solutions

Making data science actionable

Sample internal report

Data science has become table stakes for data-centric businesses. Although many companies have a traditional BI tool, these do not support advanced analytics or data science insights, so data teams use the open-source Python ecosystem to create value for marketing, sales, and operations teams.

What is broken?

Python scripts and notebooks are not accessible to non-data scientists, so generating data science insights for stakeholders is a manual process. Stakeholders rely on the data science team to generate individual results for each query, which they manually package into static reports where data and interactive elements are lost. Due to this manual workflow, this process cannot be easily automated or replicated.

Why does this matter?

Non-technical teams cannot access data science results and use data science effectively, so end up stuck on a backlog. Additionally, the data science team spends time building ad-hoc reports, instead of focusing on core product R&D.

What value does Datapane bring?

Datapane's report generation and automation APIs allow the whole organization to use data science self-service to drive decisions, instead of waiting on a manual backlog. Additionally, data science teams are able to hand over scripts and reports and focus on high-value tasks.

Reporting API

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.

Script Automation API

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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