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1.5. Collaboration - Non Technical

While Excel spreadsheets are the most familiar example of sharing analysis with non-technical users, many other common approaches still rely heavily on manual steps or static outputs. Python opens the door to more efficient, reliable, and user-friendly collaboration methods:

Static Reports (PDF, Word, HTML)

  • Often analysts generate static reports (PDFs, Word docs, or HTML files) with key tables and charts to share with business users.
  • These are usually created manually or with semi-automated scripts.
  • Python libraries like ReportLab, WeasyPrint, or python-docx can automate generating these reports on a schedule.
  • This reduces manual effort, ensures consistency, and allows quick updates with fresh data.

Interactive Dashboards and Web Apps

  • Instead of static reports, interactive dashboards let users explore the data themselves-filter by date, segment, or other dimensions.
  • Tools like Plotly Dash enable analysts to build simple web apps without requiring users to write code.
  • This greatly enhances user engagement and understanding, while freeing analysts from repetitive “ad-hoc” requests.

Scheduled Emails with Summary Insights

  • Python scripts can automatically generate key insights or alert emails based on pricing data (e.g., unusual trends, KPIs).
  • Integrations with email APIs (like SendGrid) allow automated delivery to stakeholders at set intervals.
  • This keeps users informed proactively without manual report distribution.

APIs for Data Access and Integration

  • Building lightweight APIs with Python (using frameworks like FastAPI or Flask) allows non-technical teams or other software systems to query pricing outputs on demand.
  • This replaces emailing files or copy-pasting data and ensures everyone accesses consistent, up-to-date information.
  • APIs can power internal tools, BI platforms, or third-party integrations.

Version-Controlled Notebooks and Documentation

  • Sharing Jupyter Notebooks enables users to see analysis code, results, and commentary all in one place.
  • For non-technical users, notebooks can be pre-built to only show results or use widgets to interact without touching code.
  • Documentation alongside notebooks clarifies assumptions and methodology.

Data Visualizations Embedded in Presentations or Portals

  • Python scripts can export high-quality charts with libraries such as Plotly that are easily embedded in PowerPoint or web portals.
  • Automating this reduces manual chart copying and helps maintain consistency across reports.