What is Data Modeling?
Just like an ERP implementation needs a discovery step to set up a plan as well as the research and development of the project, launching a new data strategy needs an outline, otherwise known as data modeling.
But, what exactly is data modeling?
In a nutshell, data modeling is a roadmap, outline, or blueprint that emphasizes the different data flows through diagrams. For companies who are launching a new database, data warehouse, or data software, it’s essential to begin with designing diagrams that will describe in detail how the data will go in and out of the system.
The objective is to set up a visual representation of the data process in order to provide teams with a better understanding of how the data will communicate between the different infrastructures and data points.
Questions that should be answered are:
- What is the relationship between the data types?
- How is the data going to be grouped?
- How will the data be organized and attributed?
- How will the data be used and stored with the system?
Just like any other IT project, these questions will differ from business to business depending on the objectives and requirements of the company.
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How to Build a Data Modeling Strategy
Just like an architecture creates a blueprint before asking engineers and contractors to come in and build, data modeling is all about pre-project schemas and techniques. This is the best way to manage consistent results between data resources.
And remember, while there is no specific out-of-the-box plan, there is a base procedure that can be followed when building out a data model. Keep in mind that this strategy can evolve as the business grows and scales.
Identify Entities
The first step entails identifying entities, such as events and concepts that will make up the modeled data set.
In other words, what data attributes are we creating properties for? E-Commerce businesses will require entities for customers, addresses, and transactions in order to keep clean records of the different customers and purchases made.
For example, an address entity will have all the attributions associated with location including street name, city, region, and country. Whereas a customer entity will more likely include properties for name and phone number.
Create Data Relationships
Moving forward to step two, we’ll start going into the nitty-gritty. What are the relationships of each entity?
Going back to the e-commerce example, a business will need a customer name and address in order to deliver products.
Therefore, a customer entity will need an address, which would expand to include another entity around orders. When an order is placed the property around that order will include both the customer and address entity to create a relationship.
Certain language called unified modeling language (UML) is used to document each data element.
Map Out Entities
A very important question is answered here: how will the business use the data?
Once answered, developers can strategically set up a data structure with patterns and designs that will be used throughout the company.
Assignment
This is where a conceptual data model is created. Generally speaking, most companies will use Normalization, a technique that groups data models together.
As mentioned earlier under Data Relationships, normalization will organize these key entities and assign them to groups without creating any redundancies. For customers who have an associated address as well as an order history, this data will be grouped together without risk of repetition elsewhere in the data structure.
Test
As is the case with most things in life, it is important to test before finalizing and validating any data tool. Better to know what is working before the go-live than go back and fix issues while the new data structure is in place.
It’s also a good idea to keep in mind that the data modeling process should be refined throughout the years as the business continues to adapt and evolve.
Key Benefits of Data Modeling for Data Projects
It’s hard to envision a project and software without any type of roadmap or outline. How can a company get developers, architects, analysts, and business stakeholders on board if there is nothing to present?
A data model puts the project into perspective where all team members can have a top-level view and full understanding of the data relationships within the database or data warehouse.
The process of creating a data model also:
- Lowers the margin of error throughout the development
- Manages consistency within the data and system
- Streamlines performance across the database or data warehouse
- Facilitates the operational efficiency of data mapping across the board
- Enforces collaboration between both developers and product teams
- All levels of design are done in a faster and more effective way
The 3 Main Types of Data Models Explained
There are three types of data models to consider when working on database design.
Conceptual Data Model
Similar to how the word concept is defined, this type of data model offers a visualization of what the system will look like, how it will be organized, and the different business rules to be incorporated.
A conceptual model is done at the beginning of the process.
Logical Data Model
This is the place where developers expand on the details, by providing a more concrete and in-depth model. Concepts and relationships within the program are also analyzed.
Developers will advise on the attributes, including data types, entity relationships, and properties.
Physical Data Model
Here is where it gets, well, physical. A physical schema will be presented describing how the data will be stored within the database.
Most likely, a finalized design will be introduced that specifies the relationships between entities, primary and foreign keys that maintain said entities, as well as the properties included in the DBMS (database management system).
Data modeling is an essential part of any new data project. Companies moving forward with their data strategies, such as implementing AI4NetSuite, need to truly understand their data requirements and the importance of solid data modeling. AI4NetSuite builds on this foundation by unifying your NetSuite data into a single governed data model in Google BigQuery, giving you dashboards and conversational AI on data you can actually trust.
Build Your Data Strategy with GURUS Solutions
GURUS Solutions has been implementing and customizing NetSuite ERP for over 20 years across 2,500+ projects, with deep expertise in data modeling, data warehousing, and NetSuite analytics. AI4NetSuite builds on solid data modeling principles to unify your NetSuite data into a single governed data model in Google BigQuery, giving you dashboards and conversational AI on data you can actually trust.
Whether you're launching a new data warehouse, designing a data strategy from the ground up, or scaling analytics across growing data volumes, our team configures NetSuite and AI4NetSuite to fit how your business operates, not the other way around. Contact GURUS Solutions to discuss your data modeling and analytics needs and get started.
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Frequently Asked Questions
What is data modeling?
Data modeling is the practice of creating a visual blueprint that shows how data moves through a system. It defines entities, their relationships, and the rules that govern them so a database or data warehouse can be built with structure and consistency.
What are the three types of data models?
The three types are the conceptual data model (a high-level visualization of the system and business rules), the logical data model (a more detailed view of attributes and relationships), and the physical data model (the finalized schema describing how data is stored in the database).
Why is data modeling important?
Data modeling reduces errors during development, keeps data consistent, improves database performance, and gives every stakeholder a shared understanding of the project before a single line of code is written.
What is the first step in building a data model?
The first step is identifying entities, meaning the events, concepts, and objects the data set will describe, such as customers, addresses, and transactions, along with the properties that belong to each one.
What is normalization in data modeling?
Normalization is a technique that organizes entities into groups without creating redundancies, so related data such as a customer, their address, and their order history is stored together without being repeated elsewhere in the structure.
