Systems and methods for computer modeling and visualizing entity attributes

A non-transitory computer readable medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising:

• receiving data from a plurality of disparate data sources, the data including a plurality of variables, wherein each variable of the plurality of variables is associated with a data type and an entity of a plurality of entities in a first position;
• distilling the data into a plurality of indexes to convert the data into the plurality of indexes to be usable by a single data structure, by:
• assigning a binary value to each variable of the plurality of variables, wherein each variable is a categorical value, a numerical value, or an ordinal value;
• generating, using the binary value of each variable, an index for each data type and each entity of the plurality of entities;
• and
• storing each index in a database;
• retrieving a first set of data elements associated with the plurality of entities from the plurality of indexes, wherein the first set of data elements includes information associated with a velocity index, attrition, and network analytic index;
• generating a first probability of moving to a first different position for each of the plurality of entities, wherein the first probability is calculated by a first mathematical transformation that includes the velocity index, attrition, and network analytic index;
• generating a second probability of moving to a second different position for each of the plurality of entities, wherein the second probability is calculated by a second mathematical transformation that includes the velocity index, attrition, and network analytic index;
• predicting a number of second entities, wherein the number of second entities is a number of entities expected to move to the first position;
• generating a second set of data elements associated with a second plurality of entities, wherein the generating includes applying a third mathematical transformation to the first probability, the second probability, and the prediction of the number of second entities;
• and
• generating an expected composition of entities in the first position, wherein the generating includes identifying at least one data category of each of the second plurality of entities.

A method comprising:

• receiving data from a plurality of disparate data sources, the data including a plurality of variables, wherein each variable of the plurality of variables is associated with a data type and an entity of a plurality of entities in a first position;
• distilling the data into a plurality of indexes to convert the data into the plurality of indexes to be usable by a single data structure, by:
• assigning a binary value to each variable of the plurality of variables, wherein each variable is a categorical value, a numerical value, or an ordinal value;
• generating, using the binary value of each variable, an index for each data type and each entity of the plurality of entities;
• and
• storing each index in a database;
• retrieving a first set of data elements associated with the plurality of entities from the plurality of indexes, wherein the first set of data elements includes information associated with a velocity index, attrition, and network analytic index;
• generating a first probability of moving to a first different position for each of the plurality of entities, wherein the first probability is calculated by a first mathematical transformation that includes the velocity index, attrition, and network analytic index;
• generating a second probability of moving to a second different position for each of the plurality of entities, wherein the second probability is calculated by a second mathematical transformation that includes the velocity index, attrition, and network analytic index;
• predicting a number of second entities, wherein the number of second entities is a number of entities expected to move to the first position;
• generating a second set of data elements associated with a second plurality of entities, wherein the generating includes applying a third mathematical transformation to the first probability, the second probability, and the prediction of the number of second entities;
• and
• generating an expected composition of entities in the first position, wherein the generating includes identifying at least one data category of each of the second plurality of entities.

A system comprising:

• at least one processor configured to:
• receive data from a plurality of disparate data sources, the data including a plurality of variables, wherein each variable of the plurality of variables is associated with a data type and an entity of a plurality of entities in a first position;
• distill the data into a plurality of indexes to convert the data into the plurality of indexes to be usable by a single data structure, by:
• assigning a binary value to each variable of the plurality of variables, wherein each variable is a categorical value, a numerical value, or an ordinal value;
• generating, using the binary value of each variable, an index for each data type and each entity of the plurality of entities;
• and
• storing each index in a database;
• retrieve a first set of data elements associated with the plurality of entities from the plurality of indexes, wherein the first set of data elements includes information associated with a velocity index, attrition, and network analytic index;
• generate a first probability of moving to a first different position for each of the plurality of entities, wherein the first probability is calculated by a first mathematical transformation that includes the velocity index, attrition, and network analytic index;
• generate a second probability of moving to a second different position for each of the plurality of entities, wherein the second probability is calculated by a second mathematical transformation that includes the velocity index, attrition, and network analytic index;
• predict a number of second entities, wherein the number of second entities is a number of entities expected to move to the first position;
• generate a second set of data elements associated with a second plurality of entities, wherein the generating includes applying a third mathematical transformation to the first probability, the second probability, and the prediction of the number of second entities;
• and
• generate an expected composition of entities in the first position, wherein the generating includes identifying at least one data category of each of the second plurality of entities.