Why you need data modeling in your business
Collecting large amounts of data supports organizational goals and helps businesses of all kinds improve their competitive positioning.
But before you can put that data to work for your company, you must first learn how to visualize the various ways it’s connected. Doing so allows you to organize, store, and access it better.
To really bring your data to life, you’ll need to understand data modeling.
In this guide:
- What is data modeling?
- Why is data modeling important?
- Data modeling versus data analysis
- Types of data models
- Data modeling examples
- The data modeling process
- Evaluate a platform to actualize your data model
What is data modeling?
Data modeling is the process of mapping and visualizing different data sources into models. These models are then put together in a way that makes the information easier to understand, manipulate, and analyze.
The goal of data modeling is to produce a clear picture of a company’s data through high-quality, consistent, and structured models. Using those models, business teams can clearly define and communicate the requirements for a computer system or database design, and the technical teams can build the design in response to those requirements.
Data modeling also helps businesses identify missing or redundant information. For example, a business can use data modeling to locate inefficiencies that may be leading to unnecessary spending. By modeling data from various departments, the business can analyze how resources are used across the organization, discover which areas have too many or too few, and reallocate them accordingly.
How are data models built?
Data models are created by IT in collaboration with commercial teams. They are centered on business requirements and incorporate feedback from business stakeholders on what is needed from the information system or database design. These are known as business rules. Once the data requirements are clearly defined, the data architects, data engineers, and other technical experts will build it.
Why is data modeling important?
When you empower your team members to apply their skills in data modeling by investing in the right technologies, you can more efficiently organize the information your company collects. In turn, you’ll get the most out of your data and leverage it to support critical business processes.
Data consistency
Data modeling tears down silos and ensures information consistency across all departments, systems, and databases.
Improved data quality
When your team can easily share and access information, overall data quality and usability increase significantly.
More efficient database design
Data modeling reveals potential information logjams, meaning you can eliminate these bottlenecks and create a more efficient data management system.
Reduced errors and redundancies
Through data modeling, you can optimize workflows, reduce the need for redundant manual input, and decrease the likelihood of costly data entry errors.
Improved transparency throughout the company
By using data modeling, you can improve information transparency across the entire company and provide decision-makers with the information they need to capitalize on emerging opportunities.
Data modeling vs. data analysis
Data modeling and data analysis are often thought to be the same. But they’re different concepts that require entirely different skill sets.
Data analysis
Data analysis is what you do with the data you have access to. It’s about filtering data to find the most important insights in the form of reports, predictions, graphs, or other visualizations. Data analysis helps determine why aspects of a business are working the way they are.
Data modeling
Data modeling is about creating the right conditions to make that analysis possible. Creating and fitting together a company’s data models needs to happen before teams can perform data analysis. It’s about determining which types of data to bring together — and in which ways — to get the desired results.
Types of data models
There are three main types of data model — conceptual, logical, and physical. Which of these you choose depends on:
- how complex your model is
- your business requirements for the model
- how you will use it
Each of the three options has its own way of storing and organizing data as well as handling data retrieval:
Conceptual data modeling
Also called enterprise data modeling. This type follows a big-picture approach that shapes the data around the main business requirements and needs of your stakeholders such as business analysts. A conceptual model identifies your different data entities and shows how they are related.
Logical data modeling
Logical data modeling identifies the technical details and how they will support business goals. It works by exploring in detail how each set of data relates to the others. A logical data model expands on a conceptual one by factoring in attributes within data entities and how those attributes interact.
Physical data modeling
This model is the blueprint for a business’s database design — generally built for a particular data storage system. It lays out precisely how each database will be built and how all databases, applications, and features will interact.
Data modeling examples
Relational
Relational data modeling is a technique that IBM developed in the 1970s, and it maps the relationship between data elements stored in different tables. The relational data modeling method is popular among many modelers because it doesn’t require a deep understanding of physical information storage.
Entity-relationship
Entity-relationship (ER) data modeling is a more complex variant of the relational data model, mapping entities and identifying specific attributes as they relate to these entities. An entity could be customer data, employee data, product information, or an invoice, while an attribute might be a customer email address, employee last name, product price, or the date that an invoice was created.
Hierarchical
Hierarchical data models typically feature a tree-like visualization of parent and child relationships. IBM’s Information Management System (IMS) is the most well-known example of a hierarchical data model and is still used by many businesses, though relational data models have otherwise largely replaced hierarchical ones. Extensible Markup Language (XML, as it’s more commonly known) is an alternative to IMS that is also still in use.
Dimensional
Dimensional data modeling is primarily used in data marts and warehouses for business intelligence (BI) applications. The technique is designed to optimize data retrieval speeds so organizations can efficiently tap into the information stored within their data warehouses. A dimensional data model consists of fact tables, each of which contains information about events or transactions, such as product purchases.
Object-oriented
Object-oriented data modeling is similar to ER modeling, but in this framework, entities are abstracted into objects. Objects that share similar attributes can be moved into classes and grouped hierarchically. Object-oriented databases can incorporate tables, but they also support more complex data relationships. Many hypertext and multimedia databases use object-oriented modeling.
The data modeling process
Data modeling is a fluid process that can be shaped to the needs of your organization. However, most teams start by creating a conceptual model, followed by a logical model, and finally a physical data model. This enables your organization to progress from a high-level visualization of your data to a more technical representation of the database itself.
Following the established data modeling process will support stakeholders as they meet to discuss the company’s data processing and storage needs in extreme detail. During these discussions, decision-makers will generally progress through a workflow that goes as follows:
- Identify the entities. Organizational leaders will identify and list the various entities and departments that exist within the business.
- Identify any key properties for each entity. Decision-makers will identify attributes that differentiate each of the company’s entities.
- Identify entity relationships. Business leaders will develop a model that outlines interconnected entities and shows how they relate to each other visually.
- Identify the data attributes that need to be in the model. Company leaders will list the specific attributes that need to be included in the model, such as customer email addresses and customer names.
- Map the attributes. Once data attributes are created, they will then be mapped to each entity.
- Finalize and validate its accuracy. Before finalizing the data model, decision-makers will validate its accuracy and ensure that all relevant entities and attributes are included in the initial framework.
By following this reproducible process, organizational leaders can effectively develop increasingly technical data models and clearly identify the information needs of the business.
Evaluate a platform to actualize your data model
Having a visual representation of how your data is connected will help you organize your data so you can meet your company’s need for information. However, before you can start designing your data model, you must first implement a robust data collection and storage platform.
Adobe Real-Time CDP collects B2C and B2B data from across systems and immediately unifies it into profiles ready for activation across any channel.
To learn more about Adobe Real-Time CDP, watch the overview video or take an interactive tour.
