At its core, data modeling is about understanding how data flows through a system. Just as a map can help us understand the layout of a city, data modeling can help us understand the complexity of a data system, its structures, formats and processing functions.
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By mapping the data flow, we can identify bottlenecks and inefficiencies. We also see opportunities for improvement. Data modeling lives on after the database is created and implemented, allowing us to track changes and adapt our systems accordingly. But to fully understand and make the most of data models, it is important to first understand the different types of data models and what they can do.
Why use data models?
From a business perspective, data modeling offers a number of important benefits. It helps create an efficient and logical database by eliminating redundancy, which saves storage space for large organizations with extensive digital resources.
The data modeling process also gives each system access to a single source, ensuring accurate reporting at all levels from analysis to visualization. That is why data modeling is a crucial process in the development of any digital enterprise that wants to become more data-driven.
The different types of data models
There are different types of data models that companies can use. The three most common types are relational, dimensional, and entity-relationship (ER).
The most popular database model format is relational, which stores data in fixed-format records and organizes them into tables with rows and columns. The most basic data model has two components: measures and dimensions. Raw data can be a measure or a dimension.
- Measures: These numerical values are used in mathematical calculations, such as sum or average.
- Dimensions: Text or numeric values. They are not used in calculations and contain locations or descriptions.
When designing relational databases, “relations”, “attributes”, “tuples” and “domains” are some of the most commonly used terms. Additional terms and structural criteria also define a relational database, but the importance of relationships within that structure is what matters. Key data elements (or keys) connect tables and datasets together. Explicit relationships such as parent-child or one-to-one/many connections can also be established.
A dimensional model is a type of data model that is less rigid and structured than other types of models. It is best for a contextual data structure that is more related to the business use or context. Dimensional models are optimized for online queries and data warehousing tools.
Critical data points, such as transaction quantity, are referred to as “facts.” In addition to these facts, there are reference data known as “dimensions,” which include things like product ID, unit price, and transaction price.
A fact table is the primary table of a dimensional model. Retrieval can be fast and effective because data for a specific activity is kept together. However, the absence of links can complicate analytical retrieval and data use.
Entity-relationship (ER) model
The entity-relationship model is a graphical representation of a company’s data structure. It contains boxes with different shapes and lines to represent activities, functions or ‘entities’ and associations, dependencies or ‘relationships’ respectively.
The ER model provides a framework for understanding, analyzing, and designing databases. This type of data model is most commonly used to design relational databases.
In an ER diagram, entities are represented by rectangles and relationships are represented by diamonds. An entity is anything that can be identified as distinct from other things. A relationship is an association between two or more entities. Attributes are the properties or characteristics of an entity or a relationship.
ER diagrams can be divided into three types: one-to-one, one-to-many, and many-to-many relationships.
- One-to-one relationship: An example of a one-to-one relationship is a citizen service number (BSN) and a person. Each BSN can only be assigned to one person and each person can have only one SSN.
- One-to-many relationship: An example of a one-to-many relationship is a company and employees. A company can have many employees, but each employee usually only works for one company.
- Many-to-many relationship: An example of a many-to-many relationship is students and classes. A student can take many classes and many students can be registered for a class.
Levels of data abstraction
There are also different layout permutation options for all types of data models. These three types of data abstraction modeling levels are most common:
Conceptual data model
The conceptual data model is the highest level of abstraction, which represents the general structure and content of a database, but lacks details about the data. It contains a description of the data, but not the actual data itself. This type of model is intended to show how data flows within the organization, capture business requirements, and define what types of data are needed.
Logical data model
The logical data model contains more details than the conceptual data model and includes all entities, relationships, attributes and rules that apply to the data. This type of model is used to design the database.
Physical data model
The physical data model contains all the details about how the logical model will be implemented. This model format includes table names, column names, types, lengths, primary keys, foreign keys, indexes, and relationships.
Consider different model types and strategies for your business
The data model types and formats mentioned earlier are the most popular, but they are not the only ones that exist for business use. Some companies will opt for hierarchical, network, object-oriented and/or multi-value models, depending on their specific situation and business use cases.
Regardless of which data model(s) you want to incorporate into your company’s data strategy, it’s important to have the right people and processes in place to make these models work. Hire a big data modeler is a good first step towards selecting and using successful data models for your business.
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