I'm Tivon Johnson.
I’m a data analyst who came up through finance and accounting. I bring that discipline to analytics work in SQL, Python, and Power BI, framing business questions, checking the data, and explaining what the results support.
I spent years in finance and accounting before moving into analytics. That background shaped how I think about data. I learned early that small details can make a big difference, so I pay close attention to definitions, timing, assumptions, and whether the numbers actually line up.
Over time, my work expanded into customer, operational, financial, and market data. On this site, I break down real analytical projects in plain language, showing how I move from an unclear business question to a structured analysis and a defensible result.
I also share this work to connect with others who care about thoughtful analysis and clear communication. I’ve learned a lot from people who show how they think through problems, and I hope sharing my own work contributes something useful to the broader conversation.
How I approach analytics
- Start with the business question, even when it's unclear
- Identify what data is available and what isn't
- Define what the analysis needs to measure or detect before adding complexity
- Make assumptions explicit instead of pretending the data is perfect
- Use the right tools to get a useful answer
- Be honest about uncertainty, tradeoffs, and next steps
The goal is analysis that is solid enough to support real decisions.
You won’t find theory for its own sake here. If I cover modeling, it will be tied to a specific business question and the decision it supports.
Why this site exists
This site is where I document how I work through real analytical questions from start to finish.
My goal is to make the analytical process easier to follow, especially for people who know SQL, Python, or other analytics tools but want a clearer way to apply them to real business problems. Some projects focus on reporting and business analysis. Others move into applied data science, including text analysis, modeling, and pattern detection. In each case, the point is to use the right level of analysis to answer the question clearly and accurately.
You’ll find practical examples, plain-language explanations, and transparent reasoning about how I move from a business question to a defensible result.
Links
GitHub → SQL + Python walkthroughs and supporting files
LinkedIn → professional background
Medium → cross-posted articles and broader analytics writing
Feedback welcome
I’m always refining these posts. If you spot an error, a clearer explanation, or a better way to do something, just reply to any newsletter email.
If you are not subscribed, use LinkedIn to send a note.