Caution with Streamlit

Streamlit ( is a lovely Python module that helps data scientists build interactive dataviz apps.

Use it when a BI is overkill — as this Streamlit dashboard that I wrote to manage my personal investments —, or where there is no BI, such as very small companies. Or where there is no interactive app developers to create a native app.

Investorzilla and its Streamlit app

Streamlit proliferation in mid to large size companies might however be a bad sign of several things:

1️⃣ Application and/or integration developer’s job wrongly assigned to Data Scientists
2️⃣ Lack of a solid BI platform and practice
3️⃣ Siloed data that isn’t flowing due to lack of data streaming or API architecture
4️⃣ All the above.

Use Streamlit with caution; we don’t want it to become the new, data science-era spreadsheet for corporate reporting, with all the burden that spreadsheet proliferation have caused.

Best Data Scientist’s time is spent getting insights from Exploratory Data Analysis, and then using it to model outstanding estimators and predictors. Definitively not writing nice looking apps.

Also in my LinkedIn

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