Jack Bauer and Enforcing Data Governance Policies

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:

• The Acronymicon
• Podcast: Business Technology and Human-Speak
• The Third Law of Data Quality
• Eternal September and Tacit Knowledge
• Not So Strange Case of Dr. Technology and Mr. Business 

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:

• The Point of View Paradox
• Shut Your Mouth
• Podcast: Open Your Ears
• Hailing Frequencies Open
• The Game of Darts – An Allegory

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:

• DQ-Tip: “There is no point in monitoring data quality…”
• Oughtn’t you audit?
• The First Law of Data Quality
• Data Governance and Data Quality
• Adventures in Data Profiling 

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:

• Hyperactive Data Quality (Second Edition)
• A Tale of Two Q’s
• What going to the dentist taught me about data quality
• Microwavable Data Quality 
• DQ-Tip: “Data quality is about more than just improving your data...” 

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:

• The HedgeFoxian Hypothesis
• The Circle of Quality
• Is DG a D-O-G?
• Data Quality and The Middle Way
• Days Without A Data Quality Issue  

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.

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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

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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

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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 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!

Persistence

In a recent eLearningCurve MDM and Data Governance webinar, Dan Power quoted former U.S. President Calvin Coolidge:

“Nothing in the world can take the place of persistence.  Talent will not; nothing is more common than unsuccessful men with talent.  Genius will not; unrewarded genius is almost a proverb.  Education will not; the world is full of educated derelicts.  Persistence and determination are omnipotent.  The slogan ‘press on’ has solved and always will solve the problems of the human race.”

Although I had heard this excellent quote many times, it perhaps resonated with me more this particular time because I recently finished reading the latest Daniel Pink book Drive: The Surprising Truth About What Motivates Us.

In one of the many case studies cited in the book, Pink recounts the findings of an academic study performed to determine why some (approximately one in twenty) prospective cadets at the U.S. Military Academy at West Point, drop out before completing the mandatory seven weeks of basic training during the summer before what would be their first year at the academy.

The study tried to isolate the personal attributes that made the difference, such as physical strength, athleticism, intelligence, leadership ability, or perhaps a well-balanced combination of these factors traditionally considered to be crucial characteristics.

However, what the research discovered was that although all of the traditional characteristics were important, not one of them was the best predictor of success.  Instead, it was the prospective cadets’ rating on a non-cognitive, non-physical trait known as grit, defined as “perseverance and passion for long-term goals,” which truly made all the difference.

In related research examining the most accurate predictor of the academic performance of West Point Cadets, grit was once again found to be the determining factor in success.  As the researchers thoughtfully concluded:

“Whereas the importance of working harder is easily apprehended, the importance of working longer without switching objectives may be less perceptible.

In every field, grit may be as essential as talent to high accomplishment.”

This conclusion is similar to the “10,000-Hour Rule” explained by Malcolm Gladwell in his book Outliers: The Story of Success, where he claims the key to success in any field is largely a matter of practicing its primary task for approximately 10,000 hours.  However, Gladwell also acknowledges that success is far more complicated, and often relies on variables beyond our control.

I have written many times before about the common misperception of experts and their apparently easy success. 

Experts are often misunderstood as being somehow more naturally talented, more intelligent, or better educated than the rest of us.  When in truth, expertise is largely about experience, which as Oscar Wilde wrote “is simply the name we give our mistakes.”

Experts are simply those among us who have made the most mistakes, but persevered and persisted in spite of those failures, because experts see mistakes, as James Joyce wonderfully wrote, as our personal “portals of discovery.”

One of our most difficult challenges in life is the need to acknowledge the favor that our faults do for us.  Although experience is the path that separates knowledge from wisdom, the true wisdom of experience is the wisdom gained from failure.

However, expertise in any discipline is more than an accumulation of mistakes, birthdays, and 10,000 hours.  Expertise is not a static state that once achieved, signifies a comforting conclusion to all that grueling effort, which required so much perseverance.

All of this returns me to the misperceived connection between expertise and success.

Just as talent, intelligence, and education are no guarantee of success, neither are experience, perseverance, and expertise.  As much as we would like to believe that our personal success is dependent solely upon ourselves alone, the harsh reality is more often that not, variables beyond our control, such as luck, timing, and circumstance, will control our destiny as much as we do.

Please don’t misunderstand—I agree with President Coolidge that “persistence and determination are omnipotent” because we do have complete control over the effort we choose to expend. 

However, the most challenging mistake for us to overcome is when we choose entitlement over persistence.

Talent, intelligence, education, experience, and (perhaps paradoxically) expertise can all bring a sense of entitlement.  In other words, we can feel that we possess the necessary attributes and/or have completed the necessary steps required to be successful.

Therefore, we must ultimately accept that there is absolutely nothing that can guarantee our success—but far more important, we must also accept that the only guarantee of our failure would be to abandon our persistence.

Related Posts

The Once and Future Data Quality Expert

Mistake Driven Learning

The Fragility of Knowledge

The Wisdom of Failure

A Portrait of the Data Quality Expert as a Young Idiot

The Five Worst Elevator Pitches for Data Quality

Although you don’t have to actually wait until you are riding in an elevator with a member of executive management to use it, every data quality professional needs to have a well-rehearsed and highly effective elevator pitch ready to go for convincing your organization’s business stakeholders and financial decision makers of the importance of data quality initiatives.

In this blog post, I wanted to provide a few examples of what definitely won’t work as an effective elevator pitch for data quality.

The Five Worst Elevator Pitches for Data Quality

1. “I’m ramping up my job search because I hate working here so much, and my headhunter really thinks a data quality project would look great on my résumé, so how about you be a good sport and approve one?  Additionally, could you make me the leader of the project, and give me some awesome sounding title that would look great in the sans-serif font.”
2. “About 23% of the columns in our operational databases are NULL 42% of the time, and 18% of the fields in our analytical reports contain inconsistent formats 35% of the time, and duplicate rates for customer names and postal addresses vary from 8% to 16% depending on who you ask.  I don’t know what any of that means in business terms, but it can’t be good.”
3. “Like everybody, on, like, Twitter, Facebook, YouTube, and, like, most of the blogosphere, keeps saying data quality is, like, kinda really important, like some kinda best practice or something.  So I was wondering if, like, you could give us, like, oh I don’t know, like, a couple million dollars, so we could like, do a data quality project or something.  Yeah, like, that would be like, really cool of you, and I would, like totally, like say so on Twitter and Facebook and my blog, like, for real, totally.”
4. “I just came back from a major industry conference, and every one of the conference speakers, industry thought leaders, international experts, hardware, software, and consulting vendors were in unanimous and unambiguous agreement—that everything we’re currently doing is totally wrong.  We need to invest in a new master data management, data governance, and business intelligence center of excellence—all built upon a solid data quality foundation.  And it should only cost us about one billion dollars—not counting the annual maintenance fees, of course.”
5. “All of us down in IT are so bored maintaining the existing systems you use for those reports that contain made up data more than half the time anyway, so we’d like you to buy a bunch of cool new technology for us to play with.  Pick us up a few new data profiling and data cleansing tools, one of those master data management things everybody’s talking about, and throw in one of those data warehouse appliances too.  Oh, by the way, the enterprise data warehouse just went down and we’re pretty sure that thing’s never coming back up again.  Well anyway, have a great weekend, executive dude.”

Let’s hear your elevator pitch for data quality

Surely, you could do better—or even better, maybe you could do worse—than these five silly examples. 

Please share your (seriously effective or seriously funny) elevator pitch for data quality by posting a comment below.

Related Posts

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Wordless Wednesday: May 19, 2010

Metaphorically Blogging

Photo via Flickr (Creative Commons License) by: macwagen

I have always wanted to see my name in lights.  However, this photo (of the Harris Theater on Liberty Avenue in downtown Pittsburgh, Pennsylvania) is probably the closest that I will ever come to such a luminous achievement. 

In this blog post, I will simply shine the bright stage lights upon the reasoning behind my somewhat theatrical blogging style.

Metaphorically Blogging

Regular readers know (and perhaps all too well) that I have a proclivity for using metaphors in my blogging. 

Most often, I employ conceptual metaphors in an attempt to explain data quality (and its related disciplines) by providing context about a key concept I am trying to convey by casting it within a situation that (hopefully) my readers can more easily relate to, and (hopefully) later be able to use the conceptual metaphor to draw meaningful parallels to their own experiences.

Sometimes I weave metaphors into the very tapestry of the fine written-woven fabric that is my blogging style (such as with that admittedly terrible example).  Other times, the metaphor provides the conceptual framework for a blog post.  Some of my many examples of this technique include equating data quality with going to the dentist, having a bad cold, or fantasy league baseball.

However, by far my most challenging metaphors—not only for me to write, but also for my readers to understand—is when I blog either a story or a song (well, technically lyrics since—and believe me, you should be very thankful for this—I don’t sing).

Both my story posts and my song posts (please see below for links) are actually allegories since they are extended metaphors where I usually don’t include any supporting commentary, thereby hoping that they illustrate their point without explanation.

Even before the evolution of written language, storytelling played an integral role in every human culture.  Listening to stories and retelling them to others continues to be the predominant means of expressing our emotions and ideas—even if nowadays we get most of our stories from television, movies, or the Internet, and less from reading books or having in-person conversations.

And, 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 provide us with additional stories through instrumental, lyrical, and theatrical performances.

I also believe that one of the best aspects of the present social media revolution is that it’s reinvigorating the story culture of our evolutionary past, providing us with more immediate and expanded access to our collective knowledge, experience, and wisdom.

Metaphorically Speaking

Last summer, metaphor maven James Geary recorded the following fantastic TED Talk video, during which he explains how we all use metaphors to compare what we know, to what we don’t know, and he quotes the sage wisdom of Albert Einstein:

“Combinatory play seems to be the essential feature in productive thought.”

If you are having trouble viewing this video, then you can watch it on TED by clicking on this link: Metaphorically Speaking

Conclusion

Whether you blog or not, you use metaphors, stories, and sometimes songs, to help you make sense of the world around you. 

The very act of thinking is a form of storytelling.  Your brain tries to compare what you already know, or more precisely, what you think you already know, with the new information you are constantly receiving.  Especially nowadays when the very air you breath is literally teeming with digital data streams, you are being continually inundated with new information.

Your brain’s combinatory play experiments with bridging your neural pathways with different metaphors, until eventually it finds the right metaphor and your cognitive dissonance falls away in a flash of insight that brings a new depth of understanding and helps you discover a new way to rule the world—metaphorically speaking of course.

Related (Story) Posts

Video: Oh, the Data You’ll Show!

Data Quality and #FollowFriday the 13th

Spartan Data Quality

Pirates of the Computer: The Curse of the Poor Data Quality

The Quest for the Golden Copy

The Game of Darts – An Allegory

My Own Private Data

‘Twas Two Weeks Before Christmas

The Tell-Tale Data

Data Quality is People!

Related (Song) Posts

Data Love Song Mashup

I’m Bringing DQ Sexy Back

Council Data Governance

I’m Gonna Data Profile (500 Records)

A Record Named Duplicate

You Can’t Always Get the Data You Want

Data Quality is such a Rush

Imagining the Future of Data Quality

The Very Model of a Modern DQ General

New Time Human Business

Related (Blogging) Posts

Social Karma (Part 4)

The Mullet Blogging Manifesto

Collablogaunity

Brevity is the Soul of Social Media

The Two U’s and the Three C’s

Quality is more important than Quantity

Listening and Broadcasting

Please don’t become a Zombie

The Challenging Gift of Social Media

The Wisdom of the Social Media Crowd

Recently Read: May 15, 2010

Recently Read is an OCDQ regular segment.  Each entry provides links to blog posts, articles, books, and other material I found interesting enough to share.  Please note “recently read” is literal – therefore what I share wasn't necessarily recently published.

Data Quality

For simplicity, “Data Quality” also includes Data Governance, Master Data Management, and Business Intelligence.

• Something happened on the way to better data quality – Rich Murnane discusses facing the challenging reality that around 80% of data quality “issues” at his organization were not “technology” problems, but instead “social” (i.e., human) issues.
• Data Profiling with SQL is Hazardous to Your Company’s Health – Stephen Putman explains that implementing a robust data profiling system is an essential part of an effective data management environment.
• How to deliver a Single Customer View – Ken O’Connor previews his e-book (available via Data Quality Pro free download)  on how to cost effectively deliver a Single Customer View that satisfies the UK Financial Services Authority requirements.  The process steps in the e-book would also be more generally applicable to anyone planning a major data migration project.
• Nerd Appeal or Boardroom Fare? – Marty Moseley explains data quality professionals generally do a very poor job in relaying the business value of data quality, and therefore we must strive to define meaningful, business relevant metrics.
• Blind Vendor Allegiance Trumps Utility – Evan Levy examines the bizarrely common phenomenon of selecting a vendor without gathering requirements, reviewing product features, and then determining the best fit for your specific needs.
• When Data Governance Turns Bureaucratic – Dan Power describes what he calls “reactive data governance” and how it can prevent organizations from realizing the full value of MDM.
• Data Quality: The Movie – Henrik Liliendahl Sørensen explains although you can learn data quality from courses, books, and articles, it’s a bit like watching a movie and then realizing that the real world isn’t exactly the same as the movie’s world.
• Why you should data profile – James Standen explains that initial data profiling provides crucial insight necessary for accurate estimates of the effort required on your business intelligence or data migration project.
• How are you Executing your Data Quality Strategy? – Phil Wright examines the high level characteristics of three different approaches to executing your data quality strategy—by power, by process, and by promise.
• Who’ll stop the rain – Frank Harland approaches the pervasive challenge of Business-IT alignment and collaboration from a new angle—by using data to form a divine triangle of Business, IT, and Data.
• “Dirty Harry” was right, “You've got to know your limitations” – Jim Whyte explains that MDM requires a deployment strategy that chunks up organizational and business process changes into small, manageable initiatives.
• Have you built your DQ trust today? – Thorsten Radde explains that a “blame and shame” approach, although somewhat cathartic, is not an effective tool for improving an organization’s data quality.
• The Data Accident Investigation Board – Julian Schwarzenbach outlines a “no blame” approach that would result in more data quality issues being reported, as well as leading to the true root causes of those problems being identified.
• I have a dream – Graham Rhind shares his dream of a revolution in data management, where the focus is on prevention of data quality problems, rather than on trying to resolve them only after their detrimental effect becomes obvious.
• My Data Governance Hero: A True Story – Amar Ramakrishnan shares a great story about encountering an unexpected hero who demonstrated an understanding of data governance and MDM challenges without using “industry speak.”
• Attributes of a Data Rock Star – Jill Wanless provides a great summary of the attributes of a “data rock star” based on an excellent online magazine article recently written by Elizabeth Glagowski.
• Three Conversations to Have with an Executive - the Only Three – Steve Sarsfield discusses how “data champions” must be prepared to talk about the value they bring to the organization in terms that will resonate with executives.
• Demarcating The Lines In Master Data Governance Turf Battles – Judy Ko explains a common challenge, namely how different groups within an organization often argue about master data—what it is, how it is defined, and who “owns” it.
• Data profiling: Are you closing the loop? – Dylan Jones explains how only using data profiling results to drive data cleansing efforts is missing the other part of the equation, namely also capturing and implementing defect prevention rules.
• Data Management Best Practices for Today's Businesses – Tony Fisher uses the Three R's of enterprise data management (Reduce, Reuse, Recycle) to explain how data is the one asset that every company has, but not every company exploits.

Social Media

For simplicity, “Social Media” also includes Blogging, Writing, Social Networking, and Online Marketing.

• Blogging: The Good, the Bad, and the Really, Really Bad – Brenda Somich provides a brief blog post succinctly conveying a few key points and providing some useful general advice regarding the art of effective blogging.
• The need for social media training is larger than ever – John Moore recaps a recent talk about extending thought leadership positions via social media, especially by leveraging it for professional networking—and while you are still happily employed.
• Information as Theater – The Power of Humanized Description – Jay Baer relates the story of Randy Lauson, the best flight attendant that he has ever seen, as a great story about how information isn’t boring by accident—you make it that way.
• New Adventures in Wi-Fi – Track 2: Twitter – Peter Thomas applies his very comprehensive but not overwhelming blogging style to the subject of Twitter, and thereby provides us with an excellent overview of my favorite social networking service.
• The 4 Es of Social Media Strategy – Jill Dyché explains that although over time your social media strategy can incorporate each of the 4 Es (Expose, Engage, Entertain, Educate), a single prevailing need will likely drive your initial efforts.
• What Role For The CMO In Social? – Mary Beth Kemp examines the possible roles that a Chief Marketing Officer (CMO), and the marketing department, could play in an organization’s social media strategy.  Includes a very useful diagram.
• Is Social Media a Fad? – On Day 6 of her 28 day blogging challenge, Tamara Dull shared a great video about social media, which includes some very compelling statistics provided by the Erik Qaulman book Socialnomics.
• Social Media Resistance: Déjà Vu All Over Again – Phil Simon compares the current resistance to social media adoption shown by many organizations, with their similar reluctance in the 1990s regarding the creation of a corporate website.
• Can you have a social system without a community or a collective? – Mark McDonald explains that not only can you have a social system without a community, approaching social media from this perceptive expands its true potential.

• Social Media and BI – Kelly Pennock explains that the newest frontier for data collection is the vast universe of social media, which you need to incorporate into your company’s overall business intelligence strategy.

Related Posts

Recently Read: March 22, 2010

Recently Read: March 6, 2010

Recently Read: January 23, 2010

Recently Read: December 21, 2009

Recently Read: December 7, 2009

Recently Read: November 28, 2009

Recently Read Resources

Data Quality via My Google Reader

Blogs about Data Quality, Data Governance, Master Data Management, and Business Intelligence

Books about Data Quality, Data Governance, Master Data Management, and Business Intelligence

Social Media via My Google Reader

Blogs and Websites about Social Media, Social Networking, and Online Marketing

Books about Social Media, Blogging, Social Networking, and Online Marketing

Data Rock Stars: The Rolling Forecasts

As is often the case with these sorts of things, it all started with a tweet, based on an online magazine article about rock stars.

The tweet (shown above) was sent by Jill Dyché in regards to the article Are You a Data Rock Star? by Elizabeth Glagowski.

The Rolling Forecasts

After the original tweet went viral, our group had very little choice other than to get the band back together and prepare for our Data Rock Star World Tour 2010.  Jean-Michel Franco named us The Rolling Forecasts.  You can follow us on Twitter:

Jill Dyché – @JillDyche	Elizabeth Glagowski – @1to1MediaEditor
Jean-Michel Franco – @jmichel_franco	Giedre Aleknonyte – @googlea
Michael W Cristiani – @mcristia	Phil Simon – @PhilSimon
Jill Wanless – @sheezaredhead	Jim Harris – @ocdqblog

We are currently working through some “creative differences” while recording our latest studio album, which is scheduled to drop sometime this summer.  For now, please enjoy the following lyrics from one of our greatest hits of all time.  Rock On!

You Can’t Always Get the Data You Want *

I saw her looking for business direction
A document of requirements in her hand
I knew she would find a database connection
And search for the business value they demand

No, you can’t always get the data you want
You can’t always get the data you want
You can’t always get the data you want
But if you try sometimes, you might find
You get the insight you need

I saw her struggle with data’s imperfection
When at the cursor she declared her command
I knew she questioned her SQL selection
Because the result set wasn’t what she planned

You can’t always get the data you want
You can’t always get the data you want
You can’t always get the data you want
But if you try sometimes, well you might find
You get the insight you need

Oh yeah, hey hey hey, oh...

And I went down to the vendor’s product demonstration
To listen to the salesman’s fair share of lies and abuse
Singing: “Now we’re gonna vent our customer frustration
Because we are sick of hearing your sorry ass excuse”
Sing it to me now...

You can’t always get the data you want
You can’t always get the data you want
You can’t always get the data you want
But if you try sometimes, well you just might find
You get the insight you need
Oh baby, yeah, yeah!

I went down to the operational datastore
To get your source data request fulfilled
I was standing in the cubicle of DBA Jimmy
And man, did his data look pretty ill

We decided that we should talk about data quality
Master data management and data governance too
I sung my song to DBA Jimmy
Yeah, and he said one word to me, and that was “Screw!”
I said to him

You can’t always get the data you want, no!
You can’t always get the data you want, I’m telling ya baby
You can’t always get the data you want, oh no
But if you try sometimes, you just might find
You get the insight you need
Oh yes!  Woo!

You get the business insight you need
Yeah baby!
Oh, yeah!

I saw her today at the executive presentation
She knew telling the truth would not win her any fans
But she was tired of practicing the art of deception
And I could tell she finally understands
Sing it!

You can’t always get the data you want
You can’t always get the data you want
You can’t always get the data you want
But if you try sometimes, you just might find
Oh, you just might find
You get the insight you need

Oh, yeah!
Oh, baby!
Woo!

Ah, you can’t always get the data you want
No, no baby

You can’t always get the data you want
Telling you right now

You can’t always get the data you want, oh no!
But if you try sometimes, you just might find
You just might find, that yeah!
You get the business insight you need!
Oh, yeah!

I’m telling the truth about data...

___________________________________________________________________________________________________________________

* In 1969, The Rolling Stones released a similar song called “You Can’t Always Get What You Want” on their album Let It Bleed.

Podcast: Business Technology and Human-Speak

An excellent recent Marty Moseley blog post called for every one of us, regardless of where we sit within our organization chart, to learn conversational business-speak. 

This common call to action, perhaps first sounded by the George Colony blog post in August of 2006, rightfully emphasizes that “business is technology and technology is business” and therefore traditional IT needs to be renamed BT (Business Technology) and techies need to learn how to “engage in a discussion of process, customers, and operations, not esoteric references to SOA, Web services, and storage management.” 

Therefore, we need to always frame enterprise information initiatives (such as data governance and master data management) in a business context by using business language such as mitigated risks, reduced costs, or increased revenue, in order to help executives understand, as the highly recommended Tony Fisher book details, the need to view data as a strategic corporate asset.

While I do not disagree with any of these viewpoints, as I was reading the latest remarkable Daniel Pink book, I couldn’t help but wonder if what we really need to do is emphasize both Business Technology and (for lack of a better term) Human-Speak.

In this brief (approximately 9 minutes) OCDQ Podcast, I share some of my thoughts on this subject:

You can also download this podcast (MP3 file) by clicking on this link: Business Technology and Human-Speak

Related Posts

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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.

What going to the dentist taught me about data quality

Photo via Flickr (Creative Commons License) by: Paul Lowry

Dear kind readers, while some of you are reading this very blog post, I will be getting ruthlessly tortured by my maniacal dentist.

Well okay, the truth is that I will simply be getting two cavities filled at my dentist’s office on Thursday morning.  Dr. Blass and her entire staff is far from maniacal—they are, in fact, all very wonderful people. 

I am simply deathly afraid of the object of terror pictured above—the dental drill.  I would argue that this evil object produces one of the most horrifying sounds ever heard in the entire history of humankind.

What does any of this have to do with data quality?

In previous blog posts, I have used a variety of metaphors to compare and contrast the proactive (i.e., defect prevention) and reactive (i.e., data cleansing) approaches to data quality.  With this blog post, I will add an oral hygiene metaphor. 

Brushing and flossing our teeth is defect prevention, where instead of preventing data quality issues before they happen, we are trying to prevent tooth decay and gum disease.  If we neglect these preventative measures (e.g., if, like me, you only floss when you get something stuck in your teeth), then we could develop cavities and gingivitis. 

Removing the decayed portion of a tooth and filling the cavity is data cleansing, where instead of correcting data quality issues after they happen, we are trying to correct the problem before it gets worse (e.g., leads to partial or complete tooth loss). 

Just as data cleansing doesn’t address the root cause (no pun intended) of data quality issues, correcting tooth decay doesn’t address the lapse in oral hygiene that caused it.  However, once the damage is done, corrective action is necessary, or at least preferred before the problem worsens.  Just like data cleansing is often viewed as a considerable cost with little to no ROI, so is getting a cavity filled (especially when, like me, you do not currently have any dental insurance).

I know that this particular metaphor doesn’t really add anything new to what is one of the most deeply polarizing topics for the data quality profession.  However, it is perhaps a more tangible metaphor. 

The vast majority of people have a tendency to neglect their oral hygiene until an obvious (and usually quite physically painful) problem presents itself (e.g., wow, my tooth really hurts, I have to go see the dentist). 

The vast majority of organizations have a tendency to neglect data quality until an obvious (and usually quite financially painful) problem presents itself (e.g., a customer service nightmare, a regulatory compliance failure, or a financial reporting scandal).

My point is that we should all be brushing and flossing our data at least twice a day, and we should all be getting a routine data checkup at least once every six months.  In other words, implement defect prevention whenever and wherever possible, and perform a data quality assessment on a regular basis.

After all, your data probably dislikes data cleansing tools just as much as I dislike dental drills.  Well, almost as much.

Related Posts

Microwavable Data Quality

A Tale of Two Q’s

Hyperactive Data Quality (Second Edition)

The General Theory of Data Quality

DQ-Tip: “There is no point in monitoring data quality…”

Data Quality (DQ) Tips is an OCDQ regular segment.  Each DQ-Tip is a clear and concise data quality pearl of wisdom.

“There is no point in monitoring data quality if no one within the business feels responsible for it.” 

This DQ-Tip came from the Enterprise Data World 2010 conference presentation Monitor the Quality of your Master Data by Thomas Ravn, MDM Practice Director at Platon.

It reminds me of a similar quote from Thomas Redman: “It is a waste of effort to improve the quality of data no one ever uses.” 

A common mistake made by those advocating that data needs to be viewed as a corporate asset is discussing data independent of both its use and its business relevance.  Additionally, Marty Moseley recently blogged about how data quality metrics often do a poor job in relaying the business value of data quality, strategic or otherwise.

Data profiling can help you begin to understand your data characteristics and usage.  A full data quality assessment can help you create the metrics that establish an initial baseline measurement, as well as continue monitoring your data quality over time.

However, without data quality metrics that meaningfully represent tangible business relevance, you should neither expect anyone within your organization to feel responsible for data quality, nor expect anyone to view data as a corporate asset.

 

Related Posts

DQ-Tip: “Start where you are...”

DQ-Tip: “Data quality is about more than just improving your data...”

DQ-Tip: “...Go talk with the people using the data”

DQ-Tip: “Data quality is primarily about context not accuracy...”

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

Wednesday Word: April 28, 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.

Antidisillusionmentarianism

Definition – A corporate philosophy opposed to freeing executive management from any of their own illusions or false beliefs.

Example – “I explained that we have serious data quality problems and most of them have business process or people issues as their root causes, and that according to every industry data governance maturity model, our organization was very undisciplined, and that buying more technology wasn’t the solution—and then the CEO fired me for violating antidisillusionmentarianism.”

Related Posts

Wednesday Word: April 21, 2010 – Enterpricification

Wednesday Word: April 7, 2010 – Vendor Asskisstic