Data Modeling vs. Data Architecture: What’s the Difference?
The potential benefits of cloud computing are inspiring senior IT and business leaders in many organizations to reconsider enterprise data strategy and contemplate how migrating data and applications to the cloud can motivate the modernization of a data architecture.
The concept of data organization and architecture in the past had typically been absorbed by the IT department. But with the raised awareness of data’s business value comes the realization that an effective data strategy influences how transactional and operational data helps drive analytics applications that feed into judicious decision-making and profitable outcomes.
This increased scrutiny raises questions about different facets of organizing and managing data, particularly data modeling vs. data architecture. We’ll explore how data modeling and data architecture differ, the relationship between data modeling and data architecture as part of the data management process, and the various roles of data modelers and data architects.
A data model is an abstract representation of the real-world entities that interoperate within an organization’s business environment. It represents data entities, their attributes and how those entities relate to each other. There are three types of data models: conceptual, logical and physical. Conceptual data model. This model shows a high-level view of the data the enterprise uses to support business processes. Though this model represents the conceptual objects used by the organization, it’s generally unassociated with a specific application or database management system. Rather, it’s intended to capture the business’s overall information requirements. The conceptual data model is used to communicate with the business to ensure that information processing needs will be met — for example, the definition of conceptual entities such as customer, product and location and the relationships among those entities: “A customer living at a locationpurchased a product.” Logical data model. This model captures details of the characteristics, attributes and relationships among the different entities. It provides the technical perspective of the data objects described in the conceptual model with the definition of specific attributes of each entity — for example, associating attributes of a customer, such as last name, first name and a customer number, as well as linking a customer to a specific residential location that includes street, city, state and ZIP code attributes. Physical data model. This model is specific to the application and storage framework used for that data. For each entity modeled, the physical data model lists the data elements, their data types, lengths and other characteristics relevant to the underlying database management system or alternative storage environment.
Data models are developed and refined by data modelers, who engage the business data users and solicit their requirements as a prelude to iteratively refining the conceptual, logical and physical data models. Data modelers work with application developers to understand the business processes implemented by the developed application and determine the best representation for the data that supports that application. A data modeler’s tasks include the following: Engage the business users to assess their information needs. Review the business process and conceptualize the entities that interoperate within the business process. Determine how the various entities are related and develop entity relationship diagrams that represent the connections among the entities. Identify each entity’s characteristics and properties and ensure that the entities can be differentiated within the model. Develop a logical data model and validate the model to ensure that it serves the needs of the business application and its consumers.
