solutions

Make insights accessible for everyone

Sample internal report

Data science outputs can transform business units and decisions, but is only accessible to the technical teams who can run Python scripts and notebooks.

What is broken?

To get an answer or result, stakeholders create tickets on the data backlog or email someone to run a script. The data science team spends time copying charts into static reports where data and interactive elements are lost, or rebuilding everything in a BI tool from scratch.

This process cannot be easily automated or replicated, without significant investment in having a backend and front-end team build a fully-fledged application.

Why does this matter?

Non-technical teams cannot use data science to drive actions, and the data science team spends time building ad-hoc reports, instead of focusing on core product R&D.

What value does Datapane bring?

Datapane Teams allows data teams to transform their Python Notebooks and scripts into self-service data tools which can be used by stakeholders in their browsers to generate results. Non-technical teams can get new insights in seconds, run scenarios, and schedule reports to update automatically. The data team can focus on core product and R&D instead of reporting.

Data Science Reports

Build beautiful, interactive reports for stakeholders straight from Python.

Integrates with your analysis stack
Import Datapane into your existing Python analysis environment to build friendly user-facing reports.
Beautiful and interactive
Build beautiful visualizations using Python, and provide data drill-downs, tabs, selects, and fully customizable HTML.
Embed results into your tools
Use Datapane to embed results into tools such as Salesforce, Notion, Confluence, or your own internal tools.
Security and Authentication
Focus on the data science: Datapane handles all authentication, accounts, and publishing.

Data Science Apps

Deploy Python scripts and Jupyter Notebooks as self-service tools for data science reporting and platform automation.

Python and Jupyter-first
Data teams deploy existing Jupyter Notebooks or Python scripts as automated tools.
Automated data science reporting
Scripts run automatically to refresh reports, or update your database, CRM, DMP, or internal products with data science insights.
Self-service report generation
Stakeholders can run scripts with parameters via the web to generate custom results, allowing self-service report generation without the data science backlog.
Custom Environments
Run deployed scripts with custom environments and networking which allows integration with platforms such as Snowflake, Looker, Salesforce, and Google Ads. Include custom internal libraries or custom data science dependencies.

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