Many people confuse data precision with accuracy, but it’s important to understand each and the differences, especially when applied to data quality. Precision is defined as the exactness of the measurement. A highly precise television would reflect minute differences in colors with incredibly high pixel resolution. In data quality, precision assesses the depth of detail that is encoded in the data. To strengthen the definition, one may ask themself, “how tightly can my data be defined?”
Data coverage means that all the right data is available and included. Having full data coverage doesn’t necessarily mean that the entire data set is fully exhaustive or that every value is accessible, but rather that the data is available for a necessary purpose.
According to DataCadamia, a definition of consistency is, “It specifies that two data values drawn from separate data sets must not conflict with each other, although consistency does not necessarily imply correctness.”
Data consistency means that the value is the same across all datastores within the organization. This data belongs together and describes a specific process at a specific time, meaning that the data is not changed during processing or transfers. Without consistency, there is no way to guarantee that when a piece of data is moved it is correct and the same across all places data is stored.
Dive into another Qualytics 8 to learn the importance of Conformity and why organizations should align content to requirements.
Timeliness is a measure of how often data is available when it’s expected. It can be calculated as the time difference of when information should be available and when it is actually available. Informed business decisions depend upon consistent and timely information. Therefore, critical measures of data quality include tests specifying how quickly data must be propagated and compliance with other timeliness constraints such as periodic availability.
As mentioned, with Qualytics Compare, you can ensure consistency throughout your data. Our product works for you to identify incorrect data and the root cause for the error. Additionally, with Qualytics Protect, you can capture anomalies in data pipelines and quarantine records; or identify and alert on anomalies in your historical data. With our products, businesses are alerted of problems within their data, so the problems can be solved.
How many times have you accidentally stumbled across a massive data quality problem that has gone undetected for months or…