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High-quality data leads to intelligent business decisions

For any business that manages data, there should be a constant interest in maintaining data quality as well. After all, what good is having information at your fingertips if you can't trust it?

It's important to note that "data quality" can mean different things to different types of organizations. It's all about putting the term in the right context. For example, is it important to have the most current phone number on file for a given customer? If you almost never call people, this might be an element that gets neglected. But if you're working the phones constantly, it should be priority No. 1.

According to Valerisys Consulting, a fundamental part of honing your organization's data is making sure it's a valuable resource within the context of your business. Michael Gallo, the organization's owner and principal consultant, says that because it takes resources to collect and maintain data, you want to make sure you're getting stuff that's useable.

"Unused data are unloved data," Gallo noted." And unloved data are ignored. That means there's a good chance that no one is paying attention to ensuring the data are error free and will lead to poor or decreasing data quality. Even though a lot of data today are generated to satiate our ever-increasing appetite for bureaucracy, rules and regulations, data ultimately should exist for a business or mission purpose."

Here are a few examples of business context that you should know:

Data "authority figures"
Who's in charge of a given element of data? Who within the organization created it in the first place, and who has access to it for modifying, using and acting upon it? Part of the challenge of data quality is establishing workflow patterns.

Acceptable data parameters
Getting data in the right format is also an important step. Defining the parameters can be tricky - for example, is Massachusetts abbreviated as "Mass." or "MA?" These little differences can throw off your database if left unchecked.

Proper uses of data
What's the intent behind a piece of data? Is it supposed to be used by marketing teams for creating ad campaigns, or salespeople for delivering better sales pitches? Part of data quality is making sure no piece of data gets into the wrong hands.

Knowing the full scope
Finally, it's important to define the full extent of how data can be used. Does it only extend to a certain geographical area? Chronologically, is there a certain expiration date on it? Understanding the complete picture will help any organization fine-tune its operations with a data-driven approach.

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