Bring Domain and Data Experts Together to Spur Upskilling

Scaling AI Catie Grasso

Despite the efforts to train more AI experts (across academic, governmental, and corporate entities, for example), the truth remains: There is a lack of AI experts, particularly in less tech savvy or sexy industries. This leads to the existing data experts to be bogged down by the minutiae, a demoralized workforce, and sometimes even the inability to deploy advanced analytics and AI projects.

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Enterprises can't rely on experts alone when aiming for massive, company-wide change. They need to be able to tap into knowledge workers who are already working with data in some light capacity — think engineers, supply chain managers, etc. This blog post sheds some light on how organizations can achieve this in practice by letting advanced experts do advanced data science, while also empowering domain experts to play a critical role.

So, How Can Organizations Address This Data Expert Scarcity?

For data and AI to truly become ubiquitous within an organization’s operating model for AI initiatives, everyone — regardless of their role, team, or technical expertise — needs to have appropriate access (and, with it, literacy and understanding) to the data they need to do their jobs and make decisions based on that data. In addition to setting up formal upskilling and continuous education programs, organizations can also provide tools that support those initiatives and allow domain experts to participate in the data science process — such as via features that support AutoML and augmented analytics, data cleaning, or exploration without code. 

In the video below, hear from Dataiku Field CDOs Conor Jensen and Shaun McGirr as they highlight the ways that Dataiku customers NXP and Unilever are leveraging their existing expert data talent to equip domain experts to solve data science and advanced analytics problems themselves:

Upskilling Is a Virtuous Cycle

As we have observed, companies do not want to limit data and AI initiatives to any one business unit or team. With the increase in adoption and scale, new players are continuing to join the data experts and their teams developing, deploying, and managing AI. With success from initial AI projects comes more involvement from business stakeholders who want visibility into projects and potentially even review and sign-off at key steps. We will continue to witness data and AI democratization and projects that allow collaboration between domain and data experts which will both, in turn, lead to more deployments and high-value business outcomes. 

As AI upskilling is fundamentally critical to AI staffing, organizations need to be tremendously intentional about embedding formal active continuous learning on AI into employee education programs so teams can access high-performing talent and shape the talent into the emerging needs associated with scaling AI. 

Further, helping staff understand how AI, data science, and ML fit into the company’s overall strategy can be just as critical as educating people on the concepts and technology themselves. So by clearly communicating the value, existing employees can more easily see how upskilling fits into the mix. Plus, when an upskilling program works well, it creates a virtuous cycle where domain experts (i.e., business analysts) acquire skills and create value. In turn, this increases awareness of the value of AI and new users are identified or raise their hand to be upskilled.

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