Magicians, unicorns, or data cleaners?
With advances in digital technologies, the exponential growth in the amount of data available, and organizations’ pursuit of data-driven insights, a new group of knowledge workers straddling the intersection of technology and decision-making has taken centre stage: data scientists. While data scientists emerge as key figures in the contemporary discourse on digitalization, rather little is known yet about this nascent occupation.
Addressing this research gap, a recent publication by Lukas Goretzki (Stockholm School of Economics), Martin Messner, and Maria Wurm (both University of Innsbruck) unravels the complex interplay between data scientists’ work experiences and the multifaceted challenges that shape their occupational identities. Drawing insights from interviews with data scientists across different industries, the study delves into how data scientists mobilize their educational backgrounds, work experiences, and perspectives on the current digitalization discourse to craft their occupational identities. In doing so, it uncovers pivotal narrative elements that data scientists use to craft their identities.
The first is a “scientific mindset”: Data scientists nurture this element through their educational background (mostly in the sciences), innate curiosity, and the rigorous methodologies that characterize their craft. They emphasize the ‘scientific’ dimension of their work, in terms of investigating data like a researcher would do. The second element in their identity narrative centres around "sophisticated data methodologies" as tools of their trade. Emphasizing their expertise in employing advanced tools to unveil hidden patterns and transformative insights, data scientists distinguish themselves from other, in their view, less sophisticated data workers. The third narrative element embedded in data scientists’ occupational identity is a “problem-solving attitude” and a corresponding orientation towards translating complex data into actionable insights.
While the study suggests that, despite being members of a nascent occupation, data scientists have a relatively clear identity. However, enacting the latter is sometimes challenged by what they perceive as either too low or too high expectations of their internal stakeholders, not least managers. Regarding too high expectations, the study stresses the impact of the contemporary discourses on data science and data scientists. Data scientists feel that the fashionable nature of data science often translates into unrealistic expectations that managers and others have towards their work. Thus, instead of fully embracing the “hype” around their occupation in their identity narrative, data scientists carefully filter it through their work experiences and professional aspirations.
As a result, they, for example, engage in expectations management vis-à-vis their internal stakeholders to align their stakeholders’, at times, unrealistic hopes with the nuanced realities of their craft. Data scientists thereby engage in outward-facing identity work by carrying out educational work and stressing not only the prestigious but also the mundane, non-prestigious, and at times ‘dirty’ parts of their work to ‘tame’ the ambiguity and hype they perceive in managers’ expectations.
An additional challenge the study uncovers is that data scientists experience a good amount of ambiguity around their occupation often resulting in assignments that they feel are not part of their role. Put differently, they sometimes encounter expectations that they believe are too low and do not match their expertise in, for example, statistical modelling and exploring patterns in the data.
They, therefore, aim to avoid tasks that typically fall in the areas of reporting, business intelligence, or simpler data analysis domains for they feel that those should be performed by other data workers like data analysts or accountants. Emphasizing the level of sophistication of their work and the status-related aspirations that go along with it, data scientists try to demarcate their role in the organization and dissociate themselves from others.
This somewhat paradoxical form of identity work related to managing too high and too low expectations captures the conundrum that data scientists currently face in organizations: While, from a discursive perspective, being surrounded by a certain “hype”, data scientists grapple with the complexities emanating from the fashionable status of their craft and occupation in their day-to-day work. The very prestige that elevates their role thus simultaneously introduces layers of intricacy, potentially challenging their (aspirational) occupational identity.
The study shows how data scientists act upon themselves and others in the organization to tackle the identity challenges they face. By delving into the details of data scientists' identity work, this study offers in-depth insights, emphasizing the need for cautiousness when engaging in discussions about the potential and constraints of data science concerning advancements in accounting and control.
You can read the full article (open access) here:
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