What is a Data Model?

| March 5, 2024
what is a data model

A data model depicts a company’s data organization, standardizing the relationships among data elements and their correspondence to real-world entities’ properties. It facilitates the organization of data for business processes and information systems, offering tools to effectively define and structure data.

The data models help business and technical resources to collaboratively decide on data storage, data access, data sharing, data updating, and how these processes are leveraged across an organization. It also includes the data description, data semantics, and consistency constraints of the data.

Data models establish the connections and processes for data within a system, playing a crucial role in unifying enterprise segments like IT, management, and business analysts. Together, they cooperatively design information systems needing well-defined and formatted data. Supporting a consistent, clean data exchange, data models cater to various use cases such as database modeling, information system design, and process development.

Understanding these three different types of data models, conceptual, logical, and physical, is important to grasp the structure of the databases. Each model serves a different purpose as one works through the data modeling process.

what is a data model

Conceptual Data Model

Also known as a domain model, this type represents the highest level of abstraction. The conceptual data model actively explores and details your high-level, static business structures and concepts, commonly used during the discovery stage of new projects. It includes elements such as basic business rules for application, categories or entity classes of data you intend to incorporate, and any regulations that might restrict your layout options. 

Designers often create these models as precursors or alternatives to the subsequent stage: logical data models.

Logical Data Model

This type takes into account more relational factors than the conceptual data model does. It describes data elements in detail and facilitates the development of visual understandings of data entities, their defining attributes, keys, and the relationships among them. Data warehousing plans find this model especially useful.

Physical Data Model

This model stands as the most detailed and typically represents the final step before creating a database. Designers use these models to craft the internal schema of a database, taking into account the specific properties and rules of the database management system (DBMS), including the tables, their columns, and the relationships among them. 

You will specify data points and their relationships in enough detail to produce a schema or a final actionable blueprint since the physical data model directly informs the production database design.

Designers generally use physical data models to create three types of databases: relational models for traditional operational databases, document models for NoSQL and JSON databases, and dimensional models for aggregation and business intelligence data stores, such as data warehouses and data marts.

Different Design and Infrastructure Data Methods

Apart from the three main types of data modeling, organizations can choose from several different design and infrastructure methods to visualize their data model.

Entity Relationship Model

Based on the concept of real-world entities and their relationships, this approach highlights data entities and uses diagrams to illustrate their connections. In translating real-world scenarios into the database model, the ER Model constructs entity sets, relationship sets, general attributes, and constraints to clarify how data should interconnect within a database. The ER Model focuses on entities, their attributes, and the relationships among these entities.

Object-Oriented Data Model

This design method groups entities into class hierarchies based on real-world situations, making complex data points more understandable. Developers often use object-oriented design in the early stages of multimedia technology development. In this approach, they represent information as objects, encapsulating both data and relationships within a single structure known as an object.

Hierarchical Data Model

Hierarchical data models resemble a family tree layout. It represents the data in a tree-like structure in which your data entities look like “parents” or “children” and branch off from other data that shares a relationship with them, with a single parent responsible for each record.

Relational Data Model

This model mirrors the hierarchical data model but adopts a more scientific approach. It maps the connections among various data tables, moving beyond simple parent-child relationships. As one of the most popular data models in database management systems (DBMS), it relies on first-order predicate logic and defines a table as an n-ary relation

admiral case study

Case Study: Admiral Insurance’s Transformation with Automated Data Modeling

Admiral Insurance, headquartered in Cardiff, Wales, with 9,000 employees, faced challenges with its complex data ecosystem because of reliance on manual coding and traditional methodologies. This resulted in delays that hindered strategic and operational efficiency.

Data Model Automation

In response, Admiral partnered with WhereScape, incorporating WhereScape® 3D and WhereScape® RED, Teradata, and Microsoft SQL Server to transition from manual coding to an automated data modeling framework. This move significantly reduced their time to production from a week to under a day and bug fixing from two weeks to just two hours.

Overcoming Data Modeling Challenges

Admiral’s challenges included slow database deployments, outdated documentation, no data lineage, and difficulties in scheduling. WhereScape automation enabled rapid creation and deployment of data structures, automating code generation, and improving responsiveness to end-user requests.

Key Benefits of Automation

  • Efficiency: Quick development and learning curve with WhereScape tools, enabling rapid project deployment.
  • Collaboration: Improved IT and business collaboration, using prototypes for precise requirement confirmation.
  • Adaptability: Platform-agnostic tools facilitated seamless technology migrations, ensuring future-proof data infrastructure.

Looking Ahead

The adoption of WhereScape’s automation tools has transformed Admiral’s IT service delivery, aligning it with the demands of the digital marketplace. The company now plans to expand its data platforms, including SQL Server and Cloud technologies, under WhereScape’s framework. This approach not only supports GDPR compliance but also maintains Admiral’s competitive edge by enabling agile, accurate data management and development.

This case study underscores the power of automated data modeling in modernizing data management practices, demonstrating how Admiral Insurance leveraged technology to streamline operations, enhance collaboration, and future-proof its data infrastructure

Automated Data Modeling

Automated data modeling, as demonstrated by Admiral Insurance’s collaboration with WhereScape, significantly enhances data management efficiency, collaboration, and adaptability. This transformative approach reduces production times, improves project deployment, and ensures a future-proof data infrastructure. Through models such as Conceptual, Logical, and Physical, alongside methodologies like the Entity Relationship and Object-Oriented models, businesses can effectively organize and leverage their data.

Discover how automated data modeling can revolutionize your data management. Book a demo with WhereScape today.

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