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Shifts in expertise in the data science era A fascinating project launched in April 2021

Research context

Our research aims at further understanding how multiple forms of expertise are competing within the organisation, in the wake of the data science trend.

Our first phase of field work at BNPP PF revealed the centrality of scoring practices or scores, that is, mathematical models aimed at evaluating customers. These practices are historically embedded in the organisation and have relied since the 1970s on strong mathematical skills, embodied by the Risk teams and their statisticians. However, the spread of data science—through new occupations, new tools and new sets of skills— especially computer science skills— allows a new way to produce scores in the organisation.
 

Research question

How does the diffusion of data science challenge established forms of expertise in organisations? 
Do we still need experts?

Methodology and milestones

1st phase (completed): 18 exploratory interviews with 16 Personal Finance employees.
Non-participant observation of a data science project from June 2021 to January 2022.

2nd phase (upcoming): focus on Risk teams, with a target of 20 interviews - data scientists, scoring experts, business process experts - and observations. Additionally, we plan to access non-classified archives documenting the rise of scoring practices, for a historical perspective.

 

Research team

  • Valentin Mesa (ESCP, Paris campus)
  • Géraldine Galindo (ESCP, Paris campus)

Research keytake aways

Data science reframes expertise in organisations, promoting expert work supported by computer science. This happens as a result of data scientists seeking legitimacy in the organisation.

This transformation may not be frictionless, as historical forms of expertise are embedded in the organisation through tools, processes and recognised skills.

Ultimately, more teams within the organisation can benefit from this challenge on expertise, as the supply of expert work is increasing.

Data practices emphasize the role of in-between actors - data workers who operate business software and databases - and whose knowledge and skills are key ingredients for data science.

Attention points: Talent management, History-mindful innovation and change.



 

Key outcomes

External ecosystem

Shared research outputs / Awards and articles

“Innovation et acteurs-frontières : les enjeux du capital symbolique”, Paper presented at the 32nd AGRH doctoral workshop [link]

“Famous objects : Studying organizations through the lens of symbolic capital”, Paper accepted at the 38th EGOS colloquium [link]

 


 

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