Industry leading organizations recognize and manage data as a strategic asset. By ensuring high data quality, they are able to rely upon data for critical decision making. Business intelligence and analytics spending has been increasing dramatically for several years, incorporating traditional data warehouse platforms as well as data lakes comprised of SQL and NoSQL technologies, dispersed across on-premise and cloud environments.
Major investments and effort are spent on data extraction, transformation, and load (ETL) from source systems into data warehouses and data marts. Incorrect decisions based on poor data can be disastrous, so how can we ensure that we are utilizing the proper data to begin with? In order to do so, we must be able to address the following data quality considerations:
Author: Ron Huizenga