My life is a data quality problem. I didn’t know it at first. I’m minding my own business and wham! A data quality issue smacks me in the face. It has happened again and again. The interesting thing is that your life, as an individual, is a data quality problem, too. Your corporation is a data quality problem. Your government agency is a data quality problem. Your educational institution is a data quality problem. Any organization is practically a living, breathing data quality problem. It’s just that most of them haven’t put a name to it yet. All organizations depend on data and information to provide their products and services – without exception. And the quality of that data and information, in most cases, is not up to the task.
Look at your own life. As a consumer, patient, or citizen how many times have you been overcharged on an invoice, had health care records that were wrong, or been unable to access services because government records were wrong? How many times have you tried to obtain information over the phone, but could not because the identifying data on your account was wrong, or an automated payment did not go through because the data was wrong? Or your spouse’s medical information mysteriously changed between the physician’s office and the hospital on the day of surgery? All these things have happened to me.
Look at your organization. The right-quality information helps inventory managers keep the supply chain lean, CEOs make long-term plans for growth based on dependable performance measures, and social services identify high-risk youth who need their help. Right-quality information instills trust in voters that election results are accurate and helps government officials determine where to place scarce resources during a pandemic. The right quality data is essential to getting valid results from artificial intelligence and machine learning algorithms. Whether for market research, patent applications, manufacturing improvement metrics, taking sales orders, receiving payments, or analyzing test results, information is as essential to the functioning of an organization (and a society) as it is to providing a competitive edge.
Information quality contributes to that edge by delivering the right information, at the right time, in the right place, to the right people. Neither human beings nor machines can make effective decisions with flawed, incomplete, or misleading data. They must have data and information they can trust to be correct and current if they are to do the work that provides products and services and satisfies the organization’s customers.
It is not unusual to find that data quality issues prevent organizations from realizing the full benefit of their investments in fulfilling strategies and goals, addressing issues, or taking advantage of opportunities. The business therefore does not receive the expected improvements to customer satisfaction, products, services, operations, analytics, and decision-making. It is not able to manage risk appropriately, keep costs down, or increase business value.
The good news is that we know how to deal with the data quality issues that get in the way of what any organization is trying to achieve. The Ten Steps™ methodology is a structured yet flexible approach for creating, assessing, improving, sustaining, and managing data quality within any organization. It is a proven approach that has been used by many types of organizations in various countries around the world. Join me to learn how to address your critical data quality problems in my upcoming course Ten Steps to Quality Data.
Author’s Note: This article (with a few updates) is an excerpt from Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information™, 2nd Ed (Elsevier/Academic Press, 2021) by Danette McGilvray.
