The Significance of Data Community Building
By Ravit Jain – Featured Contributor
Unfortunately, the stereotype of the isolated, anti-social programmer still exists. The prejudice, which can be traced back to the rise in popularity of personal computers in the 1980s, remains somewhat attached to software developers, data analysts, and information technology professionals. Even though these fields can promote some of the most effective team-building and collaborative cultures, some still tend to think that programmers only work alone. Because of these lingering misconceptions, concepts like data and community aren’t often considered together. Put differently, data communities may have at one point been thought of as contradictory. But with the increasing sophistication of computing technology, the rise of Big Data, and the importance of data applications in all of our daily lives, it makes sense that new data communities are sprouting in every corner of the internet and the world.
For example, in 2017, researchers conducted a study in the Association for Information Science and Technology that was designed to generate data community building. Participants were prompted to join others in using the Body Listening Project, “a participatory platform in which participants were invited to engage collectively in the building of a public repository.” The platform gave users the opportunity to share their experiences about how they listen to their bodies and how they respond. As researchers gave people the space to supply that information, they also gathered and curated a collection of data that informed how people can improve their self-management of their health. Ultimately, everyone involved formed a community “that was shaped by participants of many different backgrounds. This example illustrates just how expansive and inclusive the umbrella of data community building can be. While these researchers were data scientists who intended to concentrate on the collection, analysis, and reporting of different data sets, the project they built ultimately focused on health and collaboration. Through this collaborative approach, data community building provides a perfect opportunity for data science novices and experts to connect and begin working together. As an appealing and admittedly novel facet of the data science world, many have begun to look at the potential for data communities in a new light. Ravit Jain, Founder & Host of “The Ravit Show,” is a full-fledged data community builder and advocate who has created and maintained a YouTube channel that covers data science, machine learning, AI, and the advanced minds who work with these concepts for some of the world’s largest companies. The goal for this kind of channel has always been a focus for Ravit, who told Discover Data Science, “going on calls with different industry experts and getting their thoughts in various industries and how they’re performing gives insight into the reports they’re creating.” Ravit’s career didn’t start in data science. As a veteran of finance and financial publishing, he was already looking for ways to build community-oriented connections. “What I used to actually do,” he said, “is bridge that gap between fact-publishing, their products, the books, and the communities.” To help explain what the concept is, its principles, the impact of data literacy, and how social media impacts the data community, Ravit’s experience provides a perfect lens into the ongoing significance and connectedness of data community building.
What Is Data Community Building?
It’s no secret that data-backed decision making continues to dominate the business world. From the largest companies to small businesses, leaders tend to rely on and execute strategies based on data analysis. “Data plays an important role in practically all industries,” Ravit said, “because we use it to create reports that ultimately drive business and promote greater efficiency.” Because the dependence on data science continues to grow across sectors and industries, more people than ever have the opportunity to join different communities that grapple with data practically and conceptually. Ravit, in his work and messaging, aims to clarify how inclusive and inviting data community building can be. Specifically, he offers that the data community “is something which isn’t so tricky. People feel that it is difficult to go out there and build a community for yourself. I would say it’s not that difficult if you have an interest in something.” Most people tend to enter the data science community for different reasons. From professional drive, curious enthusiasm, and even amateur interest, Ravit Any kind of professional pursuit and hobby can be positively affected and optimized when integrated with data practices. This reality for Ravit is how people of all skill levels and knowledge bases can enter the data science community. “Community building starts from where your interest lies,” Ravit says. “I know for a fact that I need to get into different pockets of communities to create opportunities for people to learn effective data science practices. It’s engaging in different slack channels, browsing social media platforms networks, and attending conferences.” These kinds of practices Ravit adds, “play a very important role in helping newcomers to meet people.”
“You are learning from different industry experts or different leaders in that space. So that makes you part of the data community.”
Additionally, Ravit has identified how “the data space works more on feedback from different perspectives in the field considering what people think about.” For example, as Ravit points out, “if you have a project and if you wish to get feedback, there are places where you can do this.” He goes on to explain that Slack and Discord are excellent social media platforms to cultivate discussions and feedback on data science projects. Because of their smooth file sharing, their individual and group instant messaging components, and their streaming / screen sharing capabilities, these platforms ensure that people can connect more directly. The data world has gone on to promote “growing in this space where you are able to join different communities, attend events, join channels, connect through platforms like LinkedIn and YouTube, and learn from instructors who are helping you.”
Data Community Building Principles and Practices
For Ravit, one of the guiding principles for data community building, centers on pursuing a focused interest, even if it doesn’t seem like it has anything to do with data. Ravit posits that this principle “is to make sure your niche, goal, or agenda is super clear if you want to enter into a community.” On the other side, creating a new community or adding onto an already existing one, Ravit believes that outreach and making connections can prove invaluable. “If you want to build a community,” he relays, “you need to attract those folks who are interested in these topics which could be done in various ways.” Content creation and circulation are integral for the data community building process. As Ravit has identified, “there are more possibilities where people would want to follow you, when you create, share, and react to data science-related content.” Additionally, this improves overall access to the field where people of diverse skill levels and experience can interact with each other. Ravit highlights, “it’s a benefit because there will be more like-minded people in the same space talking about different things and different opportunities,” under the data science community umbrella. For Ravit, this multifaceted data science community building process that revolves around content creation and syndication includes a step-by-step progression though these five steps:
- Choose an interest or “niche” that could in some way be connected to data science practices.
- Engage with the appropriate audience directly through social media or networking capacities.
- Consume relevant content connected to the interest or professional pursuit.
- Create your own content to be shared on social media platforms.
- Continue communicating with community members.
These steps, according to Ravit, will lead to an interactive and dedicated data science-oriented community. This process doesn’t happen overnight and those who are interested in following these suggestions should expect a learning curve. Still, the connections to a new community and the opportunities to learn new technical skills make this process worth the time and energy for a beneficial, measurable return on investment.
What Is Data Literacy?
While data science as a field is becoming more inclusive and accessible, there is still a technical barrier that could dissuade some from joining. When newcomers are able to understand data and become data literate, they will be able to engage in new communities and adopt new skills. Ravit finds data literacy to be an important threshold to joining the community. He defines data literacy as “the ability to read, work with, analyze, and communicate with data.” Data literacy doesn’t require a level of mastery over the technical skills of the field, just that, as Ravit explains, those interested “know how to understand data and can talk data.” Recently, researchers in the field have begun to examine how community-based organizations can become more inclusive using data and the promotion of data literacy. In a scholarly article published in the Journal of the Association for Information Science and Technology, two researchers worked with community-based organizations across several cities in the United States. They found that several hurdles stand in the way of communities being able to utilize data effectively. These obstacles include:
- The accessibility / restriction of certain data sets.
- Open data and its decentered, un-local drawbacks.
- A limited understanding of data and lack of data literacy skills.
- Trouble in working with data structures that already exist.
To help democratize the use of data across communities, especially those who are underserved and underrepresented in technology spaces, researchers have identified potential opportunities proposing that effective data community building and data literacy learning can be accomplished by:
- Creating Educational Programming
- Building Partnerships within Data Ecosystems
- Bringing Community Voices Forward in Current Data Ecosystems
For Ravit, data literacy provides an opportunity to enter the field of data science more generally. Pointing out the cyclical nature of learning and strengthening your skillset within this industry, he says, “when you know more about data, you can begin to speak data, and you can learn data.”
How Networking Impacts Data Community Building
One of the most important aspects of data community building is the networking opportunity that it presents. Through data science-related conferences, networking events, and live webinars, there are significant opportunities for both professionals new to the field and veterans of the industry to make new connections and understand how data science is evolving.
Networking can happen in a variety of spaces. Ravit offers that “Slack channels and Discord servers offer one way of networking but there are other ways of networking like conferences and events.” These kinds of circumstances give data community members an avenue to learn from and “listen to leaders who are the real creators in this space.”
“There are so many opportunities because these are the spaces where the real folks who are running businesses – CEOs, CTOs, Founders, and speakers – are watching for folks who could join their teams.”
The Intersection of Social Media and Data Community Building
As the COVID-19 pandemic forced employers and employees to rethink the benefits of remote work, social media has continued to change the way we communicate with other people. In the world of data, social media has helped data science professionals to find new ways to connect and collaborate. Ravit believes that building up a profile on social media channels like LinkedIn and Twitter can be goal-oriented for people with a range of backgrounds, “and this goal could be anything – it could be creating a community for people, it could be creating a presence and making sure they have their voice in this space.” Through this lens, social media offers an important entry and sustainability element for data community building. In terms of the content that he creates, Ravit’s celebrated podcast The Ravit Show focuses on data communities for more professional audiences. “My show runs almost completely on LinkedIn,” he told us. “I’ve had more than 100+ interviews of CEOs, CTOs, and CDOs based in the USA or from around the world.” By creating his own content and continuously emphasizing the importance of data community building, he has also been able to curate a more educational approach to his personal brand. He has featured some of the world’s leading data scientists and technologists and has invited people to “learn more about data, data science, AI, and machine learning spaces.”
How Newcomers Can Join the Data Science Community
The data science community is expanding rapidly. It’s never been a better time for newcomers to enter the field and for professionals to learn new skills. There are hurdles that could prevent some from feeling the confidence to make the jump into the data community and are widely explored through the subject of Imposter Syndrome. Here too, Ravit counters these insecurities highlighting how eager data science professionals are to help people join. Specifically, Ravit recommends those who are interested in data science “start reaching out to people who are in this space. There are always 5 or 10 people that you will find who are experts in different domains.” By connecting with these people on social media, most will be able to start engaging with helpful materials to learn more. For example, as Ravit has outlined, “if you are wanting to read a book, you can reach out to 10-15 people out there and get their feedback about what topic would be relevant to your goals.”
“My first tip to anyone getting into this space or wanting to do something in this space is don’t be hesitant. It’s right now. Go out there and start communicating.”
Taking the first step by communicating with industry professionals can be intimidating, but the sooner you do it, the sooner you will be able to learn more about data science to help you accomplish your personal objectives. This first step is a critical component for data community building, because connection, collaboration, and learning are the bedrock foundations for the practice. Ravit recommends that newcomers, regardless of experience or skill level, begin creating content and making posts on various social media platforms. “When you want to start building up a community,” he told us, “creating content plays a very important role. It could be simply creating a 10-second video asking ‘Where can I find the best data science courses? Which is the best programming language? Which community should I be joining? What are the best conferences I should be attending? Or which are the five data leaders that I can follow?’” This process of creating your own content and circulating content that others create will contribute to data community building in a practical manner. Ravit says, “This results in a two- way conversation where you create questions and content and at the same time that you are also able to get answers from your community.” While this kind of content creation, circulation, and engagement practice can be effective for those who already have the knowledge base in data science to support it, others will need the training necessary to engage in the community. This sort of training is often best supported in the classroom. By getting a degree in data science, you will be able to learn first-hand from qualified faculty with direct experience in the field. Learn more about which program will best cater to your professional and academic goals and pave your way as a data community builder today.
Ravit is a data community builder and the founder and host of “The Ravit Show” where he interviews data science guests, experts, panels, and companies to help the community to gain valuable insights.
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