Identifying the entities is just the start; next, we determine the attributes, the specific details, we want to record for each entity. For instance, for an 'Employee', we may collect details such as Name, Addresses, Date of Birth, City, Country, and Phone Number.
Advanced Concept: It's crucial to collect only the data that is pertinent to each entity to avoid redundancy. This efficiency is a guiding principle in data modelling.
After pinpointing the entities and attributes, the next phase is to outline the interrelations among these entities. These relationships are pivotal and should reflect how entities interact with one another. In our example, an Employee 'performs' a Job Role, illustrating a direct relationship.
Determining the 'cardinality' or 'multiplicity' of these relationships is essential. This involves assessing the permissible or necessary number of linkages between entities. For example, an Employee is associated with one Job Role, but a Job Role may be associated with multiple Employees.
The final stride in data modelling is delineating the meta data. Meta data is essentially data about data, offering insights into the use and origin of your data. For example, it could provide details about when a record was added, who added it, and its accuracy.
With this overview of data modelling fundamentals, as a Business Analyst, you're now equipped to navigate and interpret data models with confidence.