Data Governance needs Searchers, not Planners

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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Best OCDQ Blog Posts of 2012

Welcome to my roundup of the best blog posts published on the Obsessive-Compulsive Data Quality (OCDQ) blog during 2012.

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, and which I have organized into four sections of ten best posts by topic or type.

Ten Best Posts on Big Data

• Dot Collectors and Dot Connectors — The multifaceted challenges of big data require the dot collectors of data management and the dot connectors of business intelligence to overcome their attention blindness and work together more collaboratively.

• HoardaBytes and the Big Data Lebowski — Don’t hoard Data, dude.  The Data must abide.  The Data must abide both the Business, by proving useful to our business activities, and the Individual, by protecting the privacy of our personal activities.

• Magic Elephants, Data Psychics, and Invisible Gorillas — As technological advancements improve our data analytical tools, we must not lose sight of the fact that tools and data remain only as effective and beneficent as the humans who wield them.

• Our Increasingly Data-Constructed World — What we now call Big Data is in fact a long-running macro trend underlying the many recent trends and innovations making our world, not just more data-driven, but increasingly data-constructed.

• Will Big Data be Blinded by Data Science? — With apologies to Thomas Dolby, will the business leaders being told to hire data scientists to derive business value from big data analytics be blind to what data science tries to show them?

• The Graystone Effects of Big Data — Using a metaphor based on the science fiction television show Caprica, I refer to the positive aspects of Big Data as the Zoe Graystone Effect, and the negative aspects of Big Data as the Daniel Graystone Effect.

• Exercise Better Data Management — Big Data may be followed by MOData (i.e., MOre Data or Morbidly Obese Data), but that doesn’t necessarily mean we require more data management, instead we just need to exercise better data management.

• A Tale of Two Datas — Inspired by Malcolm Chisholm and Charles Dickens, there are two types of data (i.e., representation and observation, not big and not-so-big) with different data uses that will require different data management approaches.

• Data Silence — Not only do we need to adopt a mindset that embraces the principles of data science, but we also have to acknowledge that the biases and preconceptions in our minds could silence the signal and amplify the noise in big data.

• The Wisdom of Crowds, Friends, and Experts — The future of wisdom will increasingly become an amalgamation of experts, friends, and crowds, with the data and techniques from all three sources often contributing to data-driven decision making.

Ten Best Posts on Data Governance and Data Quality

• Data Governance Frameworks are like Jigsaw Puzzles — Inspired by Jill Dyché and Scott Berkun, this post explains how the usefulness of data governance frameworks comes from realizing data governance frameworks are like jigsaw puzzles.

• Data Quality: Quo Vadimus? — With lots of help from Henrik Liliendahl Sørensen, Garry Ure, Bryan Larkin, and many others via the comments, I ponder where data quality is going, and whether data quality is a journey or a destination.

• Data Quality and Miracle Exceptions — Battling the dark forces of poor data quality doesn’t require any superpowers, and data quality doesn’t have any miracle exceptions, so for the love of high-quality data everywhere, stop trying to sell us one.

• Data Myopia and Business Relativity — Examines the two most prevalent definitions for data quality, real-world alignment and fitness for the purpose of use, otherwise known as the danger of data myopia and the challenge of business relativity.

• How Data Cleansing Saves Lives — Although proactive defect prevention is far superior to reactive data cleansing, the history of the Hubble Space Telescope proves that data cleansing can be not just a necessary evil, but also a necessary good.

• Data Quality and the Bystander Effect — The most common reason data quality issues are neither reported nor corrected is the Bystander Effect making people less likely to interpret bad data as a problem or, at the very least, not their responsibility.

• Data Quality and Chicken Little Syndrome — A chicken-metaphor-based post about the far-too-common and fowl folly of, instead of trying to sell the business benefits of data quality, emphasizing the negative aspects of not investing in data quality.

• Data and its Relationships with Quality — The metadata linking the data management industry to what it manages suffers from the one-to-many relationships created by never agreeing on how data, information, and quality should be defined.

• Cooks, Chefs, and Data Governance — Implementing policies requires cooks who are adept at carrying out a recipe, as well as chefs who are trusted to figure out how to best combine policies with the organizational ingredients available to them.

• Availability Bias and Data Quality Improvement — The availability heuristic explains why a reactive data cleansing project is easily approved, and availability bias explains why initiating a proactive data quality program is usually resisted.

Ten Best Podcasts

• 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.

• Saving Private Data — Recorded in December 2011, guest Daragh O Brien discusses the data privacy and data protection implications of social media, cloud computing, and big data.

• Decision Management Systems — Guest James Taylor discusses data-driven decision making and analytical concepts from his book: Decision Management Systems: A Practical Guide to Using Business Rules and Predictive Analytics.

• 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).

• Social Media for Midsize Businesses — Sponsored by IBM Midsize Business Solutions, guest Paul Gillin, author of four books, the latest, co-authored with Greg Gianforte, is Attack of the Customers, discusses social media marketing concepts.

• Data Driven — Guest Tom Redman (aka the “Data Doc”) discusses concepts from one of my favorite data quality books, which is his most recent book: Data Driven: Profiting from Your Most Important Business Asset.

• 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.

• The Evolution of Enterprise Security — Sponsored by the Enterprise CIO Forum, guest Bill Laberis discusses striking a balance between convenience and security, which is necessary in the era of cloud computing and mobile devices.

• 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.

• Getting to Know NoSQL — This episode of the Open MIKE Podcast discusses how NoSQL does not mean AntiSQL (i.e., NoSQL is not a Relational replacement), and that business-driven big data needs will often require “Not Only SQL.”

Ten Best of the Rest

• DQ-View: Data Is as Data Does — In this short video, I explain that data’s value comes from data’s usefulness, exemplifying the potential value of unstructured data based on whether or not you put what you read in data management books to use.

• DQ-View: The Five Stages of Data Quality — In this short video, using my superb acting skills, I demonstrate how coming to terms with the daunting challenge of data quality is somewhat similar to experiencing the Five Stages of Grief.

• DQ-View: MetaData makes BettahMusic — In this short video, I demonstrate how better metadata makes data better using the metadata automatically and manually created after importing my CD collection into my iTunes library.

• Metadata, Data Quality, and the Stroop Test — In this colorful (and perhaps too colorful) post, I use the Stroop Test, where colors do not match their names, to discuss the relationship between metadata and data quality.

• Quality is the Higgs Field of Data — Using one of the biggest science stories of 2012, the potential discovery of the elusive Higgs Boson (which I also attempt to explain), I attempt an analogy for data quality based on the Higgs Field.

• The Family Circus and Data Quality — Thanks to The Family Circus comic strip created by cartoonist Bil Keane, I explain how Ida Know owns the data, Not Me is accountable for data governance, and Nobody takes responsibility for data quality.

• Data Love Song Mashup — Since your data needs love too, on Valentine’s Day I wrote this post providing a mashup of love songs for your data (and Rob DuMoulin added a few more in the comments) — Happy Data Quality to you and your data!

• The Algebra of Collaboration — The trick of algebra equates collaboration with data quality and data governance success when collaboration is viewed not just as a guiding principle, but also as a call to action in your daily practices.

• The Return of the Dumb Terminal — With help from author Kevin Kelly and my old green machine, I ponder how the mobile-app-portal-to-the-cloud computing model means mobile devices are bringing about the return of the dumb terminal.

• An Enterprise Carol — Jacob Marley raises the ghosts of a few ideas to consider about how to keep the Enterprise well in the new year via the Ghosts of Enterprise Past (Legacy Applications), Present (IT Consumerization), and Future (Big Data).

Thank You for Reading OCDQ Blog in 2012

In 2012, the Obsessive-Compulsive Data Quality (OCDQ) blog published 92 posts, which received 160,000 total page views, while averaging over 400 page views and 200 unique visitors a day.

Thank you for reading OCDQ Blog in 2012.  Your readership was deeply appreciated.

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Social Business is more than Social Marketing

Although much of the early business use of social media was largely focused on broadcasting marketing messages at customers, social media transformed word of mouth into word of data and empowered customers to add their voice to marketing messages, forcing marketing to evolve from monologues to dialogues.  But is the business potential of social media limited to marketing?

During the MidMarket IBM Social Business #Futurecast, a panel discussion from earlier this month, Ed Brill, author of the forthcoming book Opting In: Lessons in Social Business from a Fortune 500 Product Manager, defined the term social business as “an organization that engages employees in a socially-enabled process that brings together how employees interact with each other, partners, customers, and the marketplace.  It’s about bringing all the right people, both internally and externally, together in a conversation to solve problems, be innovative and responsive, and better understand marketplace dynamics.”

“Most midsize businesses today,” Laurie McCabe commented, “are still grappling with how to supplement traditional applications and tools with some of the newer social business tools.  Up until now, the focus has been on integrating social media into a lot of marketing communications, and we haven’t yet seen the integration of social media into other business processes.”

“Midsize businesses understand,” Handly Cameron remarked, “how important it is to get into social media, but they’re usually so focused on daily operations that they think that a social business is simply one that uses social media, and therefore they cite the facts that they created Twitter and Facebook accounts as proof that they are a social business, but again, they are focusing on external uses of social media and not internal uses such as improving employee collaboration.”

Collaboration was a common theme throughout the panel discussion.  Brill said a social business is one that has undergone the cultural transformation required to embrace the fact that it is a good idea to share knowledge.  McCabe remarked that the leadership of a social business rewards employees for sharing knowledge, not for hoarding knowledge.  She also emphasized the importance of culture before tools since simply giving individuals social tools will not automatically create a collaborative culture.

Cameron also noted how the widespread adoption of cloud computing and mobile devices is helping to drive the adoption of social tools for collaboration, and helping to break down a lot of the traditional boundaries to knowledge sharing, especially as more organizations are becoming less bounded by the physical proximity of their employees, partners, and customers.

From my perspective, even though marketing might have been how social media got in the front door of many organizations, social media has always been about knowledge sharing and collaboration.  And with mobile, cloud, and social technologies so integrated into our personal and professional lives, life and business are both more social and collaborative than ever before.  So, even if collaboration isn’t in the genes of your organization, it’s no longer possible to put the collaboration genie back in the bottle.

This post was written as part of the IBM for Midsize Business program, which provides midsize businesses with the tools, expertise and solutions they need to become engines of a smarter planet.

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Turning the M Upside Down

I am often asked about the critical success factors for enterprise initiatives, such as data quality, master data management, and data governance.

Although there is no one thing that can guarantee success, if forced to choose one critical success factor to rule them all, I would choose collaboration.

But, of course, when I say this everyone rolls their eyes at me (yes, I can see you doing it now through the computer) since it sounds like I’m avoiding the complex concepts underlying enterprise initiatives by choosing collaboration.

The importance of collaboration is a very simple concept but, as Amy Ray and Emily Saliers taught me, “the hardest to learn was the least complicated.”

The Pronoun Test

Although all organizations must define the success of enterprise initiatives in business terms (e.g., mitigated risks, reduced costs, or increased revenue), collaborative organizations understand that the most important factor for enduring business success is the willingness of people all across the enterprise to mutually pledge to each other their communication, cooperation, and trust.

These organizations pass what Robert Reich calls the Pronoun Test.  When their employees make references to the company, it’s done with the pronoun We and not They.  The latter suggests at least some amount of disengagement, and perhaps even alienation, whereas the former suggests the opposite — employees feel like part of something significant and meaningful.

An even more basic form of the Pronoun Test is whether or not people can look beyond their too often self-centered motivations and selflessly include themselves in a collaborative effort.  “It’s amazing how much can be accomplished if no one cares who gets the credit” is an old quote for which, with an appropriate irony, it is rather difficult to identify the original source.

Collaboration requires a simple, but powerful, paradigm shift that I call Turning the M Upside Down — turning Me into We.

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Shining a Social Light on Data Quality

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

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

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

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

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

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The Algebra of Collaboration

Most organizations have a vertical orientation, which creates a division of labor between functional areas where daily operations are carried out by people who have been trained in a specific type of business activity (e.g., Product Manufacturing, Marketing, Sales, Finance, Customer Service).  However, according to the most basic enterprise arithmetic, the sum of all vertical functions is one horizontal organization.  For example, in an organization with five vertical functions, 1 + 1 + 1 + 1 + 1 = 1 (and not 5).

Other times, it seems like division is the only mathematics the enterprise understands, creating perceived organizational divides based on geography (e.g., the Boston office versus the London office), or hierarchy (e.g., management versus front-line workers), or the Great Rift known as the Business versus IT.

However, enterprise-wide initiatives, such as data quality and data governance, require a cross-functional alignment reaching horizontally across the organization’s vertical functions, fostering a culture of collaboration combining a collective ownership with a shared responsibility and an individual accountability, requiring a branch of mathematics I call the Algebra of Collaboration.

For starters, as James Kakalios explained in his super book The Physics of Superheroes, “there is a trick to algebra: If one has an equation describing a true statement, such as 1 = 1, then one can add, subtract, multiply, or divide (excepting division by zero) the equation by any number we wish, and as long as we do it to both the left and right sides of the equation, the correctness of the equation is unchanged.  So if we add 2 to both sides of 1 = 1, we obtain 1 + 2 = 1 + 2 or 3 = 3, which is still a true statement.”

So, in the Algebra of Collaboration, we first establish one of the organization’s base equations, its true statements, for example, using the higher order collaborative equation that attempts to close the Great Rift otherwise known as the IT-Business Chasm:

Business = IT

Then we keep this base equation balanced by performing the same operation on both the left and right sides, for example:

Business + Data Quality + Data Governance = IT + Data Quality + Data Governance

The point is that everyone, regardless of their primary role or vertical function, must accept a shared responsibility for preventing data quality lapses and for responding appropriately to mitigate the associated business risks when issues occur.

Now, of course, as I blogged about in The Stakeholder’s Dilemma, this equation does not always remain perfectly balanced at all times.  The realities of the fiscal calendar effect, conflicting interests, and changing business priorities, will mean that the amount of resources (money, time, people) added to the equation by a particular stakeholder, vertical function, or group will vary.

But it’s important to remember the true statement that the base equation represents.  The trick of algebra is just one of the tricks of the collaboration trade.  Organizations that are successful with data quality and data governance view collaboration not just as a guiding principle, but also as a call to action in their daily practices.

Is your organization practicing the Algebra of Collaboration?

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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.

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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.

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No Datum is an Island of Serendip

Continuing a series of blog posts inspired by the highly recommended book Where Good Ideas Come From by Steven Johnson, in this blog post I want to discuss the important role that serendipity plays in data — and, by extension, business success.

Let’s start with a brief etymology lesson.  The origin of the word serendipity, which is commonly defined as a “happy accident” or “pleasant surprise” can be traced to the Persian fairy tale The Three Princes of Serendip, whose heroes were always making discoveries of things they were not in quest of either by accident or by sagacity (i.e., the ability to link together apparently innocuous facts to come to a valuable conclusion).  Serendip was an old name for the island nation now known as Sri Lanka.

“Serendipity,” Johnson explained, “is not just about embracing random encounters for the sheer exhilaration of it.  Serendipity is built out of happy accidents, to be sure, but what makes them happy is the fact that the discovery you’ve made is meaningful to you.  It completes a hunch, or opens up a door in the adjacent possible that you had overlooked.  Serendipitous discoveries often involve exchanges across traditional disciplines.  Serendipity needs unlikely collisions and discoveries, but it also needs something to anchor those discoveries.  The challenge, of course, is how to create environments that foster these serendipitous connections.”

No Datum is an Island of Serendip

“No man is an island, entire of itself; every man is a piece of the continent, a part of the main.”

These famous words were written by the poet John Donne, the meaning of which is generally regarded to be that human beings do not thrive when isolated from others.  Likewise, data does not thrive in isolation.  However, many organizations persist on data isolation, on data silos created when separate business units see power in the hoarding of data, not in the sharing of data.

But no business unit is an island, entire of itself; every business unit is a piece of the organization, a part of the enterprise.

Likewise, no datum is an Island of Serendip.  Data thrives through the connections, collisions, and combinations that collectively unleash serendipity.  When data is exchanged across organizational boundaries, and shared with the entire enterprise, it enables the interdisciplinary discoveries required for making business success more than just a happy accident or pleasant surprise.

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.  We need to transcend our organizational boundaries, reduce our data silos, and gather our enterprise’s heroes together on the Data Island of Serendip — our United Nation of Business Success.

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The Three Most Important Letters in Data Governance

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

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

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

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

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

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Big Data and Big Analytics

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

Jill Dyché is the Vice President of Thought Leadership and Education at DataFlux.  Jill’s role at DataFlux is a combination of best-practice expert, key client advisor and all-around thought leader.  She is responsible for industry education, key client strategies and market analysis in the areas of data governance, business intelligence, master data management and customer relationship management.  Jill is a regularly featured speaker and the author of several books.

Jill’s latest book, Customer Data Integration: Reaching a Single Version of the Truth (Wiley & Sons, 2006), was co-authored with Evan Levy and shows the business breakthroughs achieved with integrated customer data.

Dan Soceanu is the Director of Product Marketing and Sales Enablement at DataFlux.  Dan manages global field sales enablement and product marketing, including product messaging and marketing analysis.  Prior to joining DataFlux in 2008, Dan has held marketing, partnership and market research positions with Teradata, General Electric and FormScape, as well as data management positions in the Financial Services sector.

Dan received his Bachelor of Science in Business Administration from Kutztown University of Pennsylvania, as well as earning his Master of Business Administration from Bloomsburg University of Pennsylvania.

On this episode of OCDQ Radio, Jill Dyché, Dan Soceanu, and I discuss the recent Pacific Northwest BI Summit, where the three core conference topics were Cloud, Collaboration, and Big Data, the last of which lead to a discussion about Big Analytics.

Big Data and Big Analytics

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 Browse the OCDQ Radio Archives and Schedule

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The Stakeholder’s Dilemma

Game theory models a strategic situation as a game in which an individual player’s success depends on the choices made by the other players involved in the game.  One excellent example is the game known as The Prisoner’s Dilemma, which is deliberately designed to demonstrate why two people might not cooperate—even if it is in both of their best interests to do so.

Here is the classic scenario.  Two criminal suspects are arrested, but the police have insufficient evidence for a conviction.  So they separate the prisoners and offer each the same deal.  If one testifies for the prosecution against the other (i.e., defects) and the other remains silent (i.e., cooperates), the defector goes free and the silent accomplice receives the full one-year sentence.  If both remain silent, both prisoners are sentenced to only one month in jail for a minor charge.  If each betrays the other, each receives a three-month sentence.  Each prisoner must choose to betray the other or to remain silent.

If you have ever regularly watched a police procedural television series, such as Law & Order, then you have seen many dramatizations of the prisoner’s dilemma, including several sample outcomes of when the prisoners make different choices.

The Iterated Prisoner’s Dilemma

In iterated versions of the prisoner’s dilemma, players remember the previous actions of their opponent and change their strategy accordingly.  In many fields of study, these variations are considered fundamental to understanding cooperation and trust.

Here is an economics scenario with two players and a banker.  Each player holds a set of two cards, one printed with the word Cooperate (as in, with each other), the other printed with the word Defect.  Each player puts one card face-down in front of the banker.  By laying them face down, the possibility of a player knowing the other player’s selection in advance is eliminated.  At the end of each turn, the banker turns over both cards and gives out the payments, which can vary, but one example is as follows.

If both players cooperate, they are each awarded $5.  If both players defect, they are each penalized $1.  But if one player defects while the other player cooperates, the defector is awarded $10, while the cooperator neither wins nor loses any money.

Therefore, the safest play is to always cooperate, since you would never lose any money—and if your opponent always cooperates, then you can both win on every turn.  However, although defecting creates the possibility of losing a small amount of money, it also creates the possibility of winning twice as much money.

It is the iterated nature of this version of the prisoner’s dilemma that makes it so interesting for those studying human behavior.

For example, if you were playing against me, and I defected on the first two turns while you cooperated, I would have won $20 while you would have won nothing.  So what would you do on the third turn?  Let’s say that you choose to defect.

But if I defected yet again, although we would both lose $1, overall I would still be +$19 while you would be -$1.  And what if I continued defecting?  This would actually be an understandable strategy for me—if I was only playing for money, since you would have to defect 19 more times in a row before I broke even, but by which time you would have also lost $20.  And if instead, you start cooperating again in order to stop your losses, I could win a lot of money—at the expense of losing your trust.

Although the iterated prisoner’s dilemma is designed so that, over the long-term, cooperating players generally do better than non-cooperating players, in the short-term, the best result for an individual player is to defect while their opponent cooperates.

The Stakeholder’s Dilemma

Organizations embarking on an enterprise-wide initiative, such as data quality, master data management, and data governance, play a version of the iterated prisoner’s dilemma, which I refer to as The Stakeholder’s Dilemma.

These initiatives often bring together key stakeholders from all around the organization, representing each business unit or business function, and perhaps stakeholders representing data and technology as well.  These stakeholders usually form a committee or council, which is responsible for certain top-down aspects of the initiative, such as funding and strategic planning.

Of course, it is unrealistic to expect every stakeholder to cooperate equally at all times.  The realities of the fiscal calendar effect, conflicting interests, and changing business priorities, will mean that during any particular turn in the game (i.e., the current phase of the initiative), the amount of resources (money, time, people) allocated to the effort by a particular stakeholder will vary.

There will be times when sacrifices for the long-term greater good of the initiative will require that cooperating stakeholders either contribute more resources during the current phase, or receive fewer benefits from its deliverables, than defecting stakeholders.

As with the iterated prisoner’s dilemma, the challenge is what happens during the next turn (i.e., the next phase of the initiative).

If the same stakeholders repeatedly defect, then will the other stakeholders continue to cooperate?  Or will the spirit of trust, cooperation, and collaboration necessary for the continuing success of the ongoing initiative be irreparably damaged?

There are many, and often complex, reasons for why enterprise-wide initiatives fail, but failing to play the stakeholder’s dilemma well is one very common reason—and it is also a reason why many future enterprise-wide initiatives will fail to garner support.

How well does your organization play The Stakeholder’s Dilemma?

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The Data Governance Oratorio

Boston Symphony Orchestra

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

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

The Data Governance Oratorio

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

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

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

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

Is your organization collectively performing the Data Governance Oratorio?

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Beware the Data Governance Ides of March

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

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

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

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

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

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

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

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

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

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Spartan Data Quality

My recent Twitter conservation with Dylan Jones, Henrik Liliendahl Sørensen, and Daragh O Brien was sparked by the blog post Case study with Data blogs, from 300 to 1000, which included a list of the top 500 data blogs ranked by influence.

Data Quality Pro was ranked #57, Liliendahl on Data Quality was ranked #87, The DOBlog was a glaring omission, and I was proud OCDQ Blog was ranked #33 – at least until, being the data quality geeks we are, we noticed that it was also ranked #165.

In other words, there was an ironic data quality issue—a data quality blog was listed twice (i.e., a duplicate record in the list)!

Hilarity ensued, including some epic photo shopping by Daragh, leading, quite inevitably, to the writing of this Data Quality Tale, which is obviously loosely based on the epic movie 300—and perhaps also the epically terrible comedy Meet the Spartans.  Enjoy!

Spartan Data Quality

In 1989, an alliance of Data Geeks, lead by the Spartans, an unrivaled group of data quality warriors, battled against an invading data deluge in the mountain data center of Thermopylae, caused by the complexities of the Greco-Persian Corporate Merger.

Although they were vastly outnumbered, the Data Geeks overcame epic data quality challenges in one of the most famous enterprise data management initiatives in history—The Data Integration of Thermopylae.

This is their story.

Leonidas, leader of the Spartans, espoused an enterprise data management approach known as Spartan Data Quality, defined by its ethos of collaboration amongst business, data, and technology experts, collectively and affectionately known as Data Geeks.

Therefore, Leonidas was chosen as the Thermopylae Project Lead.  However, Xerxes, the new Greco-Persian CIO, believed that the data integration project was pointless, Spartan Data Quality was a fool’s errand, and the technology-only Persian approach, known as Magic Beans, should be implemented instead.  Xerxes saw the Thermopylae project as an unnecessary sacrifice.

“There will be no glory in your sacrifice,” explained Xerxes.  “I will erase even the memory of Sparta from the database log files!  Every bit and byte of Data Geek tablespace shall be purged.  Every data quality historian and every data blogger shall have their Ethernet cables pulled out, and their network connections cut from the Greco-Persian mainframe.  Why, uttering the very name of Sparta, or Leonidas, will be punishable by employee termination!  The corporate world will never know you existed at all!”

“The corporate world will know,” replied Leonidas, “that Data Geeks stood against a data deluge, that few stood against many, and before this battle was over, a CIO blinded by technology saw what it truly takes to manage data as a corporate asset.”

Addressing his small army of 300 Data Geeks, Leonidas declared: “Gather round!  No retreat, no surrender.  That is Spartan law.  And by Spartan law we will stand and fight.  And together, united by our collaboration, our communication, our transparency, and our trust in each other, we shall overcome this challenge.”

“A new Information Age has begun.  An age of data-driven business decisions, an age of data-empowered consumers, an age of a world connected by a web of linked data.  And all will know, that 300 Data Geeks gave their last breath to defend it!”

“But there will be so many data defects, they will blot out the sun!” exclaimed Xerxes.

“Then we will fight poor data quality in the shade,” Leonidas replied, with a sly smile.

“This is madness!” Xerxes nervously responded as the new servers came on-line in the data center of Thermopylae.

“Madness?  No,” Leonidas calmly said as the first wave of the data deluge descended upon them.  “THIS . . . IS . . . DATA !!!”

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