Before beginning this assignment, ensure that you've thoroughly read and understood Chapters 9 (pages 197 to 235) and 10 (pages 237 to 251) on Dimensional Modeling from the course textbook.
You are stepping into the shoes of a Junior BI developer involved in a data warehouse project. As part of the requirements gathering phase, you have a discussion with Jim Riner, the Sales Manager. Jim identifies a crucial need for deeper sales data analysis that encompasses the following dimensions:
- Products
- Customers
- Dates (Seasonality)
- Orders
- Sales Territory
- Product Dimension:
● Analyze sales based on categories, subcategories, product names, colors, and models.
● This will help in identifying top-selling items in various categories and attributes.
- Customer Dimension:
● Explore sales data to determine which customers purchase which items, pinpoint top customers, and analyze sales by the customer's zip, territory, country, and city.
● This information can aid in tailoring promotional offers and understanding buying patterns of valued customers.
- Date (Seasonality) Dimension:
● Analyze which products have high sales during specific seasons, days, weeks, or years.
● The granularity of this dimension should include: Date Surrogate Key, Date Value, Month, Year, IsHoliday, and Holiday Name.
- Order Dimension:
● Sales analysis based on Order ID, Order Detail ID, and Customer ID.
- Sales Territory Dimension:
● The analysis should cover territory name, territory group, country, or region codes.
● The objective is to determine the profitability of specific geographic locations, products sold there, and revenue comparison between regions.
Full Answer Section
Product Dimension
Jim would like to be able to analyze sales based on categories, subcategories, product names, colors, and models. This information would help him to identify top-selling items in various categories and attributes.
For example, Jim could use this information to:
- Identify which product categories are driving the most sales
- Determine which product subcategories are performing well and which ones are not
- Identify the most popular product names, colors, and models
- Track sales trends over time to identify seasonal fluctuations
Customer Dimension
Jim would also like to be able to explore sales data to determine which customers purchase which items, pinpoint top customers, and analyze sales by the customer's zip, territory, country, and city.
This information would help him to:
- Identify his most valuable customers
- Understand the buying patterns of his customers
- Target promotional offers to specific customer segments
- Tailor his marketing and sales strategies to different geographic regions
Dimensional Modeling
Dimensional modeling is a data modeling technique that is well-suited for sales data analysis. Dimensional models are designed to be easy to query and analyze, making them ideal for business intelligence applications.
To create a dimensional model for sales data analysis, we would start by identifying the fact table. The fact table is the central table in the model and it contains the quantitative data that we want to analyze. In this case, the fact table would contain the following data:
- Order ID
- Customer ID
- Product ID
- Sales amount
- Order date
- Sales territory
We would then create dimension tables for each of the dimensions that Jim identified. The dimension tables would contain the descriptive information about each dimension. For example, the product dimension table would contain the following information:
- Product ID
- Product name
- Product category
- Product subcategory
- Product color
- Product model
The customer dimension table would contain the following information:
- Customer ID
- Customer name
- Customer address
- Customer city
- Customer state
- Customer country
Example Query
The following is an example of a query that we could use to analyze sales data using the dimensional model that we just described:
SELECT
product_category,
SUM(sales_amount) AS total_sales
FROM
fact_table
INNER JOIN
product_dimension
ON
fact_table.product_id = product_dimension.product_id
GROUP BY
product_category
ORDER BY
total_sales DESC;
This query would return the total sales for each product category, ordered by total sales from highest to lowest. This information could be used to identify the top-selling product categories.
Conclusion
Dimensional modeling is a powerful technique for analyzing sales data. By creating a dimensional model, we can make it easy to query and analyze the data to gain valuable insights into our sales performance.
Additional Considerations
In addition to the dimensions that Jim identified, we may also want to consider including other dimensions in the model, such as:
- Sales channel (e.g., online, retail, wholesale)
- Promotion type (e.g., coupon, discount, sale)
- Payment method (e.g., credit card, debit card, cash)
These additional dimensions could provide even more valuable insights into our sales data.
We should also consider the level of detail that we need in the model. For example, do we need to track sales for each individual product model or can we aggregate sales at the product category level? The level of detail that we need will depend on the specific questions that we want to answer.
Once we have created the dimensional model, we can use it to create a variety of reports and dashboards to analyze our sales data. This information can be used to improve our sales performance, make better decisions about our products and marketing campaigns, and better serve our customers.