Common Data Modeling is one of the core considerations when setting up a business data warehouse. Any serious company wanting to have a data warehouse will have to be first serious about data models. Building a data model takes time and it is not unusual for companies to spend two to five years just doing it.

Data Models should reflect practical and real world operations and that is why a common data modeling method of combining forward, reverse and vertical methods make perfect sense to seamlessly integrate disparate data coming in whether top down or bottom up from different sources and triggering events.

Professionals involved Enterprise Data Modeling projects understand the great importance of accurately reflecting what exactly happens in an industry without having to create entities artificially. It can be easy to overlook and side step some issues which can be analytically difficult, issues people have no experience of or issues which may be politically sensitive. When these are side stepped, data models can become seriously flawed.

Business Architects, analysts and data modelers work together to look around and look for the best practices found in the industry. These best practices are then synthesized into the enterprise model to reflect the current state of the business and the future it wants to get into.

A good Enterprise Data Model should strike a balance between conceptual entities and functional entities based on practical, real and available industry standard data. Conceptual entities are defined within the company and will take on the values of the data by the defined by the company. Examples of conceptual entities are products status, marital status, customer types, etc.

On the other hand, functional entities refer to entities that are already well defined, industry standard data ready to be placed into database tables. Examples of functional entities are D&B Paydex Rating and FICO Score.

Businesses usually start with simply and grows more complex as they progress. It may start by selling goods or providing services to clients. These goods and services delivered as well as money received were recorded and then reused. So over time, transactions pile up over another and the set up can get more and more complex. Despite the complexity, the business is still essentially a simple entity that has just grown in complexity.

This happens when the business does not have a very defined common data modeling method. Many software applications could not provide ways to integrate real world data and data within the data architecture.

This scenario where there is not common business model can worsen when disparate multiple systems are used within the company each and each of the system has differing views on the underlying data structures.

Business Intelligence can perform more efficiently with Common Data Modeling Method. As its name implies, Business Intelligence processes billions of data from the data warehouse so that a variety of statistical analysis can be reported and a recommendation on innovation to give the company more competitive edge can be presented.

With Common Data Modeling Method, processes can be made faster as the internal structure of data are made closer to reality compared to non-usage of the data model. It should be noted that the common set up of today’s business involves having data sources from as many geographical locations as possible.