Data is a valuable asset for every business. However, data alone is not enough, we need a solid foundation to exploit the real value from these numbers, and that is the role of data modeling.

Imagine building a skyscraper without a blueprint. Sounds crazy, right? That’s exactly what happens when we try to analyze data without a proper data model.

Key components in an effective data model

🔴 Dimension Tables: Data Context

Dimension tables are data tables that define attributes and characteristics related to objects in a business, such as products, customers, time, and regions. Dimension tables answer basic questions such as:

• Who are the customers?

• What was sold?

• Where does the transaction take place?

• When does the transaction take place?

Dimension tables are where contextual information is stored, allowing analysts to easily group, categorize, and query data.

For example , the customer table might contain information like name, address, customer segment, and other attributes. When combined with the event table, we can answer the question "What products were sold the most in the South region last month?"

🟡 Fact Tables: The Heart of Data Modeling

Fact tables store transaction or event data, including information about sales, revenue, costs, or financial metrics. These are the values you'll measure and analyze throughout your data work.

For example , an event table might contain sales transaction records, with information such as order number, product quantity, sales price, and total order value.

Fact tables often have foreign keys linked to dimension tables, helping to connect transaction data with context (customer, product, region, time…).

🟢 Supporting Tables: Improved Calculations and Consistency

Supporting tables often store additional data that helps support complex calculations and maintain consistency in the model. These tables can contain information about cost categories, financial structures, or classification rules.

For example , a cost sheet can store cost items such as marketing, human resources, and production, which can help analyze costs by group and calculate profit margins.

🔵 Measure Groups: Important Indicators

The metrics group is where calculations are performed to provide valuable information for reports and dashboards. These metrics can be pre-calculated values, such as gross profit, profit margin, conversion rate, or cash flow.

For example , a metric group might contain calculations such as average revenue per customer, lead-to-customer conversion rate, or number of products sold in a given period.

Why is Data Modeling Important?

✅ Increased Analysis Speed : Data models help query data quickly and efficiently, thereby increasing analysis speed.

✅ Improve Data Quality : Scientifically designed model helps ensure accuracy and minimize data redundancy.

✅ Business Decision Support : Clear data structure helps identify trends, discover opportunities and solve challenges quickly.

✅ Ensure Extensibility : The data model can grow with the business, easily adding new data dimensions and metrics as needed.

Advice for Data Analysts

Don’t skip the step of building a solid data model from the beginning. This is the foundation for creating a powerful data analytics system that can grow with your business and deliver long-term value.

If you want to improve your data analysis skills and gain a better understanding of how to build effective data models, MDA’s Business Intelligence course is the opportunity for you to learn from industry experts. The course provides solid knowledge of BI, from basic to advanced, helping you understand how to deploy and optimize data models, while improving your ability to analyze and make data-driven decisions.

• Details: Business Intelligence Course

• Contact Zalo: 0961 48 66 48

Do you have any questions about how to build effective data models? Leave a comment, we are happy to discuss and support you!