Syncsort announced today that it was named a leader in the Gartner Magic Quadrant for Data Quality Tools. You may have heard the term data quality before, but might still be wondering exactly what data quality is.
Defining Data Quality
A basic data quality definition is this: Data quality is the ability of a given data set to serve an intended purpose.
To put it another way, if you have data quality, your data can deliver the insight you hope to get out of it. Conversely, if you don’t have data quality, there is likely to be problems in your data that will prevent you from using the data to do what you hope to achieve with it.
To illustrate the definition further, let’s examine a few examples of real-world data quality challenges. Imagine that we have a data set that consists of names and addresses. Data like this is likely to contain some errors for various reasons – both simple and complicated ones.
Simple causes of data errors are names and addresses that were entered incorrectly, or address information that has changed since it was collected.
Continue reading about more complex data quality challenges and how to fix them in Syncsort’s blog post What is Data Quality? Explaining What Data Quality Actually Means.