Senior data leaders would likely agree that they’ve seen data management experience rapid growth in its scope and areas of responsibility over recent years. Though never straightforward, the field was once limited to managing a few critical processes.

Operational data was gradually generated by internal and external sources, and that data would be stored in on-premises databases around the enterprise. IT would maintain those databases and ensure access and reliability, leveraging data integration tools to move or transform the data as needed. When different teams needed data for operational or reporting purposes, IT made sure it was available within a reasonable period of time — often in days or weeks, not moments. Today, data management teams have a far broader and more demanding charter.

They are often responsible for the accuracy and completeness of an organization’s data. So, data quality and master data management tools are needed. You may require data engineering solutions to discover disparate datasets throughout the enterprise, bring those datasets into a data lake with mass ingestion capabilities and then
operationalize ongoing data pipelines.