Dashboard Guide: Data Modeling
For this guide, we’ll use a fictional online store’s sales data to create a dashboard for analyzing product performance.
Step 1: Understand your business case and objectives
Our business case is to analyze product performance, so our objectives will be:
- Identify top-selling products
- Analyze sales trends over time
- Determine the effectiveness of promotions and discounts
- Monitor customer satisfaction and returns
Step 2: Identify key metrics and dimensions
Based on our objectives, we need to select relevant metrics (measurable quantities) and dimensions (categorical attributes) from our dataset. For our business case, let’s choose the following:
- Quantity sold
- Average sales price
- Discount percentage
- Return rate
- Customer satisfaction score
- Product category
- Product name
- Time (year, quarter, month, day)
- Promotion type
Step 3: Create calculated fields
Some metrics might not be available directly from the dataset, so we’ll need to create calculated fields. For example, let’s create a calculated field for average sales price:
- In your dashboard tool, navigate to the data modeling section or the field editor.
- Click on “Create Calculated Field” or a similar option.
- Enter the formula: Revenue / Quantity Sold
- Name the field “Average Sales Price” and save it.
Step 4: Aggregate data and create hierarchies
For a more comprehensive analysis, aggregate your data at different levels. In our example, we want to analyze sales trends over time. Create a time hierarchy as follows:
- In the data modeling section, find the “Time” dimension.
- Create a hierarchy by nesting the time fields in the following order: Year > Quarter > Month > Day.
- Save the hierarchy as “Time Hierarchy.”
Step 5: Create relationships between tables
If your dataset has multiple tables, you’ll need to establish relationships between them. For instance, our dataset might have a separate table for promotions. In that case:
- In the data modeling section, find the “Relationships” or “Data Relationships” option.
- Identify the primary and foreign keys in both tables. For our example, let’s assume “Product ID” is the common key.
- Create a relationship between the tables using the common key.
Step 6: Optimize performance
To improve your dashboard’s performance, consider pre-aggregating data, indexing fields, and hiding unused fields. This step will vary depending on your dashboard tool, but some general tips include:
- Use filters and parameters to limit data
- Hide unused fields to reduce memory consumption
- Use summary tables to pre-aggregate large datasets
Step 7: Validate your data model
Finally, always validate your data model by cross-checking the numbers against your original dataset or known values. This ensures that your calculations, aggregations, and relationships are set up correctly.
That’s it! You’re now ready to create visualizations and explore insights using your optimized data model. Remember that data modeling is an iterative process, so feel free to make adjustments as you uncover new insights and objectives.