Endjin have worked in the Power BI space since its inception, delivering projects for a wide range of clients.
In this report we have chosen 25 global brands across the categories of tech, food & drink, entertainment, automotive, and financial services.
We have then ingested 20 years of financial data for each brand with the objective of using Power BI to generate some descriptive analytics over this data.
Our aim for this report is to encourage our audience to explore. We make it easy to navigate, with a quick way to clear all the filters, so our users feel confident that they can dive deep into the data, and quickly get back home. Across the different pages you can see interesting trends in financial metrics such as stock price, volume of shares traded and market capitalization.
You can clearly see the market movers by observing stock prices over the last 20 years. There are also global insights, such as the impact of the financial crisis in 2008 and the invasion of Ukraine in 2021. You can also observe the evolving markets and the domination of the big tech companies with their market capitalization that dwarfs many of the other brands.
This is also an opportunity to explore some specific features in Power BI that are suited to analysis of brands, financial data, and relative performance:
- We sourced all company logos.
- We designed a bespoke suite of icons, pictograms and logos in visuals and report navigation to enhance accessibility to support the report.
- Use of DAX measure to “rank” the companies, using this to illustrate trends in relative performance in different visuals including one inspired by a Formula 1 lap chart.
- Use of a 2D “heat map” and a RAG (red, amber, green) colour palette to visualise high density information and isolate trends in data.
- Application of dimensional modelling to implement a date dimension that enables financial data to be aggregated through daily, weekly, monthly, quarterly and yearly levels of granularity, allowing users to interactively drill up or down through this hierarchy.
- Illustrating specific visualization features that are well suited to analysis of financial data in reports such as conditional formatting (data bars, icons, and background colors), and spark lines.
The data is sourced from the Yahoo Finance service using the yfinance Python package. This data is being used for educational purposes, to bring key features in Power BI to life.
A Python pipeline was used to ingest and wrangle the data. The Python process also creates the date dimension, allowing the data to be aggregated up into different granularities.
