Recently, I was working with an organization on a data warehouse. There came a time when the organization needed a data model, which is absolutely normal in the life of a data warehouse. So this organization set out to find a data model for their business.
Indeed, after searching the Internet a bit, a data model for their industry was found. And the organization that had the data model allowed us to take a look at it.
At this point, we made a discovery – there are different kinds of data models. In this case, the data model that was presented was an operational data model. The operational data model was appropriate for the day-to-day operational needs of the organization. But when it came to building a data warehouse, the operational data model simply was a misfit.
With that short perspective in mind, it seems that there are at least three very different kinds of data models – operational data models, data warehouse (or analytical) data models, and global data models. In fact, there are probably even other types of data models.
So what is the difference between an operational data model and a data warehouse data model? There are in fact many differences. Some of those differences are:
- Operational data models contain data that is purely operational, whereas data warehouse data models contain only information useful for analytical processing. As an example, consider telephone number. There is no question that telephone number is of operational importance. But a telephone number being used for analytical processing? We don’t make analytical decisions on the basis of the digits of a person’s telephone number.
- Date stamping. All data warehouse data is time stamped. But only some operational data is time stamped. As data passes from the operational world to the data warehouse world, if a time stamp is not present, it is added.
- Granularity. Data in the data warehouse world is always granular. Always. But data in the operational world is not granular at all.
- Subject orientation. Data in the operational world is organized by function. But data in the data warehouse world is organized by subject area. This very real difference in orientations shows up in the data model.
Now when it comes to the global data model, it seems that there are even more fundamental differences. In general, the data model reflects commonality between organizations. It is in the commonality of the business that organizations find their need for shared data. The problem is that when looking at business across the globe, there often is very little common data. Across the globe – even for the same company – business practices are different. Who a customer is, what a product is, how a transaction is conducted are all very different. In many organizations, about the only commonality across the organization is a common balance sheet.
It is for these reasons then that the global data model tends to be:
- Simple, and
- Focused on finance, particularly profit and loss.
These then are some of the differences between the different types of data models.
SOURCE: Three Types of Data Models
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