--- title: "Types of data models in DBMS, with examples" canonical: "https://www.owox.com/blog/articles/types-of-data-models-and-benefits" updated: "2026-10-07" --- # Types of data models and data modeling techniques in DBMS Data models are classified in two ways. By level of detail there are three: conceptual, logical and physical. By structure there are the classic DBMS models: hierarchical, network, relational, entity-relationship, object-oriented and dimensional, plus document and graph. This guide explains each with an example and says which to use for what. [Data Modeling](/blog/topics/data-modeling) · Updated October 7, 2026 · [Ievgen Krasovytskyi](/team/ievgen-krasovytskyi) · 7 min read ![Types of data models and data modeling techniques in DBMS](https://cdn.owox.ai/www/webflow/6a316ef85e4c2a3787d2d1d1_TYPES-OF-DATA-MODELS-AND-DATA-MODELING.png/public) There are two answers to “what are the types of data models”, because data models are classified in two ways. * **By level of detail** there are three: **conceptual, logical and physical**. They describe the same data, from a whiteboard sketch down to tables in a specific database. * **By structure** there are the classic DBMS models: **hierarchical, network, relational, entity-relationship, object-oriented and dimensional**, with document and graph models as later additions. A real project uses one from each list. A reporting warehouse, for example, is usually a dimensional structure taken through all three levels. ## What a data model is A [data model](/product/data-modeling) is a description of data: what things are recorded, what is known about each, and how they relate. It is written before the database and outlives any single report built on it. A model answers three questions. What are the entities (customer, order, product)? What attributes does each have? How are they connected, and how many of one go with how many of the other? Everything else, from table design to the totals in a dashboard, follows from those answers. For the process of building one, see [what is data modeling](/blog/articles/what-is-data-modeling). For the description of what the data means and how each number is calculated, see [semantic data models](/blog/articles/semantic-data-models). ## Types of data models at a glance | Type | Classified by | Shape | Used for | | --- | --- | --- | --- | | Conceptual | Level | Entities and relationships, no detail | Agreeing scope with the business | | Logical | Level | Entities, attributes, keys, no vendor | Designing the structure | | Physical | Level | Tables, types, indexes on one database | Building it | | Hierarchical | Structure | Tree, one parent per record | File systems, directories, XML | | Network | Structure | Graph, many parents per record | Legacy mainframe systems | | Relational | Structure | Tables joined by keys | Most business applications | | Entity-relationship | Structure | Diagram of entities and relationships | Designing relational databases | | Object-oriented | Structure | Objects with attributes and methods | Complex objects, CAD, media | | Dimensional | Structure | Fact tables and dimension tables | Reporting and analytics | | Document and graph | Structure | Nested documents, nodes and edges | Flexible content, connected data | ## The Turning Point OWOX Data Marts See your first report built in real time. _15 minutes._ 1. Connect your data warehouse 2. Pick your metrics 3. Get a live Google Sheets report In the time it takes to write a ticket. Then imagine never writing that ticket again. [Book a Demo](/book-a-call) We'll use your actual use case ## The three levels: conceptual, logical and physical The three levels are stages of one design, not alternatives. Each adds detail the previous one left out. ### Conceptual data model A conceptual model names the things the business tracks and how they relate. It has no data types and no keys, and it should be readable by someone who has never written SQL. ![Hand-drawn conceptual data model of a sales process: Customer, SalesOrder, SalesOrderItem, Product, Region and SalesRep with lines between them.](https://cdn.owox.ai/www/webflow/680917280b5ebd66a38e1fb1_AD_4nXdT5cNppdNQZ0g0fT_JlbIXoG7UJJTh3-IkNybMKcpnLVw1a8k87KUWg59lQ3n1p-dp9YCK7f9FMSzxyEqnyk89f2j-Xlo6Gbd09C--2EDiTh-ysTBFxvZ3W1v8EBb0MXoBiNIJ8A.png/public) Its job is agreement. If sales and finance mean different things by “customer”, this is the cheapest place to find out. More in [conceptual data modeling with examples](/blog/articles/conceptual-data-modeling-with-examples). ### Logical data model A logical model adds every attribute, the primary and foreign keys, and the cardinality of each relationship. It is still independent of any database product. ![Logical data model: Customer, Order, OrderItem, Product, Warehouse and Courier entities with attributes and key relationships.](https://cdn.owox.ai/www/webflow/68091727c3298551c134f32e_AD_4nXdIlIOgTE29VJR-7v7N-I-4kqAqA6tCFn0oYPoeg-KRtIm0NlaIbeihVITRyVqecpmWgihHsBiuIOhlW1bNhvycs6kkuF3yVqX5wFInQcqJW2DuC4-LqVA5W5aziV0Cib8af_X3aw.png/public) This is where structure is decided: what one row of each entity stands for, and which key connects it to the next. ### Physical data model A physical model is the logical model made concrete for one database: table and column names, data types, constraints, indexes, partitions. ![Physical data model: Customer, Order, OrderItem and Product tables with data types, not-null markers and foreign keys.](https://cdn.owox.ai/www/webflow/680917274acb0b7f0fbfc06f_AD_4nXcBLnpf5aLxA8HGpNyOGDUtez92Wqt8-ETuaUHw9JRDyO-PdJy7xUe08SB9TMKfDGqiwn-_onelJdNvolTuJeyPj9ldxn1Pi4mpJs-wLVb-f-QPKkHLaPGNiJ1iSG_3UiwhjO778Q.png/public) The same logical model produces different physical models on different platforms. A date column, a partition and a nested field are not declared the same way everywhere. For a side-by-side comparison, see [logical vs physical data models](/blog/articles/logical-vs-physical-data-models). | | Conceptual | Logical | Physical | | --- | --- | --- | --- | | Shows | Entities, relationships | Plus attributes, keys | Plus types, indexes | | Depends on a database | No | No | Yes | | Main reader | Business and analysts | Analysts, architects | Engineers | ## Types of data models in DBMS, by structure The second classification is about how records are organised and linked. These are the models a DBMS course lists, in roughly the order they appeared. ### Hierarchical data model Data is a tree. Each record has one parent and any number of children, and you reach a record by walking down from the root. IBM’s IMS, built in the 1960s, is the classic example. ![Hierarchical data model: ProductCategory is the parent of Product, which is the parent of SalesTransaction.](https://cdn.owox.ai/www/webflow/680917ac2e15d38018bb59cf_AD_4nXdSkbxR1LthjWwU9Bz9iSvOWH_ErkgLoMbqPNTxlBT6R-Ccrt2RcfN-RV9qrdYX3zcskuI-zDvcpwpNsE12E-2exBmvDBr97kAfx7ZneyeldfmmvCHDHDBY-wrZYMLth--R9KlMaQ.png/public) **Example:** category, then product, then sale. It is fast when questions follow the tree and awkward when they do not. A product in two categories has to be stored twice. ### Network data model The network model removes the one-parent limit: a record can have several parents, so the structure is a graph. It was standardised by CODASYL around 1970. **Example:** an order line belongs both to an order and to a product. This handles many-to-many relationships, at the cost of programs that have to navigate the links by hand. ### Relational data model Data lives in tables. Rows are connected by matching values in key columns, and SQL retrieves them without the program knowing how they are stored. E. F. Codd proposed the model in 1970, and it is the default for business systems. ![Relational data model: Customers, Orders, OrderDetails and Products tables linked by primary and foreign keys.](https://cdn.owox.ai/www/webflow/680917ada5b0b5576aa904b7_AD_4nXcgC7JCpdoa8LVIazCan848VQKK_aD44_0a7AnjiSSsGAnUhWzVBMxifTgz_11uESdkHdxCa_eFkgVES2ptq-BOptWUd0WHm2lSKSj_ggcA5Fln0CClGIpV8U0cZwFo9J4X-1QN.png/public) **Example:** `Customers`, `Orders`, `OrderDetails` and `Products`, joined on customer, order and product ids. ### Entity-relationship model The entity-relationship (ER) model, introduced by Peter Chen in 1976, is a way of drawing a design: entities as boxes, relationships as lines, attributes listed inside. It is a design notation more than a storage model, and most relational databases start as an ER diagram. **Example:** the three diagrams in the previous section are all ER diagrams at different levels of detail. ### Object-oriented data model Data is stored as objects that bundle attributes with the methods that act on them, organised into classes with inheritance. It suits applications with complex, nested objects. **Example:** `DigitalProduct` and `PhysicalProduct` both inherit name and price from `Product`. Details in [object-oriented data models](/blog/articles/object-oriented-data-models). ### Dimensional data model The dimensional model is built for reporting. Measurable events go into **fact tables**, and the things that describe them go into **dimension tables**. The usual layout is a star schema. ![Dimensional data model: a SalesFact table joined to ProductDim, CustomerDim and DateDim.](https://cdn.owox.ai/www/webflow/680917ad77de5b4bae5c414f_AD_4nXe6Ge6JMKO4VyIJMyQ3S6UlDDEw1LM8XEQVLTgWgiyrLrxyWPPtKb6KeoJpiqCcCJ00D6ddVVECA6bFBLvyEhjwWhk2wdjWj-T1ZstMO_zt63clbXOVwDJtKkcISzpkcBJpOkFwBQ.png/public) **Example:** a sales fact with quantity and amount, joined to product, customer and date dimensions. See [star schema](/blog/articles/star-schema-explained), [snowflake schema](/blog/articles/snowflake-schema-data-modeling) and [dimensional data modeling](/blog/articles/dimensional-data-modeling). ### Document and graph models Two later structures are common outside the classic list. A **document model** stores each record as a nested document, which suits content whose shape varies. A **graph model** stores nodes and the edges between them, which suits questions about connections, such as fraud rings or recommendations. ## Which data model to use Pick by the question the system has to answer most often. | If you need to | Use | | --- | --- | | Record transactions reliably in an application | Relational, in normalized form | | Report and aggregate across many records | Dimensional, as a star schema | | Store complex objects exactly as the program holds them | Object-oriented or document | | Follow connections several steps deep | Graph | | Agree what the data means before building anything | Conceptual model first, in any of the above | Most companies end up with two: a relational model inside each application and a dimensional model in the warehouse where the applications’ data is combined for reporting. ## What a data model gives you, and what it does not A model that is written down gives a team three things: * **One definition per thing.** “Customer” and “order” mean the same in every table. * **Joins that are decided once.** The keys and the cardinality are in the model, so nobody guesses them in a query. * **Cheaper change.** A new source or metric is fitted into a known structure instead of bolted onto a report. It does not keep itself true. A diagram in a wiki drifts from the tables within weeks, and a correct model does not stop a report from reading the wrong table. The common failure modes are in [mistakes in data modeling](/blog/articles/mistakes-in-data-modeling). ## From a data model to reports people can check For reporting, the practical test of a data model is whether the people building reports are held to it. In [OWOX Data Marts](https://www.owox.com/app-signup) the model is made of the same objects the reports read. Each entity is a **Data Mart**: SQL, a table or a view that an analyst defines, with a description, an owner and described fields. The lines in the diagram are **relationships** between Data Marts, declared on their join keys. Together, the Data Marts in one data storage are the data model. A report in Google Sheets or Looker Studio then picks fields from Data Marts and uses the declared joins. The model is not a separate document that can fall behind, because it is what the reports run on. The analyst still designs it and writes the SQL. The product publishes and governs it. To start from an existing design instead of a blank page, the [industry data models](/data-models) are worked examples, with walkthroughs for [e-commerce](/blog/articles/ecommerce-data-model), [SaaS](/blog/articles/saas-data-model), [fintech](/blog/articles/fintech-data-model), [healthcare](/blog/articles/healthcare-data-model) and [marketplaces](/blog/articles/marketplace-data-model), and [Joinable Data Marts](/features/joinable-data-marts) explains how relationships are declared. ## Your New Normal Turn your data into decisions. Governed data marts give you the clean foundation ML needs to actually work. * No AI hallucinations * Analyst-governed definitions * Every number traces to SQL [Get started free](https://www.owox.com/app-signup) ## FAQ ## Frequently Asked Questions Which data model is used for reporting and analytics? The dimensional model, usually laid out as a star schema: fact tables hold measurable events and dimension tables hold the things that describe them. Applications that record transactions use a normalized relational model, and the warehouse combines their data in dimensional form. What are the types of data models? By level of detail there are three: conceptual, logical and physical. By structure there are the classic DBMS models: hierarchical, network, relational, entity-relationship, object-oriented and dimensional, with document and graph models as later additions. A real project uses one from each list. ## Who wrote this ![Ievgen Krasovytskyi](https://cdn.owox.ai/www/webflow/68404586b341508a789a4aa5_.png/public) [Ievgen Krasovytskyi](/team/ievgen-krasovytskyi) · Head of Marketing Ievgen Krasovytskyi is the Head of Marketing at OWOX, leading strategy across content, SEO, product marketing, and AI-powered automation. With deep expertise in analytics infrastructure, data warehouses, and marketing technology, he builds systems that connect marketing performance to business outcomes. Ievgen writes about SaaS growth, analytics workflows, and the future of AI in marketing operations. [LinkedIn](https://www.linkedin.com/in/ievgen-krasovytskyi-a38a1253/) · [All articles](/team/ievgen-krasovytskyi) [Data Modeling](/blog/topics/data-modeling) ## Related articles [Data Modeling · Data aggregation: techniques, examples and tools · July 22, 2023](/blog/articles/data-aggregation-techniques-for-effective-data-analysis) [Data Modeling · Benefits of data modeling for business reporting · March 3, 2025](/blog/articles/benefits-of-data-modeling) [Data Modeling · Best Data Modeling Tools in 2026 · July 21, 2026](/blog/articles/best-data-modeling-tools) [See all articles →](/blog/articles) ## Book a call You are booked. The invitation is on its way to , with the calendar invite and the meeting link. [Reschedule](#) [Cancel](#) ## Links Pages this page links to, on this site and on docs.owox.com. Where the page has a Markdown twin, its address follows the link. - [Data Modeling](https://www.owox.com/blog/topics/data-modeling) — /blog/topics/data-modeling.md - [Ievgen Krasovytskyi](https://www.owox.com/team/ievgen-krasovytskyi) — /team/ievgen-krasovytskyi.md - [data model](https://www.owox.com/product/data-modeling) — /product/data-modeling.md - [what is data modeling](https://www.owox.com/blog/articles/what-is-data-modeling) — /blog/articles/what-is-data-modeling.md - [semantic data models](https://www.owox.com/blog/articles/semantic-data-models) — /blog/articles/semantic-data-models.md - [Book a Demo](https://www.owox.com/book-a-call) — /book-a-call.md - [conceptual data modeling with examples](https://www.owox.com/blog/articles/conceptual-data-modeling-with-examples) — /blog/articles/conceptual-data-modeling-with-examples.md - [logical vs physical data models](https://www.owox.com/blog/articles/logical-vs-physical-data-models) — /blog/articles/logical-vs-physical-data-models.md - [object-oriented data models](https://www.owox.com/blog/articles/object-oriented-data-models) — /blog/articles/object-oriented-data-models.md - [star schema](https://www.owox.com/blog/articles/star-schema-explained) — /blog/articles/star-schema-explained.md - [snowflake schema](https://www.owox.com/blog/articles/snowflake-schema-data-modeling) — /blog/articles/snowflake-schema-data-modeling.md - [dimensional data modeling](https://www.owox.com/blog/articles/dimensional-data-modeling) — /blog/articles/dimensional-data-modeling.md - [mistakes in data modeling](https://www.owox.com/blog/articles/mistakes-in-data-modeling) — /blog/articles/mistakes-in-data-modeling.md - [OWOX Data Marts](https://www.owox.com/app-signup) - [industry data models](https://www.owox.com/data-models) — /data-models.md - [e-commerce](https://www.owox.com/blog/articles/ecommerce-data-model) — /blog/articles/ecommerce-data-model.md - [SaaS](https://www.owox.com/blog/articles/saas-data-model) — /blog/articles/saas-data-model.md - [fintech](https://www.owox.com/blog/articles/fintech-data-model) — /blog/articles/fintech-data-model.md - [healthcare](https://www.owox.com/blog/articles/healthcare-data-model) — /blog/articles/healthcare-data-model.md - [marketplaces](https://www.owox.com/blog/articles/marketplace-data-model) — /blog/articles/marketplace-data-model.md - [Joinable Data Marts](https://www.owox.com/features/joinable-data-marts) — /features/joinable-data-marts.md - [Data Modeling · Data aggregation: techniques, examples and tools · July 22, 2023](https://www.owox.com/blog/articles/data-aggregation-techniques-for-effective-data-analysis) — /blog/articles/data-aggregation-techniques-for-effective-data-analysis.md - [Data Modeling · Benefits of data modeling for business reporting · March 3, 2025](https://www.owox.com/blog/articles/benefits-of-data-modeling) — /blog/articles/benefits-of-data-modeling.md - [Data Modeling · Best Data Modeling Tools in 2026 · July 21, 2026](https://www.owox.com/blog/articles/best-data-modeling-tools) — /blog/articles/best-data-modeling-tools.md - [See all articles →](https://www.owox.com/blog/articles) — /blog/articles.md