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Fixing the little things with data enrichment

Rachel Wheeler April 17, 2014 Archive When most people think of the process of improving data quality, they think of large-scale mistakes that need to be eliminated. They think of entirely mistaken identities, addresses or financial data that can lead companies in a catastrophically misguided direction. In other words, data disasters.

But in many cases, errors in data quality are really quite small, and the appropriate action isn't a large-scale overhaul but rather a minor tweak. That's where data enrichment comes in - companies can use such solutions for making minor changes to their banks of information. If data is already at a fairly high level of quality, businesses can make it even better, with no minor misspellings or incomplete fields.

According to Techopedia, this is a big thing that's happening in information management today. Companies can take data that's currently quite raw and make it demonstrably better.

"Although data enrichment can work in many different ways, many of the tools used for this goal involve a refinement of data that might include small errors," the source stated. "A common data enrichment process could, for example, correct likely misspellings or typographical errors in a database through the use of precision algorithms. Following this logic, data enrichment tools could also add information to simple data tables."

Companies everywhere are collecting data on their customers and the economy around them, but their information isn't always comprehensive. Sometimes, they need some help with filling in the blanks. Today, 94 percent of organizations are using third-party data to enhance the information they have. That figure will likely only increase as more companies come to see the benefits.

If you're a perfectionist in information management, you have an interest in eliminating every blemish on your data, no matter how small. Data enrichment is one part of that process.
 How data enrichment helps with emergency response Smart businesses can make a killing by using open government data Examining data quality errors of all shapes and sizes What's your frame of reference for data quality?

