Today we are proud to announce the open beta of Noteable, the collaborative data notebook that enables data-driven teams to use and visualize data, together.
Have you ever found data requests or projects to only involve one person or role? Probably not. The initial need for data emerges from many different areas and is driven by a number of potential inputs including stakeholder requests, customer feedback, observational pain points, and performance monitoring metrics. But most tools are built for single roles or perhaps, at most, a small group of technical people.
Noteable creates an environment for multiple roles and people across technical know-how to work on data together. Our Python notebook foundation with built-in SQL, no-code visualizations, and plug-and-play workflows democratizes data and brings them to a much broader audience, giving more users across an organization access to deeper, actionable insights.
What’s the business impact of Noteable?
Data teams minimize their costs, increase productivity, and drive business impact. Data experts waste 44% of their time searching for, preparing, and analyzing data. Data scientists spend up to 80% of their time on non-model related activities, like data prep, infrastructure management, and version control. According to Forrester Research, only 22% of companies see a significant return on data science expenditures. From a Data Science perspective, data doesn’t migrate from system to system seamlessly. Not only do most projects fail to scale well from a technology perspective, but also from an organizational perspective. This leads to few models ever reaching production. At this point, data has become too complex that organizations can no longer ignore the collaboration and communication gaps.
Notable puts everyone on the same page to enable organizations to build analytics capabilities and manage data, code, and workflows at scale. It is the unique alternative to self-service analytics tools for data teams, that brings the best of two worlds: Science and Analytics as well as natively integrate with Engineering workflows.
Who is Noteable for?
Noteable is for anyone who works with data – from the data curious to data experts. Analytics requirements in many organizations vary between free-form data analysis and tweaking reports to modifying data models. While the novices will have no problem conducting routine Analytics tasks on this platform, the savvy technical users will enjoy the tremendous flexibility of our extensible platform, such as training Machine Learning models or contributing to ETL workflows and Data Pipelines.
How does Noteable work?
Ease access into data
No more insufficient hardware and and lack of data. Save time and money by simplifying tooling and enabling your team to focus on what they do best. Featuring native integrations with BigQuery, Snowflake, Databricks, Trino, Redshift, PostgreSQL, and more, you can seamlessly connect to data no matter where it’s stored. Plus easily and securely connect to external APIs. Native SQL support within a Python notebook gives you the flexibility to work with data the way you want® without sacrificing collaboration.
Bring everyone together with data.
All your work in one place: No-code, Python, SQL, & more. With our Data Explorer (DEX), no matter your code level, you can turn your data into stories with the largest library of built-in visualizations. Plus, go beyond just communicating on the document to communicating on the actual data with deeply collaborative commenting, down to specific data points
Address explainability and reproducibility issues and help everyone iterate quickly. Only share secrets, credentials, and files at the level of granularity that is right for you by dynamically controlling access and visibility of your files. And with live collaboration and real-time updates, you can work together and simultaneously from anywhere without worrying about losing your work.
Work with data the way you want®
Jupyter Notebooks are a great data exploration tool, but the gap between exploration and production can sometimes be frustrating. Free up time by productionalizing your workflows in a flexible and ecosystem-friendly workspace. Remove the friction between data exploration and production by scheduling notebooks to run automatically, even within your existing production pipelines such as Airflow, Dagster, AWS Step Functions, and more. Speed up data analysis and enable more users to explore data with the best no-code Excel-like interactive tables. Lastly, share your data’s story with easy-to-build, easy-to-share, and customizable published notebooks.