DAMA DMBOK: Deliverables & Steps of the Data Modeling Process

Hello everyone,

Welcome to my fourth article about data modeling. In this article I will try to explain modeling deliverables and steps of the Data Modeling Process. Have fun!

The deliverables of the data modeling process encompass a range of essential elements that contribute to the effective representation and organization of data.

The deliverables of the data modeling process include:

Diagram

A data model contains one or more diagrams. The diagram is the visual that captures the requirements in a precise form. It depicts a level of detail (e.g., conceptual, logical, or physical), a scheme (relational, dimensional, object-oriented, fact-based, time-based, or NoSQL), and a notation within that scheme.

Definitions

Definitions for entities, attributes, and relationships are essential to maintaining the precision on a data model.

Outstanding questions

Frequently the data modeling process raises issues and questions that may not be addressed during the data modeling phase.

Lineage

For physical and sometimes logical data models, it is important to know the data lineage, that is, where the data comes from. Often lineage takes the form of a source/target mapping, where one can capture the source system attributes and how they populate the target system attributes.

Steps of Developing a Data Model

The data modeling process entails a series of systematic steps designed to facilitate the structured and comprehensive representation of data, ensuring its accuracy, consistency, and usability.

The data modeling process typically involves the following steps:

Identify the business requirements

The first step in the data modeling process is to identify the business requirements and the data that is needed to support those requirements. This involves working with business stakeholders to understand their needs and the data that is required to support their business processes.

Develop a conceptual data model

The next step is to develop a conceptual data model, which is a high-level representation of the data structures and relationships that are needed to support the business requirements. The conceptual data model is typically developed using a modeling notation, such as the Entity-Relationship (ER) model, and is used to communicate the overall structure of the data to stakeholders.

Refine the conceptual data model

After the conceptual data model has been developed, it is typically refined and expanded to provide more detailed information about the data structures and relationships. This may involve adding additional entities, attributes, and relationships to the model, or making changes to the existing elements of the model.

Develop a logical data model

The next step is to develop a logical data model, which is a more detailed representation of the data structures and relationships that will be implemented in the database. The logical data model is typically developed using a modeling notation, such as the Relational Data Model (RDM), and is used to specify the technical details of the data structures and relationships.

Validate the logical data model

After the logical data model has been developed, it is typically reviewed and validated to ensure that it meets the business requirements and is technically sound. This may involve reviewing the data structures and relationships for consistency and correctness, and identifying any potential issues or problems with the model.

Develop a physical data model

The final step in the data modeling process is to develop a physical data model, a physical data model is a representation of a database or data structure that specifies how data is physically stored and organized on a storage medium, such as a hard drive or database management system. Physical data models are typically created based on logical data models, which are high-level representations of the data and relationships within an organization. The physical data model includes details such as the data types and sizes of fields, the names and structures of tables and indexes, and the location and storage of data on the storage medium. It also includes information about performance and scalability, such as the use of indexing and partitioning to improve query performance.

In next article I’ll be explaining “Data Modeling Tools”, see you soon :)