Data Model

What is a data model?

A data model is a conceptual representation of data. It is a way of understanding and organizing data to be effectively used, typically by computers and digital tools, such as analytics.

At its most basic level, it is made up of three critical components called entities, attributes, and relationships:

• Entities are the individual elements represented, such as customers or products.
• Attributes are the descriptive properties of those entities, such as a customer’s name or age.
• Relationships are the connections between entities, such as a customer’s relationship to a particular product they bought or the fact that a customer is the parent of another customer.

In business, a data model allows users to take large amounts of disorganized information and transform them into a format that is easy to understand and work with. Not only does this enable organizations to derive insights from the data, but it can also help teams within any part of that organization make more informed decisions and improve overall business performance.

What is data modeling?

Data modeling is the process of creating and refining the structure of a data model. This includes determining the entities and attributes that will be included in it and deciding how those attributes are organized and connected.

It is an integral part of many analytics processes because it helps ensure that information is organized in a way that can be easily understood and interpreted by various tools and systems. This enables organizations to fully leverage their data for analytics, decision-making, innovation, and other critical business functions.

Modern organizations often automate aspects of data modeling by using specialized software tools. These tools allow teams to quickly build and refine them while providing intuitive visualizations that make understanding the data and its underlying structure easier.

For example, modern decision intelligence (DI) platforms assist users with data modeling without requiring technical background or training, supporting their efforts through an easy-to-use self-service portal.

What are some of the different types?

Each type is designed for a unique purpose or set of purposes, such as formatting for a specific application or arranging for a visual representation of information. Some of the most common types include:

• Relational data models: These are organized around relationships between entities and are often used for transactional applications, such as customer relationship management (CRM) systems or accounting software.
• Dimensional data models: These are designed to make it easier to analyze information and typically include time dimensions, which allow users to view trends or other insights over time quickly.
• Hierarchical data models: These use a tree-like structure to represent information and can organize information hierarchically, such as in an organizational chart.

Some are more specific to unique types of organizations or special business functions. For example:

• Entity-relationship (ER) data models: This type is commonly used in the design phase of many projects because an ER data model can be used to define and visualize key entities, attributes, and relationships in a dataset.
• Forecasting data models: These contain historical information and are formatted for advanced analytics tools to help users predict future trends. They can be used for various purposes, such as determining sales targets or projecting customer demand.
• Data warehouse data models: These are designed to support data warehousing processes, which involve consolidating information from various sources to perform analytics.

No matter which type they use, organizations must ensure that they accurately reflect the structure and content of their data, as well as the analytical needs of users. By doing so, they can take full advantage of the many benefits offered.

What are the key benefits?

Data models streamline organizational outcomes that depend on the information they organize. Among their many benefits, they can help organizations:

• Attract and retain customers by providing a better customer experience.
• Improve operational efficiency through enhanced analytics and decision-making capabilities.
• Identify new business opportunities or address operational challenges by leveraging insights.
• Enhance compliance by ensuring data is securely stored, organized, and accessible.

The potential benefits are virtually limitless. Organizational leaders need some creativity and thoughtfulness to uncover them.

How can Pyramid Analytics help?

Pyramid Analytics is a leading provider of decision intelligence (DI) technologies—the next evolutionary stage of business intelligence (BI). Where traditional data modeling for BI was once the domain of data scientists and other technical experts, DI platforms enable virtually any business user to design and build models for analytical purposes.

Pyramid Analytics’s platforms are designed to be easy to use, flexible, and powerful, affording a wide range of users the ability to build, customize, and share data models. With these capabilities, Pyramid Analytics can help organizations achieve critical benefits, including enhanced data visualization and analytics, improved operational efficiency, faster decision-making, and more.

Contact us today to learn more about how we can help your business with data models.