The state of open source in data science
Open-source datasets and software have become a staple of data science. In recent years, innovative startups have open-sourced tools to enable data teams to do better work with data, such as Airbnb’s Airflow workflow management platform and Lyft’s data discovery engine.
Publicly available datasets provide valuable training data for the latest machine learning algorithms. Open-source packages in Python and R enable data scientists to streamline their workflows. Data scientists can build frameworks that reduce the barrier to entry for working with data across the organization. The list goes on.
Open source is catalyzing the development of data-driven and data-generating technologies, heralding a fourth industrial revolution (Salesforce). There are now more bytes of data than there are stars in the observable universe, and the amount of data in the world continues to double every two years, and organizations are increasingly using open source tools to make the most of this data.
The advantages of open source for data science
Just as the open-source revolution has led to a transformation of software development, so too has it been driving the development and democratization of data science and artificial intelligence. Open source has become a critical enabler of enterprise data science solutions, with the majority of data scientists using open-source tools (Kaggle).
Open source is more secure
Indeed, the world now largely runs on open-source solutions, whether we’re referring to Linux-powered data centers, Apache web servers, or web apps programmed in Java. The thriving communities that have grown around these solutions means they’re widely supported, which is good news not just from a support perspective, but also when it comes to security, updates, and optimizations.
Since open source promotes a community-based approach to data science and software development, popular projects get valuable input from hundreds or even thousands of industry experts. This means potential security vulnerabilities are identified and remediated more quickly, quality is guaranteed by widespread consensus, and new opportunities are more easily identified.
Open source provides flexibility
One of the key differentiators between proprietary and open-source software is flexibility and customization. Ultimately, proprietary software is controlled and managed by its developers, whereas open-source software has much more flexible licensing. This enables organizations to customize software for the workflows and provides them more control over the tools and solutions they develop (Inc). Moreover, open-source software is interoperable, meaning that it can work with a variety of data formats, and is designed for cloud and cloud-native technologies. Finally, open-source software enables organizations to avoid vendor lock-in and allows them to test and try software before committing to a solution (InfoWorld).
Open source promotes employee acquisition and retention
One of the key aspects of the open-source revolution is how it intersects with talent acquisition and retention. Whereas skills pertaining to proprietary technologies lack mobility since they are only relevant in a specific closed environment, contributing to open-source projects makes it easier for organizations to attract and retain suitable talent. Open-source tools are already the standard in academic and industry circles, promoting skills-sharing and development across the board.
Upskill your teams in open-source data science
While the benefits of open-source in data science are without doubt, learning the necessary skills still takes time and effort. Upskilling your team on popular open-source data science tools and packages is essential for future-proofing your business and for promoting a culture of continuous innovation, learning, and improvement.
The essential aspect of making the most of data, is to ensure that your team is equipped to analyze it efficiently and act upon it with smarter, more timely decision-making.
Download our whitepaper to learn more about the benefits of open-source data science.