In a world where data is seemingly everywhere, how can we be confident in its accuracy? The answer lies in extensible data observability, or the tracking of data quality. By DQO’ing your data, you can increase your confidence in its accuracy and make better business decisions as a result.
What is DQO?
DQO is a process of defining, measuring, and improving the quality of data. The goal of DQO is to ensure that data is fit for its intended use. To DQO your data, you need to understand three things: what are you trying to achieve with your data (the goal), what are the specific characteristics of good data (the criteria), and how will you know if your data meets the criteria (the measurement).
DQO begins with understanding the business goal that the data will be used for. Once the goal is understood, criteria for good data can be determined. For example, if the goal is to target ads to potential customers, then the criterion would be that the data must be accurate and up-to-date. To measure whether or not the criterion has been met, one could compare the customer’s current address against what’s on record. If they match, then we can assume that the customer’s address is current and accurate.
The DQO process can be applied to any type of data, including but not limited to: customer data, financial data, inventory data, etc. By DQO’ing your data, you can ensure that it is of the highest quality and fit for its intended use. As a result, you will have increased confidence in your data and can make better business decisions. https://dqo.ai/