Stat­Soft defi­nes data warehousing as a pro­cess of orga­ni­zing the sto­rage of lar­ge, mul­ti­va­ria­te data sets in a way that faci­li­ta­tes the retrie­val of infor­ma­ti­on for ana­ly­tic pur­po­ses.

The most effi­ci­ent data warehousing archi­tec­tu­re will be capa­ble of incor­po­ra­ting or at least refe­ren­cing all data available in the rele­vant enter­pri­se-wide infor­ma­ti­on manage­ment sys­tems, using desi­gna­ted tech­no­lo­gy sui­ta­ble for cor­po­ra­te data base manage­ment (e.g., Ora­cle, Syba­se, MS SQL Ser­ver). Also, a fle­xi­ble, high-per­for­mance (see IDP Tech­no­lo­gy), open archi­tec­tu­re approach to data warehousing, that fle­xi­bly inte­gra­tes with the exis­ting cor­po­ra­te sys­tems and allows users to orga­ni­ze and effi­ci­ent­ly refe­rence for ana­ly­tic pur­po­ses enter­pri­se repo­si­to­ries of data of prac­ti­cal­ly any com­ple­xi­ty, is offe­red in Stat­Soft Enter­pri­se Sys­tems such as STATISTICA Enter­pri­se, which can also work in con­junc­tion with STATISTICA Data Miner and STATISTICA Enter­pri­se Ser­ver appli­ca­ti­ons.

See also, Data Mining, Explo­ra­to­ry Data Ana­ly­sis (EDA) and Data Mining Tech­ni­ques, Extra­ct, Trans­form, and Load (ETL), and On-Line Ana­ly­tic Pro­ces­sing (OLAP).