A CXO’s Guide to Collaboration Between Citizen Data Scientists and Data Science Teams
According to Gartner, the primary job function of citizen data scientists is predominantly outside the field of statistics and analytics. However, they create or generate models that leverage predictive or prescriptive analytics.
The profile name “citizen data scientist” bears a close association with “citizen science,” where the findings from the general public contribute to the scientific research carried out by professional scientists.
In the business world, citizen data scientists today play a complementary function to professional data scientists. Traditional data scientists’ professional knowledge is frequently expensive and hard to come by. Citizen data scientists are effective assets to close the present skills gap.
This article serves as a guide to CXOs and covers how citizen data scientists and data science teams should be more collaborative than competitive.
The Need for Collaboration Between Citizen Data Scientists and Data Science Teams
Although the practice of citizen data science is growing in popularity, this collaboration is rarely considered or addressed. However, this needs to change to nurture more collaboration among individuals working to drill down into data and unearth actionable insights.
Here’s why the need for collaboration between citizen data scientists and data science teams is highly reasonable.
Fostering a Collaborative Data Science Ecosystem
Data science training for non-data scientists is an essential step in any company’s journey towards:
Nurturing data science competency within the organization
Acquiring a greater understanding of the current gaps in the offerings, market, workflows, etc.
Increasing transparency and trust among people working on data
Advancing the democratization of data science and facilitating equal opportunities for all stakeholders to contribute to decision-making
Citizen data scientists need to work hand-in-hand with professional data scientists to ensure that relevant insights are churned out from the entire stack. Such an ecosystem would spawn more innovative solutions and processes while ensuring that quality data is quickly made available to the entire team.
In such an ecosystem, CXOs would be able to have a greater say in the formulation of goals, and they would be able to order more precisely the resources needed from vendors and other teams.
For this, however, it’s critical that data science roles are clearly defined. In case of discrepancy, accountability Data scientists must be aware and open to collaboration with citizen data scientists at all levels of the organization.
Reaping Maximum Benefits from Augmented Analytics
Extending the capabilities of the analytics initiatives is something that CXOs aim for to accommodate a holistic and more profound view of the business functions, especially as the business scales and evolves.
With citizen data scientists on board, organizations can lay out a concrete augmented analytics roadmap that follows a phased approach to creating a holistic data science ecosystem.
For example, let’s assume that citizen data scientists carry out activities, including fundamental reporting, exploratory analysis, and data curation. The enterprise wants to augment these capabilities with functions that facilitate data storytelling, data querying, etc.
Instead of opting for a big bang approach where they completely change the toolkit and processes to accommodate the next set of capabilities, it’s more lucrative for CXOs to implement a phased approach where citizen data scientists are equipped with enhanced resources to perform advanced analytics.
Driving Collaborative Model Development and Deployment
Citizen data scientists can collect data and ensure that it is accurate and indicative of the target business environment. They can outline the pertinent characteristics, factors, and practical limitations affecting the problem domain.
Data scientists can handle complex analytical problems and guarantee that the models follow the best standards. They can help with algorithm selection, model parameter adjustment, and the use of strict validation approaches like cross-validation and out-of-sample testing.
This collaborative approach ensures that the predictive or prescriptive models are accurate, efficient, and in line with the unique needs and business objectives. This, in turn, results in better decision-making and more effective model deployment throughout the organization.
Enhancing Business Resilience
Often, businesses find themselves in the peculiar situation of not having enough expert data scientists and having to implement innovative solutions with limited resources.
Whether it’s because of talent shortage, lack of skillsets, or simply the fact that a data science team is still in its nascent stages of development, it’s common for enterprises to have a dearth of in-house data scientists to address critical business problems.
In such situations, it’s incumbent upon the CXOs to find a way in which they can utilize the expertise of their small data science teams — something that becomes even more critical in a scenario like the economic downturn of today.
This is where they can look toward skilled information analysts in their teams. These professionals might not have an out-and-out data science degree, but they have in-depth knowledge of statistical modeling and forecasting.
When equipped with AI, NLP, and ML tools, these analysts complement the work of the data science team.
Role of a CXO in Facilitating Collaboration Between Citizen Data Scientists and Data Science Teams
CXOs should be aware of the difference between an information analyst and a data scientist. They should have a clear understanding of the right mix of talent needed for the business, as well as have a sense of how their team can best utilize the data science stack to further develop their capabilities.
A clear vision and plan must be laid out by the CXOs for data-driven activities across the company. This includes:
Underlining the significance of collaboration between citizen data scientists and data science teams
Cultivating a culture that encourages cooperation
Dismantling team silos and fostering information sharing
Equipping citizen data scientists with the right resources and technology infrastructure
Positioning citizen data scientists for greater accountability, responsibility, and authority
Providing mentorship and training to expose them to cutting-edge tools and techniques
It’s here that they can immensely benefit from a unified data science platform like Rubiscape.