The Bimodal Data Team and the Rise of the Full-Stack Artisan
Tristan Handy is CEO of the company formed by the combination of dbt Labs and Fivetran, whose new name is pending. In a wide-ranging interview on DataCamp, Handy observed that “we are in the midst of one of the biggest shifts in the way that data teams operate” over the last 10 years. This claim caught my attention because of its implications for the Insights Supply Chain (ISC).
Handy describes a data team “bifurcated into two main buckets”: platform maintenance and data enablement. According to Handy, data practitioners (Handy’s terminology) in platform maintenance focus on the centralized infrastructure, including data pipelines, data governance, and data security. Data practitioners in data enablement focus on democratizing data access throughout the organization and partner with stakeholders to standardize business definitions into semantic models. The ISC describes this division of labor as “upstream specialization,” in which data engineering provides data services to analysts. In Handy’s bifurcation, an upstream platform-maintenance bucket supports a downstream data-enablement bucket. Together, these buckets form what I call the bimodal data team which accounts for contexts where data practitioners can switch between platform maintenance and data enablement.
At the same time, Handy rejects conventional role definitions centered on technical skills. Instead, Handy defines roles by how they create business value, insisting that “the best teams are focusing on full-stack data practitioners who can own projects end-to-end.” Although the DataCamp discussion did not explain how full-stack practitioners fit within the two buckets, Handy’s comments place a broad practitioner model alongside a bifurcated organizational model. The advent of AI and its application in data workflows and teams supports the emergence of the full-stack role. AI is blurring conventional distinctions between roles by augmenting the technical capabilities of data practitioners and expanding their capacity for cross-functional communication and collaboration.
Handy’s description of the full-stack data practitioner also evokes the notion of an artisan in the ISC, although John Thompson describes the artisan role as belonging to a data scientist. Viewed through the ISC, Handy’s full-stack practitioner becomes a full-stack artisan, extending the artisan concept beyond data scientists to a broader range of data practitioners. The full-stack data practitioner also extends Colin Zima’s conception of the data analyst as curator, communicator, and product thinker: a holistic, business-focused data analyst.
Handy’s full-stack practitioner, Thompson’s artisan, and Zima’s holistic analyst all combine broader practitioner capabilities with a direct focus on business value. AI puts this model within reach of more data practitioners. In the Insights Supply Chain, the bimodal data team divides the work; the full-stack artisan connects that work to business value.
