In Software Engineering, data modeling is the process that creates a data model by applying specific formal techniques for an information system. There are some rules to do data modeling, such as Data integrity should be enforced by data modeling, all entities should have a primary key, all primary fundamental values must be unique, and a primary key's value cannot be null. There is a difference between data modeling and data profiling , as data modeling is the process that creates a data model. Data profiling is a process that examines, analyzes, and creates valuable summaries of data which is used to aid in discovering any issues or risks in data quality and overall trends.
Tips For Best Practices To Enhance Data Modeling
When anyone is engaging in a task or project of data modeling, one always has that knowledge of data modeling through best practices for data modeling to enhance data model, so the following are the tips for best practices that one should never forget:
- First, understand the needs of the business and the required outcomes.
- Then for visualization, make the design of the data model.
- Always keep in mind to recognize the business demands and have the aim for a relevant result.
- Always start with simple and easy data modeling and then keep expanding later.
Apart from this, for data modeling best practices , there are some more steps, such as:
- First of all, gather all the requirements things of the business.
- Then, must make identification of the entities.
- Make a conceptual data model.
- Then finalize the attributes and design the logical data model.
- Last but not least, the database creates physical tables.
Best Practices For Data Modeling: Standards, Conventions, And Techniques
There is a common standard for data modeling best practices that a common data model contains a metadata's uniform set, which includes a standardized set, entities, attributes, semantic metadata, and relationships. And then allow the data across applications and its meaning to be shared.
As data modeling is a new field, there are conventions to be defined, which are in three levels. They are,
- Syntactic: Syntactic is about those symbols which to used. Some examples of syntactic conventions include Barker's technique, IDEF1X, and UML.
- Positional: Positional conventions help dictate how entity types laid out. This guide of convention gives the shape of the model. But this type of convention only sometimes followed as its results in models are chaotic, very difficult to read, and confusing.
- Semantic: This convention describes the standard ways to represent typical business situations. The semantic convention was first described in a 1995 book by David Hay, so these conventions are new in the industry.
These three convention sets are entirely independent of each other.
There are three primary data modeling techniques; Network, relational, and entity, as there are three types of data models conceptual, logical, and physical.
- Network Technique: A networking technique involves designing a database which will be the flexible model which represents objects and their relationships. It has a schema to provide the logical view in a graphical form of the database. This technique supports multiple parent and child records to make it easier to handle complex relationships.
Relational Technique: The relational technique used to describe the relationships between the data elements stored in rows and columns. This technique used by data modelers to minimize the complexity and to ensure a clear overview of the data.
- Entity-Relationship Modeling Technique: It a technique used to define the data elements, entities, and database relationships. This technique creates an entity-relationship diagram to comprise entities, attributes, and relationships in a graphical format. Entity-relationship modeling technique to implemented as a database also serves as a conceptual blueprint.
Conclusion
Before moving forward with analytics, data modeling is about understanding your business and data. With the knowledge of standards, conventions of modern data modeling techniques, and best practices, equipping yourself will help you build a data model that can serve your business and the end user's requirements.
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