Here's how you can tackle data analysis and modeling in a business architecture role.

Kam, a Business Architect, tackles data analysis by using Python to clean and analyze customer data, identifying key usage patterns. He employs machine learning models to forecast trends, integrating these insights into the business architecture framework. This approach helps optimize process workflows and aligns strategic decisions with data-driven evidence, enhancing overall business efficiency and growth.

Highly recommend using data and charts to tell a story. Especially trend lines, averages and mean. Most people are visual people and this only helps.

In a business architecture role, you can tackle data analysis and modeling by aligning data strategies with business objectives, ensuring data integrity and usability, and utilising advanced analytical tools to derive actionable insights for informed decision-making.

Being able to analyze trends is critical to growth of a company. This allows leaders to align resources to trends. Need to ensure the trend lines show possible future direction.

Understanding how to communicate to your stakeholders is essential for work. Must be able to communicate clearly and effectively. If not, there will be confusion and not use resources efficiently or effectively.

Architecture involving operational and analytical data strategies is typically nuanced. As a business representative working in data, our job is to produce documentation. Producing documentation is an excellent opportunity for business folk to state purpose/value and flex your understanding of the extended enterprise architecture. Wireframes, ppts, pivot tables, and infographics can add flair to a vanilla data topic. While visualizing data strategy will go a long way w senior leader/s and engineers, it is not a replacement for understanding the nuance and value of your data strategy.