The astonishing dominance of relational databases since the mid-1980s has led, in practice, to a blurring of the boundaries between the three models, and it is not uncommon nowadays for a single model to be built, again in the form of an ERD. This ERD is then converted straight into a set of tables in the database. The conceptual model, logical model, and physical model are treated as the same thing. This means that any changes that are made to the design for, say, performance-enhancing reasons are reflected in the conceptual model as well as the physical model. The inescapable conclusion is that the business requirements are being changed for performance reasons. Under normal circumstances, in OLTP-type databases for instance, we might be able to debate the pros and cons of this approach because the business users don't ever get near the data model, and it is of no interest to them. They can, therefore, be shielded from it. But data warehouses are different. There can be no debate; the users absolutely have to understand the data in the data warehouse. At least they have to understand that part of it that they use. For this reason, the conceptual data model, or something that can replace its intended role, must be reintroduced as a necessary part of the development lifecycle of data warehouses.
There is another reason why we need to reinvent the conceptual data model for data warehouse development. As we observed earlier, in the past 15 years, the relational database has emerged as a de facto standard in most business applications. However, to use the now well worn phrase, data warehouses are different. Many of the OLAP products are nonrelational, and their logical and physical manifestations are entirely different from the relational model. So the old reasons for having a three-tier approach have returned, and we should respond to this.
The astonishing dominance of relational databases since the mid-1980s has led, in practice, to a blurring of the boundaries between the three models, and it is not uncommon nowadays for a single model to be built, again in the form of an ERD. This ERD is then converted straight into a set of tables in the database. The conceptual model, logical model, and physical model are treated as the same thing. This means that any changes that are made to the design for, say, performance-enhancing reasons are reflected in the conceptual model as well as the physical model. The inescapable conclusion is that the business requirements are being changed for performance reasons. Under normal circumstances, in OLTP-type databases for instance, we might be able to debate the pros and cons of this approach because the business users don't ever get near the data model, and it is of no interest to them. They can, therefore, be shielded from it. But data warehouses are different. There can be no debate; the users absolutely have to understand the data in the data warehouse. At least they have to understand that part of it that they use. For this reason, the conceptual data model, or something that can replace its intended role, must be reintroduced as a necessary part of the development lifecycle of data warehouses.
There is another reason why we need to reinvent the conceptual data model for data warehouse development. As we observed earlier, in the past 15 years, the relational database has emerged as a de facto standard in most business applications. However, to use the now well worn phrase, data warehouses are different. Many of the OLAP products are nonrelational, and their logical and physical manifestations are entirely different from the relational model. So the old reasons for having a three-tier approach have returned, and we should respond to this.