Data quality is a critical aspect of any data warehouse project. With an ever increasing number of data sources and technologies, scoping and prioritization is daunting. In this document, we share our data quality framework for planning data quality actions, current state assessments, and prioritizing data quality initiatives.
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Data quality is often been overlooked as businesses focus on immediate value, with increased revenues and decreased costs.
With data becoming increasingly integrated with strategy and operations, the bottom-line impact of data quality is increasing. Relatively newer corporate functions like sales operations and revenue operations use data heavily and with increasing depth. The professionals in these functions are very intentional about how they want to use data to generate value, and the data has to be accurate, timely, complete, and reliable. They rely on the data to make business decisions. They design processes and automate systems that use this data. Data quality has become a priority.
Despite the increasing awareness of data quality, challenges still get in the way of data quality initiatives:
This document will share our data quality framework for planning data quality actions, current state assessments, and prioritizing data quality initiatives.
Before we dive deeper, we want to remind the reader that our scope is the cloud data warehouse, where the data transformations are managed with a project, as is most commonly the case with dbt.
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