The First Law of Data Quality explained the importance of understanding your Data Usage, which is essential to the proper preparation required before launching your data quality initiative.
The Second Law of Data Quality explained the need for maintaining your Data Quality Inertia, which means a successful data quality initiative requires a program—and not a one-time project.
The Third Law of Data Quality explained a fundamental root case of data defects is assuming data quality is someone else’s responsibility, which is why Data Quality is Everyone’s Responsibility.
The Data-Information Continuum
Whether it is 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, all data is an abstract description of reality.
These abstract descriptions can never be perfected since there will always be what I call a digital distance between data and reality.
I also make a distinction between data and information, which I view as interrelated entities forming what I like to call The Data-Information Continuum.
Although a common definition for data quality is fitness for the purpose of use, the common challenge is that all data has multiple uses—and each specific use has its own specific fitness requirements.
Viewing each specific use as the information that is derived from data, I define information as data in use or data in action.
Therefore, information is customized to meet the subjective needs of a particular business unit and/or a particular tactical or strategic initiative. In other words, the information is customized data used as the basis for making a business decision.
This is why data quality has both objective and subjective dimensions.
Although data’s quality can be objectively measured separate from its many uses (i.e., data can be fit to serve as the basis for each and every purpose by attempting to maintain an accurate description of reality), information’s quality can only be subjectively measured according to its specific use.
The Fourth Law of Data Quality
Most organizations suffer from a lack of a shared business understanding, or what I like to call a Shared Version of the Truth.
Objective 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.
Subjective 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.
Therefore, The Fourth Law of Data Quality states that:
“When establishing data quality standards, you must include both objective data quality and subjective information quality.”
Remarkable Data Quality
As Seth Godin explained in Purple Cow: Transform Your Business by Being Remarkable, the opposite of “remarkable” is not “bad” or “mediocre” or “poorly done.”
The opposite of remarkable is “very good.”
In other words, don’t just establish data quality standards and set goals to meet them.
Your goal should be to exceed your goals.
Perfection is impossible—but remarkable data quality is not.