Data model

Board’s Data model technology is designed to ensure maximum efficiency in managing very large volumes of data and to provide top-level performance. The implementation of exclusive multidimensional management techniques completely avoids the database explosion problem typically associated with multidimensional databases (also referred to as M-OLAP).

Board’s Data models are multidimensional and are therefore optimal for online analytical processing (OLAP). Conceptually, a multidimensional Data model uses the idea of a data cube where the cube cells contain values and the dimensions represent the different possible perspectives on data. For example, a "sales" cube, would contain sales values in its cells and could be viewed by various dimensions such as product (i.e. the sales figure per product), geography (i.e. sales figure by city or region), time and so on.

Data, such as turnover, assets and liabilities, expenses and revenues, etc., are normalized and stored in multidimensional objects called Cubes that are structured by Entities (such as Month, Customer, and Product).

Entities can be linked through relationships to establish hierarchical structures (e.g. Customer → Country → State), while some Entities, that we define as Unbalanced, can accommodate indirect hierarchical structures. Users can read, write and update Cubes regardless of their data sources.

Board supports write back not only on its Cubes, but also directly on relational data sources, making it possible to effectively integrate performance management processes with enterprise applications.

A Board Data model is made of:

  1. Entities: these are information sets, generally text and codes. For example, inside a single Entity there could be a list of Customers, Products or Cities. Entities (and hierarchies) are the Cubes dimensions
  2. Relationships: when two or more entities have a many-to-one relation, then a Relationship (or hierarchy) can be defined. For example, the entities Customer, City and State can be organized into the Relationship "Customer → City → State" since there is a many-to-one relationship existing between Customer and City and between City and State
  3. Cubes: Cubes contain data (often numerical but not strictly) that can be analyzed and viewed by its different dimensions and hierarchy levels.

Entities, Relationships and Cubes form the multidimensional Data model of the company or, more generally, the modeled system. We will use the term "dimension" to refer to an independent Entity or an entire Relationship, that can be used as an axis for a Cube. For example, the entity Currency can be a dimension for Cubes such as "Orders value" and "Invoiced value". The three entities Customer, City, and State are hierarchically related  and therefore form a unique dimension, referred to as the Customer dimension: the dimension is named after the base level Entity of the Relationship, the one that represents its most detailed level.


Board provides the capability to connect, integrate and federate data across:

  1. Relational databases and Data Warehouse(s)
  2. Enterprise applications (e.g. SAP ERP)
  3. Multidimensional sources (including SAP BW)
  4. Web Services via API calls
  5. Excel, CSV and TXT files
  6. Cloud based sources

Data is usually imported into Board via Data Readers. Data Readers also handle the mapping of data to Entities, Relationships and Cubes.
Imported data can be later manipulated using Data model procedures.

From a technical point of view, Board leverages the following list of data providers in order to import data from external sources:

  1. Open Database Connectivity (ODBC) standard and OLE DB to connect with relational databases
  2. CSV and TXT files