Even as companies invest significant amounts of money in digital transformation and data science initiatives, there remains a dearth of data science talent. For example, a Dice report found that the position of ‘data scientist’ was one of the top five fastest-growing roles this year. Similarly, according to LinkedIn’s 2021 jobs report, hiring for the position has grown nearly 46 percent since 2019.
What can organizations do to address the gap? According to a recent article by Gartner’s Anirudh Ganeshan, one solution is to open data science and machine learning up to nontraditional roles. Or, to put it another way, now is the time to empower citizen data scientists (CDSs).
But how can you ensure that your citizen data scientists are being properly primed for success? Below, we go over four critical steps for creating a business environment that supports citizen data scientists.
1. Build a CDS Ecosystem
In order to empower citizen data scientists, or CDSs, organizations need a complete ecosystem that includes people, tools, data, and processes. It’s a mistake to assume that citizen data scientists have all the requisite skills to access, transform, and investigate data for analysis. Rather, these individuals should be provided with a data literacy program to train them to access, use, and make sense of the organization’s data.
Another consideration is collaboration. It’s important that complementary roles such as business translators, developers, data engineers, and machine learning architects make themselves available to citizen data scientists. These individuals can often fill in the skills gap that the former lacks.
2. Add Capabilities that Enable Augmented Analytics
Another consideration centers on tools, as investing heavily in new platforms and technologies could potentially overwhelm citizen data scientists. A better approach is incrementally adding capabilities to extend the analytics tools already in use. To start, companies must first analyze their existing environment and identify any gaps. Ideally, tools should complement citizen data scientists’ abilities in areas such as data storytelling, data preparation, and direct querying using natural language queries.
Augmented analytics provides a guided, intelligent approach to conducting several steps, including augmented data preparation, augmented data discovery, and augmented data science. By adding these to the existing CDS toolkit, companies can make it easier for these roles to familiarize themselves with data analytics.
3. Start Business Extension Projects that Involve CDSs
Business extension projects are an ideal opportunity for citizen data scientists to demonstrate immediate value. Data leaders can begin by identifying existing processes that require repetitive decision-making, then tap citizen data scientists to take on these and other redundant activities, freeing expert data scientists to focus on more complex tasks.
4. Encourage Collaboration Between Citizen Data Scientists and Expert Data Scientists
The goal of the citizen data scientist movement is never to replace expert data scientists but rather to complement the latter and other existing analytics roles. As such, companies should focus on building and facilitating communication and collaboration across the analytics process. Both expert and citizen data roles should be involved in defining the collaborative process and approach.
For more on creating the best environment for citizen data scientists, head over to CDOTrends.