Beware the Data Governance Ides of March

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Morte de Césare (Death of Caesar) by Vincenzo Camuccini, 1798

Today is the Ides of March (March 15), which back in 44 BC was definitely not a good day to be Julius Caesar, who was literally stabbed in the back by the Roman Senate during his assassination in the Theatre of Pompey (as depicted above), which was spearheaded by Brutus and Cassius in a failed attempt to restore the Roman Republic, but instead resulted in a series of civil wars that ultimately led to the establishment of the permanent Roman Empire by Caesar’s heir Octavius (aka Caesar Augustus).

“Beware the Ides of March” is the famously dramatized warning from William Shakespeare’s play Julius Caesar, which has me pondering whether a data governance program implementation has an Ides of March (albeit a less dramatic one—hopefully).

Hybrid Approach (starting Top-Down) is currently leading my unscientific poll about the best way to approach data governance, acknowledging executive sponsorship and a data governance board will be required for the top-down-driven activities of funding, policy making and enforcement, decision rights, and arbitration of conflicting business priorities as well as organizational politics.

The definition of data governance policies illustrates the intersection of business, data, and technical knowledge spread throughout the organization, revealing how interconnected and interdependent the organization is.  The policies provide a framework for the communication and collaboration of business, data, and technical stakeholders, and establish an enterprise-wide understanding of the roles and responsibilities involved, and the accountability required to support the organization’s daily business activities.

The process of defining data governance policies resembles the communication and collaboration of the Roman Republic, but the process of implementing and enforcing data governance policies resembles the command and control of the Roman Empire.

During this transition of power, from policy definition to policy implementation and enforcement, lies the greatest challenge for a data governance program.  Even though no executive sponsor is the Data Governance Emperor (not even Caesar CEO) and the data governance board is not the Data Governance Senate, a heavy-handed top-down approach to data governance can make policy compliance feel like imperial rule and policy enforcement feel like martial law.  Although a series of enterprise civil wars is unlikely to result, the data governance program is likely to fail without the support of a strong and stable bottom-up foundation.

The enforcement of data governance policies is often confused with traditional management notions of command and control, but the enduring success of data governance requires an organizational culture that embodies communication and collaboration, which is mostly facilitated by bottom-up-driven activities led by the example of data stewards and other peer-level change agents.

“Beware the Data Governance Ides of March” is my dramatized warning about relying too much on the top-down approach to implementing data governance—and especially if your organization has any data stewards named Brutus or Cassius.

Data Quality in Six Verbs

Once upon a time when asked on Twitter to identify a list of critical topics for data quality practitioners, my pithy (with only 140 characters in a tweet, pithy is as good as it gets) response was, and especially since I prefer emphasizing the need to take action, to propose six critical verbs: Investigate, Communicate, Collaborate, Remediate, Inebriate, and Reiterate.

Lest my pith be misunderstood aplenty, this blog post provides more detail, plus links to related posts, about what I meant.

1 — Investigate

Data quality is not exactly a riddle wrapped in a mystery inside an enigma.  However, understanding your data is essential to using it effectively and improving its quality.  Therefore, the first thing you must do is investigate.

So, grab your favorite (preferably highly caffeinated) beverage, get settled into your comfy chair, roll up your sleeves and starting analyzing that data.  Data profiling tools can be very helpful with raw data analysis.

However, data profiling is elementary, my dear reader.  In order for you to make sense of those data elements, you require business context.  This means you must also go talk with data’s best friends—its stewards, analysts, and subject matter experts.

Six blog posts related to Investigate:

2 — Communicate

After you have completed your preliminary investigation, the next thing you must do is communicate your findings, which helps improve everyone’s understanding of how data is being used, verify data’s business relevancy, and prioritize critical issues.

Keep in mind that communication is mostly about listening.  Also, be prepared to face “data denial” whenever data quality is discussed.  This is a natural self-defense mechanism for the people responsible for business processes, technology, and data, which is understandable because nobody likes to be blamed (or feel blamed) for causing or failing to fix data quality problems.

No matter how uncomfortable these discussions may be at times, they are essential to evaluating the potential ROI of data quality improvements, defining data quality standards, and most importantly, providing a working definition of success.

Six blog posts related to Communicate:

3 — Collaborate

After you have investigated and communicated, now you must rally the team that will work together to improve the quality of your data.  A cross-disciplinary team will be needed because data quality is neither a business nor a technical issue—it is both.

Therefore, you will need the collaborative effort of business and technical folks.  The business folks usually own the data, or at least the business processes that create it, so they understand its meaning and daily use.  The technical folks usually own the hardware and software comprising your data architecture.  Both sets of folks must realize they are all “one company folk” that must collaborate in order to be successful.

No, you don’t need a folk singer, but you may need an executive sponsor.  The need for collaboration might sound rather simple, but as one of my favorite folk singers taught me, sometimes the hardest thing to learn is the least complicated.

Six blog posts related to Collaborate:

4 — Remediate

Resolving data quality issues requires a combination of data cleansing and defect prevention.  Data cleansing is reactive and its common (and deserved) criticism is that it essentially treats the symptoms without curing the disease. 

Defect prevention is proactive and through root cause analysis and process improvements, it essentially is the cure for the quality ills that ail your data.  However, a data governance framework is often necessary for defect prevention to be successful.  As is patience and understanding since it will require a strategic organizational transformation that doesn’t happen overnight.

The unavoidable reality is that data cleansing is used to correct today’s problems while defect prevention is busy building a better tomorrow for your organization.  Fundamentally, data quality requires a hybrid discipline that combines data cleansing and defect prevention into an enterprise-wide best practice.

Six blog posts related to Remediate:

5 — Inebriate

I am not necessarily advocating that kind of inebriation.  Instead, think Emily Dickinson (i.e., “Inebriate of air am I” – it’s a line from a poem about happiness that, yes, also happens to make a good drinking song). 

My point is that you must not only celebrate your successes, but celebrate them quite publicly.  Channel yet another poet (Walt Whitman) and sound your barbaric yawp over the cubicles of your company: “We just improved the quality of our data!”

Of course, you will need to be more specific.  Declare success using words illustrating the business impact of your achievements, such as mitigated risks, reduced costs, or increased revenues — those three are always guaranteed executive crowd pleasers.

Six blog posts related to Inebriate:

6 — Reiterate

Like the legend of the phoenix, the end is also a new beginning.  Therefore, don’t get too inebriated, since you are not celebrating the end of your efforts.  Your data quality journey has only just begun.  Your continuous monitoring must continue and your ongoing improvements must remain ongoing.  Which is why, despite the tension this reality, and this bad grammatical pun, might cause you, always remember that the tense of all six of these verbs is future continuous.

Six blog posts related to Reiterate:

What Say You?

Please let me know what you think, pithy or otherwise, by posting a comment below.  And feel free to use more than six verbs.

Finding Data Quality

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Have you ever experienced that sinking feeling, where you sense if you don’t find data quality, then data quality will find you?

In the spring of 2003, Pixar Animation Studios produced one of my all-time favorite Walt Disney Pictures—Finding Nemo

This blog post is an hommage to not only the film, but also to the critically important role into which data quality is cast within all of your enterprise information initiatives, including business intelligence, master data management, and data governance. 

I hope that you enjoy reading this blog post, but most important, I hope you always remember: “Data are friends, not food.”

Data Silos

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“Mine!  Mine!  Mine!  Mine!  Mine!”

That’s the Data Silo Mantra—and it is also the bane of successful enterprise information management.  Many organizations persist on their reliance on vertical data silos, where each and every business unit acts as the custodian of their own private data—thereby maintaining their own version of the truth.

Impressive business growth can cause an organization to become a victim of its own success.  Significant collateral damage can be caused by this success, and most notably to the organization’s burgeoning information architecture.

Earlier in an organization’s history, it usually has fewer systems and easily manageable volumes of data, thereby making managing data quality and effectively delivering the critical information required to make informed business decisions everyday, a relatively easy task where technology can serve business needs well—especially when the business and its needs are small.

However, as the organization grows, it trades effectiveness for efficiency, prioritizing short-term tactics over long-term strategy, and by seeing power in the hoarding of data, not in the sharing of information, the organization chooses business unit autonomy over enterprise-wide collaboration—and without this collaboration, successful enterprise information management is impossible.

A data silo often merely represents a microcosm of an enterprise-wide problem—and this truth is neither convenient nor kind.

Data Profiling

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“I see a light—I’m feeling good about my data . . . Good feeling’s gone—AHH!”

Although it’s not exactly a riddle wrapped in a mystery inside an enigma,  understanding your data is essential to using it effectively and improving its quality—to achieve these goals, there is simply no substitute for data analysis.

Data profiling can provide a reality check for the perceptions and assumptions you may have about the quality of your data.  A data profiling tool can help you by automating some of the grunt work needed to begin your analysis.

However, it is important to remember that the analysis itself can not be automated—you need to translate your analysis into the meaningful reports and questions that will facilitate more effective communication and help establish tangible business context.

Ultimately, I believe the goal of data profiling is not to find answers, but instead, to discover the right questions. 

Discovering the right questions requires talking with data’s best friends—its stewards, analysts, and subject matter experts.  These discussions are a critical prerequisite for determining data usage, standards, and the business relevant metrics for measuring and improving data quality.  Always remember that well performed data profiling is highly interactive and a very iterative process.

Defect Prevention

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“You, Data-Dude, takin’ on the defects.

You’ve got serious data quality issues, dude.

Awesome.”

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

Although defect prevention is most commonly associated with business and technical process improvements, after identifying the burning root cause of your data defects, you may predictably need to apply some of the principles of behavioral data quality.

In other words, understanding the complex human dynamics often underlying data defects is necessary for developing far more effective tactics and strategies for implementing successful and sustainable data quality improvements.

Data Cleansing

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“Just keep cleansing.  Just keep cleansing.

Just keep cleansing, cleansing, cleansing.

What do we do?  We cleanse, cleanse.”

That’s not the Data Cleansing Theme Song—but it can sometimes feel like it.  Especially whenever poor data quality negatively impacts decision-critical information, the organization may legitimately prioritize a reactive short-term response, where the only remediation will be fixing the immediate problems.

Balancing the demands of this data triage mentality with the best practice of implementing defect prevention wherever possible, will often create a very challenging situation for you to contend with on an almost daily basis.

Therefore, although comprehensive data remediation will require combining reactive and proactive approaches to data quality, you need to be willing and able to put data cleansing tools to good use whenever necessary.

Communication

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“It’s like he’s trying to speak to me, I know it.

Look, you’re really cute, but I can’t understand what you’re saying.

Say that data quality thing again.”

I hear this kind of thing all the time (well, not the “you’re really cute” part).

Effective communication improves everyone’s understanding of data quality, establishes a tangible business context, and helps prioritize critical data issues. 

Keep in mind that communication is mostly about listening.  Also, be prepared to face “data denial” when data quality problems are discussed.  Most often, this is a natural self-defense mechanism for the people responsible for business processes, technology, and data—and because of the simple fact that nobody likes to feel blamed for causing or failing to fix the data quality problems.

The key to effective communication is clarity.  You should always make sure that all data quality concepts 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 key concepts and other terminology actually mean.

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.

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

Collaboration

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“Oh and one more thing:

When facing the daunting challenge of collaboration,

Work through it together, don't avoid it.

Come on, trust each other on this one.

Yes—trust—it’s what successful teams do.”

Most organizations suffer from a lack of collaboration, and as noted earlier, without true enterprise-wide collaboration, true success is impossible.

Beyond the data silo problem, the most common challenge for collaboration is the divide perceived to exist between the Business and IT, where the Business usually owns the data and understands its meaning and use in the day-to-day operation of the enterprise, and IT usually owns the hardware and software infrastructure of the enterprise’s technical architecture.

However, neither the Business nor IT alone has all of the necessary knowledge and resources required to truly be successful.  Data quality requires that the Business and IT forge an ongoing and iterative collaboration.

You must rally the team that will work together to improve the quality of your data.  A cross-disciplinary team will truly be necessary because data quality is neither a business issue nor a technical issue—it is both, truly making it an enterprise issue.

Executive sponsors, business and technical stakeholders, business analysts, data stewards, technology experts, and yes, even consultants and contractors—only when all of you are truly working together as a collaborative team, can the enterprise truly achieve great things, both tactically and strategically.

Successful enterprise information management is spelled E—A—C.

Of course, that stands for Enterprises—Always—Collaborate.  The EAC can be one seriously challenging place, dude.

You don’t know if you know what they know, or if they know what you know, but when you know, then they know, you know?

It’s like first you are all like “Whoa!” and they are all like “Whoaaa!” then you are like “Sweet!” and then they are like “Totally!”

This critical need for collaboration might seem rather obvious.  However, as all of the great philosophers have taught us, sometimes the hardest thing to learn is the least complicated.

Okay.  Squirt will now give you a rundown of the proper collaboration technique:

“Good afternoon. We’re gonna have a great collaboration today.

Okay, first crank a hard cutback as you hit the wall.

There’s a screaming bottom curve, so watch out.

Remember: rip it, roll it, and punch it.”

Finding Data Quality

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As more and more organizations realize the critical importance of viewing data as a strategic corporate asset, data quality is becoming an increasingly prevalent topic of discussion.

However, and somewhat understandably, data quality is sometimes viewed as a small fish—albeit with a “lucky fin”—in a much larger pond.

In other words, data quality is often discussed only in its relation to enterprise information initiatives such as data integration, master data management, data warehousing, business intelligence, and data governance.

There is nothing wrong with this perspective, and as a data quality expert, I admit to my general tendency to see data quality in everything.  However, regardless of the perspective from which you begin your journey, I believe that eventually you will be Finding Data Quality wherever you look as well.

 

Is DG a D-O-G?

Is DG a DOG.png

Convincing your organization to invest in a sustained data quality program implemented within a data governance framework can be a very difficult task requiring an advocate with a championship pedigree.  But sometimes it seems like no matter how persuasive your sales pitch is, even when your presentation is judged best in show, it appears to fall on deaf ears.

Perhaps, data governance (DG) is a D-O-G.  In other words, maybe the DG message is similar to a sound only dogs can hear.

Galton’s Whistle

In the late 19th century, Francis Galton developed a whistle (now more commonly called a dog whistle), which he used to test the range of frequencies that could be heard by various animals.  Galton was conducting experiments on human faculties, including the range of human hearing.  Although not its intended purpose, today Galton’s whistle is used by dog trainers.  By varying the frequency of the whistle, it emits a sound (inaudible to humans) used either to simply get a dog’s attention, or alternatively to inflict pain for the purpose of correcting undesirable behavior.

Bad Data, Bad, Bad Data!

Many organizations do not become aware of the importance of data governance until poor data quality repeatedly “bites” critical business decisions.  Typically following a very nasty bite, executives scream “bad data, bad, bad data!” without stopping to realize the enterprise’s poor data management practices unleashed the perpetually bad data now running amuck within their systems.

For these organizations, advocacy of proactive defect prevention was an inaudible sound, and now the executives blow harshly into their data whistle and demand a one-time data cleansing project to correct the current data quality problems.

However, even after the project is over, it’s often still a doggone crazy data world.

The Data Whisperer

Executing disconnected one-off projects to deal with data issues when they become too big to ignore doesn’t work because it doesn’t identify and correct the root causes of data’s bad behavior.  By advocating root cause analysis and business process improvement, data governance can essentially be understood as The Data Whisperer.

Data governance defines policies and procedures for aligning data usage with business metrics, establishes data stewardship, prioritizes data quality issues, and facilitates collaboration among all of the business and technical stakeholders.

Data governance enables enterprise-wide data quality by combining data cleansing (which will still occasionally be necessary) and defect prevention into a hybrid discipline, which will result in you hearing everyday tales about data so well behaved that even your executives’ tails will be wagging.

Data’s Best Friend

Without question, data governance is very disruptive to an organization’s status quo.  It requires patience, understanding, and dedication because it will require a strategic enterprise-wide transformation that doesn’t happen overnight.

However, data governance is also data’s best friend. 

And in order for your organization to be successful, you have to realize that data is also your best friend.  Data governance will help you take good care of your data, which in turn will take good care of your business.

Basically, the success of your organization comes down to a very simple question — Are you a DG person?

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Demystifying Social Media

In this eight-minute video, I attempt to demystify social media, which is often over-identified with the technology that enables it, when, in fact, we have always been social, and we have always used media, because social media is about human communication, about humans communicating in the same ways they have always communicated, by sharing images, memories, stories, words, and more often nowadays, we are communicating by sharing photographs, videos, and messages via social media status updates.

This video briefly discusses the three social media services used by my local Toastmasters clubPinterest, Vimeo, and Twitter — and concludes with an analogy inspired by The Emerald City and The Yellow Brick Road from The Wizard of Oz:

If you are having trouble viewing this video, then you can watch it on Vimeo by clicking on this link: Demystifying Social Media

You can also watch a regularly updated page of my videos by clicking on this link: OCDQ Videos

 

Social Karma Blog Series

 

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Shining a Social Light on Data Quality

Last week, when I published my blog post Lightning Strikes the Cloud, I unintentionally demonstrated three important things about data quality.

The first thing I demonstrated was even an obsessive-compulsive data quality geek is capable of data defects, since I initially published the post with the title Lightening Strikes the Cloud, which is an excellent example of the difference between validity and accuracy caused by the Cupertino Effect, since although lightening is valid (i.e., a correctly spelled word), it isn’t contextually accurate.

The second thing I demonstrated was the value of shining a social light on data quality — the value of using collaborative tools like social media to crowd-source data quality improvements.  Thankfully, Julian Schwarzenbach quickly noticed my error on Twitter.  “Did you mean lightning?  The concept of lightening clouds could be worth exploring further,” Julian humorously tweeted.  “Might be interesting to consider what happens if the cloud gets so light that it floats away.”  To which I replied that if the cloud gets so light that it floats away, it could become Interstellar Computing or, as Julian suggested, the start of the Intergalactic Net, which I suppose is where we will eventually have to store all of that big data we keep hearing so much about these days.

The third thing I demonstrated was the potential dark side of data cleansing, since the only remaining trace of my data defect is a broken URL.  This is an example of not providing a well-documented audit trail, which is necessary within an organization to communicate data quality issues and resolutions.

Communication and collaboration are essential to finding our way with data quality.  And social media can help us by providing more immediate and expanded access to our collective knowledge, experience, and wisdom, and by shining a social light that illuminates the shadows cast upon data quality issues when a perception filter or bystander effect gets the better of our individual attention or undermines our collective best intentions — which, as I recently demonstrated, occasionally happens to all of us.

 

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The Three Most Important Letters in Data Governance

The Data Governance Imperative

OCDQ Radio is a vendor-neutral podcast about data quality and its related disciplines, produced and hosted by Jim Harris.

During this episode, Steve Sarsfield and I discuss how data governance is about changing the hearts and minds of your company to see the value of data quality, the characteristics of a data champion, and creating effective data quality scorecards.

Steve Sarsfield is a leading author and expert in data quality and data governance.  His book The Data Governance Imperative is a comprehensive exploration of data governance focusing on the business perspectives that are important to data champions, front-office employees, and executives.  He runs the Data Governance and Data Quality Insider, which is an award-winning and world-recognized blog.  Steve Sarsfield is the Product Marketing Manager for Data Governance and Data Quality at Talend.

 

The Data Governance Imperative

Additional listening options:

 

Win a copy of the Book

Steve Sarsfield wants to give one OCDQ Radio listener a free copy of The Data Governance Imperative

 

Here is how the book contest will work:

 

(1) Book Contest Question — Name at least one of the characteristics of a data champion that Steve Sarsfield described during this OCDQ Radio episode.

 

(2) Book Contest Deadline — By or before April 30, 2012, Email Jim Harris with your answer to the book contest question.

 

(3) Book Contest Winner — In May 2012, one winner will be randomly selected from the emails containing the correct answer to the contest question, and Steve Sarsfield (or his publisher) will email the winner requesting a shipping address for the book.

 

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Related OCDQ Radio Episodes

Clicking on the link will take you to the episode’s blog post:

  • Data Governance Star Wars — Special Guests Rob Karel and Gwen Thomas joined this extended, and Star Wars themed, discussion about how to balance bureaucracy and business agility during the execution of data governance programs.
  • The Johari Window of Data Quality — Guest Martin Doyle discusses helping people better understand their data and assess its business impacts, not just the negative impacts of bad data quality, but also the positive impacts of good data quality.

Data Driven

OCDQ Radio is a vendor-neutral podcast about data quality and its related disciplines, produced and hosted by Jim Harris.

This is Part 1 of 2 from my recent discussion with Tom Redman.  In this episode, Tom and I discuss concepts from one of my favorite data quality books, which is his most recent book: Data Driven: Profiting from Your Most Important Business Asset.

Our discussion includes viewing data as an asset, an organization’s hierarchy of data needs, a simple model for culture change, and attempting to achieve the “single version of the truth” being marketed as a goal of master data management (MDM).

Dr. Thomas C. Redman (the “Data Doc”) is an innovator, advisor, and teacher.  He was first to extend quality principles to data and information in the late 80s.  Since then he has crystallized a body of tools, techniques, roadmaps and organizational insights that help organizations make order-of-magnitude improvements.

More recently Tom has developed keen insights into the nature of data and formulated the first comprehensive approach to “putting data to work.”  Taken together, these enable organizations to treat data as assets of virtually unlimited potential.

Tom has personally helped dozens of leaders and organizations better understand data and data quality and start their data programs.  He is a sought-after lecturer and the author of dozens of papers and four books.

Prior to forming Navesink Consulting Group in 1996, Tom conceived the Data Quality Lab at AT&T Bell Laboratories in 1987 and led it until 1995. Tom holds a Ph.D. in statistics from Florida State University.  He holds two patents.

 

Data Driven

Additional listening options:

 

Win a copy of the Book

Tom Redman wants to give one OCDQ Radio listener a free copy of Data Driven: Profiting from Your Most Important Business Asset

Here is how the book contest will work:

(1) Book Contest Question — Name at least one of the five aspects of the hierarchy of data and information needs that was described by Tom Redman during this OCDQ Radio episode.

 

(2) Book Contest Deadline — By or before March 31, 2012, Email Jim Harris with your answer to the book contest question.

 

(3) Book Contest Winner — In April 2012, one winner will be randomly selected from the emails containing the correct answer to the contest question, and Tom Redman (or his publisher) will email the winner requesting a shipping address for the book.

 

 

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Related OCDQ Radio Episodes

Clicking on the link will take you to the episode’s blog post:

  • Organizing for Data Quality — Guest Tom Redman (aka the “Data Doc”) discusses how your organization should approach data quality, including his call to action for your role in the data revolution.
  • The Johari Window of Data Quality — Guest Martin Doyle discusses helping people better understand their data and assess its business impacts, not just the negative impacts of bad data quality, but also the positive impacts of good data quality.
  • Studying Data Quality — Guest Gordon Hamilton discusses the key concepts from recommended data quality books, including those which he has implemented in his career as a data quality practitioner.

The Three Most Important Letters in Data Governance

In his book I Is an Other: The Secret Life of Metaphor and How It Shapes the Way We See the World, James Geary included several examples of the psychological concept of priming.  “Our metaphors prime how we think and act.  This kind of associative priming goes on all the time.  In one study, researchers showed participants pictures of objects characteristic of a business setting: briefcases, boardroom tables, a fountain pen, men’s and women’s suits.  Another group saw pictures of objects—a kite, sheet music, a toothbrush, a telephone—not characteristic of any particular setting.”

“Both groups then had to interpret an ambiguous social situation, which could be described in several different ways.  Those primed by pictures of business-related objects consistently interpreted the situation as more competitive than those who looked at pictures of kites and toothbrushes.”

“This group’s competitive frame of mind asserted itself in a word completion task as well.  Asked to complete fragments such as wa_, _ight, and co_p__tive, the business primes produced words like war, fight, and competitive more often than the control group, eschewing equally plausible alternatives like was, light, and cooperative.”

Communication, collaboration, and change management are arguably the three most critical aspects for implementing a new data governance program successfully.  Since all three aspects are people-centric, we should pay careful attention to how we are priming people to think and act within the context of data governance principles, policies, and procedures.  We could simplify this down to whether we are fostering an environment that primes people for cooperation—or primes people for competition.

Since there are only three letters of difference between the words cooperative and competitive, we could say that these are the three most important letters in data governance.

 

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The Blue Box of Information Quality

OCDQ Radio is a vendor-neutral podcast about data quality and its related disciplines, produced and hosted by Jim Harris.

On this episode, Daragh O Brien and I discuss the Blue Box of Information Quality, which is much bigger on the inside, as well as using stories as an analytical tool and change management technique, and why we must never forget that “people are cool.”

Daragh O Brien is one of Ireland’s leading Information Quality and Governance practitioners.  After being born at a young age, Daragh has amassed a wealth of experience in quality information driven business change, from CRM Single View of Customer to Regulatory Compliance, to Governance and the taming of information assets to benefit the bottom line, manage risk, and ensure customer satisfaction.  Daragh O Brien is the Managing Director of Castlebridge Associates, one of Ireland’s leading consulting and training companies in the information quality and information governance space.

Daragh O Brien is a founding member and former Director of Publicity for the IAIDQ, which he is still actively involved with.  He was a member of the team that helped develop the Information Quality Certified Professional (IQCP) certification and he recently became the first person in Ireland to achieve this prestigious certification.

In 2008, Daragh O Brien was awarded a Fellowship of the Irish Computer Society for his work in developing and promoting standards of professionalism in Information Management and Governance.

Daragh O Brien is a regular conference presenter, trainer, blogger, and author with two industry reports published by Ark Group, the most recent of which is The Data Strategy and Governance Toolkit.

 

The Blue Box of Information Quality

Additional listening options:

 

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The Data Governance Oratorio

Boston Symphony Orchestra

An oratorio is a large musical composition collectively performed by an orchestra of musicians and choir of singers, all of whom accept a shared responsibility for the quality of their performance, but also requires individual performers accept accountability for playing their own musical instrument or singing their own lines, which includes an occasional instrumental or lyrical solo.

During a well-executed oratorio, individual mastery combines with group collaboration, creating a true symphony, a sounding together, which produces a more powerful performance than even the most consummate solo artist could deliver on their own.

 

The Data Governance Oratorio

Ownership, Responsibility, and Accountability comprise the core movements of the Data Governance ORA-torio.

Data is a corporate asset collectively owned by the entire enterprise.  Data governance is a cross-functional, enterprise-wide initiative requiring that everyone, regardless of their primary role or job function, accept a shared responsibility for preventing data quality issues, and for responding appropriately to mitigate the associated business risks when issues do occur.  However, individuals must still be held accountable for the specific data, business process, and technology aspects of data governance.

Data governance provides the framework for the communication and collaboration of business, data, and technical stakeholders, and establishes an enterprise-wide understanding of the roles and responsibilities involved, and the accountability required to support the organization’s business activities, and materialize the value of the enterprise’s data as positive business impacts.

Collective ownership, shared responsibility, and individual accountability combine to create a true enterprise-wide symphony, a sounding together by the organization’s people, who, when empowered by high quality data and enabled by technology, can optimize business processes for superior corporate performance.

Is your organization collectively performing the Data Governance Oratorio?

 

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Listening and Broadcasting

Photo via Flickr by: Colleen AF Venable

Photo via Flickr by: Anders Pollas

As we continue to witness the decline of traditional media and the corresponding rise of social media, the business world is attempting to keep up with the changing times.  Many organizations are “looking to do whatever it is that’s intended to replace advertising,” explained Douglas Rushkoff in a recent blog post about how marketing threatens the true promise of social media by “devolving to the limited applications of social marketing” and trying to turn the “social landscape back into a marketplace.”

We can all relate to Rushkoff’s central concern—the all-too-slippery slope separating social networking from social marketing.

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 writing, speaking, and consulting services.

You and/or your company are probably using social media to help sell your products and services as well.

Effective social networking is about community participation, which requires actively listening, inviting others to get involved, sharing meaningful ideas, contributing to conversations—and not just broadcasting your sales and marketing messages.

An often cited reason for the meteoric rise of social media is its exchange of a broadcast medium for a conversation medium.  However, some people, including Mitch Joel and Jay Baer, have pondered whether social media conversations are a myth.

“One of the main tenets of social media,” Joel blogged, “was the reality that brands could join a conversation, but by the looks of things there aren’t really any conversations happening at all.” 

Joel wasn’t being negative, just observational.  He pointed out that most blog comments provide feedback, not a back and forth conversation between blogger and reader, Twitter “feels more like everyone screaming a thought at once than a conversation that can be followed and engaged with” and “Facebook has some great banter with the wall posts and status updates, but it’s more chatty than conversational and it’s not an open/public environment.”

“To expect social media to truly emulate conversation as we know it is a fools errand,” Baer blogged.  “The information exchange is asynchronous.  However, there’s a difference between striving for conversation and settling for broadcasting.  The success path must lie somewhere in the middle of those two boundaries.”

Regardless of how we are striving for conversation, whether it be blogging, tweeting, Facebooking, or a face-to-face discussion, we must remember the importance of empathically listening to each other—and not just waiting for our turn to broadcast.

An effective social media strategy is essential for organizations as well as individual professionals, but it is a constant struggle to find the right balance between the headphones and the bullhorn—between listening and broadcasting.

 

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