Why choose an open source tool for data science

88% of organizations agree that open source is critical to innovation in data science and machine learning. Open source communities anticipate trends and will advance development to expand on the core software functionalities. Data science tools, like KNIME, that embrace openness as their core principle will therefore always be among the first to integrate any innovation, KNIME has consistently done that with big data, autoML, and now GenAI.

The community-powered innovation in open source tools allows you to work with the latest analytics techniques that closed source tools can’t keep pace with.

With KNIME, you’re no longer bound to a vendor’s development schedule to wait for functionality and extensions. You’re no longer limiting your competitive ability at a time when integrating the newest data science, machine learning, and (Gen)AI techniques has never been so important.

Let’s have a closer look at how open source tools improve your competitive edge.

Your data engineers need the fastest data processing capabilities to deal efficiently with your company’s increasing volumes of data. Your computational scientists want to experiment with different machine learning techniques to build the best predictive models. Your data science team is made up of scientists who prefer to work in scripting languages, while others would like to try the new AI coding assistants. Your business users would like easier access to advanced techniques.

You can make all this happen immediately with an open source tool.

If you want to explore how your company can use generative AI and machine learning, you can. If you want to get that new subsidiary to level-up their data science skills, you can. If you want to integrate additional specialized libraries and environments, you can. No up-front licensing costs are required with an open source tool like KNIME.

• Put GenAI into their hands: Generative AI in KNIME enables business users to upskill faster with even easier access to advanced analytics techniques. Beginners to data science can use KNIME’s K-AI assistant to automatically generate analyses for all their data analytics tasks based on a simple chat, and provide tips and support when they start building their own analytics workflows. Data experts can benefit from KNIME’s new AI Extension to connect to, integrate, and customize large language models for specific use cases, or use AI coding assistants to create Python scripts and visualizations, removing the heavy lift of writing code manually.

• Integrate with scripting languages and environments: Integrations for multiple programming languages and environments in KNIME mean your data scientists don’t have to choose but have the flexibility to work with a broad range of scripting languages if they want to – from Python, to R, to Groovy, Matlab, and more — all within the same platform as their non-scripting colleagues.

• Access all machine learning and AI techniques: With all the popular machine learning libraries and latest GenAI techniques at their fingertips, your computational scientists can build complex predictive models to explore new opportunities for your enterprise.

• Get universal connectivity: The ability to connect to 300+ data sources, as well as community-driven APIs, means your engineers can easily access AWS, Google Cloud, Microsoft Azure or MySQL, PostgreSQL, Oracle, and more, and connect up with all the BI tools your business users like to work with. This cheat sheet shows just some of the connectors available with KNIME Analytics Platform.

Your competitive edge will sharpen when the entire enterprise is truly data-driven. However, less than 20% of organizations say they are. Analytics at scale is more likely to succeed if your analytics tool can facilitate collaboration, knowledge-sharing, and deployment of analytics solutions across different departments and teams, skill sets, and roles.

Open platforms make it especially easy to share and collectively develop data-driven insight.

The easier it is for people to access training resources, best practices, and hands-on examples, the easier it is for more people to learn and develop their own solutions.

• Share insight: With consistent, easy access to thousands of examples and training resources shared by the open source community on KNIME Community Hub, beginners can get started quickly and data experts can dive deeper into learning new skills. 100,000+ KNIME Analytics Platform users are available on the KNIME Forum answering questions and sharing their own expertise with others. 

• Develop new functionality: If you need specialized functionality, you can browse the KNIME Community Hub to see if it’s already available as a community-developed extension. Or your data experts can extend the open source KNIME Analytics Platform, by developing customized functionality themselves.

• Advocate for new features: KNIME takes feedback and ideas for new functionality very seriously. Discussions on the KNIME Forum about existing features, improvements and new ideas, or feedback about the most recent developments are reviewed immediately for integration into upcoming releases.

Teams from global organizations are adopting open platforms to get started on real use cases quickly and use new knowledge to build their own solutions.

“The biggest wow factors we’ve had came from the clinically minded folks who took the time to learn KNIME and really embed that knowledge and then utilize the underlying power of that data to get the patients on the right drugs,” says Scott Morrison, Head of Data Science, Diaceutics.

Open platforms for data innovation are more powerful than closed source solutions because they’re highly integrative, developed around transparency and trust, and they help organizations become more agile and collaborative in their data innovation. Not to mention that they come with fewer risks and can be rolled out at lower cost and in less time. It’s because of these advantages that we’re seeing a lot of large global organizations and institutions actively consider and adopt open platforms for their data science teams.

If you’re interested in learning more about choosing or transitioning to an open source solution, watch this on-demand webinar From Alteryx to KNIME – Getting Started.