Conceptual Modeling for Data Warehouse design

A foundational element of indyco is that is based on what’s called a Conceptual Model.

Through Conceptual Modeling you can create Conceptual Schemas: “a conceptual schema is a high-level description of a business’s informational needs. It typically includes only the main concepts and the main relationships among them”

This means that indyco uses conceptual schemas for multidimensional modeling, which is a key issue in Data Warehouse design. While practitioners often deal this task by directly designing star or snowflake schemas, distinguishing between a phase of Conceptual Design (that delivers an implementation-independent and expressive representation of multidimensional cubes) and one of Logical Design (that creates a corresponding logical schema on the chosen platform) brings doubtless advantages to both designers and end-users.

A necessary foundation

Conceptual Modeling provides a high level of abstraction in describing multidimensional data, and is aimed at achieving independence of implementation issues. It is widely recognised to be the necessary foundation for building a database that is well-documented and that fully satisfies user requirements; usually, it relies on a graphical notation that facilitates writing, understanding, and managing conceptual schemata by both designers and business users.

This means that, on top of being a good practice for IT people, it’s a must-have when you need to design your System hand in hand with your Business Users… and this is usually the case, isn’t it?




The main benefits of Conceptual Modeling can be summarised as follows:

  • it gives designers and end-users a platform-independent, non-ambiguous, comprehensive picture of the Data Warehouse content
  • it is 100% independent of the OLAP multidimensional engine chosen for deployment
  • it enables effective communication between designers and end-users with the goal of formalizing more accurately the requirement specifications
  • it decreases the overall complexity of design by breaking it into two distinct but inter-related phases
  • it streamlines the Data Warehouse life-cycle by enabling logical design to be automated based on widely recognised best practices
  • it provides clear and expressive design documentation, readable by both ICT and business people, which improves the overall system maintainability
  • it provides a graphical environment for expressing OLAP queries in a more intuitive way
  • it enables early testing of requirements based on the core workload expressed by users, thus reducing the probability of errors and misunderstanding
  • it encourages self-service BI by helping business users understand the information content of their Data Warehouse
  • it enables the computation of metrics, in order to assess the quality of design effectively

Avoid skepticism

Skeptics may argue that the conceptual models sometimes used in this context might miss their goal. This is mainly due to two reasons: they are either too general for the peculiarities of multidimensional data, or too complex to be understood by end-users, or too poor to be used in real projects. The conceptual model adopted by indyco, the Dimensional Fact Model (DFM), has been specifically devised to overcome these problems. The DFM has been successfully experimented over the last 20 years in both the academic and industrial worlds. It gives a graphical and intuitive representation of facts, measures, and dimensions; dimensional, descriptive, and cross-dimensional attributes; optional and multiple arcs; convergences; shared, incomplete, and recursive hierarchies; additivity; temporal scenarios.

Since DFM has been specifically created to provide a conceptual representation of the multidimensional model it implements the full expressivity of a multidimensional cube (i.e. what you model is what you can implement on a Data Warehouse tool). Furthermore all the information you model in a DFM will be useful, sooner or later, in one of the Data Warehouse design phases (e.g. requirement analysis, logical design, OLAP meta-data implementation). For this reason, it is better to frame them in a systematic documentation, rather than losing sight of them in the foot notes of an analysis session report.

What is indyco

What is indyco? Drawing on an innovative graphical representation of requirements, indyco enables the co-creation of an enterprise data warehouse, automatically validated by the Dimensional Fact Model.