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

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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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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.

Do you enjoy writing?

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

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Hyperactive Data Quality (Second Edition)

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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...”

Comic Relief: Dilbert on Project Management

For truly comic relief, there is perhaps no better resource than Scott Adams and the Dilbert comic strip.

Since I don’t read newspapers very often (does anyone anymore?), nowadays I get my Dilbert fix online.  However, I don’t always find the time to read the comic strip on a regular basis.  Therefore, I catch up by reading several weeks of it all at once. 

I like to find one or more recurring themes (which is very easy to do with Scott Adams) and then share some of my favorites.

Today’s blog post provides some recent Dilbert Views on the wonderful world of project management.  Enjoy!

Dilbert on Project Management

The first step in project management is proper planning, which starts by selecting a good acronym:

The next step is to properly establish realistic estimates for the primary tasks in the project plan:

Of course, the most important resource allocation is the project leader, so you must choose wisely:

Finalizing the delivery date can involve some tricky math, it’s much easier to just add more resources:

However if simply adding additional resources won’t really help, there is always an alternative approach:

 

Once it becomes impossible to meet the project’s carefully determined deadline, you know what must be done:

But fear not, your project can be brought to a graceful conclusion following this standard best practice:

Related Posts

Comic Relief: Dilbert to the Rescue

A Superb Lyrebird is a Superb Liar

The Superb Lyrebird is a small ground-dwelling Australian bird that is most notable for its superb ability to mimic almost any sound.  During an excellent special that I watched recently on the Discovery Channel, a Superb Lyrebird demonstrated this extraordinary ability by mimicking not only the sounds of many animals, which also included the human voice, but also various musical instruments, power tools such as drills and chainsaws, electronic devices such as car and fire alarms, and even some incredibly realistic sounding gunshots and explosions.

Male lyrebirds use this ability mainly during their song and dance courtship rituals.

As fascinating (well, I find it fascinating) as this information is, you are probably wondering why I am blogging about it. 

No, despite the rumors circulating the Twitterverse, I am not auditioning for my own primetime show on Animal Planet. 

However, I have recently been participating in the song and dance courtship ritual otherwise known as job interviews.

Résumés

I have always found the very concept of a résumé (or a curriculum vitae or far more often nowadays, a LinkedIn profile) to be truly fascinating.  The idea that a well-written document (printed, electronic, or online) that provides a mixture of summarized and detailed information about your professional experience, career goals, job history, academic qualifications, and references, can somehow encapsulate what kind of employee you would make is highly specious—at least in my humble opinion.

I think that the Superb Lyrebird is an excellent metaphor for a résumé because the job seeker is essentially attempting to mimic the sounds that the employer wants to hear.  Do you have an academic degree in a discipline relevant to the job opening?  If not, did you at least graduate from a prestigious college or university?  Does your job history include professional experience relevant to the job opening?  If not, did you at least have some past jobs with either impressive descriptions or titles?  Are your career goals ambitious enough—but not so ambitious that they could be considered potentially threatening to your new direct manager?

I am not suggesting that these questions are completely irrelevant, nor am I suggesting that some level of screening can’t be effectively performed using them.  However, is it really difficult to make sure that your résumé at least sounds good?

Gaming the System

Although you can’t embellish your education, you can easily get quite creative with the rest, such as using the right keywords in your job descriptions.  A cursory review (either manual or automated) of keywords is still a very common practice performed by human resources (HR) during the preliminary screening to determine what résumés will reach the desks of hiring managers.

So it would seem that “gaming the system” is what you have to do if you want to secure gainful employment.  In fact, it could be easily argued that the system is purposely designed to be gamed.

This is akin to my university literature professor not really caring what I actually thought about Don Quixote.  If I wanted to pass the final exam, then I had to mimic the professor’s belief that Miguel de Cervantes intended his wonderful novel to be an allegory for the critical but sometimes dangerous role that an active imagination can play in the human experience.

Telling my literature professor what he wanted to hear doesn’t mean that I truly appreciated or even understood the brilliance of the novel.  Although I gained the experience of reading it, passed a course that contributed to my graduation, and can sound good at a dinner party where guests have an interest in discussing the novel with me, does that really qualify me as an expert?

I can play buzzword bingo with the best of the best.  I can quote from the books and blogs of industry thought leaders.  I can customize my résumé so its loaded with all the right keywords.  I can use my Internet prowess to wow you during a telephone interview by using Google and Wikipedia to sound like the smartest man on the planet.  I can cram for the in-person interview like I crammed for my literature final exam because if I do my research well, I will know every question you are going to ask, and I will know exactly how you want me to answer them.

A Superb Lyrebird is a Superb Liar

Just like a Superb Lyrebird convincingly mimicking a lion only makes it sound like a lion, and convincingly mimicking what my professor wants to hear only makes me sound like a great student, convincingly mimicking what you are looking for only makes me sound like a potentially great employee.  But how many “lions on paper” or “lions during the interview” have you or your organization hired only to end up with a mostly flightless bird incapable of doing anything other than sounding impressive?

The reason that this happens is incredibly simple—a Superb Lyrebird is a Superb Liar.

However, my point is not to suggest that either job seekers are deceptive or that employers are easily deceived. 

My point is I believe that the system is fundamentally broken because it actually encourages job seekers to act like lyrebirds and actually encourages employers to hire lyrebirds.

In my career, I have been on both sides of the interview desk.  I have made hiring recommendations that resulted in terrible employees, as well as disagreed with hiring decisions that resulted in excellent employees.  I have performed poorly during interviews that resulted in getting hired anyway, as well as performed brilliantly during interviews that resulted in no offer. 

I acknowledge that some truly qualified people, who would make great employees, simply do not interview well.  Some people (including so-called “professional students”) excel at interviews (and in the classroom), but at absolutely nothing else.  Also, some interviewers simply do not know how to conduct a truly effective interview (or in some cases, how to conduct a legal interview).

Therefore, I completely accept that there is no way to perfect the process (and that I am also making sweeping generalizations).

Tilting at Windmills

Recently I have been very disappointed with both the questions that I have and have not been asked during an interview. 

I have also been very disappointed to observe interviewers getting frustrated with me for telling them the truth as opposed to telling them what they wanted to hear. 

Perhaps I should just play along like a good little Superb Lyrebird?  It certainly sounds like that is what is expected of me.

After all, The Ingenious Hidalgo Don Quixote of La Mancha is really an allegory about deception, both self-deception and the deception imposed on us by others—and about acknowledging not only the negative, but also the positive aspects of deception.

My good friend Sancho has just arrived, meaning it’s time once again to do battle with the hulking giants and try to slay them. 

Even though I know that I am really only tilting at windmills, for whatever reason, it still always makes me feel better anyway.

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

Commendable Comments (Part 6)

Last September, and on the exact day of the sixth mensiversary (yes, that’s a real word, look it up) of my blog, I started this series as an ongoing celebration of the truly commendable comments that I regularly receive from my heroes—my readers.

Commendable Comments

On The Circle of Quality, Kelly Lautt commented:

“One of the offerings I provide as a consultant is around data readiness specifically for BI.  Sometimes, you have to sneak an initial data quality project into a company tightly connected to a project or initiative with a clear, already accepted (and budgeted) ROI.  Once the client sees the value of data quality vis a vis the BI requirements, it is easier to then discuss overall data quality (from multiple perspectives).

And, I have to add, I do feel that massive, cumbersome enterprise DQ programs sometimes lose the plot by blindly ‘improving’ data without any value in sight.  I think there has to be a balance between ignoring generalized DQ versus going overboard when there will be a diminishing return at some point.

Always drive effort and investment in any area (including DQ) from expected business value!”

On The Poor Data Quality Jar, Daragh O Brien commented:

“We actually tried to implement something like this with regard to billing data quality issues that created compliance problems.  Our aim was to have the cost of fixing the problem borne by the business area which created the issue, with the ‘swear jar’ being the budget pool for remediation projects.

We ran into a few practical problems:

1) Many problems ultimately had multiple areas with responsibility (line-of-business workers bypassing processes, IT historically ‘right-sizing’ scope on projects, business processes and business requirements not necessarily being defined properly resulting in inevitable errors)

2) Politics often prevented us from pushing the evidence we did have too hard as to the weighting of contributions towards any issue.

3) More often than not it was not possible to get hard metrics on which to base a weighting of contribution, and people tended to object to being blamed for a problem that was obviously complex with multiple inputs.

That said, the attempt to do it did help us to:

1) Justify our ‘claims’ that these issues were often complex with multiple stakeholders involved.

2) Get stakeholders to think about the processes end-to-end, including the multiple IT systems that were involved in even the simplest process.

3) Ensure we had human resources assigned to projects because we had metrics to apply to a business case.

4) Start building a focus on prevention of defect rather than just error detection and fix.

We never got around to using electric shocks on anyone.  But I’d be lying if I said it wasn’t a temptation.”

On The Poor Data Quality Jar, Julian Schwarzenbach commented:

“As data accuracy issues in some cases will be identified by front line staff, how likely are they going to be to report them?  Whilst the electric chair would be a tempting solution for certain data quality transgressions, would it mean that more data quality problems are reported?

This presents a similar issue to that in large companies when they look at their accident reporting statistics and reports of near misses/near hits:

* Does a high number of reported accidents and near hits mean that the company is unsafe, or does it mean that there are high levels of reporting coupled with a supportive, learning culture?

* Does a low number of reported accidents and near hits mean that the company is safe, or does it mean that staff are too scared of repercussions to report anything?

If staff risk a large fine/electric shock for owning up to transgressions, they will not do it and will work hard to hide the evidence, if they can.

In organizational/industrial situations, there are often multiple contributing factors to accidents and data quality problems.  To minimize the level of future problems, all contributory causes need to be identified and resolved.  To achieve this, staff should not be victimized/blamed in any way and should be encouraged to report issues without fear.”

On The Scarlet DQ, Henrik Liliendahl Sørensen commented:

“When I think about the root causes of many of the data quality issues I have witnessed, the original data entry was actually made in good faith by people trying to make data fit for the immediate purpose of use.  Honest, loyal, and hardworking employees striving to get the work done.

Who are the bad guys then?  Either it is no one or everyone or probably both.

When I have witnessed data quality problems solved it is most often done by a superhero taking the lead in finding solutions.  That superhero has been different kinds of people.  Sometimes it is a CEO, sometimes a CFO, sometimes a CRM-manager, sometimes it is anyone else.”

On The Scarlet DQ, Jacqueline Roberts commented:

“I work with engineering data and I find that the users of the data are not the creators of data, so by the time that data quality is questioned the engineering project has been completed, the engineering teams have been disbanded and moved on to other projects for other facilities. 

I am sure that if the engineers had to put the spare part components on purchasing contracts for plant maintenance, the engineers would start to understand some of the data quality issues such as incomplete part numbers or descriptions, missing information, etc.”

On The Scarlet DQ, Thorsten Radde commented:

“Is the question of ‘who is to blame’ really that important?

For me, it is more important to ask ‘what needs to be done to improve the situation.’

I don’t think that assigning blame helps much in improving the situation.  It is very rare that people cooperate to ‘cover up their mistakes.’  I found it more helpful to point out why the current situation is ‘wrong’ and then brainstorm with people on what can be done about it - which additional conventions are required, what can be checked automatically, if new functionality is needed, etc.

Of course, to be able to do that, you’ve got to have the right people on board that trust each other - and the blame game doesn’t help at all.  Maybe you need a ‘blame doll’ that everyone can beat in order to vent their frustrations and then move on to more constructive behavior?”

On Can Enterprise-Class Solutions Ever Deliver ROI?, James Standen commented:

“Fantastic question.  I think the short answer of course as always is ‘it depends’.

However, what’s important is exactly WHAT does it depend on.  And I think while the vendors of these solutions would like you to believe that it depends on the features and functionality of their various applications, that what it all depends on far more is the way they are installed, and to what degree the business actually uses them.

(Insert buzz words here like: ‘business process alignment’, ‘project ownership’, ‘Business/IT collaboration’)

But if you spend Gazillions on a new ERP, then customize it like crazy to ensure that none of your business processes have to change and none of your siloed departments have to talk to each other (which will cost another gazillion in development and consulting by the way), which will then ensure that ongoing maintenance and configuration is more expensive as well, and will eliminate any ability to use pre-built business intelligence solutions etc., etc.  Your ROI is going to be a big, negative number.

Unfortunately, this is often how it’s done.  So my first comment in this debate is - If enterprise systems enable real change and optimization in business processes, then they CAN have ROI.  But it’s hard. And doesn't happen often enough.”

On Microwavable Data Quality, Dylan Jones commented:

“Totally agree with you that data cleansing has been by far the most polarizing topic featured on our site since the launch.  Like you, I agree that data governance is a marathon not a sprint but I do object to a lot of the data cleansing bashing that goes on.

I think that sometimes we should give people who purchase cleansing software far more credit than many of the detractors would be willing to offer.  In the vast majority of cases data cleansing does provide a positive ROI and whilst some could argue it creates a cost base within the organization it is still a step in the direction of data quality maturity.

I think this particular debate is going to run and run however so thanks for fanning the flames.”

On The Challenging Gift of Social Media, Crysta Anderson commented:

“This is the biggest mindshift for a lot of people.  When we started Social Media, many wanted to build our program based only on the second circle - existing customers.  We had to fight hard to prove that the third circle not only existed (we had a hunch it did), but that it was worth our time to pursue.  Sure, we can't point to a direct sales ROI, but the value of building a ‘tribe’ that raises the conversation about data quality, MDM, data governance and other topics has been incredible and continues to grow.”

Thank You

Thank you all for your comments.  Your feedback is greatly appreciated—and truly is the best part of my blogging experience.

Since there have been so many commendable comments, please don’t be offended if one of your comments wasn’t featured. 

Please keep on commenting and stay tuned for future entries in the series.

Related Posts

Commendable Comments (Part 5)

Commendable Comments (Part 4)

Commendable Comments (Part 3)

Commendable Comments (Part 2)

Commendable Comments (Part 1)

Follow OCDQ

For more blog posts and commendable comments, 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 Challenging Gift of Social Media

I recently finished reading (and also highly recommend) the excellent book Linchpin: Are You Indispensable? by Seth Godin. 

Although it’s not the subject of the book, in this blog post I’ll focus on one of its concepts that is very applicable to social media. 

The Circles of the Gift System

Godin uses the term “Gift Culture” to describe an emerging ethos facilitated by (but not limited to) the Internet and social media, which involves what he calls “The Circles of the Gift System” that I have attempted to represent in the above diagram.

In the first circle are your true real-world friends and family, the people that you would never interact with on the basis of trying to make money (i.e., the people you freely give “true gifts” while expecting nothing in return).

In the second circle are your customers and clients, the people that you conduct commerce with and who must pay you for your time, products, and services (i.e., the people and organizations you don’t give gifts because you need them to help pay your bills).

In the third circle is the social media and extended (nowadays mostly online) community, where following the freemium model, you give freely so that you can reach as many people as possible.  It is in the third circle that you assemble your tribe comprised of blog readers, Twitter followers, Facebook fans, and other “friendlies” — the term Godin uses for our social media connections.

It is the third circle that many (if not most) people struggle with and often either resist or ignore.  However, as Godin explains:

“This circle is new.  It’s huge and it’s important, because it enables you to enlarge the second circle and make more money, and because it enables you to affect more people and improve more lives.” 

However, dedicating the necessary time and effort to enlarge the third circle doesn’t guarantee you will enlarge the second circle, which risks turning freemium into simply free.  It is on this particular aspect that I will focus the remainder of my blog post.

The Intriguing Opportunity of Social Media

It is difficult to imagine a business topic generating more widespread discussion these days than social media.  That’s not to say that it is (or that it even should be) considered the most important topic.  However, almost every organization as well as most individual professionals have at the very least considered getting involved with social media in a business context.

The intriguing opportunity of social media is difficult to ignore—even after you ignore most of the hype (which is no easy task).

But as I wrote in the Social Karma series, if we are truly honest, then we all have to admit that we have the same question:

“What’s in this for me?”

Using social media effectively can definitely help promote you, your expertise, your company, and its products and services.  The primary reason I started blogging was to demonstrate my expertise and establish my authority with regards to data quality and its related disciplines.  As an independent consultant, I am trying to help sell my consulting, speaking, and writing services.

The Sobering Reality of Social Media

A social media strategy focused entirely on your own self-promotion will be easily detected by the online community, and could therefore easily result in doing far more harm than good.  Effectively using social media for business requires true participation, sustained engagement, and making meaningful contributions to the community’s goals—and not just your own.

The sobering reality of social media is that it’s not something you can simply do whenever it’s convenient for you.

Using social media effectively, more than anything else, requires a commitment that is mostly measured in time.  It requires a long-term investment in the community, and the truth is you must be patient because any returns on this investment will take a long time to materialize. 

If you are planning on a quick get in, get out, short-term marketing campaign requiring little effort, then don’t waste your time, but much more importantly, don’t waste the community’s time.

The Challenging Gift of Social Media

Godin opens his chapter on “The Powerful Culture of Gifts” by joking that he must have been absent the day they taught the power of unreciprocated gifts at Stanford business school. 

In fact, it’s probably a safe bet that the curriculum at most business schools conveniently ignores the fifty thousand year tradition of human tribal economies based on mutual support and generosity, when power used to be about giving, not getting.

Although we maintain some semblance of this tribal spirit in our personal lives with respect to the first circle, when it comes to our professional lives in the second circle, we want money for our time, product, or service—and we usually don’t come cheap.

Therefore, by far the most common question that I get asked (and that I often ask myself) about social media is:

“Is it really worth all that time and effort, especially when you aren’t getting paid for it?”

Although I honestly believe that it is, truthfully there have been many times when I have doubted it.  But those were usually times when I allowed myself to give in to the natural tendency we all have to become hyper-focused on our own goals. 

The paradox is that the best way to accomplish our selfish goals is—first and foremost—to focus on helping others. 

Of course, helping others doesn’t guarantee they’ll reciprocate, especially with financial returns on our social media investment.  Returning to Godin’s analogy, enlarging (or even just maintaining) the third circle doesn’t guarantee enlarging the second circle.

However, true service to the social media community requires giving true gifts to the third circle. 

Godin explains that these gifts—which do not demand reciprocation—turn the third circle into your tribe.  Giving gifts fulfills your tribal obligation.  Recipients pay it forward by also giving gifts—but perhaps to another tribal member—and not back to you.

And this is the challenging gift of social media—it is a gift that you may keep on giving without ever getting anything in return.

Related Posts

Freemium is the future – and the future is now

Social Karma

True Service