If businesses are slow to adapt to the insights they glean from their transactional data, they will ultimately fail.

Traditional data architectures require organizations to manage separate transactional and analytical databases, often leading to operational burden, data silos, and governance gaps.

For example, a company may store transactional data in a relational database that it wants to analyze in a data warehouse, but use another analytics tool to perform a vector search on data from a ...

According to the agency’s Inspector General, GSA performed an inaccurate evaluation of the program when it deemed TDR a success, but in actuality, the data is unusable.

Traditionally, transactional and analytical data have been siloed, creating complexities when moving data between systems and hindering the speed required for modern development.