Systems | Development | Analytics | API | Testing

Data mesh is becoming a key component of many organizations' data management strategy. Before embarking on that journey, organizations need to understand the real complexities of data mesh and what the industry doesn’t talk about, the risks associated with this approach, and the importance of governance in a data mesh world.

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.