Poor Data Quality is a Virus

“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.”



“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:

“Prevent viral data and preserve trusted information.”

Books Referenced in this Post

Viral Data in SOA: An Enterprise Pandemic by Neal A. Fishman

Enterprise Knowledge Management: The Data Quality Approach by David Loshin

Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information by Danette McGilvray

Data Quality: The Field Guide by Thomas Redman

Juran on Quality by Design: The New Steps for Planning Quality into Goods and Services by Joseph Juran

Customer Data Integration: Reaching a Single Version of the Truth by Jill Dyché and Evan Levy


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