Data Mart

Sample Solution

Data Mart Design

Dimensional Modeling Principles

Dimensional modeling is a data warehousing approach that organizes data into dimensions and facts. Dimensions are descriptive attributes of the data, such as product, customer, and date. Facts are quantitative measurements, such as sales amount and quantity.

Dimensional modeling is well-suited for data marts because it allows for fast and easy data analysis. Dimensional data models are also easy to understand and use, which makes them ideal for business users.

Full Answer Section

Data Mart Design

The following data mart design is based on the dimensional modeling principles and the requirements specified by Jim Riner:

Fact Table: Sales

| Column | Description | |---|---|---| | Sales ID | Unique identifier for the sale | | Product ID | Foreign key to the Product dimension | | Customer ID | Foreign key to the Customer dimension | | Order ID | Foreign key to the Order dimension | | Sales Territory | Foreign key to the Sales Territory dimension | | Sales Amount | Total sales amount for the sale | | Sales Quantity | Total sales quantity for the sale | | Sales Date | Date of the sale |

Dimension Tables:

• Product:
  • Product ID: Unique identifier for the product
  • Product Category: Category of the product
  • Product Subcategory: Subcategory of the product
  • Product Name: Name of the product
  • Product Color: Color of the product
  • Product Model: Model of the product
• Customer:
  • Customer ID: Unique identifier for the customer
  • Customer Name: Name of the customer
  • Customer Zip Code: Zip code of the customer
  • Customer Territory: Sales territory of the customer
  • Customer Country: Country of the customer
  • Customer City: City of the customer
• Date:
  • Date ID: Unique identifier for the date
  • Date: Date
  • Day of Week: Day of the week
  • Week of Year: Week of the year
  • Month: Month
  • Quarter: Quarter
  • Year: Year
  • Season: Season (Spring, Summer, Fall, Winter)
• Order:
  • Order ID: Unique identifier for the order
  • Order Date: Date of the order
  • Order Status: Status of the order (Pending, Shipped, Delivered, Canceled)
• Sales Territory:
  • Sales Territory ID: Unique identifier for the sales territory
  • Sales Territory Name: Name of the sales territory

Star Schema

The following star schema illustrates the relationships between the fact table and the dimension tables:

Sales Fact Table
|
|
|-------|-------|-------|-------|-------|
| Product | Customer | Date | Order | Sales Territory |
| Dimension | Dimension | Dimension | Dimension | Dimension |

Data Analysis

The data mart design described above can be used to perform a variety of data analysis tasks, such as:

• Identifying top-selling products: The Product dimension can be used to identify top-selling products by category, subcategory, product name, color, and model. This information can be used to develop marketing and sales strategies.
• Understanding customer purchase patterns: The Customer dimension can be used to understand customer purchase patterns, such as which products customers purchase, how often they purchase, and how much they spend. This information can be used to develop targeted marketing campaigns and improve customer retention.
• Analyzing seasonal sales trends: The Date dimension can be used to analyze seasonal sales trends. This information can be used to plan inventory levels and develop marketing campaigns that target specific seasons.
• Evaluating sales performance by sales territory: The Sales Territory dimension can be used to evaluate sales performance by sales territory. This information can be used to identify areas where sales need to be improved and to allocate resources more effectively.

Conclusion

The data mart design described above is a good starting point for developing a data mart that meets the needs of Jim Riner. The data mart can be expanded to include additional dimensions and facts, as needed.