OCDQ - Obsessive-Compulsive Data Quality (OCDQ) by Jim Harris

Entries in Best of 2009 (21)

Thursday

31Dec2009

So Long 2009, and Thanks for All the . . .

Jim Harris in

Blogs,

Books,

Data Quality,

Social Media,

Videos tagged

Best of 2009

Thursday, December 31, 2009 at 3:21AM

Before I look ahead to the coming New Year and wonder what it may (or may not) bring, I wanted to pause, reflect on, and in the following OCDQ Video, share some of the many joys I was thankful for 2009 bringing to me.

If you are having trouble viewing this video, then you can watch it on Vimeo by clicking on this link: OCDQ Video

Thank You

Thank you all—and I do mean every single one of you—thank you for everything.

Happy New Year!!!

Data Quality Resources

For simplicity, “Data Quality” also includes Data Governance, Master Data Management, and Business Intelligence.

Books about Data Quality, Data Governance, Master Data Management, and Business Intelligence

Blogs about Data Quality, Data Governance, Master Data Management, and Business Intelligence

Websites about Data Quality, Data Governance, Master Data Management, and Business Intelligence

Twitter List for Data Quality, Data Governance, Master Data Management, and Business Intelligence

Data Quality on Twitter

Data Governance on Twitter

Master Data Management on Twitter

Business Intelligence on Twitter

Social Media Resources

For simplicity, “Social Media” also includes Blogging, Writing, Social Networking, and Online Marketing.

Books about Social Media, Blogging, Social Networking, and Online Marketing

Blogs and Websites about Social Media, Social Networking, and Online Marketing

Blogs and Websites about Writing and Blogging

Twitter List for Social Media, Social Networking, and Online Marketing

Twitter List for Bloggers whose blogs I actually read on a regular basis

Twitter List for Book authors and other published writers or journalists

Tuesday

01Dec2009

Adventures in Data Profiling (Part 8)

Jim Harris in

Data Quality,

Methodology tagged

Adventure in Data Profiling,

Best of 2009,

Data Profiling

Tuesday, December 1, 2009 at 1:12AM

Understanding your data is essential to using it effectively and improving its quality – and to achieve these goals, there is simply no substitute for data analysis. This post is the conclusion of a vendor-neutral series on the methodology of data profiling.

Data profiling can help you perform essential analysis such as:

Provide a reality check for the perceptions and assumptions you may have about the quality of your data
Verify your data matches the metadata that describes it
Identify different representations for the absence of data (i.e., NULL and other missing values)
Identify potential default values
Identify potential invalid values
Check data formats for inconsistencies
Prepare meaningful questions to ask subject matter experts

Data profiling can also help you with many of the other aspects of domain, structural and relational integrity, as well as determining functional dependencies, identifying redundant storage, and other important data architecture considerations.

Adventures in Data Profiling

This series was carefully designed as guided adventures in data profiling in order to provide the necessary framework for demonstrating and discussing the common functionality of data profiling tools and the basic methodology behind using one to perform preliminary data analysis.

In order to narrow the scope of the series, the scenario used was a customer data source for a new data quality initiative had been made available to an external consultant with no prior knowledge of the data or its expected characteristics. Additionally, business requirements had not yet been documented, and subject matter experts were not currently available.

This series did not attempt to cover every possible feature of a data profiling tool or even every possible use of the features that were covered. Both the data profiling tool and data used throughout the series were fictional. The “screen shots” were customized to illustrate concepts and were not modeled after any particular data profiling tool.

This post summarizes the lessons learned throughout the series, and is organized under three primary topics:

Counts and Percentages
Values and Formats
Drill-down Analysis

Counts and Percentages

One of the most basic features of a data profiling tool is the ability to provide counts and percentages for each field that summarize its content characteristics:

NULL – count of the number of records with a NULL value
Missing – count of the number of records with a missing value (i.e., non-NULL absence of data, e.g., character spaces)
Actual – count of the number of records with an actual value (i.e., non-NULL and non-Missing)
Completeness – percentage calculated as Actual divided by the total number of records
Cardinality – count of the number of distinct actual values
Uniqueness – percentage calculated as Cardinality divided by the total number of records
Distinctness – percentage calculated as Cardinality divided by Actual

Completeness and uniqueness are particularly useful in evaluating potential key fields and especially a single primary key, which should be both 100% complete and 100% unique. In Part 2, Customer ID provided an excellent example.

Distinctness can be useful in evaluating the potential for duplicate records. In Part 6, Account Number and Tax ID were used as examples. Both fields were less than 100% distinct (i.e., some distinct actual values occurred on more than one record). The implied business meaning of these fields made this an indication of possible duplication.

Data profiling tools generate other summary statistics including: minimum/maximum values, minimum/maximum field sizes, and the number of data types (based on analyzing the values, not the metadata). Throughout the series, several examples were provided, especially in Part 3 during the analysis of Birth Date, Telephone Number and E-mail Address.

Values and Formats

In addition to counts, percentages, and other summary statistics, a data profiling tool generates frequency distributions for the unique values and formats found within the fields of your data source.

A frequency distribution of unique values is useful for:

Fields with an extremely low cardinality, indicating potential default values (e.g., Country Code in Part 4)
Fields with a relatively low cardinality (e.g., Gender Code in Part 2)
Fields with a relatively small number of known valid values (e.g., State Abbreviation in Part 4)

A frequency distribution of unique formats is useful for:

Fields expected to contain a single data type and/or length (e.g., Customer ID in Part 2)
Fields with a relatively limited number of known valid formats (e.g., Birth Date in Part 3)
Fields with free-form values and a high cardinality (e.g., Customer Name 1 and Customer Name 2 in Part 7)

Cardinality can play a major role in deciding whether you want to be shown values or formats since it is much easier to review all of the values when there are not very many of them. Alternatively, the review of high cardinality fields can also be limited to the most frequently occurring values, as we saw throughout the series (e.g., Telephone Number in Part 3).

Some fields can also be analyzed using partial values (e.g., in Part 3, Birth Year was extracted from Birth Date) or a combination of values and formats (e.g., in Part 6, Account Number had an alpha prefix followed by all numbers).

Free-form fields are often easier to analyze as formats constructed by parsing and classifying the individual values within the field. This analysis technique is often necessary since not only is the cardinality of free-form fields usually very high, but they also tend to have a very high distinctness (i.e., the exact same field value rarely occurs on more than one record).

Additionally, the most frequently occurring formats for free-form fields will often collectively account for a large percentage of the records with an actual value in the field. Examples of free-form field analysis were the focal points of Part 5 and Part 7.

We also saw examples of how valid values in a valid format can have an invalid context (e.g., in Part 3, Birth Date values set in the future), as well as how valid field formats can conceal invalid field values (e.g., Telephone Number in Part 3).

Part 3 also provided examples (in both Telephone Number and E-mail Address) of how you should not mistake completeness (which as a data profiling statistic indicates a field is populated with an actual value) for an indication the field is complete in the sense that its value contains all of the sub-values required to be considered valid.

Drill-down Analysis

A data profiling tool will also provide the capability to drill-down on its statistical summaries and frequency distributions in order to perform a more detailed review of records of interest. Drill-down analysis will often provide useful data examples to share with subject matter experts.

Performing a preliminary analysis on your data prior to engaging in these discussions better facilitates meaningful dialogue because real-world data examples better illustrate actual data usage. As stated earlier, understanding your data is essential to using it effectively and improving its quality.

Various examples of drill-down analysis were used throughout the series. However, drilling all the way down to the record level was shown in Part 2 (Gender Code), Part 4 (City Name), and Part 6 (Account Number and Tax ID).

Conclusion

Fundamentally, this series posed the following question: What can just your analysis of data tell you about it?

Data profiling is typically one of the first tasks performed on a data quality initiative. I am often told to delay data profiling until business requirements are documented and subject matter experts are available to answer my questions.

I always disagree – and begin data profiling as soon as possible.

I can do a better job of evaluating business requirements and preparing for meetings with subject matter experts after I have spent some time looking at data from a starting point of blissful ignorance and curiosity.

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

Discovering the right questions is a critical prerequisite for effectively discussing data usage, relevancy, standards, and the metrics for measuring and improving quality. All of which are necessary in order to progress from just profiling your data, to performing a full data quality assessment (which I will cover in a future series on this blog).

A data profiling tool can help you by automating some of the grunt work needed to begin your analysis. However, it is important to remember that the analysis itself can not be automated – you need to review the statistical summaries and frequency distributions generated by the data profiling tool and more important – translate your analysis into meaningful reports and questions to share with the rest of your team.

Always remember that well performed data profiling is both a highly interactive and a very iterative process.

Thank You

I want to thank you for providing your feedback throughout this series.

As my fellow Data Gazers, you provided excellent insights and suggestions via your comments.

The primary reason I published this series on my blog, as opposed to simply writing a whitepaper or a presentation, was because I knew our discussions would greatly improve the material.

I hope this series proves to be a useful resource for your actual adventures in data profiling.

The Complete Series

Adventures in Data Profiling (Part 1) – Series Introduction
Adventures in Data Profiling (Part 2) – Customer ID and Gender Code
Adventures in Data Profiling (Part 3) – Birth Date, Telephone Number and E-mail Address
Adventures in Data Profiling (Part 4) – City Name, State Abbreviation, Zip Code and Country Code
Adventures in Data Profiling (Part 5) – Postal Address Line 1 and Postal Address Line 2
Adventures in Data Profiling (Part 6) – Account Number and Tax ID
Adventures in Data Profiling (Part 7) – Customer Name 1 and Customer Name 2

Saturday

14Nov2009

Collablogaunity

Jim Harris in

Blogs,

Books,

Social Media tagged

Best of 2009,

Blogging

Saturday, November 14, 2009 at 11:14PM

The meteoric rise of the Internet coupled with social media has created an amazing medium that is enabling people who are separated by vast distances and disparate cultures to come together, communicate, and collaborate in ways few would have thought possible just a few decades ago. Blogging, especially when effectively integrated with social networking, can be one of the most powerful aspects of social media.

The great advantage to blogging as a medium, as opposed to books, newspapers, magazines, and even presentations, is that blogging is not just about broadcasting a message.

This is not to say that books, newspapers, and magazines aren't useful (they certainly can be) or that presentations lack an interactive component (they certainly should not). I simply believe that, when done well, blogging better facilities effective communication by starting a conversation, encouraging collaboration, and fostering a true sense of community.

Mashing together the words collaboration, blog, and community, I use the term collablogaunity — which is pronounced “Call a Blog a Unity” — to describe how remarkable blogs do this remarkably well.

Conversation

Blogging is a conversation — with your readers.

I love the sound of my own voice and I talk to myself all the time (even in public). However, the two-way conversation that blogging provides via comments from my readers greatly improves the quality of my blog content — because it helps me better appreciate the difference between what I know and what I only think I know.

Without comments, the conversation is only one way. Engaging readers in dialogue and discussion allows some of your points to be made for you by those who take the time to comment as opposed to you just telling everyone how you see the world.

Blogging isn't about using the Internet as your own personal bullhorn for broadcasting your message. In her wonderful book The Whuffie Factor, Tara Hunt explains that you really need to:

“Turn the bullhorn around: stop talking, start listening, and create continuous conversations.”

Respond to the comments you receive (but never feed the troll). You don't have to respond immediately. Sometimes, the conversation will go more smoothly without your involvement as your readers talk amongst themselves. Other times, your response will help continue the conversation and encourage participation from others.

Always demonstrate that feedback is both welcome and appreciated. Make sure to never talk down to your readers (either in your blog post or your comment responses). It is perfectly fine to disagree and debate, just don't denigrate.

In a recent guest post on ProBlogger, Rob McPhillips explained:

“If instead, you are all the time only seeking praise and approval from everyone, then there is nothing solid, consistent or certain about your blog and so ultimately it will never gather a sizeable core of die hard fans. Only drive by readers who scan a post and never look back.”

Collaboration

Blogging is a collaboration — with other bloggers.

While conversation is primarily between you and your readers, collaboration is primarily between you and other bloggers. Although you may be inclined to view other bloggers as “the competition,” especially those within your own niche, this would be a mistake. Yes, it is true that blogs are competing with each other for readers. However, sustainable success is achieved through collaboration and friendly competition with your peers.

Brian Clark has explained in the past and continues to exemplify that strategic collaboration is the secret to 21st century success. Clark has stated that if he had to reduce his recipe for success to just three ingredients, it would be content, copywriting, and collaboration. And if he had to give up two of those, then he'd keep collaboration.

In their terrific book Trust Agents, Chris Brogan and Julien Smith explain that although people in most cultures view themselves as the central hero in their life's story, the reality is that you need to build an army because you can't do it all alone.

Collaboration between bloggers is mainly about networking and cross-promotion. You should network with other bloggers, especially those within your own niche. This can be accomplished a number of ways including e-mail introductions, Twitter direct messages (if the other blogger is following you), LinkedIn connection requests, or Facebook friend requests.

As with any networking, the most important thing is being genuine. As Darren Rowse and Chris Garrett explained in their highly recommended ProBlogger book, when you network with other bloggers, keep it real, be specific, keep it brief without being rude, and explain why you are interested in connecting. They rightfully emphasize the importance of that last point.

As we all know, although content may be king, marketing is queen. Networking with other bloggers can help you get the word out about your brilliant blog and its penchant for publishing posts that everyone must read. Adding other bloggers to your blogroll, linking to their posts when applicable to your content, and leaving meaningful comments on their posts are not only recommended best practices of netiquette, they are also just the right thing to do.

Too many bloggers have a selfish networking and marketing strategy. They only promote their own content and then wonder why nobody reads their blog. I am fond of referring to all social media as Social Karma. Focus on helping other bloggers promote their content and they will likely be more willing to return the favor. However, don't misunderstand this technique to be a pathetic peer pressure tactic – in other words, I re-tweeted your blog post, why didn't you re-tweet my blog post?

One last point on collaboration is to set realistic expectations — for others and for yourself. You should definitely try to help others when you can. However, you simply can't help everyone. Don't let people take advantage of your generosity.

Politely, but firmly, say no when you need to say no. Also extend the same courtesy to other people when they turn you down (or simply ignore you) when you try to connect with them or when you ask them for their help.

Mean and selfish people definitely suck. But let's face it, nobody's perfect — we all have bad days, we all occasionally say and do stupid things, and we all occasionally treat people worse than they deserve to be treated. So don't be too hard on people when they disappoint you, because tomorrow it will probably be your turn to have a bad day.

Community

Blogging is a community service.

If you truly believe and actually practice the principles of both conversation and collaboration, then viewing blogging as a community service comes naturally. You will truly be more interested in actually listening to what your readers have to say, and less interested in just broadcasting your message. You will see your words as simply the catalyst that gets the conversation started, and when necessary, helps continue the discussion.

You will see friends not foes when encountering your blogging peers. You will help them celebrate their successes and quickly recover from their failures. You will help others when you can and without worrying about what's in it for you.

As James Chartrand says, you will welcome people to your blog because you view blogging as a festival of people, a community strengthened by people, where everyone can speak up with great care and attention, sharing thoughts and views while openly accepting differing opinions. Blogging is a community service providing a wealth of experience, thoughts and knowledge being shared by all sorts of participants.

In the closing keynote of this year's BlogWorld conference, Chris Brogan explained (from notes taken by David B. Thomas):

“Make it about them. Stop looking at this as a cult of me.

It has to be about your audience. Turn them into a community.

The difference between an audience and a community is the way you face the chairs.

The difference between an audience and a community:

One will fall on its sword for you and the other will watch you fall.”

Collablogaunity

Pronounced: “Call a Blog a Unity”

There are literally millions of blogs on the Internet today. Your blog (to quote Seth Godin) is “either remarkable or invisible.”

Remarkable blogs primarily do three things:

Start conversations
Encourage collaboration
Foster a true sense of community

Remarkable blogs are collablogaunities. Is your blog a collablogaunity?

The Mullet Blogging Manifesto

Brevity is the Soul of Social Media

Podcast: Your Blog, Your Voice

Additional Resources

Books about Social Media, Blogging, Social Networking, and Online Marketing

Blogs and Websites about Social Media, Social Networking, and Online Marketing

Blogs and Websites about Writing and Blogging

Thursday

12Nov2009

Beyond a “Single Version of the Truth”

Jim Harris in

Blogs,

Books,

Debates tagged

Thomas Redman

Thursday, November 12, 2009 at 5:15AM

This post is involved in a good-natured contest (i.e., a blog-bout) with two additional bloggers: Henrik Liliendahl Sørensen and Charles Blyth. Our contest is a Blogging Olympics of sorts, with the United States, Denmark, and England competing for the Gold, Silver, and Bronze medals in an event we are calling “Three Single Versions of a Shared Version of the Truth.”

Please take the time to read all three posts and then vote for who you think has won the debate (see poll below). Thanks!

The “Point of View” Paradox

In the early 20th century, within his Special Theory of Relativity, Albert Einstein introduced the concept that space and time are interrelated entities forming a single continuum, and therefore the passage of time can be a variable that could change for each individual observer.

One of the many brilliant insights of special relativity was that it could explain why different observers can make validly different observations – it was a scientifically justifiable matter of perspective.

It was Einstein's apprentice, Obi-Wan Kenobi (to whom Albert explained “Gravity will be with you, always”), who stated:

“You're going to find that many of the truths we cling to depend greatly on our own point of view.”

The Data-Information Continuum

In the early 21st century, within his popular blog post The Data-Information Continuum, Jim Harris introduced the concept that data and information are interrelated entities forming a single continuum, and that speaking of oneself in the third person is the path to the dark side.

I use the Dragnet definition for data – it is “just the facts” collected as an abstract description of the real-world entities that the enterprise does business with (e.g., customers, vendors, suppliers).

Although a common definition for data quality is fitness for the purpose of use, the common challenge is that data has multiple uses – each with its own fitness requirements. Viewing each intended use as the information that is derived from data, I define information as data in use or data in action.

Quality within the Data-Information Continuum has both objective and subjective dimensions. Data's quality is objectively measured separate from its many uses, while information's quality is subjectively measured according to its specific use.

Objective Data Quality

Data quality standards provide a highest common denominator to be used by all business units throughout the enterprise as an objective data foundation for their operational, tactical, and strategic initiatives.

In order to lay this foundation, raw data is extracted directly from its sources, profiled, analyzed, transformed, cleansed, documented and monitored by data quality processes designed to provide and maintain universal data sources for the enterprise's information needs.

At this phase of the architecture, the manipulations of raw data must be limited to objective standards and not be customized for any subjective use. From this perspective, data is now fit to serve (as at least the basis for) each and every purpose.

Subjective Information Quality

Information quality standards (starting from the objective data foundation) are customized to meet the subjective needs of each business unit and initiative. This approach leverages a consistent enterprise understanding of data while also providing the information necessary for day-to-day operations.

But please understand: customization should not be performed simply for the sake of it. You must always define your information quality standards by using the enterprise-wide data quality standards as your initial framework.

Whenever possible, enterprise-wide standards should be enforced without customization. The key word within the phrase “subjective information quality standards” is standards — as opposed to subjective, which can quite often be misinterpreted as “you can do whatever you want.” Yes you can – just as long as you have justifiable business reasons for doing so.

This approach to implementing information quality standards has three primary advantages. First, it reinforces a consistent understanding and usage of data throughout the enterprise. Second, it requires each business unit and initiative to clearly explain exactly how they are using data differently from the rest of your organization, and more important, justify why. Finally, all deviations from enterprise-wide data quality standards will be fully documented.

The “One Lie Strategy”

A common objection to separating quality standards into objective data quality and subjective information quality is the enterprise's significant interest in creating what is commonly referred to as a “Single Version of the Truth.”

However, in his excellent book Data Driven: Profiting from Your Most Important Business Asset, Thomas Redman explains:

“A fiendishly attractive concept is...'a single version of the truth'...the logic is compelling...unfortunately, there is no single version of the truth.

For all important data, there are...too many uses, too many viewpoints, and too much nuance for a single version to have any hope of success.

This does not imply malfeasance on anyone's part; it is simply a fact of life.

Getting everyone to work from a single version of the truth may be a noble goal, but it is better to call this the 'one lie strategy' than anything resembling truth.”

Beyond a “Single Version of the Truth”

In the classic 1985 film Mad Max Beyond Thunderdome, the title character arrives in Bartertown, ruled by the evil Auntie Entity, where people living in the post-apocalyptic Australian outback go to trade for food, water, weapons, and supplies. Auntie Entity forces Mad Max to fight her rival Master Blaster to the death within a gladiator-like arena known as Thunderdome, which is governed by one simple rule:

“Two men enter, one man leaves.”

I have always struggled with the concept of creating a “Single Version of the Truth.” I imagine all of the key stakeholders from throughout the enterprise arriving in Corporatetown, ruled by the Machiavellian CEO known only as Veritas, where all business units and initiatives must go to request funding, staffing, and continued employment. Veritas forces all of them to fight their Master Data Management rivals within a gladiator-like arena known as Meetingdome, which is governed by one simple rule:

“Many versions of the truth enter, a Single Version of the Truth leaves.”

For any attempted “version of the truth” to truly be successfully implemented within your organization, it must take into account both the objective and subjective dimensions of quality within the Data-Information Continuum.

Both aspects of this shared perspective of quality must be incorporated into a “Shared Version of the Truth” that enforces a consistent enterprise understanding of data, but that also provides the information necessary to support day-to-day operations.

The Data-Information Continuum is governed by one simple rule:

“All validly different points of view must be allowed to enter,

In order for an all encompassing Shared Version of the Truth to be achieved.”

You are the Judge

Please take the time to read all three posts and then vote for who you think has won the debate. A link to the same poll is provided on all three blogs. Therefore, wherever you choose to cast your vote, you will be able to view an accurate tally of the current totals.

The poll will remain open for one week, closing at midnight on November 19 so that the “medal ceremony” can be conducted via Twitter on Friday, November 20. Additionally, please share your thoughts and perspectives on this debate by posting a comment below. Your comment may be copied (with full attribution) into the comments section of all of the blogs involved in this debate.

Poor Data Quality is a Virus

The General Theory of Data Quality

The Data-Information Continuum

Saturday

07Nov2009

The Once and Future Data Quality Expert

Jim Harris in

Associations,

Data Quality,

Debates tagged

Best of 2009,

IAIDQ

Saturday, November 7, 2009 at 11:11PM

Wednesday, November 11 is World Quality Day 2009.

World Quality Day was established by the United Nations in 1990 as a focal point for the quality management profession and as a celebration of the contribution that quality makes to the growth and prosperity of nations and organizations. The goal of World Quality Day is to raise awareness of how quality approaches (including data quality best practices) can have a tangible effect on business success, as well as contribute towards world-wide economic prosperity.

IAIDQ

The International Association for Information and Data Quality (IAIDQ) was chartered in January 2004 and is a not-for-profit, vendor-neutral professional association whose purpose is to create a world-wide community of people who desire to reduce the high costs of low quality information and data by applying sound quality management principles to the processes that create, maintain and deliver data and information.

Since 2007 the IAIDQ has celebrated World Quality Day as a springboard for improvement and a celebration of successes. Please join us to celebrate World Quality Day by participating in our interactive webinar in which the Board of Directors of the IAIDQ will share with you stories and experiences to promote data quality improvements within your organization.

In my recent Data Quality Pro article The Future of Information and Data Quality, I reported on the IAIDQ Ask The Expert Webinar with co-founders Larry English and Tom Redman, two of the industry pioneers for data quality and two of the most well-known data quality experts.

Data Quality Expert

As World Quality Day 2009 approaches, my personal reflections are focused on what the title data quality expert has meant in the past, what it means today, and most important, what it will mean in the future.

With over 15 years of professional services and application development experience, I consider myself to be a data quality expert. However, my experience is paltry by comparison to English, Redman, and other industry luminaries such as David Loshin, to use one additional example from many.

Experience is popularly believed to be the path that separates knowledge from wisdom, which is usually accepted as another way of defining expertise.

Oscar Wilde once wrote that “experience is simply the name we give our mistakes.” I agree. I have found that the sooner I can recognize my mistakes, the sooner I can learn from the lessons they provide, and hopefully prevent myself from making the same mistakes again.

The key is early detection. As I gain experience, I gain an improved ability to more quickly recognize my mistakes and thereby expedite the learning process.

James Joyce wrote that “mistakes are the portals of discovery” and T.S. Eliot wrote that “we must not cease from exploration and the end of all our exploring will be to arrive where we began and to know the place for the first time.”

What I find in the wisdom of these sages is the need to acknowledge the favor our faults do for us. Therefore, although experience is the path that separates knowledge from wisdom, the true wisdom of experience is the wisdom of failure.

As Jonah Lehrer explained: “Becoming an expert just takes time and practice. Once you have developed expertise in a particular area, you have made the requisite mistakes.”

But expertise in any discipline is more than simply an accumulation of mistakes and birthdays. And expertise is not a static state that once achieved, allows you to simply rest on your laurels.

In addition to my real-world experience working on data quality initiatives for my clients, I also read all of the latest books, articles, whitepapers, and blogs, as well as attend as many conferences as possible.

The Times They Are a-Changin'

Much of the discussion that I have heard regarding the future of the data quality profession has been focused on the need for the increased maturity of both practitioners and organizations. Although I do not dispute this need, I am concerned about the apparent lack of attention being paid to how fast the world around us is changing.

Rapid advancements in technology, coupled with the meteoric rise of the Internet and social media (blogs, wikis, Twitter, Facebook, LinkedIn, etc.) has created an amazing medium that is enabling people separated by vast distances and disparate cultures to come together, communicate, and collaborate in ways few would have thought possible just a few decades ago.

I don't believe that it is an exaggeration to state that we are now living in an age where the contrast between the recent past and the near future is greater than perhaps it has ever been in human history. This brave new world has such people and technology in it, that practically every new day brings the possibility of another quantum leap forward.

Although it has been argued by some that the core principles of data quality management are timeless, I must express my doubt. The daunting challenges of dramatically increasing data volumes and the unrelenting progress of cloud computing, software as a service (SaaS), and mobile computing architectures, would appear to be racing toward a high-speed collision with our time-tested (but time-consuming to implement properly) data quality management principles.

The times they are indeed changing and I believe we must stop using terms like Six Sigma and Kaizen as if they were a shibboleth. If these or any other disciplines are to remain relevant, then we must honestly assess them in the harsh and unforgiving light of our brave new world that is seemingly changing faster than the speed of light.

Expertise is not static. Wisdom is not timeless. The only constant is change. For the data quality profession to truly mature, our guiding principles must change with the times, or be relegated to a past that is all too quickly becoming distant.

Share Your Perspectives

In celebration of World Quality Day, please share your perspectives regarding the past, present, and most important, the future of the data quality profession. With apologies to T. H. White, I declare this debate to be about the difference between:

The Once and Future Data Quality Expert

Mistake Driven Learning

The Fragility of Knowledge

The Wisdom of Failure

A Portrait of the Data Quality Expert as a Young Idiot

The Nine Circles of Data Quality Hell

Additional IAIDQ Links

IAIDQ Ask The Expert Webinar: World Quality Day 2009

IAIDQ Ask The Expert Webinar with Larry English and Tom Redman

INTERVIEW: Larry English - IAIDQ Co-Founder

INTERVIEW: Tom Redman - IAIDQ Co-Founder

IAIDQ Publications Portal

Thursday

29Oct2009

The Tell-Tale Data

Jim Harris in

Data Quality tagged

Best of 2009,

DQ-Tale,

Humor

Thursday, October 29, 2009 at 10:31PM

It is a dark and stormy night in the data center. The constant humming of hard drives is mimicking the sound of a hard rain falling in torrents, except at occasional intervals, when it is checked by a violent gust of conditioned air sweeping through the seemingly endless aisles of empty cubicles, rattling along desktops, fiercely agitating the flickering glow from flat panel monitors that are struggling against the darkness.

Tonight, amid this foreboding gloom with only my thoughts for company, I race to complete the production implementation of the Dystopian Automated Transactional Analysis (DATA) system. Nervous, very, very dreadfully nervous I have been, and am, but why will you say that I am mad? Observe how calmly I can tell you the whole story.

Eighteen months ago, I was ordered by executive management to implement the DATA system. The vendor's salesperson was an oddly charming fellow named Machiavelli, who had the eye of a vulture — a pale blue eye, with a film over it. Whenever this eye fell upon me, my blood ran cold.

Machiavelli assured us all that DATA's seamlessly integrated Magic Beans software would migrate and consolidate all of our organization's information, clairvoyantly detecting and correcting our existing data quality problems, and once DATA was implemented into production, Magic Beans would prevent all future data quality problems from happening.

As soon as a source was absorbed into DATA, Magic Beans automatically did us the favor of freeing up disk space by deleting all traces of the source, somehow even including our off-site archives. DATA would then become our only system of record, truly our Single Version of the Truth.

It is impossible to say when doubt first entered my brain, but once conceived, it haunted me day and night. Whenever I thought about it, my blood ran cold — as cold as when that vulture eye was gazing upon me — very gradually, I made up my mind to simply load DATA and rid myself of my doubt forever.

Now this is the point where you will fancy me quite mad. But madmen know nothing. You should have seen how wisely I proceeded — with what caution — with what foresight — with what Zen-like tranquility, I went to work!

I was never happier than I was these past eighteen months while I simply followed the vendor's instructions step by step and loaded DATA! Would a madman have been so wise as this? I think not.

Tomorrow morning, DATA goes live. I can imagine how wonderful that will be. I will be sitting at my desk, grinning wildly, deliriously happy with a job well done. DATA will be loaded, data quality will trouble me no more.

It is now four o'clock in the morning, but still it is as dark as midnight. But as bright as the coming dawn, I can now see three strange men as they gather around my desk.

Apparently, a shriek had been heard from the business analysts and subject matter experts as soon as they started using DATA. Suspicions had been aroused, complaints had been lodged, and they (now identifying themselves as auditors) had been called in by a regulatory agency to investigate.

I smile — for what have I to fear? I welcome these fine gentlemen. I give them a guided tour of DATA using its remarkably intuitive user interface. I urge them audit — audit well. They seemed satisfied. My manner has convinced them. I am singularly at ease. They sit, and while I answer cheerily, they chat away about trivial things. But before long, I feel myself growing pale and wish them gone.

My head aches and I hear a ringing in my ears, but still they sit and chat. The ringing becomes more distinct. I talk more freely, to get rid of the feeling, but it continues and gains volume — until I find that this noise is not within my ears.

No doubt I now grow very pale — but I talk more fluently, and with a heightened voice. Yet the sound increases — and what can I do? It is a low, dull, quick sound. I gasp for breath — and yet the auditors hear it not.

I talk more quickly — more vehemently — but the noise steadily increases. I arise, and argue about trifles, in a high key and with violent gesticulations — but the noise steadily increases. Why will they not be gone? I pace the floor back and forth, with heavy strides, as if excited to fury by the unrelenting observations of the auditors — but the noise steadily increases.

What could I do? I raved — I ranted — I raged! I swung my chair and smashed my computer with it — but the noise rises over all of my attempts to silence it. It grows louder — louder — louder! And still the auditors chat pleasantly, and smile. Is it really possible they can not hear it? Is it really possible they did not notice me smashing my computer?

They hear! — they suspect! — they know! — they are making a mockery of my horror! — this I thought, and this I think. But anything is better than this agony! Anything is more tolerable than this derision! I can not bear their hypocritical smiles any longer! I feel that I must scream or die! — and now — again! — the noise! Louder! Louder!! LOUDER!!!

“DATA!” I finally shriek. “DATA has no quality! NO DATA QUALITY!!! What have I done? What — Have — I — Done?!?”

With a sudden jolt, I awaken at my desk, with my old friend Edgar shaking me by the shoulders.

“Hey, wake up! Executive management wants us in the conference room in five minutes. Apparently, there is a vendor here today pitching a new system called DATA using software called Magic Beans...”

“...and the salesperson has this weird eye...”

Tuesday

27Oct2009

Days Without A Data Quality Issue

Jim Harris in

Data Quality tagged

Best of 2009,

Data Governance,

Data Quality Assessment,

Philosophy

Tuesday, October 27, 2009 at 5:35PM

In 1970, the United States Department of Labor created the Occupational Safety and Health Administration (OSHA). The mission of OSHA is to prevent work-related injuries, illnesses, and deaths. Based on statistics from 2007, since OSHA's inception, occupational deaths in the United States have been cut by 62% and workplace injuries have declined by 42%.

OSHA regularly conducts inspections to determine if organizations are in compliance with safety standards and assesses financial penalties for violations. In order to both promote workplace safety and avoid penalties, organizations provide their employees with training on the appropriate precautions and procedures to follow in the event of an accident or an emergency.

Training programs certify new employees in safety protocols and indoctrinate them into the culture of a safety-conscious workplace. By requiring periodic re-certification, all employees maintain awareness of their personal responsibility in both avoiding workplace accidents and responding appropriately to emergencies.

Although there has been some debate about the effectiveness of the regulations and the enforcement policies, over the years OSHA has unquestionably brought about many necessary changes, especially in the area of industrial work site safety where dangerous machinery and hazardous materials are quite common.

Obviously, even with well-defined safety standards in place, workplace accidents will still occasionally occur. However, these standards have helped greatly reduce both the frequency and severity of the accidents. And most importantly, safety has become a natural part of the organization's daily work routine.

A Culture of Data Quality

Similar to indoctrinating employees into the culture of a safety-conscious workplace, more and more organizations are realizing the importance of creating and maintaining the culture of a data quality conscious workplace. A culture of data quality is essential for effective enterprise information management.

Waiting until a serious data quality issue negatively impacts the organization before starting an enterprise data quality program is analogous to waiting until a serious workplace accident occurs before starting a safety program.

Many data quality issues are caused by a lack of data ownership and an absence of clear guidelines indicating who is responsible for ensuring that data is of sufficient quality to meet the daily business needs of the enterprise. In order for data quality to be taken seriously within your organization, everyone first needs to know that data quality is an enterprise-wide priority.

Additionally, data quality standards must be well-defined, and everyone must accept their personal responsibility in both preventing data quality issues and responding appropriately to mitigate the associated business risks when issues do occur.

Data Quality Assessments

The data equivalent of a safety inspection is a data quality assessment, which provides a much needed reality check for the perceptions and assumptions that the enterprise has about the quality of its data.

Performing a data quality assessment helps with a wide variety of tasks including: verifying data matches the metadata that describes it, preparing meaningful questions for subject matter experts, understanding how data is being used, quantifying the business impacts of poor quality data, and evaluating the ROI of data quality improvements.

An initial assessment provides a baseline and helps establish data quality standards as well as set realistic goals for improvement. Subsequent data quality assessments, which should be performed on a regular basis, will track your overall progress.

Although preventing data quality issues is your ultimate goal, don't let the pursuit of perfection undermine your efforts. Always be mindful of the data quality issues that remain unresolved, but let them serve as motivation. Learn from your mistakes without focusing on your failures – focus instead on making steady progress toward improving your data quality.

Data Governance

The data equivalent of verifying compliance with safety standards is data governance, which establishes policies and procedures to align people throughout the organization. Enterprise data quality programs require a data governance framework in order successfully deploy data quality as an enterprise-wide initiative.

By facilitating the collaboration of all business and technical stakeholders, aligning data usage with business metrics, enforcing data ownership, and prioritizing data quality, data governance enables effective enterprise information management.

Obviously, even with well-defined and well-managed data governance policies and procedures in place, data quality issues will still occasionally occur. However, your goal is to greatly reduce both the frequency and severity of your data quality issues.

And most importantly, the responsibility for ensuring that data is of sufficient quality to meet your daily business needs, has now become a natural part of your organization's daily work routine.

Days Without A Data Quality Issue

Organizations commonly display a sign indicating how long they have gone without a workplace accident. Proving that I certainly did not miss my calling as a graphic designer, I created this “sign” for Days Without A Data Quality Issue:

Poor Data Quality is a Virus

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

The Only Thing Necessary for Poor Data Quality

Hyperactive Data Quality (Second Edition)

Data Governance and Data Quality

Wednesday

21Oct2009

Poor Quality Data Sucks

Jim Harris in

Blogs,

Books,

Data Quality tagged

Baseball,

Best of 2009,

Boston Red Sox,

Philosophy

Wednesday, October 21, 2009 at 12:57PM

Over the last few months on his Information Management blog, Steve Miller has been writing posts inspired by a great 2008 book that we both highly recommend: The Drunkard's Walk: How Randomness Rules Our Lives by Leonard Mlodinow.

In his most recent post The Demise of the 2009 Boston Red Sox: Super-Crunching Takes a Drunkard's Walk, Miller takes on my beloved Boston Red Sox and the less than glorious conclusion to their 2009 season.

For those readers who are not baseball fans, the Los Angeles Angels of Anaheim swept the Red Sox out of the playoffs. I will let Miller's words describe their demise: “Down two to none in the best of five series, the Red Sox took a 6-4 lead into the ninth inning, turning control over to impenetrable closer Jonathan Papelbon, who hadn't allowed a run in 26 postseason innings. The Angels, within one strike of defeat on three occasions, somehow managed a miracle rally, scoring 3 runs to take the lead 7-6, then holding off the Red Sox in the bottom of the ninth for the victory to complete the shocking sweep.”

Baseball and Data Quality

What, you may be asking, does baseball have to do with data quality? Beyond simply being two of my all-time favorite topics, quite a lot actually. Baseball data is mostly transaction data describing the statistical events of games played.

Statistical analysis has been a beloved pastime even longer than baseball has been America's Pastime. Number-crunching is far more than just a quantitative exercise in counting. The qualitative component of statistics – discerning what the numbers mean, analyzing them to discover predictive patterns and trends – is the very basis of data-driven decision making.

“The Red Sox,” as Miller explained, “are certainly exemplars of the data and analytic team-building methodology” chronicled in Moneyball: The Art of Winning an Unfair Game, the 2003 book by Michael Lewis. Red Sox General Manager Theo Epstein has always been an advocate of the so-called evidenced-based baseball, or baseball analytics, pioneered by Bill James, the baseball writer, historian, statistician, current Red Sox consultant, and founder of Sabermetrics.

In another book that Miller and I both highly recommend, Super Crunchers, author Ian Ayres explained that “Bill James challenged the notion that baseball experts could judge talent simply by watching a player. James's simple but powerful thesis was that data-based analysis in baseball was superior to observational expertise. James's number-crunching approach was particular anathema to scouts.”

“James was baseball's herald,” continues Ayres, “of data-driven decision making.”

The Drunkard's Walk

As Mlodinow explains in the prologue: “The title The Drunkard's Walk comes from a mathematical term describing random motion, such as the paths molecules follow as they fly through space, incessantly bumping, and being bumped by, their sister molecules. The surprise is that the tools used to understand the drunkard's walk can also be employed to help understand the events of everyday life.”

Later in the book, Mlodinow describes the hidden effects of randomness by discussing how to build a mathematical model for the probability that a baseball player will hit a home run: “The result of any particular at bat depends on the player's ability, of course. But it also depends on the interplay of many other factors: his health, the wind, the sun or the stadium lights, the quality of the pitches he receives, the game situation, whether he correctly guesses how the pitcher will throw, whether his hand-eye coordination works just perfectly as he takes his swing, whether that brunette he met at the bar kept him up too late, or the chili-cheese dog with garlic fries he had for breakfast soured his stomach.”

“If not for all the unpredictable factors,” continues Mlodinow, “a player would either hit a home run on every at bat or fail to do so. Instead, for each at bat all you can say is that he has a certain probability of hitting a home run and a certain probability of failing to hit one. Over the hundreds of at bats he has each year, those random factors usually average out and result in some typical home run production that increases as the player becomes more skillful and then eventually decreases owing to the same process that etches wrinkles in his handsome face. But sometimes the random factors don't average out. How often does that happen, and how large is the aberration?”

Conclusion

I have heard some (not Mlodinow or anyone else mentioned in this post) argue that data quality is an irrelevant issue. The basis of their argument is that poor quality data are simply random factors that, in any data set of statistically significant size, will usually average out and therefore have a negligible effect on any data-based decisions.

However, the random factors don't always average out. It is important to not only measure exactly how often poor quality data occur, but acknowledge the large aberration poor quality data are, especially in data-driven decision making.

As every citizen of Red Sox Nation is taught from birth, the only acceptable opinion of our American League East Division rivals, the New York Yankees, is encapsulated in the chant heard throughout the baseball season (and not just at Fenway Park):

“Yankees Suck!”

From their inception, the day-to-day business decisions of every organization are based on its data. This decision-critical information drives the operational, tactical, and strategic initiatives essential to the enterprise's mission to survive and thrive in today's highly competitive and rapidly evolving marketplace.

It doesn't quite roll off the tongue as easily, but a chant heard throughout these enterprise information initiatives is:

“Poor Quality Data Sucks!”

Books Recommended by Red Sox Nation

Mind Game: How the Boston Red Sox Got Smart, Won a World Series, and Created a New Blueprint for Winning

Feeding the Monster: How Money, Smarts, and Nerve Took a Team to the Top

Theology: How a Boy Wonder Led the Red Sox to the Promised Land

Now I Can Die in Peace: How The Sports Guy Found Salvation Thanks to the World Champion (Twice!) Red Sox

Thursday

08Oct2009

Mistake Driven Learning

Jim Harris in

Blogs,

Data Quality tagged

Best of 2009,

Humor,

Philosophy

Thursday, October 8, 2009 at 8:21PM

In his Copyblogger article How to Stop Making Yourself Crazy with Self-Editing, Sean D'Souza explains:

“Competency is a state of mind you reach when you’ve made enough mistakes.”

One of my continuing challenges is staying informed about the latest trends in data quality and its related disciplines, including Master Data Management (MDM), Dystopian Automated Transactional Analysis (DATA), and Data Governance (DG) – I am fairly certain that one of those three things isn't real, but I haven't figured out which one yet.

I read all of the latest books, as well as the books that I was supposed to have read years ago, when I was just pretending to have read all of the latest books. I also read the latest articles, whitepapers, and blogs. And I go to as many conferences as possible.

The basis of this endless quest for knowledge is fear. Please understand – I have never been afraid to look like an idiot. After all, we idiots are important members of society – we make everyone else look smart by comparison.

However, I also market myself as a data quality expert. Therefore, when I consult, speak, write, or blog, I am always at least a little afraid of not getting things quite right. Being afraid of making mistakes can drive you crazy.

But as a wise man named Seal Henry Olusegun Olumide Adeola Samuel (wisely better known by only his first name) lyrically taught us back in 1991:

“We're never gonna survive unless, we get a little crazy.”

“It’s not about getting things right in your brain,” explains D’Souza, “it’s about getting things wrong. The brain has to make hundreds, even thousands of mistakes — and overcome those mistakes — to be able to reach a level of competency.”

So, get a little crazy, make a lot of mistakes, and never stop learning.

The Fragility of Knowledge

The Wisdom of Failure

A Portrait of the Data Quality Expert as a Young Idiot

The Nine Circles of Data Quality Hell

Thursday

01Oct2009

Poor Data Quality is a Virus

Jim Harris in

Books,

Data Quality tagged

Thomas Redman

Thursday, October 1, 2009 at 12:03AM

“A storm is brewing—a perfect storm of viral data, disinformation, and misinformation.”

These cautionary words (written by Timothy G. Davis, an Executive Director within the IBM Software Group) are from the foreword of the remarkable new book Viral Data in SOA: An Enterprise Pandemic by Neal A. Fishman.

“Viral data,” explains Fishman, “is a metaphor used to indicate that business-oriented data can exhibit qualities of a specific type of human pathogen: the virus. Like a virus, data by itself is inert. Data requires software (or people) for the data to appear alive (or actionable) and cause a positive, neutral, or negative effect.”

“Viral data is a perfect storm,” because as Fishman explains, it is “a perfect opportunity to miscommunicate with ubiquity and simultaneity—a service-oriented pandemic reaching all corners of the enterprise.”

“The antonym of viral data is trusted information.”

Data Quality

“Quality is a subjective term,” explains Fishman, “for which each person has his or her own definition.” Fishman goes on to quote from many of the published definitions of data quality, including a few of my personal favorites:

David Loshin: “Fitness for use—the level of data quality determined by data consumers in terms of meeting or beating expectations.”
Danette McGilvray: “The degree to which information and data can be a trusted source for any and/or all required uses. It is having the right set of correct information, at the right time, in the right place, for the right people to use to make decisions, to run the business, to serve customers, and to achieve company goals.”
Thomas Redman: “Data are of high quality if those who use them say so. Usually, high-quality data must be both free of defects and possess features that customers desire.”

Data quality standards provide a highest common denominator to be used by all business units throughout the enterprise as an objective data foundation for their operational, tactical, and strategic initiatives. Starting from this foundation, information quality standards are customized to meet the subjective needs of each business unit and initiative. This approach leverages a consistent enterprise understanding of data while also providing the information necessary for day-to-day operations.

However, the enterprise-wide data quality standards must be understood as dynamic. Therefore, enforcing strict conformance to data quality standards can be self-defeating. On this point, Fishman quotes Joseph Juran: “conformance by its nature relates to static standards and specification, whereas quality is a moving target.”

Defining data quality is both an essential and challenging exercise for every enterprise. “While a succinct and holistic single-sentence definition of data quality may be difficult to craft,” explains Fishman, “an axiom that appears to be generally forgotten when establishing a definition is that in business, data is about things that transpire during the course of conducting business. Business data is data about the business, and any data about the business is metadata. First and foremost, the definition as to the quality of data must reflect the real-world object, concept, or event to which the data is supposed to be directly associated.”

Data Governance

“Data governance can be used as an overloaded term,” explains Fishman, and he quotes Jill Dyché and Evan Levy to explain that “many people confuse data quality, data governance, and master data management.”

“The function of data governance,” explains Fishman, “should be distinct and distinguishable from normal work activities.”

For example, although knowledge workers and subject matter experts are necessary to define the business rules for preventing viral data, according to Fishman, these are data quality tasks and not acts of data governance.

However, these data quality tasks must “subsequently be governed to make sure that all the requisite outcomes comply with the appropriate controls.”

Therefore, according to Fishman, “data governance is a function that can act as an oversight mechanism and can be used to enforce controls over data quality and master data management, but also over data privacy, data security, identity management, risk management, or be accepted in the interpretation and adoption of regulatory requirements.”

Conclusion

“There is a line between trustworthy information and viral data,” explains Fishman, “and that line is very fine.”

Poor data quality is a viral contaminant that will undermine the operational, tactical, and strategic initiatives essential to the enterprise's mission to survive and thrive in today's highly competitive and rapidly evolving marketplace.

Left untreated or unchecked, this infectious agent will negatively impact the quality of business decisions. As the pathogen replicates, more and more decision-critical enterprise information will be compromised.

According to Fishman, enterprise data quality requires a multidisciplinary effort and a lifetime commitment to: