What are the best practices for creating entity-relationship diagrams?

Consider the type of scope, the way the model is to consume and / or provide data, and the technology it will be used over. Is this an enterprise model to store atomic information? Is this a model to store data as it arrives on the fly or via batch? Is the data in an object store on cloud? Or a relational database? Or is it an object orientated data store? Is this a staging area or and ODS? Consider how the data is consumed. Is it for a data scientist when they just need a single file? Can you make a view of atomic data or any other data? Do you need to have another model to provide dimensional information? Is the output another kind of relational model? Be clear on the goals, the purpose, and how you want to achieve it.

The ERD should map out data flows like a city map guides movement, elucidating structure while allowing flexibility to reroute as needed; its purpose is illuminating connections, not restricting them.

Think about the purpose of the ERD and how it will be used. For me, I'd like to always know the PK(s), and what columns are used to link between tables (FKs). Cardinality and granularity are nice too, if not obvious from the PKs and FKs. Add more information depending on the use case, but be careful not to reduce usefulness by adding too much information! That's just that much more to maintain.

I think, the purpose depends. I use Crow's Foot and UML notation. But I use them differently and it depends on whether I'm at the Conceptual level, or down at the more technical level. Is this model directed to a relational database or is it for an object orientated database where UML notation fits better. I like adding primary identifiers to relational model and conceptual models. I don't need primary identifiers for UML and so my thought process becomes different then.

This is important. Also, important to separate entities into packages that might represent a grouping of related information like a subject area, of a data domain, or some form of grouping. Arrange the diagrams and entities in an orderly manner that reduces clutter with names that make sense and not something like 'Flag' or 'IsReady'? attributes and entity names should make sense. I am verbose with these. Some people don't like it.

Use clear and descriptive names for entities and attributes. This makes it easier for others to understand the structure of the database.

Basic questions related to normalization and validation rules. Do you have duplicate data entries across different tables? Would eliminating this redundancy improve storage efficiency and reduce maintenance overhead? Can normalization help enforce referential integrity between related tables? Do changes in one column necessitate updates in multiple tables? Can normalization minimize these dependencies and simplify data manipulation? Would normalization improve the clarity and maintainability of your data model? Can validation help enforce these constraints and prevent invalid data from entering the system? Can validation rules help maintain consistency and improve data quality? Does your data model require any specific security measures?

Very important to use colours in your model. A Red entity might be a hub or an anchor. A yellow entity might be a satellite with detailed information, or an entity that inherits from the anchor. A green entity might represent a relationship table. An orange entity might be a satellite that just holds codes and no string data. A white entity might represent reference data or the representation of a look-up table. When I have all this together and I remove the colours, it just becomes hard to follow. Also, consider creating multiple diagrams to represent concepts to remove clutter.

Use color and formatting to enhance readability. For example, use different colors for entities and relationships or highlight key information.

data models can be made to grow, grow with challenges, or not grow. Consider what the purpose is of these models. Adding columns to a model that was not designed to grow, or better yet, poorly designed is not good. In some cases it might be good to solve something quickly (Tactically), but not generally designed for growth or change. Normally, the can is kicked down the road when doing things tactically.

Consider using a specialized ERD tool to create and maintain your diagrams. These tools often provide features for collaboration, version control, and automatic generation of SQL scripts.