Data and AI Training

Superstore Dataset

The Superstore dataset represents a retail store selling to customers in the US from 2018 to 2012. It is fictional data. It is useful as an exercise in data modelling and DAX.

Data

There are several datasets – as if they were generated by the operational IT system of this retailer. The datasets are:

transaction – this is a fact table. It contains several numerical fields, sales, Quantity and Profit. These are values that we want to group aggregate. It has a lookup field (foreign key) into the four tables below
product – a dimension table that lists each product and grouping into categories and subcategories
customer – a dimension table that lists each customer and their segment
geography – a dimension table that lists the post code and broader geographical attributes (City, State, Region)
order – a dimension table that lists each individual order, the dates when it was ordered and shipped and the ship mode (how it was shipped)

The data exists on separate Excel tables in a single Excel spreadsheet here.

Alternatively these are available as separate CSV files on GitHub

If your organisation blocks access to GitHub then here is an alternative location.

You will also need a Dates table.

Semantic Model

A Power BI file containing a basic semantic model is here

Inspect the PBI semantic model and answer the following questions / tasks

Describe the semantic model. In technical terms!

What design choices did the builder of this model make? Consider these aspects:

table pattern and arrangement
relationships
column naming
sorting and hidden fields
formats
role playing dimensions

How does the model handle dates and time intelligence? Is this a good approach?

Does it meet best practices?

Are there any semantic model features that could usefully be added?