Image: Song_about_summer/Adobe Stock Data modeling, at its core, is the process of transforming raw data into meaningful insights. It involves creating representations of a database’s structure and organization. These models Continue Reading
Data modeling, at its core, is the process of transforming raw data into meaningful insights. It involves creating representations of a database’s structure and organization. These models are often created using diagramming software that allows business analysts to define tables, fields and relationships between tables.
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Data modelers use these diagrams to create an accurate picture of how databases will look before they’re built, allowing them to plan for what should be included in the design phase. In addition, data modeling helps identify data, where it comes from and how it’s used. It also helps companies comply with regulatory requirements like privacy and security. In this comprehensive guide, learn how data modeling works and what tools and resources are available.
Data modeling explained
Data modeling is the process of designing and creating a model of data from a real-world business, system or phenomenon. Many model types are used in data modeling, including relational diagrams, entity-relationship diagrams and decision trees. All these models are created to help make sense of large volumes of information and to find trends or associations within the data.
Data models typically contain three components: entities, attributes and relationships. The purpose of a data model or diagram is to map the different components of a problem and how they relate to one another. A typical diagram will start with an overview of the modeled system, which covers each component’s attributes, relationships with other components, input and output flow among components, and constraints.
Assembling these parts creates a logical picture that anyone who understands the system should be able to read. An entity-relationship diagram may also include multiple diagrams per component: One that describes its internal structure and behavior, another showing its relationship with other components and another showing constraints imposed by external factors. Creating a clean and accurate data model helps businesses to make informed decisions about database design.
Types of data models
Conceptual data model
Conceptual data models provide a high-level understanding of a system and how entities relate to one another. It is a way of representing an organization’s data and procedures as a unified whole. It also helps identify the need for changes and design modifications to meet organizational objectives.
Logical data model
Logical data models detail how entities in the conceptual model map to tables, fields, indexes and relationships within a relational database. For example, a relational table may represent an entity such as a product order. The columns in the table correspond to various order properties, such as customer ID and total cost. When related data from multiple tables are joined into a query, this process is called JOINS.
Physical data model
The physical data model specifies how physical structures such as databases will store data, including formats for tables, indexes, rows and columns. Its primary goal is to show what kind of hardware configuration and storage will be needed for the proposed database.
Key features of data modeling tools
Data modeling tools vary in their scope, but they all offer ways to visualize datasets and find insights. Common data modeling tool features are categorized as follows:
Core modeling features
- Support for different types of data models
- Custom field creation with defined attributes
- User-defined relationships for constraints and entities
- Model subsets and model validation
- Atomic data elements definition
- Business vocabulary definition
- New model development from existing models
- Collaboration features
- Object query
- Import and export capabilities
- Management of windows, toolbars and menus
- Easy-to-use graphical interface
- Diagram auto-layout
- Query creation with ordering, grouping, filters and joins
Benefits of data modeling for your business
Data modeling is a vital part of every business. Having a sound and complete understanding of your data can help you make informed decisions and create better strategies for your company. These are some of the top benefits that come from a successful data modeling strategy:
- Faster performance: Modeling enables faster processing by establishing an organized foundation for storing and accessing information.
- Reduced risk: Data modeling reduces risks by identifying gaps or inconsistencies between data sets. This saves companies time and money as they fix their problems before they become bigger issues.
- Enhanced efficiency: Modeling streamlines processes and eliminates redundancies that might be costly for a company to keep on its books.
- Better decision-making: It improves decision-making abilities by helping executives identify trends and patterns within their business operations. They can then use this knowledge to make more intelligent choices about their strategy.
- Increased accuracy: Accurate data ensures that managers are making smart investments without losing resources on unnecessary efforts.
- Fewer errors: A well-organized system eliminates typos, mistakes and other inadvertent errors from a company’s information system.
- Reduced workload: Employees can focus on higher-level tasks with less repetition and task duplication while ensuring necessary work gets done.
- Cost reduction: Data modeling may seem like an extra step in the process, but it is a cost-effective one. The process can prevent potential oversights and glitches that often cost companies dearly.
- Data democratization: Data models often make business data easier to interpret and understand. This means that stakeholders across the organization can effectively use data to their advantage.
Examples of data modeling solutions
Businesses can use data modeling tools to create a model of their desired system and generate a corresponding database design. In addition, developers or business analysts can use the solution to create and visualize complex models. These are some of the most commonly used data modeling tools across business types and industries:
erwin Data Modeler
Quest’s erwin Data Modeler lets users search, filter, design, visualize, deploy and manage data while helping them to create and refine data models. The program allows users to import data from various sources, including SQL databases and spreadsheets.
erwin Data Modeler can be used for various tasks such as automated data modeling and database schema generation, centralized model development and management, data literacy development and collaboration. It also includes modules that support advanced data manipulation, data mapping, reporting and dashboards.
ER/Studio is an end-to-end data modeling and physical database design tool by IDERA, Inc. It provides tools to construct data models for existing databases or new database designs from scratch.
This tool allows developers, data architects, modelers, database administrators and business analysts to create and manage database designs and document and reuse data assets. ER/Studio creates ER diagrams from existing database schemas and provides reverse engineering capabilities to read physical tables, join views and create new models.
IBM InfoSphere Data Architect
IBM InfoSphere Data Architect is a cross-platform application that enables users to visually design, create, discover, model, relate, standardize and maintain databases. IDA includes tools for creating logical and physical designs. It also offers metadata discovery and exploration features that help users to analyze existing database structures.
IDA key features include native data querying, import and export of constant mappings and source control management. The solution integrates with related products such as IBM Db2, IBM Informix, Oracle, Sybase, Microsoft SQL Server, MySQL and Teradata source systems.