“GarGarbage Dumpbage in, garbage out” – we know that already, right?  Well … what we know about information quality and what we do are not always in sync. Just for kicks, consider information quality through the lens of the industrial quality movement.

Looking down from 30,000 feet, the history of industrial quality goes something like this – Medieval Guild craftsmanship, then Industrial Revolution product inspection, and then the post-World War II focus on quality process management.  It sounds arcane, until one remembers the 1980’s visceral fear that Japanese manufacturers were beating the pants off of U.S. manufacturing in terms of quality and value. Enter W. Edward Deming, who had been deeply influential in Japan’s post-war industrial recovery, and who became the evangelist for quality management practices in U.S. industry.  Deming exhorted American management to adopt product and service quality as the driving force in all business practices.

What’s that got to do with Information Governance?  It’s this – regardless of industry, in today’s world you’re actually in the information business.  So, business quality increasingly means information quality.  

Key attributes of data for business are sometimes referred to as the four Vs: volume, variety, velocity, and veracity.  Most folks focus on the first three, but the veracity of data – its integrity, its reliability, its quality – is crucial for business decision-making.   In a 2016 survey of executives by the Chartered Institute of Management Accountants, 80% of respondents admitted that their organization used flawed information to make a strategic decision at least once in the last three years. And IBM estimates that poor data quality costs the U.S. economy $3.1 trillion each year.

The IT discipline of data governance (not to be confused with the broader concept of Information Governance) has been practiced for over a decade, focusing on ensuring quality data inputs for better decision-making.  And the advent of big data analytics has highlighted the imperative for data quality.  Notorious examples of flawed results from big data gone awry?  How about the project using social media data to model unemployment trends (tied to relevant terms such as “classifieds” and “jobs”) – a seismic spike in unemployment turned out instead to be widespread social media discussion of Steve Jobs’ death.

But the significance of information quality is not limited to master data management of structured system data or to big data initiatives.  The need for reliable information is more fundamental, pertinent as well to unstructured data in the everyday operation of the organization.  In most businesses today, the vast bulk of information is unstructured data, scattered across network and local drives, email repositories, and other largely uncontrolled locations.  Truth be told, we’re often not sure that what we eventually find is indeed the latest draft, the final document, the official agreement, the up-to-date version … in other words, the right, reliable information.

And where are we in managing the business practices that have resulted in such an unstructured data mess?  Many organizations are stuck back in the Medieval Guild days, when each “artisan” followed his or her own local (often secret) rules, or perhaps the Industrial Revolution, with its production output focus similar to today’s blunt approaches to unstructured data repository sizes and quotas.

Information quality isn’t really about the information itself, no more than time management is about time. Data is simply data, and calling some data “bad” distracts us from the true issue, which is the quality of our business practices in handling data and in using it to make decisions.  In other words, information quality is about us, how we manage our creation, retention, and use of information.

So, back to Mr. Deming, who half a century ago distilled his thoughts on organizational quality into 14 Points for Management.  If you simply substitute “information” for “product and service,” you have a pretty clear road map for improving information quality:

  • Create constancy of purpose for improvement of product and service.
  • Adopt the new philosophy.
  • Cease dependence on mass inspection.
  • End the practice of awarding business on price tag alone.
  • Constantly and forever improve the system of production and service.
  • Institute modern methods of training on the job.
  • Institute modern methods of supervision.
  • Drive out fear.
  • Break down barriers between staff areas.
  • Eliminate numerical goals for the work force.
  • Eliminate work standards and numerical quotas.
  • Remove barriers that hinder the hourly worker.
  • Institute a vigorous program of education and training.
  • Put everybody in the company to work to accomplish the transformation, which is everybody’s job.

Making information quality a management priority?  Not a bad decision.