The Johari Window of Data Quality

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.

The Johari Window of Data Quality

Additional listening options:

• Click here to Download the MP3 file for this OCDQ Radio episode

• Click here to Subscribe to OCDQ Radio via iTunes

• Click here to Subscribe to OCDQ Radio via its RSS feed (non iTunes)

• Click here to Listen to OCDQ Radio on your Mobile with Stitcher SmartRadio

• Click here to Browse the OCDQ Radio Archives and Schedule

Related Posts

There is No Such Thing as a Root Cause

The Dichotomy Paradox, Data Quality and Zero Defects

The Asymptote of Data Quality

To Our Data Perfectionists

DQ-View: The Cassandra Effect

The Data Quality Wager

DQ-View: Data Is as Data Does

Selling the Business Benefits of Data Quality

Related OCDQ Radio Episodes

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

• Redefining Data Quality — Guest Peter Perera discusses his proposed redefinition of data quality

• The Blue Box of Information Quality — Guest Daragh O Brien on why Information Quality is bigger on the inside

• Studying Data Quality — Guest Gordon Hamilton on the key concepts from recommended Data Quality books

• Organizing for Data Quality — Guest Tom Redman (aka the “Data Doc”) discusses Data Quality best practices

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DQ-View: MetaData makes BettahMusic

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

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

Related Posts

You Say Potato and I Say Tater Tot

The Metadata Continuum

The Metadata Crisis

Listen to an OCDQ Radio episode about how we are now living in a world where everyone is a Data Geek

What’s the Meta with your Data?

Let’s Meta a Data

Listen to Peter Benson discuss Metadata, Data, and Information on the Knights of the Data Roundtable

Data Quality Music (DQ-Songs)

In other words, the following links are to lyrical data quality blog posts inspired by music:

Data Quality Mondegreens

Council Data Governance

Over the Data Governance Rainbow

I’m Gonna Data Profile (500 Records)

A Record Named Duplicate

New Time Human Business

People

You Can’t Always Get the Data You Want

A spoonful of sugar helps the number of data defects go down

Data Quality is such a Rush

I’m Bringing DQ Sexy Back

Imagining the Future of Data Quality

The Very Model of a Modern DQ General

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Big Data el Memorioso

This blog post is sponsored by the Enterprise CIO Forum and HP.

Funes el memorioso is a short story by Jorge Luis Borges, which describes a young man named Ireneo Funes who, as a result of a horseback riding accident, has lost his ability to forget.  Although Funes has a tremendous memory, he is so lost in the details of everything he knows that he is unable to convert the information into knowledge and unable, as a result, to achieve wisdom.

In Spanish, the word memorioso means “having a vast memory.”  Without question, Big Data has a vast memory comprised of fast-moving large volumes of varying data seemingly providing details about everything your organization could ever want to know about our increasingly digitized and pixelated world.  But what if Big Data is the Ireneo Funes of the Information Age?

What if Big Data el Memorioso is the not-so-short story in which your organization becomes so lost in the details of everything big data delivers that you’re unable to connect enough of the dots to convert the information into knowledge and unable, as a result, to achieve the wisdom necessary to satisfice specific business needs?

Adrian Bridgwater recently compared this challenge to “trying to balance a stack of papers on a moving walkway, in a breeze, without knowing the full length or speed of the walkway itself.  If you want to extend the metaphor one step further — there are other passengers on our walkway and they could bump into us and/or add papers to our stack.  Oh, did I mention that the pieces of paper might not even all be the same size, shape, or color — and some may have tattered edges and coffee stains?”

In other words, as Bridgwater went on to explain, “our information optimization goals will typically include the need to manage information and assess its quantitative and qualitative values.  We will also need to analyze streams of both structured and unstructured data, the latter including video, emails, and other less ‘straight edged’ data.”

While examining some of the technology options that can assist with this challenge, Paul Muller recently remarked “whether it be structured, unstructured, big, small, real-time, or historical — data of all kinds are top-of-mind for business executives.  It may already feel like you’re drowning in data, but it’s important to get to grips with the changing technology landscape to ensure you’re not drowning in an incoherent mess of information management architectures too.”

Edd Dumbill recently wrote an introduction to the big data landscape, which concluded that “big data is no panacea.  You can find patterns and clues in your data, but then what?”  As Dumbill recommends, you need to know where you want to go.  You need to know what problem you want to solve, i.e., you need to pick a real business problem to guide your implementation.

Without this implementation guide, big data will have, as Borges said of Funes, “a certain stammering greatness,” but amount to, as William Shakespeare said in The Tragedy of Macbeth, “a tale told by an idiot, full of sound and fury, signifying nothing.”

This blog post is sponsored by the Enterprise CIO Forum and HP.

Related Posts

Neither the I Nor the T is Magic

Information Overload Revisited

The Speed of Decision

The Data-Decision Symphony

A Decision Needle in a Data Haystack

The Big Data Collider

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Data, Information, and Knowledge Management

Is your data complete and accurate, but useless to your business?

The Real Data Value is Business Insight

Data, data everywhere, but where is data quality?

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Dot Collectors and Dot Connectors

The attention blindness inherent in the digital age often leads to a debate about multitasking, which many claim impairs our ability to solve complex problems.  Therefore, we often hear that we need to adopt monotasking, i.e., we need to eliminate all possible distractions and focus our attention on only one task at a time.

However, during the recent Harvard Business Review podcast The Myth of Monotasking, Cathy Davidson, author of the new book Now You See It: How the Brain Science of Attention Will Transform the Way We Live, Work, and Learn, explained how “the moment that you start not paying attention fully to the task at hand, you actually start seeing other things that your attention would have missed.”  Although Davidson acknowledges that attention blindness is a serious problem, she explained that there really is no such thing as monotasking.  Modern neuroscience research has revealed that the human brain is, in fact, always multitasking.  Furthermore, she explained how multitasking can be extremely useful for a new and expansive form of attention.

“We all see selectively, but we don’t select the same things to see,” Davidson explained.  “So if we can learn to work together, we can actually account for, and productively work around, our own individual attention blindness by seeing collaboratively in a way that compensates for that blindness.”

During the podcast, an analogy was made that focusing attention on specific tasks can result in a lot of time spent collecting dots without spending enough time connecting those dots.  This point caused me to ponder the division of organizational labor that has historically existed between the dot collection of data management, which focuses on aspects such as data integrity and data quality, and the dot connection of business intelligence, which focuses on aspects such as data analysis and data visualization.

I think most data management professionals are dot collectors since it often seems like they spend a lot of their time, money, and attention on collecting (and profiling, modeling, cleansing, transforming, matching, and otherwise managing) data dots.

But since data’s value comes from data’s usefulness, merely collecting data dots doesn’t mean anything if you cannot connect those dots into meaningful patterns that enable your organization to take action or otherwise support your business activities.

So I think most business intelligence professionals are dot connectors since it often seems like they spend a lot of their time, money, and attention on connecting (and querying, aggregating, reporting, visualizing, and otherwise analyzing) data dots.

However, the attention blindness of data management and business intelligence professionals means that they see selectively, often intentionally selecting to not see the same things.  But as more of our personal and professional lives become digitized and pixelated, the big picture of the business world is inundated with the multifaceted challenges of big data, where the fast-moving large volumes of varying data are transforming the way we have to view traditional data management and business intelligence.

We need to replace our perspective of data management and business intelligence as separate monotasking activities with an expansive form of organizational multitasking where the dot collectors and dot connectors work together more collaboratively.

Related Posts

Channeling My Inner Beagle: The Case for Hyperactivity

Mind the Gap

The Wisdom of the Social Media Crowd

No Datum is an Island of Serendip

DQ-View: Data Is as Data Does

The Real Data Value is Business Insight

Information Overload Revisited

Neither the I Nor the T is Magic

The Big Data Collider

OCDQ Radio - Big Data and Big Analytics

OCDQ Radio - So Long 2011, and Thanks for All the . . .

The Interconnected User Interface

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Scary Calendar Effects

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

During this episode, recorded on the first of three occurrences of Friday the 13th in 2012, I discuss scary calendar effects.

In other words, I discuss how schedules, deadlines, and other date-related aspects can negatively affect enterprise initiatives such as data quality, master data management, and data governance.

Please Beware: This episode concludes with the OCDQ Radio Theater production of Data Quality and Friday the 13th.

Scary Calendar Effects

Additional listening options:

• Click here to Download the MP3 file for this OCDQ Radio episode

• Click here to Subscribe to OCDQ Radio via iTunes

• Click here to Subscribe to OCDQ Radio via its RSS feed (non iTunes)

• Click here to Listen to OCDQ Radio on your Mobile with Stitcher SmartRadio

• Click here to Browse the OCDQ Radio Archives and Schedule

Related Posts

Data Quality and #FollowFriday the 13th

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“What is is the was of what shall be”

Popular OCDQ Radio Episodes

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

• So Long 2011, and Thanks for All the . . . — Don’t Panic! This is the OCDQ Radio 2011 Year in Review episode

• The Fall Back Recap Show — Celebrating the autumnal equinox with a look back at the Best of OCDQ Radio

• The Blue Box of Information Quality — Guest Daragh O Brien on why Information Quality is bigger on the inside

• Redefining Data Quality — Guest Peter Perera discusses his proposed redefinition of data quality

• Studying Data Quality — Guest Gordon Hamilton on the key concepts from recommended Data Quality books

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Data Governance Frameworks are like Jigsaw Puzzles

In her recent Data Quality Pro Interview, Jill Dyché explained a common misconception, namely that a data governance framework is not a strategy.  “Unlike other strategic initiatives that involve IT,” Jill explained, “data governance needs to be designed.  The cultural factors, the workflow factors, the organizational structure, the ownership, the political factors, all need to be accounted for when you are designing a data governance roadmap.”

“People need a mental model, that is why everybody loves frameworks,” Jill continued.  “But they are not enough and I think the mistake that people make is that once they see a framework, rather than understanding its relevance to their organization, they will just adapt it and plaster it up on the whiteboard and show executives without any kind of context.  So they are already defeating the purpose of data governance, which is to make it work within the context of your business problems, not just have some kind of mental model that everybody can agree on, but is not really the basis for execution.”

“So it’s a really, really dangerous trend,” Jill cautioned, “that we see where people equate strategy with framework because strategy is really a series of collected actions that result in some execution — and that is exactly what data governance is.”

And in her excellent article Data Governance Next Practices: The 5 + 2 Model, Jill explained that data governance requires a deliberate design so that the entire organization can buy into a realistic execution plan, not just a sound bite.  As usual, I agree with Jill, since, in my experience, many people expect a data governance framework to provide eureka-like moments of insight.

In The Myths of Innovation, Scott Berkun debunked the myth of the eureka moment using the metaphor of a jigsaw puzzle.

“When you put the last piece into place, is there anything special about that last piece or what you were wearing when you put it in?” Berkun asked.  “The only reason that last piece is significant is because of the other pieces you’d already put into place.  If you jumbled up the pieces a second time, any one of them could turn out to be the last, magical piece.”

“The magic feeling at the moment of insight, when the last piece falls into place,” Berkun explained, “is the reward for many hours (or years) of investment coming together.  In comparison to the simple action of fitting the puzzle piece into place, we feel the larger collective payoff of hundreds of pieces’ worth of work.”

Perhaps the myth of the data governance framework could also be debunked using the metaphor of a jigsaw puzzle.

Data governance requires the coordination of a complex combination of a myriad of factors, including executive sponsorship, funding, decision rights, arbitration of conflicting priorities, policy definition, policy implementation, data quality remediation, data stewardship, business process optimization, technology enablement, change management — and many other puzzle pieces.

How could a data governance framework possibly predict how you will assemble the puzzle pieces?  Or how the puzzle pieces will fit together within your unique corporate culture?  Or which of the many aspects of data governance will turn out to be the last (or even the first) piece of the puzzle to fall into place in your organization?  And, of course, there is truly no last piece of the puzzle, since data governance is an ongoing program because the business world constantly gets jumbled up by change.

So, data governance frameworks are useful, but only if you realize that data governance frameworks are like jigsaw puzzles.

Related Posts

Listen to Jill Dyché discuss Data Governance on the Knights of the Data Roundtable

The Three Most Important Letters in Data Governance

Data Governance and the Adjacent Possible

Aristotle, Data Governance, and Lead Rulers

Data Governance Star Wars: Balancing Bureaucracy And Agility

OCDQ Radio - Data Governance Star Wars

The Stakeholder’s Dilemma

The Collaborative Culture of Data Governance

The Big Data Collider

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The Dumb and Dumber Guide to Data Quality

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DQ-View: Data Is as Data Does

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 following list contains the books shown in the video, simply listed in the order they appeared on my book shelf:

• Customer Data Integration: Reaching a Single Version of the Truth by Jill Dyché and Evan Levy

• Master Data Management by David Loshin

• Master Data Management in Practice: Achieving True Customer MDM by Dalton Cervo and Mark Allen, who discussed their book on OCDQ Radio: Master Data Management in Practice

• Master Data Management and Data Governance (Second Edition) by Alex Berson and Larry Dubov

• The Data Governance Imperative by Steve Sarsfield

• The Data Asset: How Smart Companies Govern Their Data for Business Success by Tony Fisher

• Making EIM Work for Business by John Ladley, who discussed his book on OCDQ Radio: Making EIM Work for Business

• Data Driven: Profiting from Your Most Important Business Asset by Thomas Redman

• Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information by Danette McGilvray

• The Practitioner’s Guide to Data Quality Improvement by David Loshin

• Data Quality and Record Linkage Techniques by Thomas Herzog, Fritz Scheuren, and William Winkler

• Entity Resolution and Information Quality by John Talburt

• 101 Lightbulb Moments in Data Management: Tales from the Data Roundtable by Jill Dyché, Jim Harris, Dylan Jones, David Loshin, Joyce Norris-Montanari, Rich Murnane, and Phil Simon

Previous DQ-View Videos

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

DQ-View: Baseball and Data Quality

DQ-View: Occam’s Razor Burn

DQ-View: Roman Ruts on the Road to Data Governance

DQ-View: Talking about Data

DQ-View: The Poor Data Quality Blizzard

DQ-View: New Data Resolutions

DQ-View: From Data to Decision

DQ View: Achieving Data Quality Happiness

Data Quality is not a Magic Trick

DQ-View: The Cassandra Effect

DQ-View: Is Data Quality the Sun?

DQ-View: Designated Asker of Stupid Questions

Video: Oh, the Data You’ll Show!

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Best OCDQ Blog Posts of 2011

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: Balancing Bureaucracy and Agility — In character as OCDQ-Wan, I argue in favor of business agility and explain that Collaboration is the Data Governance Force.

• 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:

• The Three Most Important Letters in Data Governance — There are only three letters of difference between the words cooperative and competitive, which we could say are the three most important letters in 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.

• Data Governance and the Buttered Cat Paradox — The fearless felines of the buttered-toast-paratrooper brigade ponder how to approach data governance — top-down or bottom-up.  See the follow-up post: Zig-Zag-Diagonal Data Governance

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:

• So Long 2011, and Thanks for All the . . . — The OCDQ Radio 2011 Year in Review, featuring Jarrett Goldfedder, who discusses Big Data, Nicola Askham, who discusses Data Governance, and Daragh O Brien, who discusses Data Privacy.

• 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.

• Big Data and Big Analytics — Special Guests Jill Dyché and Dan Soceanu discuss big trends in Business Intelligence, including Cloud, Collaboration, and Big Data, the last of which lead to a discussion about Big Analytics.

• 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.

• Making EIM Work for Business — Guest John Ladley discusses his book Making EIM Work for Business, exploring what makes information management, not just useful, but valuable to the enterprise.

• The Blue Box of Information Quality — Guest Daragh O Brien on why Information Quality is bigger on the inside, using stories as an analytical tool and change management technique, and why we must never forget that “people are cool.”

• Master Data Management in Practice — Guests Dalton Cervo and Mark Allen discuss their book MDM in Practice, and how to properly prepare for a new MDM program.

• 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.

• Good-Enough Data for Fast-Enough Decisions — Guest Julie Hunt discusses Data Quality and Business Intelligence, including the speed versus quality debate of near-real-time decision making, and the future of predictive analytics.

• 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

• Plato’s Data — Data shapes our perception of the real world, but sometimes we forget that data is only a partial reflection of reality.  This theme was also discussed on the OCDQ Radio episode Redefining Data Quality with Peter Perera.

• There is No Such Thing as a Root Cause — There are no root causes, only strong correlations. And correlations are strengthened by continuous monitoring.  This post received excellent comments, including great banter with Martin Doyle.

• You only get a Return from something you actually Invest in — Invest in doing the hard daily work of continuously improving your data quality and putting into practice your data governance principles, policies, and procedures.

• The Dichotomy Paradox, Data Quality and Zero Defects — Has your data quality practice become motionless by trying to prove that Zero Defects is more than just theoretically possible?

• The Data Quality Wager — Inspired by Gordon Hamilton, my rendering of Pascal’s Wager in a data quality context.

• 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.

Related Posts

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

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So Long 2011, and Thanks for All the . . .

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

Don’t Panic!  Welcome to the mostly harmless OCDQ Radio 2011 Year in Review episode.  During this approximately 42 minute episode, I recap the data-related highlights of 2011 in a series of sometimes serious, sometimes funny, segments, as well as make wacky and wildly inaccurate data-related predictions about 2012.

Special thanks to my guests Jarrett Goldfedder, who discusses Big Data, Nicola Askham, who discusses Data Governance, and Daragh O Brien, who discusses Data Privacy.  Additional thanks to Rich Murnane and Dylan Jones.  And Deep Thanks to that frood Douglas Adams, who always knew where his towel was, and who wrote The Hitchhiker’s Guide to the Galaxy.

So Long 2011, and Thanks for All the . . .

Additional listening options:

• Click here to Download the MP3 file for this OCDQ Radio episode

• Click here to Subscribe to OCDQ Radio via iTunes

• Click here to Subscribe to OCDQ Radio via its RSS feed (non iTunes)

• Click here to Listen to OCDQ Radio on your Mobile with Stitcher SmartRadio

• Click here to Browse the OCDQ Radio Archives and Schedule

Previous OCDQ Radio Episodes

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

• Redefining Data Quality — Guest Peter Perera discusses his proposed redefinition of data quality

• Making EIM Work for Business — Guest John Ladley discusses his book: Making EIM Work for Business

• The Fall Back Recap Show — Celebrating the autumnal equinox with a look back at the Best of OCDQ Radio

• The Blue Box of Information Quality — Guest Daragh O Brien on why Information Quality is bigger on the inside

• Good-Enough Data for Fast-Enough Decisions — Guest Julie Hunt on Data Quality and Business Intelligence

• Studying Data Quality — Guest Gordon Hamilton on the key concepts from recommended Data Quality books

• DAMA International — Guest Loretta Mahon Smith provides an overview of the DAMA DMBOK and CDMP

• The Higher Education of Data Quality — Guest Mark Horseman discusses Data Quality in Higher Education

• International Data Quality — Guest Graham Rhind discusses international name and address Data Quality

• Big Data and Big Analytics — Special Guests Jill Dyché and Dan Soceanu on big trends in Business Intelligence

• Organizing for Data Quality — Guest Tom Redman (aka the “Data Doc”) discusses Data Quality best practices

• The Age of the Platform — Guest Phil Simon discusses his fourth book: The Age of the Platform

• Social Media Strategy — Guest Crysta Anderson of IBM Initiate on Social Media strategy and content marketing

• Data Profiling Early and Often — Guest James Standen discusses Data Profiling concepts and practices

• Data Governance Star Wars — Special Guests Rob Karel and Gwen Thomas discuss Data Governance programs

• Master Data Management in Practice — Guests Dalton Cervo and Mark Allen on their book: MDM in Practice

• The Art of Data Matching — Guest Henrik Liliendahl Sørensen discusses Data Matching concepts and practices

• Data Quality Pro — Guest Dylan Jones discusses new features coming soon to DataQualityPro.com

• A Brave New Data World — We are now living in a brave new data world where everyone is a data geek

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Neither the I Nor the T is Magic

This blog post is sponsored by the Enterprise CIO Forum and HP.

It’s that time when we reflect on the past year and try to predict the future, such as Paul Muller, Joel Rothman, and Pearl Zhu did with their recent blog posts.  Although I have previously written about why most predictions don’t come true, in this post, I throw my fortune-telling hat into the 2012 prediction ring.

The information technology (IT) trends of 2011 included consumerization and decentralization, application modernization and information optimization, cloud computing and cloud security (and, by extension, enterprise security).  However, perhaps the biggest IT trend of the year was that 2011 is going out with a Big Bang about Big Data in 2012 and beyond.

Since its inception, the IT industry has both benefited from and battled against the principle known as Clarke’s Third Law:

“Any sufficiently advanced technology is indistinguishable from magic.”

This principle often fuels the Diderot Effect of New Technology, enchanting our organizations with the mad desire to stock up on new technologically magic things.  As such, many are predicting 2012 will be the Year of the Magic Elephant named Hadoop because, as Gartner Research predicts about big data, “the size, complexity of formats, and speed of delivery exceeds the capabilities of traditional data management technologies; it requires the use of new or exotic technologies simply to manage the volume alone.  Many new technologies are emerging, with the potential to be disruptive.  Analytics has become a major driving application.”  As a corollary, the potential business value of integrating big data into business analytics seems to be conjuring up an alternative version of Clarke’s Third Law:

“Any sufficiently advanced information is indistinguishable from magic.”

In other words, many big data proponents (especially IT vendors selling Hadoop-based solutions) extol its virtues as if its information is capable of providing clairvoyant business insight, as if big data was the Data Psychic of the Information Age.

Although both sufficiently advanced information and technology will have important business-enabling IT roles to play in 2012, never forget that neither the I nor the T is magic — no matter what the Data Psychics and Magic Elephants may say.

This blog post is sponsored by the Enterprise CIO Forum and HP.

Related Posts

Information Overload Revisited

The Data Encryption Keeper

The Cloud Security Paradox

The Good, the Bad, and the Secure

Securing your Digital Fortress

Shadow IT and the New Prometheus

Are Cloud Providers the Bounty Hunters of IT?

The Diderot Effect of New Technology

The IT Consumerization Conundrum

The IT Prime Directive of Business First Contact

A Sadie Hawkins Dance of Business Transformation

Are Applications the La Brea Tar Pits for Data?

Why does the sun never set on legacy applications?

The Partly Cloudy CIO

The IT Pendulum and the Federated Future of IT

Suburban Flight, Technology Sprawl, and Garage IT

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Redefining Data Quality

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

During this episode, I have an occasionally spirited discussion about data quality with Peter Perera, partially precipitated by his provocative post from this past summer, The End of Data Quality...as we know it, which included his proposed redefinition of data quality, as well as his perspective on the relationship of data quality to master data management and data governance.

Peter Perera is a recognized consultant and thought leader with significant experience in Master Data Management, Customer Relationship Management, Data Quality, and Customer Data Integration.  For over 20 years, he has been advising and working with Global 5000 organizations and mid-size enterprises to increase the usability and value of their customer information.

Redefining Data Quality

Additional listening options:

• Click here to Download the MP3 file for this OCDQ Radio episode

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• Click here to Subscribe to OCDQ Radio via its RSS feed (non iTunes)

• Click here to Listen to OCDQ Radio on your Mobile with Stitcher SmartRadio

• Click here to Browse the OCDQ Radio Archives and Schedule

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Information Overload Revisited

This blog post is sponsored by the Enterprise CIO Forum and HP.

Information Overload is a term invoked regularly during discussions about the data deluge of the Information Age, which has created a 24 hours a day, 7 days a week, 365 days a year, world-wide whirlwind of constant information flow, where the very air we breath is literally teeming with digital data streams — continually inundating us with new, and new types of, information.

Information overload generally refers to how too much information can overwhelm our ability to understand an issue, and can even disable our decision making in regards to that issue (this latter aspect is generally referred to as Analysis Paralysis).

But we often forget that the term is over 40 years old.  It was popularized by Alvin Toffler in his bestselling book Future Shock, which was published in 1970, back when the Internet was still in its infancy, and long before the Internet’s progeny would give birth to the clouds contributing to the present, potentially perpetual, forecast for data precipitation.

A related term that has become big in the data management industry is Big Data, which, as Gartner Research explains, although the term acknowledges the exponential growth, availability, and use of information in today’s data-rich landscape, big data is about more than just data volume.  Data variety (i.e., structured, semi-structured, and unstructured data, as well as other types, such as the sensor data emanating from the Internet of Things) and data velocity (i.e., how fast data is being produced and how fast the data must be processed to meet demand) are also key characteristics of the big challenges of big data.

John Dodge and Bob Gourley recently discussed big data on Enterprise CIO Forum Radio, where Gourley explained that big data is essentially “the data that your enterprise is not currently able to do analysis over.”  This point resonates with a similar one made by Bill Laberis, who recently discussed new global research where half of the companies polled responded that they cannot effectively deal with analyzing the rising tide of data available to them.

Most of the big angst about big data comes from this fear that organizations are not tapping the potential business value of all that data not currently being included in their analytics and decision making.  This reminds me of psychologist Herbert Simon, who won the 1978 Nobel Prize in Economics for his pioneering research on decision making, which included comparing and contrasting the decision-making strategies of maximizing and satisficing (a term that combines satisfying with sufficing).

Simon explained that a maximizer is like a perfectionist who considers all the data they can find because they need to be assured that their decision was the best that could be made.  This creates a psychologically daunting task, especially as the amount of available data constantly increases (again, note that this observation was made over 40 years ago).  The alternative is to be a satisficer, someone who attempts to meet criteria for adequacy rather than identify an optimal solution.  And especially when time is a critical factor, such as it is with the real-time decision making demanded by a constantly changing business world.

Big data strategies will also have to compare and contrast maximizing and satisficing.  Maximizers, if driven by their angst about all that data they are not analyzing, might succumb to information overload.  Satisficers, if driven by information optimization, might sufficiently integrate just enough of big data into their business analytics in a way that satisfies specific business needs.

As big data forces us to revisit information overload, it may be useful for us to remember that originally the primary concern was not about the increasing amount of information, but instead the increasing access to information.  As Clay Shirky succinctly stated, “It’s not information overload, it’s filter failure.”  So, to harness the business value of big data, we will need better filters, which may ultimately make for the entire distinction between information overload and information optimization.

This blog post is sponsored by the Enterprise CIO Forum and HP.

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Making EIM Work for Business

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.

Making EIM Work for Business

Additional listening options:

• Click here to Download the MP3 file for this OCDQ Radio episode

• Click here to Subscribe to OCDQ Radio via iTunes

• Click here to Subscribe to OCDQ Radio via its RSS feed (non iTunes)

• Click here to Listen to OCDQ Radio on your Mobile with Stitcher SmartRadio

• Click here to Browse the OCDQ Radio Archives and Schedule

Win a copy of the Book

John Ladley and Morgan Kaufmann Publishers want to give one OCDQ Radio listener a free copy of Making EIM Work for Business

Here is how the book contest will work:

(1) Book Contest Question — During this OCDQ Radio episode, John Ladley explained how EIM requires a change in the data and business environment using what sports analogy?

(2) Book Contest Deadline — By December 31, 2011, Email Jim Harris with your answer to the book contest question.

(3) Book Contest Winner — In January 2012, one winner will be randomly selected from the emails containing the correct answer to the contest question, and then Morgan Kaufmann will email the winner requesting a postal address to ship the book.

Previous OCDQ Radio Episodes

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

• The Fall Back Recap Show — Celebrating the autumnal equinox with a look back at the Best of OCDQ Radio

• The Blue Box of Information Quality — Guest Daragh O Brien on why Information Quality is bigger on the inside

• Good-Enough Data for Fast-Enough Decisions — Guest Julie Hunt on Data Quality and Business Intelligence

• Studying Data Quality — Guest Gordon Hamilton on the key concepts from recommended Data Quality books

• DAMA International — Guest Loretta Mahon Smith provides an overview of the DAMA DMBOK and CDMP

• The Higher Education of Data Quality — Guest Mark Horseman discusses Data Quality in Higher Education

• International Data Quality — Guest Graham Rhind discusses international name and address Data Quality

• Big Data and Big Analytics — Special Guests Jill Dyché and Dan Soceanu on big trends in Business Intelligence

• Organizing for Data Quality — Guest Tom Redman (aka the “Data Doc”) discusses Data Quality best practices

• The Age of the Platform — Guest Phil Simon discusses his fourth book: The Age of the Platform

• Social Media Strategy — Guest Crysta Anderson of IBM Initiate on Social Media strategy and content marketing

• Data Profiling Early and Often — Guest James Standen discusses Data Profiling concepts and practices

• Data Governance Star Wars — Special Guests Rob Karel and Gwen Thomas discuss Data Governance programs

• Master Data Management in Practice — Guests Dalton Cervo and Mark Allen on their book: MDM in Practice

• The Art of Data Matching — Guest Henrik Liliendahl Sørensen discusses Data Matching concepts and practices

• Data Quality Pro — Guest Dylan Jones discusses new features coming soon to DataQualityPro.com

• A Brave New Data World — We are now living in a brave new data world where everyone is a data geek

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Two Weeks Before Christmas

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

Season’s Greetings fellow data management enthusiasts and welcome to a special holiday-themed episode of OCDQ Radio.

With the Christmas, Hanukkah, Kwanzaa, and Festivus seasons now upon us, I revisited my ‘Twas Two Weeks Before Christmas blog post from 2009, which is based on the poem A Visit from St. Nicholas.  During this brief podcast, I perform a recital.

The entire OCDQ Blog family wishes you and yours all the best during this holiday season and the coming new year.

Two Weeks Before Christmas

Additional listening options:

• Click here to Download the MP3 file for this OCDQ Radio episode

• Click here to Subscribe to OCDQ Radio via iTunes

• Click here to Subscribe to OCDQ Radio via its RSS feed (non iTunes)

• Click here to Listen to OCDQ Radio on your Mobile with Stitcher SmartRadio

• Click here to Browse the OCDQ Radio Archives and Schedule

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You only get a Return from something you actually Invest in

In my previous post, I took a slightly controversial stance on a popular three-word phrase — Root Cause Analysis.  In this post, it’s another popular three-word phrase — Return on Investment (most commonly abbreviated as the acronym ROI).

What is the ROI of purchasing a data quality tool or launching a data governance program?

Zero.  Zip.  Zilch.  Intet.  Ingenting.  Rien.  Nada.  Nothing.  Nichts.  Niets.  Null.  Niente.  Bupkis.

There is No Such Thing as the ROI of purchasing a data quality tool or launching a data governance program.

Before you hire “The Butcher” to eliminate me for being The Man Who Knew Too Little about ROI, please allow me to explain.

Returns only come from Investments

Although the reason that you likely purchased a data quality tool is because you have business-critical data quality problems, simply purchasing a tool is not an investment (unless you believe in Magic Beans) since the tool itself is not a solution.

You use tools to build, test, implement, and maintain solutions.  For example, I spent several hundred dollars on new power tools last year for a home improvement project.  However, I haven’t received any return on my home improvement investment for a simple reason — I still haven’t even taken most of the tools out of their packaging yet.  In other words, I barely even started my home improvement project.  It is precisely because I haven’t invested any time and effort that I haven’t seen any returns.  And it certainly isn’t going to help me (although it would help Home Depot) if I believed buying even more new tools was the answer.

Although the reason that you likely launched a data governance program is because you have complex issues involving the intersection of data, business processes, technology, and people, simply launching a data governance program is not an investment (unless you believe in the Hedgehog’s framework) since it does not conjure the three most important letters.

Data is only an Asset if Data is a Currency

In his book UnMarketing, Scott Stratten discusses this within the context of the ROI of social media (a commonly misunderstood aspect of social media strategy), but his insight is just as applicable to any discussion of ROI.  “Think of it this way: You wouldn’t open a business bank account and ask to withdraw $5,000 before depositing anything. The banker would think you are a loony.”

Yet, as Stratten explained, people do this all the time in social media by failing to build up what is known as social currency.  “You’ve got to invest in something before withdrawing. Investing your social currency means giving your time, your knowledge, and your efforts to that channel before trying to withdraw monetary currency.”

The same logic applies perfectly to data quality and data governance, where we could say it’s the failure to build up what I will call data currency.  You’ve got to invest in data before you could ever consider data an asset to your organization.  Investing your data currency means giving your time, your knowledge, and your efforts to data quality and data governance before trying to withdraw monetary currency (i.e., before trying to calculate the ROI of a data quality tool or a data governance program).

If you actually want to get a return on your investment, then actually invest in your data.  Invest in doing the hard daily work of continuously improving your data quality and putting into practice your data governance principles, policies, and procedures.

Data is only an asset if data is a currency.  Invest in your data currency, and you will eventually get a return on your investment.

You only get a return from something you actually invest in.

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