The problem
Most businesses are sitting on more data than they use. It’s in the systems, it’s accurate enough — but between the day-to-day and the effort of pulling it together, the questions it could answer go unasked: what’s actually driving the numbers, where the time and money leak, which patterns repeat.
The approach
iTech88 does the pulling-together and the asking: assembling the relevant data, doing the analysis, and returning the result in plain terms — what the data shows, what it doesn’t show, and how sure the answer is. An analysis that overstates its own confidence is worse than none, so the caveats travel with the findings.
The outcome
Decisions made on evidence the business already owned, instead of on the general impression of it.
Evidence
The core statistical work is done in Python’s data-analysis stack: pandas and NumPy. One example is categorizing and breaking down professional-sports player data by position and conference.
Interactive tools sit on top of that. A browser-based data-analysis environment, built with a FastAPI backend and a React front end (Vite, Tailwind, CodeMirror), runs real Python analysis against a dataset directly in the browser. Streamlit dashboards, backed by a DuckDB database and visualized with Plotly, turn a large structured dataset (sourced from a public API and transformed with pandas) into something explorable.