Most organizations experience some level of data quality challenge. Solving data quality challenges and cleansing data can exist in three ways:

Data at source: requires business owners and subject matter experts to ensure data quality at the point of entry. It becomes important to identify what data quality issues exist, and identify ways to ensure a certain level of quality before any ETL/ELT takes place.

Data in staging: requires a data cleansing process during the building of pipelines and connecting data across systems. In most cases, this falls within the traditional ETL models whereby cleansing processes and quality controls are put into place.

Data at the destination: requires transformations at the destination to ensure data is accurate and reliable at consumption but is not transformed until it is stored in a data warehouse or at a destination source

This LinkedIn Live will discuss:
What are the benefits and risks associated with each approach?
How do organizations decide which approach to use?
What should companies consider when deciding to select ETL or ELT or both?
What data quality best practices work best?