Definition:
Our connected age and continued advances in information technology and communication through ever-expanding channels bring both challenges and opportunities. It is easier than ever before to acquire data on your customers, making it simpler to design ad campaigns, fundraising efforts, and more tailored and personalized content. This, in turn, increases the odds of success, if the data is accurate.
On the other hand, the fact that there are many points of data entry on both the customer and company-end increases the chances of errors being introduced into organizational databases. If steps are not taken to prevent this inaccurate data entry and to cleanse databases of incorrect client data, marketing efforts, awareness campaigns, and other outreaches to your users can actually be less effective.
To successfully meet the challenges of our data-driven age and take full advantage of the opportunities it offers, companies and organizations must strive for the highest data quality.
What is data quality?
A company, nonprofit organization, or other entity cannot have the highest data quality without an accurate understanding of what quality data looks like. There are a variety of ways to define data quality, but all definitions have some important points in common.
According to data quality experts, data is of high quality when it satisfies the requirements of its intended use. In other words, companies know that they have good quality data when they are able to use it to communicate effectively with their constituents, determine clients' needs, and find effective ways to serve their client base.
This data quality definition is broad enough to help companies with varying products, markets, and missions to understand if their data is up to standards. Additional aspects of data that organizations can use to determine if their data satisfies its intended use include:
Accuracy
Accurate data conveys true information about a company's clientele. If there are errors in client data, contact with customers is impossible, and it is extremely difficult to reach a larger audience.
Relevancy
Data should not only be accurate, it must also be relevant to the needs and purposes of a business. Companies waste valuable storage space if they collect information that is extraneous to their purpose, and irrelevant data may also prevent key customer targets from being identified in reporting and analysis.
Completeness
Data quality is also defined by its completeness. To get a full picture of customer needs, as well as maintain open channels of communication, a business must have data that includes all of the pertinent information and is up-to-date on customers' contact information.
Capable of being understood
Key to the data quality definition is the concept of whether the data is understood. Massive databases full of data are useless if reporting and modeling cannot understand what the data says about your users and how best to reach them.
Improve your data quality
High-quality data can mean the difference between high profitability and a company that just barely gets by. Experian Data Quality is here to help your company acquire, retain, and understand client data.
Contact us today for more information on all of our data quality solutions.
Related links:
Learn more about data quality tools.
Learn more about data quality management.
Learn how to fix data quality issues and maintain data accuracy by exploring Experian's data quality management platform
