OCDQ Blog

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

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.  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.

Related Posts

Poor Data Quality is a Virus

The General Theory of Data Quality

The Data-Information Continuum