The citizen data scientist is a fast-emerging trend, as more non-experts from operational areas (marketing, finance, HR, etc.) increasingly perform data science tasks that were once the exclusive function of expert data analysts and statisticians. It’s important to understand that the emergence of the citizen data scientist will not eliminate the need for today’s data science professionals, as this post explains.
The 3-part ecosystem of data management
The “new” landscape of data management is an increasingly-democratized ecosystem that blends three components: (1) a burgeoning number of accessible, user-friendly digital tools and platforms (powered by artificial intelligence and machine learning) that enable relevant data to be easily accessed and leveraged; (2) the increasingly enhanced capabilities (by tools and by practice) of citizen data scientists who bring operational expertise (they’re marketers, HR pros, etc.) to data; and (3) the continuing need for the expertise of trained, professional data scientists to help generate actionable insights from data.
Contrast this “blended ecosystem” of data science practice with the traditional approach, where professional data scientists controlled all the tools and all the knowledge, sharing their data-enabled insights with a few people at the top of the organization. The emerging democratization of data science comes at an important moment. We now inhabit a fast-moving, ever-changing landscape where customers have come to expect that companies know their needs in real-time and personalize their offerings, including marketing messages.
The formerly tight control of data (it’s only for those who “need to know”) is woefully inadequate for today’s unpredictable, fast-paced, and data-powered world. Companies simply don’t know who needs to know what data and when. Approaching data and analytics as “done by the few for the few” is a risk no organization, and no marketer, can accept. When data science is opened up, as the customer data scientist is doing, innovation and real-time course corrections can happen in a process of continuous, iterative improvement fueled by data.
Complementary collaborators: Citizen data scientist and data science professional
The deep knowledge and experience of professional data scientists continues to be relevant, even as the capacities of citizen data scientists grow. To be most effective, citizen data scientists must work together with professional analysts and data scientists: the two form a great partnership. For example, as citizen data scientists conduct their own data analytics, they’ll need the help of data scientists in order to deeply explore and understand a particular set of findings. Trained, professional data scientists will increasingly focus on areas where their expertise is most in need, such as high-level data curation, model building, and governance. As more “basic” data science gets done by empowered citizen data scientists, trained data scientists will become even more specialized on fewer, but far more strategic and high-level, data management tasks.
Organizations need a holistic, collaborative approach
Gartner advises that, as organizations develop more citizen data scientists, they should also consider developing supportive structures to blend the citizen data scientist with professional, full-time data scientists. “Data science productivity [will improve] with citizen data science by defining and providing guidance for the interactions and responsibilities of both disciplines [CDS and data professionals],” says Gartner. “Organizations will still need specialist data scientists to validate and operationalize models, findings, and applications.”
By taking a more holistic approach, organizations can optimize each of the three blended components of the “new” data management ecosystem -- first, by developing more CDSs with functional knowledge of their business (i.e., marketers who also “do” data science), second, by adopting and integrating the latest, best data management tools and platforms, and finally, by gaining the essential know-how of data science professionals in setting up data management systems/infrastructures and in “making meaning” from specific datasets and findings.
The key takeaway here is that data science is democratizing, becoming much more accessible to individuals and organizations alike. You don’t need an expensive, hard-to-hire team of data scientists anymore. What you need is the dynamic capacity to blend your functional citizen data scientists with the right tools/platforms and the deep knowledge of data science professionals. And as the tools (automation, AI, etc.) do more, as citizen data scientists become even more empowered, data scientists will continue to play an important role in defining structures, processes, and unlocking the massive potential of your organization’s data.
Chuck Leddy is a Zylotech contributing writer.
If you liked this post, check out our recent blog post: Empowering the citizen data scientist: The democratization of customer analytics.