Data Quality Whitepapers are Worthless

During a 1609 interview, William Shakespeare was asked his opinion about an emerging genre of theatrical writing known as Data Quality Whitepapers.  The "Bard of Avon" was clearly not a fan.  His famously satirical response was:

Data quality's but a writing shadow, a poor paper

That struts and frets its words upon the page

And then is heard no more:  it is a tale

Told by a vendor, full of sound and fury

Signifying nothing.

Four centuries later, I find myself in complete agreement with Shakespeare (and not just because Harold Bloom told me so).

Today is April Fool's Day, but I am not joking around - call Dennis Miller and Lewis Black - because I am ready to RANT.

I am sick and tired of reading whitepapers.  Here is my "Bottom Ten List" explaining why: 

1. Ones that make me fill out a "please mercilessly spam me later" contact information form before I am allowed to download them remind me of Mrs. Bun: "I DON'T LIKE SPAM!"
2. Ones that after I read their supposed pearls of wisdom, make me shake my laptop violently like an Etch-A-Sketch.  I have lost count of how many laptops I have destroyed this way.  I have starting buying them in bulk at Wal-Mart.
3. Ones comprised entirely of the exact same information found on the vendor's website make www = World Wide Worthless.
4. Ones that start out good, but just when they get to the really useful stuff, refer to content only available to paying customers.  What a great way to guarantee that neither I nor anyone I know will ever become your paying customer!
5. Ones that have a "Shock and Awe" title followed by "Aw Shucks" content because apparently the entire marketing budget was spent on the title.
6. Ones that promise me the latest BUZZ but deliver only ZZZ are not worthless only when I have insomnia.
7. Ones that claim to be about data quality, but have nothing at all to do with data quality:  "...don't make me angry.  You wouldn't like me when I'm angry."
8. Ones that take the adage "a picture is worth a thousand words" too far by using a dizzying collage of logos, charts, graphs and other visual aids.  This is one reason we're happy that Pablo Picasso was a painter.  However, he did once write that "art is a lie that makes us realize the truth."  Maybe he was defending whitepapers.
9. Ones that use acronyms without ever defining what they stand for remind me of that scene from Good Morning, Vietnam: "Excuse me, sir.  Seeing as how the VP is such a VIP, shouldn't we keep the PC on the QT?  Because if it leaks to the VC he could end up MIA, and then we'd all be put out in KP."
10. Ones that really know they're worthless but aren't honest about it.  Don't promise me "The Top 10 Metrics for Data Quality Scorecards" and give me a list as pointless as this one.

I am officially calling out all writers of Data Quality Whitepapers. 

Shakespeare and I both believe that you can't write anything about data quality that is worth reading. 

Send your data quality whitepapers to Obsessive-Compulsive Data Quality and if it is not worthless, then I will let the world know that you proved Shakespeare and I wrong.

And while I am on a rant roll, I am officially calling out all Data Quality Bloggers.

The International Association for Information and Data Quality (IAIDQ) is celebrating its five year anniversary by hosting:

El Festival del IDQ Bloggers – A Blog Carnival for Information/Data Quality Bloggers

For more information about the blog carnival, please follow this link:  IAIDQ Blog Carnival

You're So Vain, You Probably Think Data Quality Is About You

Don't you?

"Data Quality is an IT issue because information is stored in databases and applications that they manage.  Therefore, if there are problems with the data, then IT is responsible for cleaning up their own mess."

"Data Quality is a Business issue because information is created by business processes and users that they manage.  Therefore, if there are problems with the data, then the Business is responsible for cleaning up their own mess."

Responding to these common views (channeling the poet Walt Whitman), I sound my barbaric yawp over the roofs of the world:

"Data Quality is not an IT issue.  Data Quality is not a Business issue.  Data Quality is everyone's issue."

Unsuccessful data quality projects are most often characterized by the Business meeting independently to define the requirements and IT meeting independently to write the specifications.  Typically, IT then follows the all too common mantra of “code it, test it, implement it into production, and declare victory” that leaves the Business frustrated with the resulting “solution.”

Successful data quality projects are driven by an executive management mandate for the Business and IT to forge an ongoing and iterative collaboration throughout the entire project. The Business usually owns the data and understands its meaning and use in the day to day operation of the enterprise and must partner with IT in defining the necessary data quality standards and processes.

Here are some recommendations for fostering collaboration on your data quality project:

• Provide Leadership – not only does the project require an executive sponsor to provide oversight and arbitrate any issues of organization politics, but the Business and IT must each designate a team leader for the initiative.  Choose these leaders wisely.  The best choice is not necessarily those with the most seniority or authority.  You must choose leaders who know how to listen well, foster open communication without bias, seek mutual understanding on difficult issues, and truly believe it is the people involved that make projects successful.  Your team leaders should also collectively meet with the executive sponsor on a regular basis in order to demonstrate to the entire project team that collaboration is an imperative to be taken seriously.
  

• Formalize the Relationship – consider creating a service level agreement (SLA) where the Business views IT as a supplier and IT views the Business as a customer.  However, there is no need to get the lawyers involved.  My point is that this internal strategic partnership should be viewed no differently than an external one.  Remember that you are formalizing a relationship based on mutual trust and cooperation.
  

• Share Ideas – foster an environment in which a diversity of viewpoints is freely shared without prejudice.  For example, the Business often has practical insight on application development tasks, and IT often has a pragmatic view about Business processes.  Consider including everyone as optional invitees to meetings.  You may be pleasantly surprised at how often people not only attend but also make meaningful contributions.  Remember that you are all in this together.

Conclusion

Data quality is not about you.  Data quality is about us.

I believe in us.

Don't you?

The Data Quality Goldilocks Zone

In astronomy, the habitable region of space where stellar conditions are favorable for life as it is found on Earth is referred to as the "Goldilocks Zone" because such a region of space is neither too close to the sun (making it too hot) nor too far away from the sun (making it too cold), but is "just right."

In data quality, there is also a Goldilocks Zone, which is the habitable region of time when project conditions are favorable for success.

Too many projects fail because of lofty expectations, unmanaged scope creep, and the unrealistic perspective that data quality problems can be permanently “fixed” as opposed to needing eternal vigilance.  In order to be successful, projects must always be understood as an iterative process.  Return on investment (ROI) will be achieved by targeting well defined objectives that can deliver small incremental returns that will build momentum to larger success over time. 

Data quality projects are easy to get started, even easier to end in failure, and often lack the decency of at least failing quickly.  Just like any complex problem, there is no fast and easy solution for data quality.

Projects are launched to understand and remediate the poor data quality that is negatively impacting decision critical enterprise information.  Data-driven problems require data-driven solutions.  At that point in the project lifecycle when the team must decide if the efforts of the current iteration are ready for implementation, they are dealing with the Data Quality Goldilocks Zone, which instead of being measured by proximity to the sun, is measured by proximity to full data remediation, otherwise known as perfection.

The obvious problem is that perfection is impossible.  An obsessive-compulsive quest to find and fix every data quality problem is a laudable pursuit but ultimately a self-defeating cause.  Data quality problems can be very insidious and even the best data remediation process will still produce exceptions.  As a best practice, your process should be designed to identify and report exceptions when they occur.  In fact, many implementations will include logic to provide the ability to suspend exceptions for manual review and correction.

Although all of this is easy to accept in theory, it is notoriously difficult to accept in practice.

For example, let’s imagine that your project is processing one billion records and that exhaustive analysis has determined that the results are correct 99.99999% of the time, meaning that exceptions occur in only 0.00001% of the total data population.  Now, imagine explaining these statistics to the project team, but providing only the 100 exception records for review.  Do not underestimate the difficulty that the human mind has with large numbers (i.e. 100 is an easy number to relate to but one billion is practically incomprehensible).  Also, don’t ignore the effect known as “negativity bias” where bad evokes a stronger reaction than good in the human mind - just compare an insult and a compliment, which one do you remember more often?  Focusing on the exceptions can undermine confidence and prevent acceptance of an overwhelmingly successful implementation.

If you can accept there will be exceptions, admit perfection is impossible, implement data quality improvements in iterations, and acknowledge when the current iteration has reached the Data Quality Goldilocks Zone, then your data quality initiative will not be perfect, but it will be "just right."

Identifying Duplicate Customers

I just finished publishing a five part series of articles on data matching methodology for dealing with the common data quality problem of identifying duplicate customers. 

The article series was published on Data Quality Pro, which is the leading data quality online magazine and free independent community resource dedicated to helping data quality professionals take their career or business to the next level.

Topics covered in the series:

• Why a symbiosis of technology and methodology is necessary when approaching the common data quality problem of identifying duplicate customers
• How performing a preliminary analysis on a representative sample of real project data prepares effective examples for discussion
• Why using a detailed, interrogative analysis of those examples is imperative for defining your business rules
• How both false negatives and false positives illustrate the highly subjective nature of this problem
• How to document your business rules for identifying duplicate customers
• How to set realistic expectations about application development
• How to foster a collaboration of the business and technical teams throughout the entire project
• How to consolidate identified duplicates by creating a “best of breed” representative record

To read the series, please follow these links:

• Identifying Duplicate Customers (Part 1)
• Identifying Duplicate Customers (Part 2)
• Identifying Duplicate Customers (Part 3)
• Identifying Duplicate Customers (Part 4)
• Identifying Duplicate Customers (Part 5)

The Very Model of a Modern DQ General

With apologies to fellow fans of Gilbert and Sullivan and Sir Henry Lytton, I offer the following Data Quality (DQ) General's Song.  It is certainly not up to the high standards of The Pirates of Penzance or any other comic opera for that matter.  However, I hope that you find it entertaining.

The DQ General's Song

I am the very model of a modern DQ General,
I've cleansed data customer, product, and informational,
I know the challenges of data quality, and I quote issues historical,
From the Business to IT, in order categorical,
I'm very well acquainted, too, with matters very practical,
I understand application development is really quite iterational,
About teamwork and collaboration, I'm teeming with a lot o' news,
With many cheerful facts about how to succeed and not to lose.

I know the key to successful projects is the people, golly gee,
From executive sponsors and team leaders down to every busy bee,
Only together can we achieve great things tactically and strategically,
I have learned what progress has been made with modern technology,
But I understand that those are business problems we all see,
And nothing can be achieved without effective methodology;
In short, with data customer, product, and informational,
I am the very model of a modern DQ General.

I know understanding data is essential to using it effectively,
And that data's best friends are its stewards, analysts, and SMEs,
Profiling and statistical analysis can be a wonderful tool,
But if I forget the business context then I'll look like a fool,
I check for completeness and accuracy in all of my fields,
But always verify relevancy to boost my analytical yields.

I'm very good at matching and linking records probabilistically,
But I know often it can be done just as well deterministically,
And have even seen it performed quite supercalifragilistically;
In short, with data customer, product, and informational,
I am the very model of a modern DQ General.

Even with my impressive knowledge, I am still learning and must stay adventury,
With hard work and dedication, I will know everything by the end of the century;
But still, with data customer, product, and informational,
I am the very model of a modern DQ General.

Do you have obsessive-compulsive data quality (OCDQ)?

Obsessive-compulsive data quality (OCDQ) affects millions of people worldwide.

The most common symptoms of OCDQ are:

• Obsessively verifying data used in critical business decisions
• Compulsively seeking an understanding of data in business terms
• Repeatedly checking that data is complete and accurate before sharing it
• Habitually attempting to calculate the cost of poor data quality
• Constantly muttering a mantra that data quality must be taken seriously

While the good folks at Prescott Pharmaceuticals are busy working on a treatment, I am dedicating this independent blog as group therapy to all those who (like me) have dealt with OCDQ their entire professional lives.

Over the years, the work of many individuals and organizations has been immensely helpful to those of us with OCDQ.

Some of these heroes deserve special recognition:

Data Quality Pro – Founded and maintained by Dylan Jones, Data Quality Pro is a free independent community resource dedicated to helping data quality professionals take their career or business to the next level. With the mission to create the most beneficial data quality resource that is freely available to members around the world, Data Quality Pro provides free software, job listings, advice, tutorials, news, views and forums. Their goal is "winning-by-sharing” and they believe that by contributing a small amount of their experience, skill or time to support other members then truly great things can be achieved. With the new Member Service Register, consultants, service providers and technology vendors can promote their services and include links to their websites and blogs.

International Association for Information and Data Quality (IAIDQ) – Chartered in January 2004, IAIDQ 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. IAIDQ was co-founded by Larry English and Tom Redman, who are two of the most respected and well-known thought and practice leaders in the field of information and data quality.IAIDQ also provides two excellent blogs: IQ Trainwrecks and Certified Information Quality Professional (CIQP).

Beth Breidenbach – her blog Confessions of a database geek is fantastic in and of itself, but she has also compiled an excellent list of data quality blogs and provides them via aggregated feeds in both Feedburner and Google Reader formats.

Vincent McBurney – his blog Tooling Around in the IBM InfoSphere is an entertaining and informative look at data integration in the IBM InfoSphere covering many IBM Information Server products such as DataStage, QualityStage and Information Analyzer.

Daragh O Brien – is a leading writer, presenter and researcher in the field of information quality management, with a particular interest in legal aspects of information quality. His blog The DOBlog is a popular and entertaining source of great material.

Steve Sarsfield – his blog Data Governance and Data Quality Insider covers the world of data integration, data governance, and data quality from the perspective of an industry insider. Also, check out his new book: The Data Governance Imperative.