Big Data is Just Another Brick in the Wall

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The title of my recent blog post Chaos in the Big Data Brickyard made Mike Wheeler think it was a reference to the Indianapolis Motor Speedway, which is known as “The Brickyard” because it was paved entirely with bricks way back in 1909 (today, three feet of the original bricks remain at the start/finish line).  This was a reasonable assumption by Wheeler since he is a NASCAR fan (thus making his last name a great example of an aptronym) and thus prompted his blog post Yeah, But Who Won The Race?

“The term brickyard taken without any context,” Wheeler explained, “turned out to be another random brick of fact laid on an already crowded foundation.  Context is what provides relevance to facts.  Without a frame of reference into which a fact can be inserted it can easily become meaningless or, even worse, detrimental to the decision-making process.”

As usual, I agree with Wheeler (except about being a NASCAR fan — my apologies to Mike and his fellow auto racing fans).

In my post Big Data, Sporks, and Decision Frames, I blogged about how having the right decision frame (i.e., understanding the business context of a decision) is essential to whether big data and data science can provide meaningful business insight.

Additional context often missing from discussions about big data and data science is that they are not the only bricks in the yard.

Data modeling is still important and data quality still matters.  As does metadata, data management and business intelligence, data monitoring, communication, collaboration, change management and the many other aspects of data governance.

“A successful man,” David Brinkley once said, “is one who can lay a firm foundation with the bricks others have thrown at him.”  A successful big data initiative is one that can lay a firm foundation with the bricks of best practices that the data management industry has been rightfully throwing at us for a long time now.  Big data does not obviate the need for those best practices — even though it does occasionally require adapting our best practices as well as adopting new practices.

Big data is not the be all and end all, as it is sometimes overhyped, but instead, to paraphrase the great philosophers Pink Floyd:

All in all, big data is just another brick in the wall.

Related Podcasts

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

  • Defining Big Data — This episode of the Open MIKE Podcast, with assistance from Robert Hillard, discusses how big data refers to big complexity, not big volume, even though complex datasets tend to grow rapidly, thus making them voluminous.
  • Data Quality and Big Data — Guest Tom Redman (aka the “Data Doc”) discusses Data Quality and Big Data, including if data quality matters less in larger data sets, and if statistical outliers represent business insights or data quality issues.
  • Demystifying Data Science — Guest Melinda Thielbar, a Ph.D. Statistician, discusses what a data scientist does and provides a straightforward explanation of key concepts such as signal-to-noise ratio, uncertainty, experimentation, and correlation.

 

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Data Governance needs Searchers, not Planners

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In his book Everything Is Obvious: How Common Sense Fails Us, Duncan Watts explained that “plans fail, not because planners ignore common sense, but rather because they rely on their own common sense to reason about the behavior of people who are different from them.”

As development economist William Easterly explained, “A Planner thinks he already knows the answer; A Searcher admits he doesn’t know the answers in advance.  A Planner believes outsiders know enough to impose solutions; A Searcher believes only insiders have enough knowledge to find solutions, and that most solutions must be homegrown.”

I made a similar point in my post Data Governance and the Adjacent Possible.  Change management efforts are resisted when they impose new methods by emphasizing bad business and technical processes, as well as bad data-related employee behaviors, while ignoring unheralded processes and employees whose existing methods are preventing other problems from happening.

Demonstrating that some data governance policies reflect existing best practices reduces resistance to change by showing that the search for improvement was not limited to only searching for what is currently going wrong.

This is why data governance needs Searchers, not Planners.  A Planner thinks a framework provides all the answers; A Searcher knows a data governance framework is like a jigsaw puzzle.  A Planner believes outsiders (authorized by executive management) know enough to impose data governance solutions; A Searcher believes only insiders (united by collaboration) have enough knowledge to find the ingredients for data governance solutions, and a true commitment to change always comes from within.

 

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Beware the Data Governance Ides of March

Aristotle, Data Governance, and Lead Rulers

Data Governance and the Adjacent Possible

The Three Most Important Letters in Data Governance

The Data Governance Oratorio

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Over the Data Governance Rainbow

Getting Your Data Governance Stuff Together

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The Data Governance Imperative

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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.

Popular OCDQ Radio Episodes

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

  • Demystifying Data Science — Guest Melinda Thielbar, a Ph.D. Statistician, discusses what a data scientist does and provides a straightforward explanation of key concepts such as signal-to-noise ratio, uncertainty, and correlation.
  • Data Quality and Big Data — Guest Tom Redman (aka the “Data Doc”) discusses Data Quality and Big Data, including if data quality matters less in larger data sets, and if statistical outliers represent business insights or data quality issues.
  • Demystifying Master Data Management — Guest John Owens explains the three types of data (Transaction, Domain, Master), the four master data entities (Party, Product, Location, Asset), and the Party-Role Relationship, which is where we find many of the terms commonly used to describe the Party master data entity (e.g., Customer, Supplier, Employee).
  • 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.
  • 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.

Data Driven

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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.

Popular OCDQ Radio Episodes

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

  • Demystifying Data Science — Guest Melinda Thielbar, a Ph.D. Statistician, discusses what a data scientist does and provides a straightforward explanation of key concepts such as signal-to-noise ratio, uncertainty, and correlation.
  • Data Quality and Big Data — Guest Tom Redman (aka the “Data Doc”) discusses Data Quality and Big Data, including if data quality matters less in larger data sets, and if statistical outliers represent business insights or data quality issues.
  • Demystifying Master Data Management — Guest John Owens explains the three types of data (Transaction, Domain, Master), the four master data entities (Party, Product, Location, Asset), and the Party-Role Relationship, which is where we find many of the terms commonly used to describe the Party master data entity (e.g., Customer, Supplier, Employee).
  • 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.
  • 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 Johari Window of Data Quality

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

The Johari Window is a term from psychology for a technique used to help people better understand their personality and behavior by combining a self assessment with assessments from their peers.  In relation to data, the Johari Window is a metaphor for 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.

During this episode, I discuss the Johari Window of Data Quality with Martin Doyle.  Our discussion, inspired by our blog comment banter on my post There is No Such Thing as a Root Cause, includes root cause analysis, the pursuit of data perfection, metadata, communication, Business-IT collaboration, change management, defect prevention, and continuous improvement.

Martin Doyle is a Data Quality Improvement Evangelist and the CEO of DQ Global, which is a UK-based data quality software and services vendor providing data cleansing, international address and email verification, data deduplication, and data matching solutions for Customer Relationship Management, Single Customer View, and Master Data Management.  DQ Global has worked with over 500 businesses worldwide on a variety of projects, providing their clients with improved data quality, making their data fit for business use, and enabling them to trust their data and make decisions based on a foundation of fact.

Popular OCDQ Radio Episodes

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

  • Demystifying Data Science — Guest Melinda Thielbar, a Ph.D. Statistician, discusses what a data scientist does and provides a straightforward explanation of key concepts such as signal-to-noise ratio, uncertainty, and correlation.
  • Data Quality and Big Data — Guest Tom Redman (aka the “Data Doc”) discusses Data Quality and Big Data, including if data quality matters less in larger data sets, and if statistical outliers represent business insights or data quality issues.
  • Demystifying Master Data Management — Guest John Owens explains the three types of data (Transaction, Domain, Master), the four master data entities (Party, Product, Location, Asset), and the Party-Role Relationship, which is where we find many of the terms commonly used to describe the Party master data entity (e.g., Customer, Supplier, Employee).
  • 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.
  • 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.

Best OCDQ Blog Posts of 2011

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Welcome to my roundup of the best blog posts published on the Obsessive-Compulsive Data Quality (OCDQ) blog during 2011.

My selections were based on a pseudo-scientific, quasi-statistical combination of page views, comments, and re-tweets (as well as choosing a few of my personal favorites).  Instead of ordering the posts chronologically, I decided to organize them by theme.

 

The Metadata Trilogy

Although it has an incredibly important role to play in data quality and its related disciplines, I don’t write about metadata very often.  But the reader feedback that I received lead me to writing three blog posts about metadata in the span of a few weeks:

  • The Metadata Crisis — There is a running debate within many organizations over the meaning of commonly used terms, which complicates what on the surface seem like straightforward business questions.
  • The Metadata Continuum — There is a continuum, where at one end we have the uniformity of controlled vocabularies, and at the other end we have the flexibility of chaotic folksonomies.  However, both flexibility and uniformity provide value.
  • You Say Potato and I Say Tater Tot — The demarcations of the borders between metadata, data, and information are important, but sometimes difficult to discern.  In this post, I offer an explanation about these demarcations using potatoes.

 

The Data Governance Star Wars (one less than a) Trilogy

In June, Rob Karel of Forrester Research and I used a Star Wars themed blog mock debate to take on one of data governance’s biggest challenges — how to balance bureaucracy and business agility.  Gwen Thomas of the Data Governance Institute joined Rob and I to continue the discussion during a special, extended, and Star Wars themed episode of OCDQ Radio:

  • Data Governance Star Wars on OCDQ Radio — In Part 1, Rob Karel and I discuss our blog mock debate, which is followed by a brief Star Wars themed intermission, and then in Part 2, Gwen Thomas joins us to provide her excellent insights.

 

Although not Star Wars themed, here are some additional Best OCDQ Blog Posts of 2011 on the topic of data governance:

  • Data Governance and the Adjacent Possible — It’s important to demonstrate that some data governance policies reflect existing best practices, which helps reduce resistance to change, and therefore I advise: “If it ain’t broke, bricolage it.”
  • Aristotle, Data Governance, and Lead Rulers — Well-constructed data governance policies are like lead rulers — flexible rules that empower us with an understanding of the principle of the policy, and how to enforce it in a particular context.
  • The Stakeholder’s Dilemma — There will be times when sacrifices for the long-term greater good will require that stakeholders either contribute more resources during the current phase, or receive fewer benefits from its deliverables.
  • Beware the Data Governance Ides of March — 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.

 

OCDQ Radio

In June, I launched OCDQ Radio, which is a vendor-neutral podcast about data quality and the audio complement to this blog, providing me with a platform for recorded discussions with the great folks working in the data management industry.  So far, there have been 21 episodes of OCDQ Radio, including 22 guests from 7 countries.  Here are a few of the most popular episodes:

  • The Fall Back Recap Show — A look back at the Best of OCDQ Radio, including discussions about Data, Information, Business-IT Collaboration, Change Management, Big Analytics, Data Governance, and the Data Revolution.
  • 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.
  • 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.
  • Social Media Strategy — Guest Crysta Anderson of IBM Initiate explains social media strategy and content marketing, including three recommended practices: (1) Listen intently, (2) Communicate succinctly, and (3) Have fun.

 

The Best of the Rest

  • DQ-View: Talking about Data — DQ-View video discussion about how data professionals should talk about data when invited to participate in business discussions within their organizations.
  • The Speed of Decision — Examines the constraints that time puts on data-driven decision making, pondering whether decision speed is more important than data quality and decision quality.
  • The Data Cold War — Examines how Google and Facebook have performed the Master Data Management Magic Trick and socialized data (“Information wants to be free!”) in order to capitalize data as a true corporate asset.
  • A Farscape Analogy for Data Quality — Ponders whether data is not viewed as an asset because data has so thoroughly pervaded the enterprise that data has become invisible to those who are so dependent upon its quality.
  • No Datum is an Island of Serendip — Our organizations need to create collaborative environments that foster serendipitous connections bringing all of our business units and people together around our shared data assets.

 

Thank You for Reading OCDQ Blog in 2011

In 2011, the Obsessive-Compulsive Data Quality (OCDQ) blog published 112 posts, which received 130,000 total page views, averaging 350 page views and 150 unique visitors a day.

Thank you for reading OCDQ Blog in 2011.  Your readership was deeply appreciated.

 

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So Long 2011, and Thanks for All the . . . – The OCDQ Radio 2011 Year in Review

2011 Quarterly Review of the Data Roundtable (Part 3)

2011 Quarterly Review of the Data Roundtable (Part 2)

2011 Quarterly Review of the Data Roundtable (Part 1)

Commendable Comments (Part 10) – The 300th OCDQ Blog Post

730 Days and 264 Blog Posts Later – The Second Blogiversary of OCDQ Blog

OCDQ Blog Bicentennial – The 200th OCDQ Blog Post

Commendable Comments (Part 5) – The 100th OCDQ Blog Post

The Best Data Quality Blog Posts of 2010

Making EIM Work for Business

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

During this episode, I discuss Enterprise Information Management (EIM) with John Ladley, the author of the excellent book Making EIM Work for Business, exploring what makes information management, not just useful, but valuable to the enterprise.

John Ladley is a business technology thought leader with 30 years of experience in improving organizations through the successful implementation of information systems.  He is a recognized authority in the use and implementation of business intelligence and enterprise information management.  John Ladley frequently writes and speaks on a variety of technology and enterprise information management topics.  His information management experience is balanced between strategic technology planning, project management, and, most important, the practical application of technology to business problems.

Popular OCDQ Radio Episodes

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

  • Demystifying Data Science — Guest Melinda Thielbar, a Ph.D. Statistician, discusses what a data scientist does and provides a straightforward explanation of key concepts such as signal-to-noise ratio, uncertainty, and correlation.
  • Data Quality and Big Data — Guest Tom Redman (aka the “Data Doc”) discusses Data Quality and Big Data, including if data quality matters less in larger data sets, and if statistical outliers represent business insights or data quality issues.
  • Demystifying Master Data Management — Guest John Owens explains the three types of data (Transaction, Domain, Master), the four master data entities (Party, Product, Location, Asset), and the Party-Role Relationship, which is where we find many of the terms commonly used to describe the Party master data entity (e.g., Customer, Supplier, Employee).
  • 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.
  • 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

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Three+Letters+Before.jpg

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.

Three+Letters+After.jpg

Data Governance and the Adjacent Possible

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I am reading the book Where Good Ideas Come From by Steven Johnson, which examines recurring patterns in the history of innovation.  The first pattern Johnson writes about is called the Adjacent Possible, which is a term coined by Stuart Kauffman, and is described as “a kind of shadow future, hovering on the edges of the present state of things, a map of all the ways in which the present can reinvent itself.  Yet it is not an infinite space, or a totally open playing field.  The strange and beautiful truth about the adjacent possible is that its boundaries grow as you explore those boundaries.”

Exploring the adjacent possible is like exploring “a house that magically expands with each door you open.  You begin in a room with four doors, each leading to a new room that you haven’t visited yet.  Those four rooms are the adjacent possible.  But once you open any one of those doors and stroll into that room, three new doors appear, each leading to a brand-new room that you couldn’t have reached from your original starting point.  Keep opening new doors and eventually you’ll have built a palace.”

If it ain’t broke, bricolage it

“If it ain’t broke, don’t fix it” is a common defense of the status quo, which often encourages an environment that stifles innovation and the acceptance of new ideas.  The status quo is like staying in the same familiar and comfortable room and choosing to keep all four of its doors closed.

The change management efforts of data governance often don’t talk about opening one of those existing doors.  Instead they often broadcast the counter-productive message that “everything is so broken, we can’t fix it.”  We need to destroy our existing house and rebuild it from scratch with brand new rooms — and probably with one of those open floor plans without any doors.

Should it really be surprising when this approach to change management is so strongly resisted?

The term bricolage can be defined as making creative and resourceful use of whatever materials are at hand regardless of their original purpose, stringing old parts together to form something radically new, transforming the present into the near future.

“Good ideas are not conjured out of thin air,” explains Johnson, “they are built out of a collection of existing parts.”

The primary reason that the change management efforts of data governance are resisted is because they rely almost exclusively on negative methods—they emphasize broken business and technical processes, as well as bad data-related employee behaviors.

Although these problems exist and are the root cause of some of the organization’s failures, there are also unheralded processes and employees that prevented other problems from happening, which are the root cause of some of the organization’s successes.

It’s important to demonstrate that some data governance policies reflect existing best practices, which helps reduce resistance to change, and so a far more productive change management mantra for data governance is: “If it ain’t broke, bricolage it.”

Data Governance and the Adjacent Possible

As Johnson explains, “in our work lives, in our creative pursuits, in the organizations that employ us, in the communities we inhabit—in all these different environments, we are surrounded by potential new ways of breaking out of our standard routines.”

“The trick is to figure out ways to explore the edges of possibility that surround you.”

Most data governance maturity models describe an organization’s evolution through a series of stages intended to measure its capability and maturity, tendency toward being reactive or proactive, and inclination to be project-oriented or program-oriented.

Johnson suggests that “one way to think about the path of evolution is as a continual exploration of the adjacent possible.”

Perhaps we need to think about the path of data governance evolution as a continual exploration of the adjacent possible, as a never-ending journey which begins by opening that first door, building a palatial data governance program one room at a time.

 

Related Posts

The Fall Back Recap Show

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

On this episode, I celebrate the autumnal equinox by falling back to look at the Best of OCDQ Radio, including discussions about Data, Information, Business-IT Collaboration, Change Management, Big Analytics, Data Governance, and the Data Revolution.

Thank you for listening to OCDQ Radio.  Your listenership is deeply appreciated.

Special thanks to all OCDQ Radio guests.  If you missed any of their great appearances, check out the full episode list below.

Popular OCDQ Radio Episodes

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

  • Demystifying Data Science — Guest Melinda Thielbar, a Ph.D. Statistician, discusses what a data scientist does and provides a straightforward explanation of key concepts such as signal-to-noise ratio, uncertainty, and correlation.
  • Data Quality and Big Data — Guest Tom Redman (aka the “Data Doc”) discusses Data Quality and Big Data, including if data quality matters less in larger data sets, and if statistical outliers represent business insights or data quality issues.
  • Demystifying Master Data Management — Guest John Owens explains the three types of data (Transaction, Domain, Master), the four master data entities (Party, Product, Location, Asset), and the Party-Role Relationship, which is where we find many of the terms commonly used to describe the Party master data entity (e.g., Customer, Supplier, Employee).
  • 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.
  • 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 Blue Box of Information Quality

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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.

Popular OCDQ Radio Episodes

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

  • Demystifying Data Science — Guest Melinda Thielbar, a Ph.D. Statistician, discusses what a data scientist does and provides a straightforward explanation of key concepts such as signal-to-noise ratio, uncertainty, and correlation.
  • Data Quality and Big Data — Guest Tom Redman (aka the “Data Doc”) discusses Data Quality and Big Data, including if data quality matters less in larger data sets, and if statistical outliers represent business insights or data quality issues.
  • Demystifying Master Data Management — Guest John Owens explains the three types of data (Transaction, Domain, Master), the four master data entities (Party, Product, Location, Asset), and the Party-Role Relationship, which is where we find many of the terms commonly used to describe the Party master data entity (e.g., Customer, Supplier, Employee).
  • 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.
  • 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.

DQ-View: New Data Resolutions

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Data Quality (DQ) View is an OCDQ regular segment.  Each DQ-View is a brief video discussion of a data quality key concept.

 

If you are having trouble viewing this video, then you can watch it on Vimeo by clicking on this link: DQ-View on Vimeo

The graphics shown in the video were created under a Creative Commons Attribution License using: Wordle

 

New Data Resolutions

If one of your New Year’s Resolutions was not to listen to my rambling, here is the video’s (spoiler alert!) thrilling conclusion:

Now, of course, in order for this to truly count as one of your New Data Resolutions for 2011, you will have to provide your own WHY and WHAT that is specific to your organization’s enterprise data initiative.

After all, it’s not like I can eat healthier or exercise more often for you in 2011.  Happy New Year!

 

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DQ-View: Designated Asker of Stupid Questions

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Create a Slippery Slope

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Enterprise information initiatives, such as data governance, master data management, data quality, and business intelligence all face a common challenge—they require your organization to take on a significant and sustained change management effort.

Organizational change requires behavioral change.

Behavioral change requires more than just an executive management decree and a rational argument.  You need to unite the organization around a shared purpose, encourage collaboration, and elevate the change to a cause. 

Although some people within the organization will answer this call to action and become champions for the cause, many others will need more convincing.  As Guy Kawasaki advises, overcome this challenge by intentionally creating a slippery slope:

“Provide a safe first step.  Don’t put up any big hurdles in the beginning of the process.
The path to adopting a cause needs a slippery slope.”

Therefore, to get your enterprise information initiative off to a good start, make it easy for people to adopt the cause.

Create a slippery slope.

 

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DQ View: Achieving Data Quality Happiness

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Data Quality (DQ) View is an OCDQ regular segment.  Each DQ-View is a brief video discussion of a data quality key concept.

Continuing the happiness meme making its way around the data quality blogosphere, which I contributed to with my previous blog posts Delivering Data Happiness and Why isn’t our data quality worse?, in this new DQ-View segment I want to discuss achieving data quality happiness.

 

DQ View: Achieving Data Quality Happiness

 

If you are having trouble viewing this video, then you can watch it on Vimeo by clicking on this link: DQ-View on Vimeo

 

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Delivering Data Happiness

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Recently, a happiness meme has been making its way around the data quality blogosphere.

Its origins have been traced to a lovely day in Denmark when Henrik Liliendahl Sørensen, with help from The Muppet Show, asked “Why do you watch it?” referring to the typically negative spin in the data quality blogosphere, where it seems we are:

“Always describing how bad data is everywhere.

Bashing executives who don’t get it.

Telling about all the hard obstacles ahead. Explaining you don’t have to boil the ocean but might get success by settling for warming up a nice little drop of water.

Despite really wanting to tell a lot of success stories, being the funny Fozzie Bear on the stage, well, I am afraid I also have been spending most of my time on the balcony with Statler and Waldorf.

So, from this day forward: More success stories.”

In his recent blog posts, The Ugly Duckling and Data Quality Tools: The Cygnets in Information Quality, Henrik has been sharing more success stories, or to phrase it in an even happier way: delivering data happiness.

 

Delivering Data Happiness

I am reading the great book Delivering Happiness: A Path to Profits, Passion, and Purpose by Tony Hsieh, the CEO of Zappos.

Obviously, the book’s title inspired the title of this blog post. 

One of the Zappos core values is “build a positive team and family spirit,” and I have been thinking about how that applies to data quality improvements, which are often pursued as one of the many aspects of a data governance program.

Most data governance maturity models describe an organization’s evolution through a series of stages intended to measure its capability and maturity, tendency toward being reactive or proactive, and inclination to be project-oriented or program-oriented.

Most data governance programs are started by organizations that are confronted with a painfully obvious need for improvement.

The primary reason that the change management efforts of data governance are resisted is because they rely almost exclusively on negative methods—they emphasize broken business and technical processes, as well as bad data-related employee behaviors.

Although these problems exist and are the root cause of some of the organization’s failures, there are also unheralded processes and employees that prevented other problems from happening, which are the root cause of some of the organization’s successes.

“The best team members,” writes Hsieh while explaining the Zappos core values, “take initiative when they notice issues so that the team and the company can succeed.” 

“The best team members take ownership of issues and collaborate with other team members whenever challenges arise.” 

“The best team members have a positive influence on one another and everyone they encounter.  They strive to eliminate any kind of cynicism and negative interactions.”

The change management efforts of data governance and other enterprise information initiatives often make it sound like no such employees (i.e., “best team members”) currently exist anywhere within an organization. 

The blogosphere, as well as critically acclaimed books and expert presentations at major industry conferences, often seem to be in unanimous and unambiguous agreement in the message that they are broadcasting:

“Everything your organization is currently doing regarding data management is totally wrong!”

Sadly, that isn’t much of an exaggeration.  But I am not trying to accuse anyone of using Machiavellian sales tactics to sell solutions to non-existent problems—poor data quality and data governance maturity are costly realities for many organizations.

Nor am I trying to oversimplify the many real complexities involved when implementing enterprise information initiatives.

However, most of these initiatives focus exclusively on developing new solutions and best practices, failing to even acknowledge the possible presence of existing solutions and best practices.

The success of all enterprise information initiatives requires the kind of enterprise-wide collaboration that is facilitated by the “best team members.”  But where, exactly, do the best team members come from?  Should it really be surprising whenever an enterprise information initiative can’t find any using exclusively negative methods, focusing only on what is currently wrong?

As Gordon Hamilton commented on my previous post, we need to be “helping people rise to the level of the positive expectations, rather than our being codependent in their sinking to the level of the negative expectations.”

We really need to start using more positive methods for fostering change.

Let’s begin by first acknowledging the best team members who are currently delivering data happiness to our organizations.

 

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