In the role of Data Analyst for a fictional global retailer that sells electrionics, the project goal was to build an interactive report and to conduct an exploratory analysis starting from raw data in csv files.

This project took 4 steps:

1. Profile and prepare the data

Connecting to raw data with Power Query, profiling the data and preparing for loading to data model. During this step some new columns were added to build a calendar e to add new attributes to customers (for example Customer Age)

2. Build a relational data model

Building the data model was the second step. The model was initially a star schema with a fact table (Sales) and some dimension tables (Products, Customers, Stores, Calendar, Exchange Rates). From here the table of Products was splitted into three (Products, Subcategories and Categories) to normalize del model, obtaing a snowflake schema:

3. Enrich and explore the data

During this step several measures were created and added to a new dedicated measures table to enable analysis, using DAX iterators (SUMX, AVERAGEX) and lookup functions (RELATED):

4. Build an interactive report

During the last step an interactive report was created following dashboard design principles to enable exploratory analysis:

Some important evidences emerged:

• Sales plummeted across all product categories at the beginning of 2020:

• Delivery Time for products sold by the online store improved over time, from 7,3 days on average in 2016 to 3,8 days on 2021