A method for managing flow cytometry data, the method comprising: (a) receiving the flow cytometry data from a flow cytometry instrument, the flow cytometry data comprising raw event data; (b) creating a first extensible dimension table capable of being pivoted to create a variable number of columns, wherein the columns of the first extensible dimension table include keywords from received data; (c) creating a second extensible dimension table capable of being pivoted to create a variable number of columns; (d) classifying the received data based on characteristics of subpopulations of the received data; (e) populating the first and second extensible dimension tables (314, 308) with data describing values of dimensions of the classified data, wherein the dimensions are classes of flow cytometry attributes; (f) populating a first fact table with the classified data; (g) populating a second fact table with a summary of the data from the first fact table, wherein the summary is linked to a flow cytometry data file, wherein the flow cytometry data file comprises the flow cytometry data from a flow cytometry instrument comprising raw event data and wherein the columns of the second extensible dimension table correspond to parameters associated with the second fact table; (h) storing the populated tables and the flow cytometry data file in a computer useable storage medium; (i) monitoring for subsequent received data, wherein the subsequent received data is received after step (d); and if the monitoring step discovers subsequently received data: (h) modifying the first extensible dimension table to include columns corresponding to keywords from the subsequently received data; (i) modifying the second extensible dimension table to include columns corresponding to parameters from the subsequently received data; and repeating steps (c)-(g); (j) parsing the received data before step (a) to determine if the raw event data contains phenotypic classifiers, wherein step (e) further comprises populating the first fact table with classified data including phenotypic classifiers; or (k) modifying a third extensible dimension table capable of being pivoted to create a variable number of columns to include columns having region identifiers associated with the first fact table, wherein the region identifiers identify regions within the raw event data; the method further comprising: determining proteomic data in the received data; and correlating the flow cytometry data with the proteomic data based upon the sample identifier; or (n) determining genomic data in the received data; and correlating the flow cytometry data with the genomic data based upon the sample identifierwherein the sample identifier is a patient identifier. (a) receiving the flow cytometry data from a flow cytometry instrument, the flow cytometry data comprising raw event data; (b) creating a first extensible dimension table capable of being pivoted to create a variable number of columns, wherein the columns of the first extensible dimension table include keywords from received data; (c) creating a second extensible dimension table capable of being pivoted to create a variable number of columns; (d) classifying the received data based on characteristics of subpopulations of the received data; (e) populating the first and second extensible dimension tables (314, 308) with data describing values of dimensions of the classified data, wherein the dimensions are classes of flow cytometry attributes; (f) populating a first fact table with the classified data; (g) populating a second fact table with a summary of the data from the first fact table, wherein the summary is linked to a flow cytometry data file, wherein the flow cytometry data file comprises the flow cytometry data from a flow cytometry instrument comprising raw event data and wherein the columns of the second extensible dimension table correspond to parameters associated with the second fact table; (h) storing the populated tables and the flow cytometry data file in a computer useable storage medium; (i) monitoring for subsequent received data, wherein the subsequent received data is received after step (d); and if the monitoring step discovers subsequently received data: (h) modifying the first extensible dimension table to include columns corresponding to keywords from the subsequently received data; (i) modifying the second extensible dimension table to include columns corresponding to parameters from the subsequently received data; and repeating steps (c)-(g); (h) modifying the first extensible dimension table to include columns corresponding to keywords from the subsequently received data; (i) modifying the second extensible dimension table to include columns corresponding to parameters from the subsequently received data; and repeating steps (c)-(g); (j) parsing the received data before step (a) to determine if the raw event data contains phenotypic classifiers, wherein step (e) further comprises populating the first fact table with classified data including phenotypic classifiers; or (k) modifying a third extensible dimension table capable of being pivoted to create a variable number of columns to include columns having region identifiers associated with the first fact table, wherein the region identifiers identify regions within the raw event data; determining proteomic data in the received data; and correlating the flow cytometry data with the proteomic data based upon the sample identifier; or (n) determining genomic data in the received data; and correlating the flow cytometry data with the genomic data based upon the sample identifierwherein the sample identifier is a patient identifier.