Since how data quality is defined has a significant impact on how data quality is perceived, measured, and managed, in this post I examine the two most prevalent perspectives on defining data quality, real-world alignment and fitness for the purpose of use, which respectively represent what I refer to as the danger of data myopia and the challenge of business relativity.

Real-World Alignment: The Danger of Data Myopia

Whether it’s an abstract description of real-world entities (i.e., master data) or an abstract description of real-world interactions (i.e., transaction data) among entities, data is an abstract description of reality. The creation and maintenance of these abstract descriptions shapes the organization’s perception of the real world, which I philosophically pondered in my post “Plato’s Data.”

The inconvenient truth is that the real world is not the same thing as the digital worlds captured within our databases.

And, of course, creating and maintaining these digital worlds is no easy task, which is exactly the danger inherent with the real-world alignment definition of data quality — when the organization’s data quality efforts are focused on minimizing the digital distance between data and the constantly changing real world that data attempts to describe, it can lead to a hyper-focus on the data in isolation, otherwise known as data myopia.

Even if we create and maintain perfect real-world alignment, what value does high-quality data possess independent of its use?

Real-world alignment reflects the perspective of the data provider, and its advocates argue that providing a trusted source of data to the organization will be able to satisfy any and all business requirements, i.e., high-quality data should be fit to serve as the basis for every possible use. Therefore, in theory, real-world alignment provides an objective data foundation independent of the subjective uses defined by the organization’s many data consumers.

However, providing the organization with a single system of record, a single version of the truth, a single view, a golden copy, or a consolidated repository of trusted data has long been the rallying cry and siren song of enterprise data warehousing (EDW), and more recently, of master data management (MDM). Although these initiatives can provide significant business value, it is usually poor data quality that undermines the long-term success and sustainability of EDW and MDM implementations.

Perhaps the enterprise needs a Ulysses pact to protect it from believing in EDW or MDM as a miracle exception for data quality?

A significant challenge for the data provider perspective on data quality is that it is difficult to make a compelling business case on the basis of trusted data without direct connections to the specific business needs of data consumers, whose business, data, and technical requirements are often in conflict with one another.

In other words, real-world alignment does not necessarily guarantee business-world alignment.

So, if using real-world alignment as the definition of data quality has inherent dangers, we might be tempted to conclude that the fitness for the purpose of use definition of data quality is the better choice. Unfortunately, that is not necessarily the case.

Fitness for the Purpose of Use: The Challenge of Business Relativity

In M. C. Escher’s famous 1953 lithograph “Relativity,” although we observe multiple, and conflicting, perspectives of reality, from the individual perspective of each person, everything must appear normal, since they are all casually going about their daily activities.

I have always thought this is an apt analogy for the multiple business perspectives on data quality that exists within every organization.

Like truth, beauty, and art, data quality can be said to be in the eyes of the beholder, or when data quality is defined as fitness for the purpose of use — the eyes of the user.

Most data has both multiple uses and users. Data of sufficient quality for one use or user may not be of sufficient quality for other uses and users. These multiple, and often conflicting, perspectives are considered irrelevant from the perspective of an individual user, who just needs quality data to support their own business activities.

Therefore, the user (i.e., data consumer) perspective establishes a relative business context for data quality.

Whereas the real-world alignment definition of data quality can cause a data-myopic focus, the business-world alignment goal of the fitness for the purpose of use definition must contend with the daunting challenge of business relativity. Most data has multiple data consumers, each with their own relative business context for data quality, making it difficult to balance the diverse data needs and divergent data quality perspectives within the conflicting, and rather Escher-like, reality of the organization.

The data consumer perspective on data quality is often the root cause of the data silo problem, the bane of successful enterprise data management prevalent in most organizations, where each data consumer maintains their own data silo, customized to be fit for the purpose of their own use. Organizational culture and politics also play significant roles since data consumers legitimately fear that losing their data silos would revert the organization to a one-size-fits-all data provider perspective on data quality.

So, clearly the fitness for the purpose of use definition of data quality is not without its own considerable challenges to overcome.

How Does Your Organization Define Data Quality?

As I stated at the beginning of this post, how data quality is defined has a significant impact on how data quality is perceived, measured, and managed. I have witnessed the data quality efforts of an organization struggle with, and at times fail because of, either the danger of data myopia or the challenge of business relativity — or, more often than not, some combination of both.

Although some would define real-world alignment as data quality and fitness for the purpose of use as information quality, I have found adding the nuance of data versus information only further complicates an organization’s data quality discussions.

But for now, I will just conclude a rather long (sorry about that) post by asking for reader feedback on this perennial debate.

How does your organization define data quality? Please share your thoughts and experiences by posting a comment below.

This post originally appeared at OCDQ Blog.

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