DQ-Tip: “An information centric organization...”

Data Quality (DQ) Tips is an OCDQ regular segment.  Each DQ-Tip is a clear and concise data quality pearl of wisdom.

“An information centric organization is an organization driven from high-quality, complete, and timely information that is relevant to its goals.”

This DQ-Tip is from the new book Patterns of Information Management by Mandy Chessell and Harald Smith.

“An organization exists for a purpose,” Chessell and Smith explained.  “It has targets to achieve and long-term aspirations.  An organization needs to make good use of its information to achieve its goals.”  In order to do this, they recommend that you define an information strategy that lays out why, what, and how your organization will manage its information:

• Why — The business imperatives that drive the need to be information centric, which helps focus information management efforts on the activities that deliver value to the organization.

• What — The type of information that you must manage to deliver on those business imperatives, which includes the subject areas to cover, which attributes within each subject area that need to be managed, the valid values for those attributes, and the information management policies (such as retention and protection) that the organization wants to implement.

• How — The information management principles that provide the general rules for how information is to be managed by the information systems and the people using them along with how information flows between them.

Developing an information strategy, according to Chessell and Smith, “creates a set of objectives for the organization, which guides the investment in information management technology and related solutions that support the business.  Starting with the business imperatives ensures the information management strategy is aligned with the needs of the organization, making it easier to demonstrate its relevance and value.”

Chessell and Smith also noted that “technology alone is not sufficient to ensure the quality, consistency, and flexibility of an organization’s information.  Classify the people connected to the organization according to their information needs and skills, provide common channels of communication and knowledge sharing about information, and user interfaces and reports through which they can access the information as appropriate.”

Chessell and Smith explained that the attitudes and skills of the organization’s people will be what enables the right behaviors in everyday operations, which is a major determination of the success of an information management program.

Related Posts

DQ-Tip: “The quality of information is directly related to...”

DQ-Tip: “Undisputable fact about the value and use of data...”

DQ-Tip: “Data quality tools do not solve data quality problems...”

DQ-Tip: “There is no such thing as data accuracy...”

DQ-Tip: “Data quality is primarily about context not accuracy...”

DQ-Tip: “There is no point in monitoring data quality...”

DQ-Tip: “Don't pass bad data on to the next person...”

DQ-Tip: “...Go talk with the people using the data”

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

DQ-Tip: “Start where you are...”

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Doing Data Governance

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 the practical aspects of doing data governance with John Ladley, the author of the excellent book Data Governance: How to Design, Deploy and Sustain an Effective Data Governance Program.  Our discussion includes understanding the difference and relationship between data governance and information management, the importance of establishing principles before creating policies, data stewardship, and three critical success factors for data governance.

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 (EIM).

John Ladley is the author of Making EIM Work for Business, and 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.

Doing Data Governance

Additional listening options:

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Win a copy of the Book

John Ladley wants to give one OCDQ Radio listener a free copy of his book Data Governance: How to Design, Deploy and Sustain an Effective Data Governance Program.

Here is how the book contest will work:

(1) Book Contest Question — During this OCDQ Radio episode, what metaphor did John Ladley use in order to explain the difference between data governance and information management?

(2) Book Contest Deadline — By or before March 31, 2013, Email Jim Harris with your answer to the book contest question.

(3) Book Contest Winner — In April 2013, one winner will be randomly selected from the emails containing the correct answer to the contest question, and John Ladley will email the winner requesting a postal address for sending a free copy of the book.

Related OCDQ Radio Episodes

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

• 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.

• Data Governance Star Wars — Special Guests Rob Karel and Gwen Thomas joined this extended, and Star Wars themed, discussion about how to balance bureaucracy and business agility during the execution of data governance programs.

• The Data Governance Imperative — Guest Steve Sarsfield discusses his book The Data Governance Imperative, exploring the business value of data quality, the characteristics of a data champion, and creating effective data quality scorecards.

• 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.

• 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.

Related Posts

Data Governance needs Searchers, not Planners The Hawthorne Effect, Helter Skelter, and Data Governance Cooks, Chefs, and Data Governance Data Governance Frameworks are like Jigsaw Puzzles Data Governance and the Buttered Cat Paradox Data Governance Star Wars: Bureaucracy versus Agility Beware the Data Governance Ides of March Aristotle, Data Governance, and Lead Rulers Data Governance and the Adjacent Possible The Three Most Important Letters in Data Governance

The Pull and Push of Data Governance An Unsettling Truth about Data Governance The Godfather of Data Governance Over the Data Governance Rainbow Getting Your Data Governance Stuff Together Datenvergnügen Council Data Governance A Tale of Two G’s Declaration of Data Governance The Collaborative Culture of Data Governance

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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.

Related Posts

The Hawthorne Effect, Helter Skelter, and Data Governance Cooks, Chefs, and Data Governance Data Governance Frameworks are like Jigsaw Puzzles Data Governance and the Buttered Cat Paradox Data Governance Star Wars: Bureaucracy versus Agility Beware the Data Governance Ides of March Aristotle, Data Governance, and Lead Rulers Data Governance and the Adjacent Possible The Three Most Important Letters in Data Governance The Data Governance Oratorio

An Unsettling Truth about Data Governance The Godfather of Data Governance Over the Data Governance Rainbow Getting Your Data Governance Stuff Together Datenvergnügen Council Data Governance A Tale of Two G’s Declaration of Data Governance The Role Of Data Quality Monitoring In Data Governance The Collaborative Culture of Data Governance

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Open MIKE Podcast — Episode 12

Method for an Integrated Knowledge Environment (MIKE2.0) is an open source delivery framework for Enterprise Information Management, which provides a comprehensive methodology that can be applied across a number of different projects within the Information Management space.  For more information, click on this link: openmethodology.org/wiki/What_is_MIKE2.0

The Open MIKE Podcast is a video podcast show, hosted by Jim Harris, which discusses aspects of the MIKE2.0 framework, and features content contributed to MIKE 2.0 Wiki Articles, Blog Posts, and Discussion Forums.

Episode 12: Information Development Book

If you’re having trouble viewing this video, you can watch it on Vimeo by clicking on this link: Open MIKE Podcast on Vimeo

MIKE2.0 Content Featured in or Related to this Podcast

Information Development Book: openmethodology.org/wiki/Information_Development_Book

Information Development: openmethodology.org/wiki/Information_Development

Previous Episodes of the Open MIKE Podcast

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

Episode 01: Information Management Principles Episode 02: Information Governance and Distributing Power Episode 03: Data Quality Improvement and Data Investigation Episode 04: Metadata Management Episode 05: Defining Big Data Episode 06: Getting to Know NoSQL

Episode 07: Guiding Principles for Open Semantic Enterprise Episode 08: Information Lifecycle Management Episode 09: Enterprise Data Management Strategy Episode 10: Information Maturity QuickScan Episode 11: Information Maturity Model

You can also find the videos and blog post summaries for every episode of the Open MIKE Podcast at: ocdqblog.com/MIKE

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The Hawthorne Effect, Helter Skelter, and Data Governance

In his book The Half-life of Facts: Why Everything We Know Has an Expiration Date, Samuel Arbesman introduced me to the Hawthorne Effect, which is “when subjects behave differently if they know they are being studied.  The effect was named after what happened in a factory called Hawthorne Works outside Chicago in the 1920s and 1930s.”

“Scientists wished to measure,” Arbesman explained, “the effects of environmental changes on the productivity of workers.  They discovered whatever they did to change the workers’ behaviors — whether they increased the lighting or altered any other aspect of the environment — resulted in increased productivity.  However, as soon as the study was completed, productivity dropped.  The researchers concluded that the observations themselves were affecting productivity and not the experimental changes.”

I couldn’t help but wonder how the Hawthorne Effect could affect a data governance program.  When data governance policies are first defined, and their associated procedures and processes are initially implemented, after a little while, and usually after a little resistance, productivity often increases and the organization begins to advance its data governance maturity level.

Perhaps during these early stages employees are well-aware that they’re being observed to make sure they’re complying with the new data governance policies, and this observation itself accounts for advancing to the next maturity level.  Especially since after progress stops being studied so closely, it’s not uncommon for an organization to backslide to an earlier maturity level.

You might be tempted to conclude that continuous monitoring, especially of the Orwellian Big Brother variety, might be able to prevent this from happening, but I doubt it.  Data governance maturity is often misperceived in the same way that expertise is misperceived — as a static state that once achieved signifies a comforting conclusion to all the grueling effort that was required, either to become an expert, or reach a particular data governance maturity level.

However, just like the five stages of data quality, oscillating between different levels of data governance maturity, and perhaps even occasionally coming full circle, may be an inevitable part of the ongoing evolution of a data governance program, which can often feel like a top-down/bottom-up amusement park ride of the Beatles “Helter Skelter” variety:

When you get to the bottom, you go back to the top, where you stop and you turn, and you go for a ride until you get to the bottom — and then you do it again.

Come On Tell Me Your Answers

Do you, don’t you . . . think the Hawthorne Effect affects data governance?

Do you, don’t you . . . think data governance is Helter Skelter?

Tell me, tell me, come on tell me your answers — by posting a comment below.

Related Posts

Cooks, Chefs, and Data Governance Data Governance Frameworks are like Jigsaw Puzzles Data Governance and the Buttered Cat Paradox Data Governance Star Wars: Bureaucracy versus Agility Beware the Data Governance Ides of March Aristotle, Data Governance, and Lead Rulers Data Governance and the Adjacent Possible The Three Most Important Letters in Data Governance The Data Governance Oratorio Jack Bauer and Enforcing Data Governance Policies

An Unsettling Truth about Data Governance The Godfather of Data Governance Over the Data Governance Rainbow Getting Your Data Governance Stuff Together Datenvergnügen Council Data Governance A Tale of Two G’s Declaration of Data Governance The Role Of Data Quality Monitoring In Data Governance The Collaborative Culture of Data Governance

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Open MIKE Podcast — Episode 11

Method for an Integrated Knowledge Environment (MIKE2.0) is an open source delivery framework for Enterprise Information Management, which provides a comprehensive methodology that can be applied across a number of different projects within the Information Management space.  For more information, click on this link: openmethodology.org/wiki/What_is_MIKE2.0

The Open MIKE Podcast is a video podcast show, hosted by Jim Harris, which discusses aspects of the MIKE2.0 framework, and features content contributed to MIKE 2.0 Wiki Articles, Blog Posts, and Discussion Forums.

Episode 11: Information Maturity Model

If you’re having trouble viewing this video, you can watch it on Vimeo by clicking on this link: Open MIKE Podcast on Vimeo

MIKE2.0 Content Featured in or Related to this Podcast

Information Maturity Model: openmethodology.org/wiki/Information_Maturity_Model

Reactive Data Governance: openmethodology.org/wiki/Reactive_Data_Governance_Organisation

Proactive Data Governance: openmethodology.org/wiki/Proactive_Data_Governance_Organisation

Managed Data Governance: openmethodology.org/wiki/Managed_Data_Governance_Organisation

Optimal Data Governance: openmethodology.org/wiki/Optimal_Data_Governance_Organisation

Previous Episodes of the Open MIKE Podcast

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

Episode 01: Information Management Principles Episode 02: Information Governance and Distributing Power Episode 03: Data Quality Improvement and Data Investigation Episode 04: Metadata Management Episode 05: Defining Big Data

Episode 06: Getting to Know NoSQL Episode 07: Guiding Principles for Open Semantic Enterprise Episode 08: Information Lifecycle Management Episode 09: Enterprise Data Management Strategy Episode 10: Information Maturity QuickScan

You can also find the videos and blog post summaries for every episode of the Open MIKE Podcast at: ocdqblog.com/MIKE

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Data Quality and Anton’s Syndrome

In his book Incognito: The Secret Lives of the Brain, David Eagleman discussed aspects of a bizarre, and rare, brain disorder called Anton’s Syndrome in which a stroke renders a person blind — but the person denies their blindness.

“Those with Anton’s Syndrome truly believe they are not blind,” Eagleman explained.  “It is only after bumping into enough furniture and walls that they begin to feel that something is amiss.  They are experiencing what they take to be vision, but it is all internally generated.  The external data is not getting to the right places because of the stroke, and so their reality is simply that which is generated by the brain, with little attachment to the real world.  In this sense, what they experience is no different from dreaming, drug trips, or hallucinations.”

Data quality practitioners often complain that business leaders are blind to the importance of data quality to business success, or that they deny data quality issues exist in their organization.  As much as we wish it wasn’t so, often it isn’t until business leaders bump into enough of the negative effects of poor data quality that they begin to feel that something is amiss.  However, as much as we would like to, we can’t really attribute their denial to drug-induced hallucinations.

Sometimes an illusion-of-quality effect is caused when data is excessively filtered and cleansed before it reaches business leaders, perhaps as the result of a perception filter for data quality issues created as a natural self-defense mechanism by the people responsible for the business processes and technology surrounding data, since no one wants to be blamed for causing, or failing to fix, data quality issues.  Unfortunately, this might really leave the organization’s data with little attachment to the real world.

In fairness, sometimes it’s also the blind leading the blind because data quality practitioners often suffer from business blindness by presenting data quality issues without providing business context, without relating data quality metrics in a tangible manner to how the business uses data to support a business process, accomplish a business objective, or make a business decision.

A lot of the disconnect between business leaders, who believe they are not blind to data quality, and data quality practitioners, who believe they are not blind to business context, comes from a crisis of perception.  Each side in this debate believes they have a complete vision, but it’s only after bumping into each other enough times that they begin to envision the organizational blindness caused when data quality is not properly measured within a business context and continually monitored.

Related Posts

Data Quality and Chicken Little Syndrome Data Quality and Miracle Exceptions Data Quality: Quo Vadimus? Availability Bias and Data Quality Improvement Finding Data Quality “Some is not a number and soon is not a time” The Data Quality of Dorian Gray The Data Quality Wager DQ-View: The Five Stages of Data Quality Data Quality and the Bystander Effect Data Quality and the Q Test Why isn’t our data quality worse?

The Illusion-of-Quality Effect Perception Filters and Data Quality WYSIWYG and WYSIATI Predictably Poor Data Quality Data Psychedelicatessen Data Geeks and Business Blindness The Real Data Value is Business Insight Is your data accurate, but useless to your business? Data Quality Measurement Matters Data Myopia and Business Relativity Data and its Relationships with Quality Plato’s Data

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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.

Related Posts

Best OCDQ Blog Posts of 2011

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

2012 Quarterly Review of the Data Roundtable (Part 4)

2012 Quarterly Review of the Data Roundtable (Part 3)

2012 Quarterly Review of the Data Roundtable (Part 2)

2012 Quarterly Review of the Data Roundtable (Part 1)

2011 Quarterly Review of the Data Roundtable (Part 4)

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)

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Open MIKE Podcast — Episode 10

Method for an Integrated Knowledge Environment (MIKE2.0) is an open source delivery framework for Enterprise Information Management, which provides a comprehensive methodology that can be applied across a number of different projects within the Information Management space.  For more information, click on this link: openmethodology.org/wiki/What_is_MIKE2.0

The Open MIKE Podcast is a video podcast show, hosted by Jim Harris, which discusses aspects of the MIKE2.0 framework, and features content contributed to MIKE 2.0 Wiki Articles, Blog Posts, and Discussion Forums.

Episode 10: Information Maturity QuickScan

If you’re having trouble viewing this video, you can watch it on Vimeo by clicking on this link: Open MIKE Podcast on Vimeo

MIKE2.0 Content Featured in or Related to this Podcast

Information Maturity (IM) QuickScan: openmethodology.org/wiki/Information_Maturity_QuickScan

IM QuickScan Template Documents: openmethodology.org/wiki/QuickScan_MS_Office_survey

Information Maturity Model: openmethodology.org/wiki/Information_Maturity_Model

Previous Episodes of the Open MIKE Podcast

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

Episode 01: Information Management Principles Episode 02: Information Governance and Distributing Power Episode 03: Data Quality Improvement and Data Investigation Episode 04: Metadata Management Episode 05: Defining Big Data

Episode 06: Getting to Know NoSQL Episode 07: Guiding Principles for Open Semantic Enterprise Episode 08: Information Lifecycle Management Episode 09: Enterprise Data Management Strategy

You can also find the videos and blog post summaries for every episode of the Open MIKE Podcast at: ocdqblog.com/MIKE

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An Enterprise Carol

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

Since ‘tis the season for reflecting on the past year and predicting the year ahead, while pondering this post my mind wandered to the reflections and predictions provided by the ghosts of A Christmas Carol by Charles Dickens.  So, I decided to let the spirit of Jacob Marley revisit my previous Enterprise CIO Forum posts to bring you the Ghosts of Enterprise Past, Present, and Future.

The Ghost of Enterprise Past

Legacy applications have a way of haunting the enterprise long after they should have been sunset.  The reason that most of them do not go gentle into that good night, but instead rage against the dying of their light, is some users continue using some of the functionality they provide, as well as the data trapped in those applications, to support the enterprise’s daily business activities.

This freaky feature fracture (i.e., technology supporting business needs being splintered across new and legacy applications) leaves many IT departments overburdened with maintaining a lot of technology and data that’s not being used all that much.

The Ghost of Enterprise Past warns us that IT can’t enable the enterprise’s future if it’s stuck still supporting its past.

The Ghost of Enterprise Present

While IT was busy battling the Ghost of Enterprise Past, a familiar, but fainter, specter suddenly became empowered by the diffusion of the consumerization of IT.  The rapid ascent of the cloud and mobility, spirited by service-oriented solutions that were more focused on the user experience, promised to quickly deliver only the functionality required right now to support the speed and agility requirements driving the enterprise’s business needs in the present moment.

Gifted by this New Prometheus, Shadow IT emerged from the shadows as the Ghost of Enterprise Present, with business-driven and decentralized IT solutions becoming more commonplace, as well as begrudgingly accepted by IT leaders.

All of which creates quite the IT Conundrum, forming yet another front in the war against Business-IT collaboration.  Although, in the short-term, the consumerization of IT usually better services the technology needs of the enterprise, in the long-term, if it’s not integrated into a cohesive strategy, it creates a complex web of IT that entangles the enterprise much more than it enables it.

And with the enterprise becoming much more of a conceptual, rather than a physical, entity due to the cloud and mobile devices enabling us to take the enterprise with us wherever we go, the evolution of enterprise security is now facing far more daunting challenges than the external security threats we focused on in the past.  This more open business environment is here to stay, and it requires a modern data security model, despite the fact that such a model could become the weakest link in enterprise security.

The Ghost of Enterprise Present asks many questions, but none more frightening than: Can the enterprise really be secured?

The Ghost of Enterprise Future

Of course, the T in IT wasn’t the only apparition previously invisible outside of the IT department to recently break through the veil in a big way.  The I in IT had its own coming-out party this year also since, as many predicted, 2012 was the year of Big Data.

Although neither the I nor the T is magic, instead of sugar plums, Data Psychics and Magic Elephants appear to be dancing in everyone’s heads this holiday season.  In other words, the predictive power of big data and the technological wizardry of Hadoop (as well as other NoSQL techniques) seem to be on the wish list of every enterprise for the foreseeable future.

However, despite its unquestionable potential, as its hype starts to settle down, the sobering realities of big data analytics will begin to sink in.  Data’s value comes from data’s usefulness.  If all we do is hoard data, then we’ll become so lost in the details that we’ll be unable to connect enough of the dots to discover meaningful patterns and convert big data into useful information that enables the enterprise to take action, make better decisions, or otherwise support its business activities.

Big data will force us to revisit information overload as we are occasionally confronted with the limitations of historical analysis, and blindsided by how our biases and preconceptions could silence the signal and amplify the noise, which will also force us to realize that data quality still matters in big data and that bigger data needs better data management.

As the Ghost of Enterprise Future, big data may haunt us with more questions than the many answers it will no doubt provide.

“Bah, Humbug!”

I realize that this post lacks the happy ending of A Christmas Carol.  To paraphrase Dickens, I endeavored in this ghostly little post to raise the ghosts of a few ideas, not to put my readers out of humor with themselves, with each other, or with the season, but simply to give them thoughts to consider about how to keep the Enterprise well in the new year.  Happy Holidays Everyone!

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

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Why does the sun never set on legacy applications? Are Applications the La Brea Tar Pits for Data? The Diffusion of the Consumerization of IT The Cloud is shifting our Center of Gravity More Tethered by the Untethered Enterprise? A Swift Kick in the AAS The UX Factor Sometimes all you Need is a Hammer Shadow IT and the New Prometheus The IT Consumerization Conundrum

OCDQ Radio - The Evolution of Enterprise Security The Cloud Security Paradox The Good, the Bad, and the Secure The Weakest Link in Enterprise Security Can the Enterprise really be Secured? Magic Elephants, Data Psychics, and Invisible Gorillas Big Data el Memorioso Information Overload Revisited The Limitations of Historical Analysis Data Silence

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Open MIKE Podcast — Episode 09

Method for an Integrated Knowledge Environment (MIKE2.0) is an open source delivery framework for Enterprise Information Management, which provides a comprehensive methodology that can be applied across a number of different projects within the Information Management space.  For more information, click on this link: openmethodology.org/wiki/What_is_MIKE2.0

The Open MIKE Podcast is a video podcast show, hosted by Jim Harris, which discusses aspects of the MIKE2.0 framework, and features content contributed to MIKE 2.0 Wiki Articles, Blog Posts, and Discussion Forums.

Episode 09: Enterprise Data Management Strategy

If you’re having trouble viewing this video, you can watch it on Vimeo by clicking on this link: Open MIKE Podcast on Vimeo

MIKE2.0 Content Featured in or Related to this Podcast

Enterprise Data Management Strategy: openmethodology.org/wiki/Enterprise_Data_Management_Strategy_Solution_Offering

Executive Overview on EDM Strategy: openmethodology.org/w/images/6/6c/Executive_Overview_on_EDM_Strategy.pdf

You can also find the videos and blog post summaries for every episode of the Open MIKE Podcast at: ocdqblog.com/MIKE

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Open MIKE Podcast — Episode 08

Method for an Integrated Knowledge Environment (MIKE2.0) is an open source delivery framework for Enterprise Information Management, which provides a comprehensive methodology that can be applied across a number of different projects within the Information Management space.  For more information, click on this link: openmethodology.org/wiki/What_is_MIKE2.0

The Open MIKE Podcast is a video podcast show, hosted by Jim Harris, which discusses aspects of the MIKE2.0 framework, and features content contributed to MIKE 2.0 Wiki Articles, Blog Posts, and Discussion Forums.

Episode 08: Information Lifecycle Management

If you’re having trouble viewing this video, you can watch it on Vimeo by clicking on this link: Open MIKE Podcast on Vimeo

MIKE2.0 Content Featured in or Related to this Podcast

Information Asset Management: openmethodology.org/wiki/Information_Asset_Management_Offering_Group

Information Lifecycle Management: openmethodology.org/wiki/Information_Lifecycle_Management_Solution_Offering

You can also find the videos and blog post summaries for every episode of the Open MIKE Podcast at: ocdqblog.com/MIKE

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Availability Bias and Data Quality Improvement

The availability heuristic is a mental shortcut that occurs when people make judgments based on the ease with which examples come to mind.  Although this heuristic can be beneficial, such as when it helps us recall examples of a dangerous activity to avoid, sometimes it leads to availability bias, where we’re affected more strongly by the ease of retrieval than by the content retrieved.

In his thought-provoking book Thinking, Fast and Slow, Daniel Kahneman explained how availability bias works by recounting an experiment where different groups of college students were asked to rate a course they had taken the previous semester by listing ways to improve the course — while varying the number of improvements that different groups were required to list.

Counterintuitively, students in the group required to list more necessary improvements gave the course a higher rating, whereas students in the group required to list fewer necessary improvements gave the course a lower rating.

According to Kahneman, the extra cognitive effort expended by the students required to list more improvements biased them into believing it was difficult to list necessary improvements, leading them to conclude that the course didn’t need much improvement, and conversely, the little cognitive effort expended by the students required to list few improvements biased them into concluding, since it was so easy to list necessary improvements, that the course obviously needed improvement.

This is counterintuitive because you’d think that the students would rate the course based on an assessment of the information retrieved from their memory regardless of how easy that information was to retrieve.  It would have made more sense for the course to be rated higher for needing fewer improvements, but availability bias lead the students to the opposite conclusion.

Availability bias can also affect an organization’s discussions about the need for data quality improvement.

If you asked stakeholders to rate the organization’s data quality by listing business-impacting incidents of poor data quality, would they reach a different conclusion if you asked them to list one incident versus asking them to list at least ten incidents?

In my experience, an event where poor data quality negatively impacted the organization, such as a regulatory compliance failure, is often easily dismissed by stakeholders as an isolated incident to be corrected by a one-time data cleansing project.

But would forcing stakeholders to list ten business-impacting incidents of poor data quality make them concede that data quality improvement should be supported by an ongoing program?  Or would the extra cognitive effort bias them into concluding, since it was so difficult to list ten incidents, that the organization’s data quality doesn’t really need much improvement?

I think that the availability heuristic helps explain why most organizations easily approve reactive data cleansing projects, and availability bias helps explain why most organizations usually resist proactively initiating a data quality improvement program.

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Related OCDQ Radio Episodes

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

• 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.

• Organizing for Data Quality — Guest Tom Redman (aka the “Data Doc”) discusses how your organization should approach data quality, including his call to action for your role in the data revolution.

• The Johari Window of Data Quality — Guest Martin Doyle discusses helping people better understand their data and assess its business impacts, not just the negative impacts of bad data quality, but also the positive impacts of good data quality.

• Redefining Data Quality — Guest Peter Perera discusses his proposed redefinition of data quality, as well as his perspective on the relationship of data quality to master data management and data governance.

• 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.”

• 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.

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A Tale of Two Datas

Is big data more than just lots and lots of data?  Is big data unstructured and not-so-big data structured?  Malcolm Chisholm explored these questions in his recent Information Management column, where he posited that there are, in fact, two datas.

“One type of data,” Chisholm explained,  “represents non-material entities in vast computerized ecosystems that humans create and manage.  The other data consists of observations of events, which may concern material or non-material entities.”

Providing an example of the first type, Chisholm explained, “my bank account is not a physical thing at all; it is essentially an agreed upon idea between myself, the bank, the legal system, and the regulatory authorities.  It only exists insofar as it is represented, and it is represented in data.  The balance in my bank account is not some estimate with a positive and negative tolerance; it is exact.  The non-material entities of the financial sector are orderly human constructs.  Because they are orderly, we can more easily manage them in computerized environments.”

The orderly human constructs that are represented in data, in the stories told by data (including the stories data tell about us and the stories we tell data) is one of my favorite topics.  In our increasingly data-constructed world, it’s important to occasionally remind ourselves that data and the real world are not the same thing, especially when data represents non-material entities since, with the possible exception of Makers using 3-D printers, data-represented entities do not re-materialize into the real world.

Describing the second type, Chisholm explained, “a measurement is usually a comparison of a characteristic using some criteria, a count of certain instances, or the comparison of two characteristics.  A measurement can generally be quantified, although sometimes it’s expressed in a qualitative manner.  I think that big data goes beyond mere measurement, to observations.”

Chisholm called the first type the Data of Representation, and the second type the Data of Observation.

The data of representation tends to be structured, in the relational sense, but doesn’t need to be (e.g., graph databases) and the data of observation tends to be unstructured, but it can also be structured (e.g., the structured observations generated by either a data profiling tool analyzing structured relational tables or flat files, or a word-counting algorithm analyzing unstructured text).

“Structured and unstructured,” Chisholm concluded, “describe form, not essence, and I suggest that representation and observation describe the essences of the two datas.  I would also submit that both datas need different data management approaches.  We have a good idea what these are for the data of representation, but much less so for the data of observation.”

I agree that there are two types of data (i.e., representation and observation, not big and not-so-big) and that different data uses will require different data management approaches.  Although data modeling is still important and data quality still matters, how much data modeling and data quality is needed before data can be effectively used for specific business purposes will vary.

In order to move our discussions forward regarding “big data” and its data management and business intelligence challenges, we have to stop fiercely defending our traditional perspectives about structure and quality in order to effectively manage both the form and essence of the two datas.  We also have to stop fiercely defending our traditional perspectives about data analytics, since there will be some data use cases where depth and detailed analysis may not be necessary to provide business insight.

A Tale of Two Datas

In conclusion, and with apologies to Charles Dickens and his A Tale of Two Cities, I offer the following A Tale of Two Datas:

It was the best of times, it was the worst of times.
It was the age of Structured Data, it was the age of Unstructured Data.
It was the epoch of SQL, it was the epoch of NoSQL.
It was the season of Representation, it was the season of Observation.
It was the spring of Big Data Myth, it was the winter of Big Data Reality.
We had everything before us, we had nothing before us,
We were all going direct to hoarding data, we were all going direct the other way.
In short, the period was so far like the present period, that some of its noisiest authorities insisted on its being signaled, for Big Data or for not-so-big data, in the superlative degree of comparison only.

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Data and its Relationships with Quality

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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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