Washing your car can be rewarding. Scrubbing your kitchen is satisfying. Organizing your home office facilitates productivity. And cleaning your bedroom can help you relax or, even, sleep better. And know what else can help you sleep better at night? Maintaining a squeaky-clean database.
What exactly constitutes a clean database? It’s one free of outdated, incorrect or just plain junk data. It also involves consolidating datasets to create a more efficient system.
But, against all best efforts, bad data has a way of sneaking its way into databases. It’s simply a fact of life. To add to the database-maintenance complexity, time passes, people move, phone numbers change. Think about it: Do you have the same phone number you did 10 years ago? Home address? Email address? Almost certainly, somewhere, there’s a database with your old information stored.
And what are the companies, consultants or government agencies doing with that bad information? Sending advertisements to old email addresses? Making crucial business decisions based on faulty data? Relying on outdated information to make decisions about infrastructure improvements?
Bad data hurts. In fact, according to findings from IBM, poor data quality costs the U.S. economy more than $3 trillion per year. That’s trillion—with a T. And, according to Gartner research, poor data quality costs organizations an average of almost $10 million per year.
And it’s clear to see how bad data takes the financial toll it does. For instance, unqualified leads that have wiggled their way into a marketing database waste advertising dollars. Opening a storefront based on errant data about traffic in the area can lead to less-than-satisfying sales, or worse. And crucial business decisions using erroneous demographic information is usually disastrous.