For those working in the field of data science, communities are springing up and offering support arenas. This includes various resources, including blogs, articles, webinars, and online courses. Added to these are community forums and discussion groups.
Explaining how and why these communities make a difference for the evolving field of data science for Digital Journal is Rosaria Silipo, Head of Data Science Evangelism at KNIME.
Digital Journal: For starters, what is a data science community?
Rosaria Silipo: A data science community is typically made up of data analysts, data engineers, data scientists, and the eventual consumers and all four must be represented in order to be effective. Many data science communities are public. They are a great source for tutorials, examples, use case solutions, blueprints, and constructive discussions. There is always something to learn and some inspiration to get as an active member of the community. In exchange for all those benefits, there are usually two main paybacks required: sharing questions and answers publicly and giving back expertise.
DJ: Should companies encourage data scientists to participate in public data science communities (those outside of an organization)? Why are they such an important resource for companies in 2023?
Silipo: Yes, of course. Communities are often seen with suspicion by the corporate world: a free, public, technical, engaging environment more useful for students and hobbyists to learn than for companies to make a profit. This suspicion, however, has become obsolete. At a time when data science is critical to accelerating modern decision-making, the need for companies to rely on data science communities has never been higher.
Data science techniques and tools are constantly evolving, but keeping up with these changes doesn’t always require hiring more data scientists. Instead, organizations should prioritize leaning into data science communities, whenever possible.
DJ: When would this be not possible?
Silipo: The “public” character of data science communities outside of one’s own organization might not fit the highly-secure nature of some businesses.
In this case, sharing information or providing examples is not possible and therefore participation in public communities might become a problem. A company could create and support its internal private data science community, where data analysts, data engineers, data scientists, and consumers discuss and learn from each other, which is highly valuable for the same reasons as participating in a public data science community.
DJ: What are the keys to creating a successful community inside of an organization?
Silipo: In both types of communities, collaboration and communication among the technical data experts is key and it must be paired with an understanding of consumer needs, as consumers are the ultimate end-users and judges of whether data science can be put into action effectively.
DJ: What do data science communities need to thrive in an organization? And how can organizations ensure their data scientist communities aren’t left alone on an island?
Silipo: To avoid this problem and to ensure communities are adequately supported internally, companies must ensure that four basic needs of data science communities are met:
- Tutorials from past examples: By relying on an environment where data scientists can document their work, junior data scientists within the organization can reference previous data science projects and start from the highest-rated application to learn new skills. Thus, they can learn faster and quickly be introduced to new technologies.
- An ongoing repository of blueprints to jump-start the next project: Example workflows and scripts are not only beneficial to junior data scientists. Seasoned data scientists can benefit from existing blueprints and quickly adapt them to their new project. Building everything from scratch for each new project is quite expensive in terms of time and resources. The most successful data science communities offer a repository of close and adaptable prototypes to help speed up the proof-of-concept phase as well as the implementation of the final application.
- An open space for discussion: Data scientists are often more than willing to share and discuss their scripts and workflows with other data scientists in their networks and even outside of their organizations. Making it easy for these data scientists to share a solution and discuss it more broadly with other data scientists can help reveal bugs or improve the data flow, making it more efficient. One mind, as brilliant as it may be, can only achieve to a certain extent. Many minds working together with different points of view can go much farther.
- The opportunity to give back to the industry at large: Data science communities survive and thrive thanks to the contributions of their senior members. Past successful training projects become tutorials, past successful implementations become blueprints, knowledge becomes advice, and so on. Every community should envision the option for senior members to contribute back in terms of content and knowledge, and therefore to the success of the community.
