Dynamic AI does not use data aggregation, or cubes. Will it be fast enough?
For several reasons, derived from Dynamic AIs position as a new generation system, these unwieldy techniques are no longer required in order to provide for usable performance.
Todays Client Server DBMS systems are high performance and scalable. Dynamic AI relies on this power, where previous generation systems had to work around the limitations of underpowered DBMS systems - and have not subsequently moved on.
Dynamic AI uses state of the art technology, generating fully optimized SQL statements. The characteristics of the type of reports generated, together with the option to define an array of servers in an Enterprise setup make it an extremely high performing system.
Aggregation techniques require much effort to be put into transforming financial, operational or research data into data structures defined to meet the needs of specific informational requirements. As well as being a very expensive process in terms of both setting up the aggregating process and in subsequent operation, this results in very inflexible Business Intelligence Systems, as almost any subsequent change will need the re-definition and extension of the aggregation process.
The techniques that Dynamic AI uses support the use of data just as collected, not as idealised for specific purpose, and removes this barrier to flexibility.