Data Workflows and Tech Stacks are changing. And with it - the expectations of Business Oriented Data Professionals.

Excel Cubes and PowerPoint still have their place, but newer technical workflows are gaining traction and this is only going to increase in the domain of the business professional. Tools such as `cognos` and others existed to help those less savvy with `sql` to access cubes of data that are embedded SQL queries. Fast forward, today, advances in the democratisation of an organisations data has seen mass adoption of data warehousing (i.e. `snowflake / google big query / Athena`) where any analyst is able to query and extract the data they need to create answers to the questions management have about the business. This represents an ongoing shift to the Data Professionals workflow where Excel has previously (and continues) to play a large role, but more focus is now on the acquisition of SQL and Visualisation Competencies to complement the ability to query and access data from these Data Warehouses. In this article, we'll highlight the the shifting workflow expectations employers have and how this transition looks today with a sample financial case study highlighting how we make use of the simple technical workflow of SQL --> Python --> Deepnote Presentation, to improve on the legacy Excel --> PPT workflow where we'll cover Product Profitability Analysis split into three segments:

Revenue Analysis

Expense Analysis

EBIT Analysis

Product Profitability Analysis⚡⚡⚡

Theme: Revenue Breakdown Analysis Per Energy Asset

Theme: Expense Breakdown Analysis Per Energy Asset