A solid understanding of data modeling best practices can help ensure data-driven enterprises get maximum value from their data modeling projects.
Data modeling is an important technique that helps data-driven businesses cope with the challenges of managing data resources effectively.
When it is performed efficiently, data modeling can provide tangible business benefits by allowing organizations to handle big data assets productively.
Data modeling is used extensively in database creation and to provide the foundation for data governance initiatives. The quality of the data models used in various activities has an enormous impact on the end product. Poorly constructed models will not help accomplish the goals for which they were developed.
What Makes a Good Data Model?
Good data models share several characteristics that set them apart from those that provide less value to their creators. Ensuring that your data models conform to a set of standards will go a long way toward developing useful artifacts that further enterprise business goals.
Following are the top 8 data modeling best practices that go into creating good data models …
Top 8 Best Practices for Data Modeling
- Obtaining a clear understanding of the business requirements and expected results is a mandatory first step when creating data models. Without this knowledge, it is virtually impossible to develop useful models.
- Use data visualization techniques to gain a different perspective on the data elements that will be incorporated into the model. This practice can help reduce redundancy and assist in developing more consistent models.
- Begin with simple and extensible models. Minimizing the complexity in the early stages of development allows you to concentrate on the model’s accuracy. Once the initial model is verified, more data elements can be added to extend it.
- Define business requirements in terms of facts, dimensions, filters, and order to create models that can answer the questions posed by end-users.
- Restrict the model’s complexity by only using the data elements needed to address requirements. Incorporating extraneous data into models can lead to waste and degraded performance.
- Verify each stage of data model development before proceeding with the next phase. For example, performing testing to ensure the proper primary keys have been chosen can help reduce the complexity of many-to-many relationships that contribute to unmanageable data models.
- Look for causal relationships between data elements that can be incorporated into data models. The difference between the correlation of data components and the causation of their behavior are important distinctions that need to be reflected in the associated models.
- Allow data models to evolve to address changes in data sources or business requirements. Storing models in an accessible repository and using a consistent data dictionary facilitates the creation of models that can be updated when circumstances demand.
Approaching data modeling with these factors in mind will result in more effective models that add value to data-driven businesses.
Using the Right Data Modeling Tools
IDERA offers a suite of data modeling tools that can be used to efficiently create conceptual, logical, and physical data models. The various flavors of ER/Studio address different data modeling and business requirements as indicated below.
Business Architect facilitates the creation of conceptual and business process models. The tool can be used to:
- Create conceptual and process models that replicate business goals;
- Model the relationships between people, processes, and data;
- Locate sensitive data assets to bolster data security and regulatory compliance;
- Export conceptual models to ER/Studio Data Architect for building logical models.
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Data Architect can be used to perform many data modeling activities including:
- Building out data models to streamline database development;
- Discovering and documenting existing data assets;
- Creating and populating data warehouses;
- Extracting and integrating complex metadata;
- Analyzing how new policies will affect data models and databases.
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Enterprise Team Edition is a tool designed to foster team collaboration when:
- Developing enterprise data models;
- Discovering and documenting data assets from across the computing environment;
- Improving the consistency between data models and databases;
- Creating the necessary data glossaries and dictionaries to be used for data governance.
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Organizations that implement data modeling best practices supplemented by these tools are well-prepared to extract the maximum value from their data resources. In today’s data-centric market, effective data modeling can be one of the deciding factors in the success or failure of a business.
You can test drive the ER/Studio family of data modeling tools with a free 14-day trial that gives you access to the application’s complete functionality or get a demo.