Data Modelling

In ForecastingApp data is organized in model cubes - massive tables with uniform layout and cube can hold billions or hundreds of billions data points.

Each cube has predefined set of dimensions such as product, customer or category. Some dimensions are provided by system such as Data Type which defines whether we are looking at actual or forecasted data. Some dimensions are user defined such as Product or Category. In addition to those dimensions each cube has metrics or numeric values attributable to each data point, and one or more time (temporal) dimensions describing months, weeks or days.

Each dimension has one or more elements which can be organized into hierarchies. In this way you can easily navigate from details to summary and vice versa. The cube automatically takes care of correct aggregation of different metrics as some metrics are plain sum or total of elements underneath whereas others are averages over weeks, averages over existing values and so forth. As you select an element from the context picker the system automatically determines appropriate rollup rule and applies it to your data.