challenging Gift of Social Media

True Service

The Game of Darts – An Allegory

“I can make glass tubes”

My #ThemeWord for 2010: KARMA

Promoting Poor Data Quality

/

A few months ago, during an e-mail correspondence with one of my blog readers from Brazil (I’ll let him decide if he wishes to remain anonymous or identify himself in the comments section), I was asked the following intriguing question:

“Who profits from poor data quality?”

The specific choice of verb (i.e., “profits”) may have been a linguistic issue, by which I mean that since I don’t know Portuguese, our correspondence had to be conducted in English. 

Please don’t misunderstand me—his writing was perfectly understandable. 

As I discussed in my blog post Can Social Media become a Universal Translator?, my native language is English, and like many people from the United States, it is the only language I am fluent in.  My friends from Great Britain would most likely point that I am only fluent in the American “version” of the English language, but that’s a topic for another day—and another blog post.

When anyone communicates in another language—and especially in writing—not every word may be exactly right. 

For example: Muito obrigado por sua pergunta!

Hopefully (and with help from Google Translate), I just wrote “thank you for your question” in Portuguese.

My point is that I believe he was asking why poor data quality continues to persist as an extremely prevalent issue, especially when its detrimental effects on effective business decisions has become painfully obvious given the recent global financial crisis.

However, being mentally stuck on my literal interpretation of the word “profit” has delayed my blog post response—until now.

 

Promoting Poor Data Quality

In economics, the term “flight to quality” describes the aftermath of a financial crisis (e.g., a stock market crash) when people become highly risk-averse and move their money into safer, more reliable investments.  A similar “flight to data quality” often occurs in the aftermath of an event when poor data quality negatively impacted decision-critical enterprise information. 

The recent recession provides many examples of the financial aspect of this negative impact.  Therefore, even companies that may not have viewed poor data quality as a major risk—and a huge cost greatly decreasing their profits—are doing so now.

However, the retail industry has always been known for its paper thin profit margins, which are due, in large part, to often being forced into the highly competitive game of pricing.  Although dropping the price is the easiest way to sell just about any product, it is also virtually impossible to sustain this rather effective, but short-term, tactic as a viable long-term business strategy. 

Therefore, a common approach used to compete on price without risking too much on profit is to promote sales using a rebate, which I believe is a business strategy intentionally promoting poor data quality for the purposes of increasing profits.

 

You break it, you slip it—either way—you buy it, we profit

The most common form of a rebate is a mail-in rebate.  The basic premise is simple.  Instead of reducing the in-store price of a product, it is sold at full price, but a rebate form is provided that the customer can fill out and mail to the product’s manufacturer, which will then mail a rebate check to the customer—usually within a few business weeks after approving the rebate form. 

For example, you could purchase a new mobile phone for $250 with a $125 mail-in rebate, which would make the “sale price” only $125—which is what the store will advertise as the actual sale price with “after a $125 mail-in rebate” written in small print.

Two key statistics significantly impact the profitability of these type of rebate programs, breakage and slippage.

Breakage is the percentage of customers who, for reasons I will get to in a moment, fail to take advantage of the rebate, and therefore end up paying full price for the product.  Returning to my example, the mobile phone that would have cost $125 if you received the $125 mail-in rebate, instead becomes exactly what you paid for it—$250 (plus applicable taxes, of course).

Slippage is the percentage of customers who either don’t mail in the rebate form at all, or don’t cash their received rebate check.  The former is the most common “slip,” while the latter is usually caused by failing to cash the rebate check before it expires, which is typically 30 to 90 days after it is processed (i.e., expiration dated)—and regardless of when it is actually received.

Breakage, and the most common form of slippage, are generally the result of making the rebate process intentionally complex. 

Rebate forms often require you to provide a significant amount of information, both about yourself and the product, as well as attach several “proofs of purchase” such as a copy of the receipt and the barcode cut out of the product’s package. 

Data entry errors are perhaps the most commonly cited root cause of poor data quality. 

Rebates seem designed to guarantee data entry errors (by encouraging the customer to fill out the rebate form incorrectly). 

In this particular situation, the manufacturer is hyper-vigilant about data quality and for an excellent reason—poor data quality will either delay or void the customer’s rebate. 

Additionally, the fine print of the rebate form can include other “terms and conditions” voiding the rebate—even if the form is filled out perfectly.  A common example is the limitation of “only one rebate per postal address.”  This sounds reasonable, right? 

Well, one major electronics manufacturer used this disclaimer to disqualify all customers who lived in multiple unit dwellings, such as an apartment building, where another customer “at the same postal address” had already applied for a rebate.

 

Conclusion

Statistics vary by product and region, but estimates show that breakage and slippage combine on average to result in 40% of retail customers paying full price when making a purchasing decision based on a promotional price requiring a mail-in rebate.

So who profits from poor data quality?  Apparently, the retail industry does—sometimes. 

Poor data quality (and poor information quality in the case of intentionally confusing fine print) definitely has a role to play with mail-in rebates—and it’s a supporting role that can definitely lead to increased profits. 

Of course, the long-term risks and costs associated with alienating the marketplace with gimmicky promotions take their toll. 

In fact, the major electronics manufacturer mentioned above was actually substantially fined in the United States and forced to pay hundreds of thousands of dollars worth of denied mail-in rebates to customers.

Therefore, poor data quality, much like crime, doesn’t pay—at least not for very long.

I am not trying to demonize the retail industry. 

Excluding criminal acts of intentional fraud, such as identity theft and money laundering, this was the best example I could think of that allowed me to respond to a reader’s request—without using the far more complex example of the mortgage crisis.

 

What Say You?

Can you think of any other examples of the possible benefits—intentional or accidental—derived from poor data quality?

The Prince of Data Governance

/

Machiavelli

The difference between politics and policies was explained in the recent blog post A New Dimension in Data Governance Directives: Politics by Jarrett Goldfedder, who also discussed the need to consider the political influences involved, as they can often have a far greater impact on our data governance policies than many choose to recognize.

I definitely agree, especially since the unique corporate culture of every organization carries with it the intricacies and complexities of politics that Niccolò Machiavelli (pictured) wrote about in his book The Prince.

The book, even despite the fact it was written in the early 16th century, remains a great, albeit generally regarded as satirical, view on politics.

The Prince provides a classic study of the acquisition, expansion, and effective use of political power, where the ends always justify the means.

An example of a Machiavellian aspect of the politics of data governance is when a primary stakeholder, while always maintaining the illusion of compliance, only truly complies with policies when it suits the very purposes of their own personal agenda, or when it benefits the interests of the business unit that they represent on the data governance board.

 

Creating Accountability

In her excellent comment on my recent blog post Jack Bauer and Enforcing Data Governance Policies, Kelle O'Neal provided a link to the great article Creating Accountability by Nancy Raulston, which explains that there is a significant difference between increasing accountability (e.g., for compliance with data governance policies) and simply getting everyone to do what they’re told (especially if you have considered resorting to the use of a Jack Bauer approach to enforcing data governance policies).

Raulston shares her high-level thoughts about the key aspects of alignment with vision and goals, achieving clarity on actions and priorities, establishing ownership of processes and responsibilities, the structure of meetings, and the critical role of active and direct communication—all of which are necessary to create true accountability.

“Accountability does not come from every single person getting every single action item done on time,” explains Raulston.  “It arises as groups actively manage the process of making progress, raising and resolving issues, actively negotiating commitments, and providing direct feedback to team members whose behavior is impeding the team.”

Obviously, this is often easier said than done.  However, as Raulston concludes, “ultimate success comes from each person being willing to honestly engage in the process, believing that the improved probability of success outweighs any momentary discomfort from occasionally having to admit to not having gotten something done.”  Or perhaps more important, occasionally having to be comfortable with not having gotten what would suit their personal agenda, or benefit the interests of their group.

 

The Art of the Possible

“Right now, our only choice,” as Goldfedder concluded his post, “is to hope that the leaders in charge of the final decisions can put their own political goals aside for the sake of the principles and policies they have been entrusted to uphold and protect.”

Although I agree, as well as also acknowledge that the politics of data governance will always make it as much art as it is science, I can not help but be reminded of the famous words of Otto von Bismarck:

“Politics is the art of the possible.”

The politics of data governance are extremely challenging, and yes, at times rather Machiavellian in their nature. 

Although it is certainly by no means an easy endeavor for either you or your organization to undertake, neither is achieving a successful and sustainable data governance program impossible. 

Politics may be The Prince of Data Governance, but as long as Communication and Collaboration reign as King and Queen, then Data Governance is the Art of the Possible.

 

Please share your thoughts about the politics of data governance, as well as your overall perspectives on data governance.

 

Follow OCDQ

If you enjoyed this blog post, then please subscribe to OCDQ via my RSS feed, my E-mail updates, or Google Reader.

You can also follow OCDQ on Twitter, fan the Facebook page for OCDQ, and connect with me on LinkedIn.


The Importance of Envelopes

/

No, this is not going to be a blog post about postal address data quality.

It is understandable, however, if that was your first impression.  An envelope is commonly associated with mailing some form of written correspondence, either of a personal nature (e.g., a greeting card) or of a business nature (e.g., a bill requesting payment).

Although the history of envelopes is somewhat interesting, and its future is somewhat uncertain in our increasingly digital world, in this post, I’m going to use envelopes—regular readers will be less than shocked—as a metaphor for effective communication.

 

The History of Human Communication (An Abridged Version)

Long before written language evolved, humans shared their thoughts and feelings with others using hand and facial gestures, monosyllabic and polysyllabic grunting, as well as crude drawings and other symbolism.

As spoken language evolved, it increased our ability to communicate by using words as verbal symbols for emotions and ideas.  Listening to stories, and retelling them to others, became the predominant means of education and “recording” our history.

Improved symbolism via more elaborate drawings, sculptures, and other physical and lyrical works of artistic expression, greatly enhanced our ability to not only communicate, but also leave a lasting legacy beyond the limits of our individual lives.

Written language, it could be argued, provided a quantum leap in human evolution.  Writing (and reading) greatly improved our ability to communicate, educate, record our history, and thereby pass on our knowledge and wisdom to future generations.

Of course, both before and after the evolution of written language, music played a vital role in the human experience, and without doubt will continue to powerfully communicate with us through instrumental, lyrical, and theatrical performances.

Even if nowadays we get most of our stories from television, movies, or the Internet, and less from reading books or from having in-person conversations, listening to the stories of others continues to play an integral role in human communication.

One of the best aspects of the current digital communication revolution is that it is reinvigorating the story culture of our evolutionary past, providing us with more immediate and expanded access to our collective knowledge, experience, and wisdom.

 

The Symbiotic Relationship of Message and Medium

Marshall McLuhan coined the phrase “the medium is the message” to indicate that the form of a medium embeds itself within the message, thereby creating a symbiotic relationship through which the medium influences how the message is perceived.

McLuhan believed that the medium through which you receive a message effects your understanding of it.  Going even further, he adamantly believed that how the message is delivered is more important than the information content of the message itself.

Compare that perspective with the 7%-38%-55% rule of Albert Mehrabian, which explains that when others are evaluating your in-person communication of your feelings and attitudes (e.g., whether you like or dislike something), here is how the relative importance of the factors involved are distributed:

  • 7% Verbal (i.e., the words that you speak)
  • 38% Vocal (i.e., the tone of your voice)
  • 55% Visual (i.e., your facial expressions and other gestures)

Mehrabian repeatedly emphasized that this formula is only accurate for face-to-face—and emotionally charged—discussions. 

In other words, he was not discounting the value of verbal communication in favor of non-verbal.  Mehrabian was trying to explain why, under certain circumstances, your words matter far less than you think they do—and believe they should.

Even as the digital age continues to bring us new mediums and new messages, both McLuhan and Mehrabian were emphasizing a somewhat similar and still extremely relevant point about the inherently complex nature of human communication:

Your message does matter—but how you deliver it, matters just as much.

 

The Importance of Envelopes

 

Envelopes are just as important as the message they deliver for the following three reasons:

  1. Envelopes tell your audience who you are
  2. Envelopes show you paid for the postage delivering your message
  3. Envelopes are personally addressed to the center of your attention: your audience 

 

Envelopes tell your audience who you are

Your envelope is your first impression—and we all know how many chances you get to make a good one of those. 

Envelopes tell your audience who you really are—and I am not talking about your résumé, LinkedIn profile, and about page.  Those things don’t tell your audience anything other than why you think you’re so damn special that they should listen to you.

Don’t make your audience hear this every time you open your mouth (or write a sentence, or interpretively dance, or whatever):

“Me, me, me, me—I am so important that you should listen to only—me, me, me, me!” 

Your envelope is about your personality, integrity, and humanity—and not about your professional and academic qualifications.  Your envelope should prove that you are a human being, first and foremost.  All of that other stuff is mostly fluff anyway.

 

Envelopes show you paid for the postage delivering your message

Paying for the postage on your envelope means that you performed the preparation necessary before delivering your message. 

Paying for the postage means that you have done your research.  You understand what your audience is looking for, and you have put thought and care into how to best deliver them useful information or provide them assistance with a specific problem. 

Proving you literally paid the postage on a mailed letter is obvious since the recipient would not otherwise receive your letter. 

Unfortunately, it is not that obvious with all of your messages, neither for you nor for your audience.  Oftentimes, it won’t be until after your message has been received that your audience will decide if you have effectively delivered your message. 

If you have not, then they’ll mark your message Return to Sender—because you clearly didn’t pay the postage on your envelope.

 

Envelopes are personally addressed to the center of your attention

Ancient Roman amphitheatres were so-named because of their shape, which resembled that of two theatres joined together, forming a central performance space surrounded by ascending seating, thus maximizing their capacity for large audiences.

Amphitheatres literally put the performance on center stage, thus making the performer the center of the audience’s attention.

When delivering your message, and regardless of the actual logistics of the venue, you are probably imagining yourself standing on center stage, with all of your audience’s eyes and ears properly focused on you, and only you, just like it should be—NOT!

Here’s the program for the actual performance:  Your audience isn’t here for you—you are here for your audience. 

Therefore, you must always be focused on what you are or aren’t doing for your audience.  If instead you are focused more on yourself than you are on your audience, then don’t be too surprised if you don’t have much of an audience—if any at all.

Your envelope must always be personally addressed to the center of your attention, which must always be your audience.

 

Effective Communication

The importance of envelopes is they remind us that the way we deliver our message is just as important as our message. 

Regardless of how we are communicating, whether it be writing, blogging, presenting, speaking, or an in-person conversation, we need to always remember the importance of envelopes—both our own envelopes as well as the envelopes of others.

Practicing effective communication requires shutting our mouth, opening our ears, and empathically listening to each other, instead of continuing to practice ineffective communication, where we merely take turns throwing word-darts at each other.

 

My message to you

Since earlier in this blog post, I used an illustration of an envelope, I thought it best to conclude with an illustration of a message:

Jack Bauer and Enforcing Data Governance Policies

/

Jack Bauer

In my recent blog post Red Flag or Red Herring?, I explained that the primary focus of data governance is the strategic alignment of people throughout the organization through the definition, and enforcement, of policies in relation to data access, data sharing, data quality, and effective data usage, all for the purposes of supporting critical business decisions and enabling optimal business performance.

Simply establishing these internal data governance policies is often no easy task to accomplish.

However, without enforcement, data governance policies are powerless to affect the real changes necessary.

(Pictured: Jack Bauer enforcing a data governance policy.)

 

Jack Bauer and Data Governance

Jill Wanless commented that “sometimes organizations have the best of intentions.  They establish strategic alignment and governing policies (no small feat!) only to fail at the enforcement and compliance.  I believe some of this behavior is due to the fact that they may not know how to enforce effectively, without risking the very alignment they have established.  I would really like to see a follow up post on what effective enforcement looks like.”

As I began drafting this requested blog post, the first image that came to my mind for what effective enforcement looks like was Jack Bauer, the protagonist of the popular (but somewhat controversial) television series 24.

Well-known for his willingness to do whatever it takes, you can almost imagine Jack explaining to executive management:

“The difference between success and failure for your data governance program is the ability to enforce your policies.  But the business processes, technology, data, and people that I deal with, don’t care about your policies.  Every day I will regret looking into the eyes of men and women, knowing that at any moment, their jobs—or even their lives—may be deemed expendable, in order to protect the greater corporate good. 

I will regret every decision and mistake I have to make, which results in the loss of an innocent employee.  But you know what I will regret the most?  I will regret that data governance even needs people like me.”

Although definitely dramatic and somewhat cathartic, I don’t think it would be the right message for this blog post.  Sorry, Jack.

 

Enforcing Data Governance Policies

So if hiring Jack Bauer isn’t the answer, what is?  I recommend the following five steps for enforcing data governance policies, which I have summarized into the following simple list and explain in slightly more detail in the corresponding sections below:

  1. Documentation – Use straightforward, natural language to document your policies in a way everyone can understand.
  2. Communication – Effective communication requires that you encourage open discussion and debate of all viewpoints.
  3. Metrics – Truly meaningful metrics can be effectively measured, and represent the business impact of data governance.
  4. Remediation – Correcting any combination of business process, technology, data, and people—and sometimes, all four. 
  5. Refinement – You must dynamically evolve and adapt your data governance policies—as well as their associated metrics.

 

Documentation

The first step in enforcing data governance policies is effectively documenting the defined policies.  As stated above, the definition process itself can be quite laborious.  However, before you can expect anyone to comply with the new policies, you first have to make sure that they can understand exactly what they mean. 

This requires documenting your polices using a straightforward and natural language.  I am not just talking about avoiding the use of techno-mumbo-jumbo.  Even business-speak can sound more like business-babbling—and not just to the technical folks.  Perhaps most important, avoid using acronyms and other lexicons of terminology—unless you can unambiguously define them.

For additional information on aspects related to documentation, please refer to these blog posts:

 

Communication

The second step is the effective communication of the defined and documented data governance policies.  Consider using a wiki in order to facilitate easy distribution, promote open discussion, and encourage feedback—as well as track all changes.

I always emphasize the importance of communication since it’s a crucial component of the collaboration that data governance truly requires in order to be successful. 

Your data governance policies reflect a shared business understanding.  The enforcement of these policies has as much to do with enterprise-wide collaboration as it does with supporting critical business decisions and enabling optimal business performance.

Never underestimate the potential negative impacts that the point of view paradox can have on communication.  For example, the perspectives of the business and technical stakeholders can often appear to be diametrically opposed. 

At the other end of the communication spectrum, you must also watch out for what Jill Dyché calls the tyranny of consensus, where the path of least resistance is taken, and justifiable objections either remain silent or are silenced by management. 

The tyranny of consensus is indeed the antithesis of the wisdom of crowds.  As James Surowiecki explains in his excellent book, the best collective decisions are the product of disagreement and contest, not consensus or compromise.

Data Governance lives on the two-way Street named Communication (which, of course, intersects with Collaboration Road).

For additional information on aspects related to communication, please refer to these blog posts:

 

Metrics

The third step in enforcing data governance policies is the creation of metrics with tangible business relevance.  These metrics must be capable of being effectively measured, and must also meaningfully represent the business impact of data governance.

The common challenge is that the easiest ones to create and monitor are low-level technical metrics, such as those provided by data profiling.  However, elevating these technical metrics to a level representing business relevance can often, and far too easily, merely establish their correlation with business performance.  Of course, correlation does not imply causation. 

This doesn’t mean that creating metrics to track compliance with your data governance policies is impossible, it simply means you must be as careful with the definition of the metrics as you were with the definition of the policies themselves. 

In his blog post Metrics, The Trap We All Fall Into, Thomas Murphy of Gartner discussed a few aspects of this challenge.

Truly meaningful metrics always align your data governance policies with your business performance.  Lacking this alignment, you could provide the comforting, but false, impression that all is well, or you could raise red flags that are really red herrings.

For additional information on aspects related to metrics, please refer to these blog posts:

 

Remediation

Effective metrics will let you know when something has gone wrong.  Francis Bacon taught us that “knowledge is power.”  However, Jackson Beck also taught us that “knowing is half the battle.”  Therefore, the fourth step in enforcing data governance policies is taking the necessary corrective actions when non-compliance and other problems inevitably arise. 

Remediation can involve any combination of business processes, technology, data, and people—and sometimes, all four. 

The most common is data remediation, which includes both reactive and proactive approaches to data quality. 

Proactive defect prevention is the superior approach.  Although it is impossible to truly prevent every problem before it happens, the more control that can be enforced where data originates, the better the overall quality will be for enterprise information.

However, and most often driven by a business triage for critical data problems, reactive data cleansing will be necessary. 

After the root causes of the data remediation are identified—and they should always be identified—then additional remediation may involve a combination of business processes, technology, or people—and sometimes, all three.

Effective metrics also help identify business-driven priorities that determine the necessary corrective actions to be implemented.

For additional information on aspects related to remediation, please refer to these blog posts:

 

Refinement

The fifth and final step is the ongoing refinement of your data governance policies, which, as explained above, you are enforcing for the purposes of supporting critical business decisions and enabling optimal business performance.

As such, your data governance policies—as well as their associated metrics—can never remain static, but instead, they must dynamically evolve and adapt, all in order to protect and serve the enterprise’s continuing mission to survive and thrive in today’s highly competitive and rapidly changing marketplace.  

For additional information on aspects related to refinement, please refer to these blog posts:

 

Conclusion

Obviously, the high-level framework I described for enforcing your data governance policies has omitted some important details, such as when you should create your data governance board, and what the responsibilities of the data stewardship function are, as well as how data governance relates to specific enterprise information initiatives, such as master data management (MDM). 

However, if you are looking to follow a step-by-step, paint-by-numbers, only color inside the lines, guaranteed fool-proof plan, then you are going to fail before you even begin—because there are simply NO universal frameworks for data governance.

This is only the beginning of a more detailed discussion, the specifics of which will vary based on your particular circumstances, especially the unique corporate culture of your organization. 

Most important, you must be brutally honest about where your organization currently is in terms of data governance maturity, as this, more than anything else, dictates what your realistic capabilities are during every phase of a data governance program.

Please share your thoughts about enforcing data governance policies, as well as your overall perspectives on data governance.

 

Follow OCDQ

If you enjoyed this blog post, then please subscribe to OCDQ via my RSS feed, my E-mail updates, or Google Reader.

You can also follow OCDQ on Twitter, fan the Facebook page for OCDQ, and connect with me on LinkedIn.


Wednesday Word: June 9, 2010

/

Wednesday Word is an OCDQ regular segment intended to provide an occasional alternative to my Wordless Wednesday posts.  Wednesday Word provides a word (or words) of the day, including both my definition and an example of recommended usage.

 

C.O.E.R.C.E.

Definition – As opposed to a C.O.E. (Center of Excellence), a C.O.E.R.C.E. is a Center of Enforced Reality called Excellence.

Example – “After a detailed cost-benefit analysis, executive management determined it would be a far more effective strategy to implement a C.O.E.R.C.E. and I have to say, so far it’s really working out quite well for us—seriously, I have to say that.”

 

Related Posts

Wednesday Word: April 28, 2010 – Antidisillusionmentarianism

Wednesday Word: April 21, 2010 – Enterpricification

Wednesday Word: April 7, 2010 – Vendor Asskisstic

Red Flag or Red Herring?

/

A few weeks ago, David Loshin, whose new book The Practitioner's Guide to Data Quality Improvement will soon be released, wrote the excellent blog post First Cuts at Compliance, which examines a challenging aspect of regulatory compliance.

David uses a theoretical, but nonetheless very realistic, example of a new government regulation that requires companies to submit a report in order to be compliant.  An associated government agency can fine companies that do not accurately report. 

Therefore, it’s in the company’s best interest to submit a report because not doing so would raise a red flag, since it would make the company implicitly non-compliant.  For the same reason, it’s in the government agency’s best interest to focus their attention on those companies that have not yet reported—since no checks for accuracy need to be performed on non-submitted reports.

David then raises the excellent question about the quality of that reported, but unverified, data, and shares a link to a real-world example where the verification was actually performed by an investigative reporter—who discovered significant discrepancies.

This blog post made me view the submitted report as a red herring, which is a literacy device, quite common in mystery fiction, where the reader is intentionally misled by the author in order to build suspense or divert attention from important information.

Therefore, when faced with regulatory compliance, companies might conveniently choose a red herring over a red flag.

After all, it is definitely easier to submit an inaccurate report on time, which feigns compliance, than it is to submit an accurate report that might actually prove non-compliance.  Even if the inaccuracies are detected—which is a big IF—then the company could claim that it was simply poor data quality—not actual non-compliance—and promise to resubmit an accurate report.

(Or as is apparently the case in the real-world example linked to in David's blog post, the company could provide the report data in a format not necessarily amenable to a straightforward verification of accuracy.)

The primary focus of data governance is the strategic alignment of people throughout the organization through the definition, and enforcement, of policies in relation to data access, data sharing, data quality, and effective data usage, all for the purposes of supporting critical business decisions and enabling optimal business performance.

Simply establishing these internal data governance policies is often no easy task to accomplish.  Just as passing a law creating new government regulations can also be extremely challenging. 

However, without enforcement and compliance, policies and regulations are powerless to affect the real changes necessary.

This is where I have personally witnessed many data governance programs and regulatory compliance initiatives fail.

 

Red Flag or Red Herring?

Are you implementing data governance policies that raise red flags, not only for implicit, but also for explicit non-compliance? 

Or are you instead establishing a system that will simply encourage the submission of unverified—or unverifiable—red herrings?

 

Related Posts

Days Without A Data Quality Issue

Data, data everywhere, but where is data quality?

The Circle of Quality

DQ-Tip: “Don't pass bad data on to the next person...”

The Only Thing Necessary for Poor Data Quality

Data Governance and Data Quality

The Point of View Paradox

/

One of my all-time favorite non-fiction books is The 7 Habits of Highly Effective People by Stephen Covey. 

One of the book’s key points is that we need to carefully examine our point of view, the way we “see” the world—not in terms of our sense of sight, but instead in terms of the way we perceive, interpret, and ultimately understand the world around us.

As Covey explains early in the book, our point of view can be divided into two main categories, the ways things are (realities) and the ways things should be (values).  We interpret our experiences from these two perspectives, rarely questioning their accuracy. 

In other words, we simply assume that the way we see things is the way they really are or the way they should be.  Our attitudes and behaviors are based on these assumptions.  Therefore, our point of view influences the way we think and the way we act.

A famous experiment that Covey shares in the book, which he first encountered at the Harvard Business School, is intended to demonstrate how two people can see the same thing, disagree—and yet both be right.  Although not logical, it is psychological.

This experiment is reproduced below using the illustrations that I scanned from the book.  Please scroll down slowly.

 

Illustrations of a Young Woman

Look closely at the following illustrations, focusing first on the one on the left—and then slowly shift over to the one on the right:

Can you see the young woman with the petite nose, wearing a necklace, and looking away from you in both illustrations? 

 

 

Illustrations of an Old Lady

Look closely at the following illustrations, focusing first on the one on the left—and then slowly shift over to the one on the right:

Can you see the old lady with the large nose, sad smile, and looking down in both illustrations?

 

 

Illustrations of a Paradox

Look closely at the following illustrations, focusing first on the one on the far left—and then on the one in the middle—and then shift your focus to the one on the far right—and then back to the one in the middle:

Can you now see both the young lady and the old woman in the middle illustration?

 

The Point of View Paradox

The above experiment is usually performed without using the secondary illustration (the one shown on the right of the first two and in the middle of the final one).  Typically in a classroom setting, half of the room has their perception “seeded” utilizing the illustration of the young woman, and the other half with the illustration of the old lady.  When the secondary illustration is then revealed to the entire classroom, arguments commence over whether a young woman or an old lady is being represented.

This experiment demonstrates how our point of view powerfully conditions us and affects the way we interact with other people.

In the world of data quality and its related disciplines, the point of view paradox often negatively impacts the communication and collaboration necessary for success. 

Business and technical perspectives often appear diametrically opposed.  Objective and subjective definitions of data quality seemingly contradict one another.  And of course, the deeply polarized camps contrasting the reactive and proactive approaches to data quality often can’t even agree to disagree.

However, as Data Quality Expert and Jedi Master Obi-Wan Kenobi taught me a long time ago:

“You’re going to find that many of the truths we cling to depend greatly on our own point of view.” 

Related Posts

Podcast: Business Technology and Human-Speak

The Third Law of Data Quality

Beyond a “Single Version of the Truth”

Poor Data Quality is a Virus

Hailing Frequencies Open

Hyperactive Data Quality (Second Edition)

Not So Strange Case of Dr. Technology and Mr. Business

You're So Vain, You Probably Think Data Quality Is About You

 

Follow OCDQ

If you enjoyed this blog post, then please subscribe to OCDQ via my RSS feed, my E-mail updates, or Google Reader.

You can also follow OCDQ on Twitter, fan the Facebook page for OCDQ, and connect with me on LinkedIn.


Mind the Gap

/

Photo via Flickr (Creative Commons License) by: futureshape

For many people, the phrase “mind the gap” conjures up images of a train platform, and perhaps most notably one used by the London Underground.  I’ll even admit to buying the T-shirt during my first business trip to England more than a decade ago.

However, lately I have been thinking about this phrase in a completely different context, specifically in relation to a recurring thought that was provoked by two blog posts, one written by James Chartrand in February, the other by Scott Berkun in May.

The gap I have in mind is the need to coordinate our acquisition of new information with its timely and practical application.

 

Information Acquisition

The Internet, and even more so, The Great Untethering (borrowing a phrase from Mitch Joel) provided by mobile technology, has created a 24 hours a day, 7 days a week, 365 days a year, world wide whirlwind of constant information flow, where the very air we breath is literally teeming with digital data streams—continually inundating us with new information. 

Of course, until they start embedding the computer chips directly into our brains at birth (otherwise known as the top secret iBaby experiment at Apple), we always have the choice of turning off all the devices and giving our full undivided attention to a single source of new information—such as a printed book or, even better, an in-person conversation with another human being.

However, when we are confronted by information overload, its accompanying stress is often caused by the sense that we have some obligation to acquire this new information—as if we were constantly cramming for a perpetually looming pop quiz. 

Contrast this perspective with Albert Einstein, who was known for not remembering even some of the most basic equations.  He argued why would he waste time memorizing something he could just look up in a book—when he needed it. 

This allowed Einstein to focus on problems nobody else could solve, as well as problems nobody had even thought of before, instead of learning what everyone else already knew.  He acquired more of his new information from his thought experiments than he did from books or other sources.

 

Filter Failure

As Clay Shirky famously stated, “it’s not information overload, it’s filter failure.”  I agree, but setting our filters is no easy task. 

Defending ourselves against information overload has become more difficult precisely because we now have greater individual responsibility for our own filters.  Not only are there more published books than ever before, but blogs, and other online sources of new information, have virtually eliminated the “built-in filter” that was provided by publishers, editors, and other gatekeepers. 

Please don’t misunderstand me—I am the complete opposite of Andrew Keen—I believe that this is a truly great thing. 

However, our time is a zero-sum game, meaning for every book, blog, or other new information source that we choose, others are excluded.  There’s no way to acquire all available information.  Additionally, cognitive load, a scientific theory that, in part, examines the limitations of our memory, explains why we often don’t remember much of the new information we do acquire.

Limiting ourselves to the few books and blogs we currently have the time to read, still requires filtering a much larger selection in order to make those choices—or we could simply choose to read only bestselling books and the blogs with the highest PageRank. 

However, can that approach guarantee access to the most valuable sources of new information?  Can any approach do this?

 

Information Application

Although acquiring new information is always potentially useful, it is when—and if—we can put it to use that makes it valuable. 

The distinction between useful and useless information is largely one of applicability.  If the gap in time between the acquisition and application of information is too great, then we would need to reacquire it, rendering the previous acquisition a wasted effort.

Perhaps the key point could be differentiating the type of potential knowledge provided by the information.  At a very high level, there are two broad categories of knowledge—explicit and tacit.

 

Explicit Knowledge

Explicit knowledge is relatively easily to acquire from either verbal or written information, and is often easily understood without extensive explanation.  Explicit knowledge can be based on a straightforward set of facts, or a specific set of instructions to follow, which after being repeatedly put to practical use just a few times, becomes easy to internalize and later recall when necessary. 

The information required for explicit knowledge is often best coordinated around when the knowledge gained would be used. 

One example is software training classes.  As an instructor, I always recommend minimizing the gap in time between when a training class is taken, and when the students would actually start using the software.  Additionally, an introductory class should focus on the most commonly used software features so students can master the basics before approaching advanced concepts.

 

Tacit Knowledge

Tacit knowledge is not only more difficult to acquire, but it is often not even easily recognizable.  Some lessons can simply not be taught, they can only be learned from experience, which is why tacit knowledge is sometimes alternatively defined as wisdom.

One of my favorite quotes about wisdom is from Marcel Proust:

“We do not receive wisdom, we must discover it for ourselves, after a journey through the wilderness, which no one can make for us, which no one can spare us, for our wisdom is the point of view from which we come at last to regard the world.”

Thought-provoking or paradigm-shifting information is often required to get us started on our journey through the wilderness of tacit knowledge, but we can easily lose sight of the deep forest it represents because we are far more immediately concerned with the explicit knowledge provided by the trees.

Whereas explicit knowledge is often more tactical in nature, tacit knowledge is often more strategic.  In general, we tend to prioritize short-term tactics over long-term strategy, thereby developing a preference for explicit, and not tacit, knowledge.

With tacit knowledge, the gap in time between information acquisition and application is much wider.  You require this time to assess the information before attempting to apply it.  You also need to realize that you will fail far more often when applying this type of information—which is to be expected since failure is a natural and necessary aspect of developing tacit knowledge.

 

Mind the Gap

As the growing stack of unread books on my nightstand, as well as the expanding list of unread blog posts in my Google Reader, can both easily attest, neither filtering nor acquiring new information is an easy task.

I have read many books—and considerably more blog posts—containing new information, which in retrospect, I can not recall. 

Obviously, in some cases, their information was neither valuable nor applicable.  However, in many cases, their information was both valuable and applicable, but I didn’t find—or more precisely, I didn’t make—the time to either put it to an immediate use, or to use it as inspiration for my own thought experiments.

I am not trying to tell you how to manage your time, or what new information sources to read, or even when to read them.

I simply encourage you to mind the gap between your acquisition of new information and its timely and practical application.

As always, your commendable comments are one of my most valuable new information sources, so please share your thoughts.

 

Related Posts

Eternal September and Tacit Knowledge

Podcast: Open Your Ears

The War of Word Craft

Will people still read in the future?

The Fragility of Knowledge

Hailing Frequencies Open

The Acronymicon

/

Image created under a Creative Commons Attribution License using: Wordle

“The beginning of wisdom is the definition of terms.” – Socrates

“The end of wisdom is the definition of acronyms.” – Jim Harris

The Acronymicon

The Necronomicon

The Necronomicon is a fictional grimoire (i.e., a textbook containing instructions on how to perform magic), which first appeared in the classic horror stories written by H. P. Lovecraft, and later appeared in other works, including some films, such as Army of Darkness, starring Bruce Campbell, which is one of my favorites—it’s a comedy and it’s highly recommended.

Therefore, the explanation for the rather unusual title of this blog post is that I could think of no better term to describe the fictional textbook containing instructions on how to discuss enterprise information initiatives by using acronyms, and only acronyms, other than:

The Acronymicon

 

Acronyms Gone Wild

For whatever reason, enterprise information initiatives (EIIs?)  have a great fondness for TLAs (two or three letter acronyms): ERP (Enterprise Resource Planning), DW (Data Warehousing), BI (Business Intelligence), MDM (Master Data Management), DG (Data Governance), DQ (Data Quality), CDI (Customer Data Integration), CRM (Customer Relationship Management), PIM (Product Information Management), BPM (Business Process Management), and so many more—truly too many to list.

Additionally, we have apparently become so accustomed to TLAs, that we needed to take it to the next level with Acronyms 2.0 by starting the fun new trend of FLAs (four letter acronyms) such as software as a service (SaaS), platform as a service (PaaS), data as a service (DaaS), service oriented development of applications (SODA), and so many frakking more four letter acronyms.

I also have it on very good authority that by the end of this decade, the Semantic Web will deliver Acronyms 3.0 by creating an Ontology of Unambiguous Acronyms (OOUA), which will be written using a RDFS (Resource Description Framework Schema), in the FOAF (Friend of a Friend) vocabulary, which we will obviously query using SPARQL, which is itself a recursive acronym for SPARQL Protocol and RDF Query Language.

 

WTF?

Now, don’t get me wrong.  I do appreciate how acronyms and other lexicons of terminology can be used as a convenient way of more efficiently discussing the complex concepts often underlying enterprise information initiatives. 

However, too often acronyms are used without ever being defined, which can lead to conversations like that scene in the movie Good Morning, Vietnam where Adrian Cronauer (played by Robin Williams) responds to the overuse of military acronyms used by an officer in charge to describe an upcoming press conference by then former Vice President Richard Nixon with the question:

“Excuse me, sir.  Seeing as how the VP is such a VIP, shouldn’t we keep the PC on the QT?  Because if it leaks to the VC, he could end up MIA, and then we’d all be put out in KP.”

An even worse offense than not defining what the acronym stands for, is only providing what it stands for as the definition. 

For example, when someone asks you the question “what is MDM?” and you respond by stating “Master Data Management,” that really doesn’t help all that much, does it?

Even when you use a better definition, such as the following one from the book Master Data Management by David Loshin:

“Master Data Management (MDM) incorporates business applications, information management methods, and data management tools to implement the policies, procedures, and infrastructures that support the capture, integration, and subsequent shared use of accurate, timely, consistent, and complete master data.”

This is only the beginning of a more detailed discussion, the specifics of which will vary based on your particular circumstances, including the unique corporate culture of your organization, which will greatly influence such things as how exactly the “policies, procedures, and infrastructures” are defined, and what “accurate, timely, consistent, and complete” actually mean.

For that matter, you shouldn’t even assume that everyone knows what you are referring to when you say “master data.”

My point is that you should always make sure that the key concepts of your enterprise information initiatives are clearly defined and in a language that everyone can understand.  I am not just talking about translating the techno-mumbojumbo, because even business-speak can sound more like business-babbling—and not just to the technical folks.

Additionally, don’t be afraid to ask questions or admit when you don’t know the answers.  Many costly mistakes can be made when people assume that others know (or pretend to know themselves) what acronyms and other terminology actually mean.

 

Instructions for using The Acronymicon

If you absolutely insist on using The Acronymicon to discuss enterprise information initiatives at your organization, please just remember that before you even open the book, you must first carefully recite the following words:

“Clatto Verata Nicto!”

No, wait—that’s not quite right.  I think it’s something more like, you must first carefully recite the following words:

“Klaatu Barada Nikto!” 

No, that doesn’t sound right either.  Somebody should just create an acronym for it—they’re much easier to recite and remember.

 

Related Posts

Podcast: Business Technology and Human-Speak

Not So Strange Case of Dr. Technology and Mr. Business

Podcast: Open Your Ears

Shut Your Mouth

Hailing Frequencies Open

The Game of Darts – An Allegory

 

Follow OCDQ

If you enjoyed this blog post, then please subscribe to OCDQ via my RSS feed, my E-mail updates, or Google Reader.

You can also follow OCDQ on Twitter, fan the Facebook page for OCDQ, and connect with me on LinkedIn.


Oh, the Data You’ll Show!

/

Congratulations!
Today is your day.
You’re off to make your data presentations!
You’re off and away!

You have brains in your head.
You have pretty charts and graphs in your slides.
With data transparency, you’ll show you have nothing to hide.
You’re on your own, and you know what you know.

But your Data Quality may decide, where it is you may go.

You looked up and down columns, then across every row, with patience and care.
About some data you said, “I think that we need better quality here.”
With your head full of brains, and data under your supervision, 
You’re too smart to advise a not-so-good business decision.

Oh! the Data You’ll Show!

You’ll be on your way up!
You’ll be providing great business insights!
You’ll join the high fliers who soar to high heights.

You won’t lag behind, because you have parallel processing speed.
You’ll analyze the whole database, and you’ll soon take the lead.
Wherever you fly, you’ll be the best of the best.
Wherever you go, your data analysis will help you top all the rest.

Except when you don’t.
Because, sometimes, you won’t.

I’m sorry to say so but, sadly, it’s true.
Poor Data Quality and Bad Business Decisions can happen—yes, even to you.

You will discover data sources without any meta-mark. 
Nothing is labeled, leaving all business context in the dark. 
Data that could cause quite a chagrin!  Do you dare to stay out?  Do you dare to go in?
How much could you lose?  How much could you win?

And if you go in, should you JOIN LEFT or JOIN RIGHT—or JOIN LEFT-and-three-quarters?
With this data, you will feel like you are SQL querying blind.
Simple it’s not, I’m afraid you will find,
For a fine mind-maker-upper to make up their mind.

You can get so confused that you’ll start racing down long winding rows at a break-necking pace,
Grinding on for gigabytes across a weirdish and wild tablespace, headed, I fear, toward a most useless place.

The Analysis Paralysis Place—for people just analyzing.

Analyzing and analyzing, with no end in sight,
Analyzing and analyzing, with no way to know what’s wrong or what’s right.
Analyzing and analyzing, until three in the morning, and until the Nth degree,
Analyzing and analyzing, refusing to seek help from any business or data SME.

No!  That’s not for you!

Somehow you’ll escape all that Analysis Paralysis,
And hopefully without any of that costly psychoanalysis.
You’ll discover a way out of that place, so dismal and so dark,
Because when it comes to clear thinking, you’re a bright little spark.

Oh! the Data You’ll Show!

There is fun to be done!  And work too, that’s for sure.  But even work feels like a game you have already won.
The magical things that you can do with data, will make you the winning-est winner of all.
Among your co-workers and friends, everyone and all, you will truly stand the tallest of the tall.
You’ll be famous as famous can be, with the whole World Wide Web watching you win on YouTube and Google TV.

Except when they don’t.
Because, sometimes, they won’t.

I’m afraid that sometimes you’ll play lonely games too.
Games you can’t win because you’ll play against you.

All Alone!  Whether you like it or not,
Alone will be something you’ll be quite a lot.

And when you’re alone, there’s a very good chance you will meet, 
Data that scares you and convinces you it’s time to retreat.
There are some operational source systems that regularly do spawn,
Data that can scare you so very much, you won’t want to go on.

But on you will go though the data quality be most foul.
On you will go though the hidden data defects do prowl.
On you will go though it might take quite awhile, and leave quite a scar, 
You’ll overcome your data’s problems, whatever they are.

Oh! the Data You’ll Show!

Proceed with great care and with great tact, always remembering that,
Data Quality is a Great Balancing Act.
Just never forget to be dexterous and deft,
And never mix up your RIGHT JOIN with your LEFT.

Kid, you’ll move data mountains!
Today is your day!
Your data analysis is waiting.
So you had better get underway!

And will you succeed?
Yes!  You will, indeed!
(99.999 percent guaranteed.)

 

* * *

As you probably already do know,
Since it really does quite easily show,
This blog post was inspired by Oh, the Places You'll Go!