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May 21, 2021

Why your organisation needs a chief data scientist

Chief data scientists are needed to ensure AI development is ethical and to optimise decision-making, writes guest author Carlie Idoine, research vice president at Gartner

By carlie

It’s no secret that the disruptions of the past year have profoundly changed the way we do business. As organisations look to thrive in a post-pandemic world, data and analytics (D&A) is critical for enabling the automation and optimisation that are required for businesses to become future-fit.

Subsequently, the advanced analytics and artificial intelligence (AI) systems that support this automation and optimisation are becoming increasingly complex, pervasive and critical. This requires dedicated executive attention, in the form of a chief data scientist.

chief data scientist

Gartner predicts that by 2023, up to 70% of commercial AI products that do not have transparent ethical processes will be stopped due to public opposition. (Photo by Zapp2Photo/Shutterstock)

What is a chief data scientist?

The chief data scientist is a leadership role tasked with coordinating and managing data science, machine learning (ML) and AI in support of businesses’ top priorities. Chief data scientists must think tactically to assess the current situation and deliver value today, while in parallel planning strategically to coordinate and maximise the use of advanced analytics for the future.

D&A leaders have been presented with a ripe opportunity to leverage the chief data scientist role to propel the use of advanced analytics forward across the organisation. Here is what D&A leaders need to know about the chief data scientist role and why your organisation needs one.

The chief data scientist is responsible for ensuring the organisation’s overall data science strategy is translated into tactical execution and measurable operational performance. The role is typically the most senior data science position within an organisation and has a specific focus on applied data science approaches. It’s important to note that the chief data scientist role is a complement to, rather than a substitute for, the chief data officer (CDO) role, and their alignment is critical, especially around security and ethical issues.

The chief data scientist is often referred to using various titles, which may also include:

  • Vice president of data science
  • Head of data science
  • Head of artificial intelligence
  • Director of artificial intelligence

While titles vary, duties consistently include a combination of both business and technical responsibilities. Some in the role are more focused on business responsibilities, some have heavily weighted the technical responsibilities, and still others have a consistent mix of both. The chief data scientist role is not one-size-fits-all.

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Regardless of the division of responsibilities, it’s essential to understand that this role is not just about technology. As the conductors responsible for steering the definition and execution of data science and machine learning projects, the chief data scientist can take on two key business responsibilities: leading the development of an ethical and human-centric AI program, and helping to re-engineer decisions for greater business impact.

Promoting human-centric AI development

Gartner predicts that by 2023, up to 70% of commercial AI products that do not have transparent ethical processes will be stopped due to public opposition or activism. The data scientist can lead the charge for using a transparent and human-centric approach to developing advanced analytics and AI solutions. They can start on day one by adopting a responsible approach to AI and model development, using frameworks and methods that capture user value and expose ethical risks.

Many of the solutions devised so far to address issues of AI trust issue have focused on explainable AI frameworks and model interpretability. Yet, a more holistic, responsible approach that addresses the human aspect, as well as technology, is needed. Such thinking is often neglected or only addressed once a solution has been deployed. Human-centred approaches to product development, such as design thinking, are becoming widely adopted, but need to be adapted for an AI context.

The chief data scientist can take the lead on adding an “empathise stage” to AI development to ensure a human-centric approach. This can include managing various activities needed to build empathy-based thinking into AI development, such as building an engaged user group, discovering attitudes and emotions towards AI, capturing information in an intuitive way and providing a mechanism for addressing concerns and gathering further feedback. This framework provides a foundation upon which further responsible AI practices can be included in the model-building stage.

Re-engineering business decisions

Another essential focus for chief data scientists is to align with key business decisions. However, increasing complexity of the business environment and digital disruption have made traditional decision-making practices ineffective. Chief data scientists have an opportunity to take a leadership role in re-engineering decisions to execute favourable and outcome-driven results.

A recent Gartner survey found that less than half of respondents use data as their first-choice approach for making decisions. The chief data scientist can support the chief data officer (CDO) in re-engineering decision-making by leveraging decision models, ensuring that data plays a role in every organisational decision. In this way, the chief data scientist role becomes a key contributor to establishing a resilient, flexible organisation that is better positioned to respond to the changing forces that impact it.

The chief data scientist is an evolving role that is key to applying advanced analytics and AI techniques beyond focusing on operational delivery, to ensuring strategic alignment to drive organisational imperatives. As the complexity, pervasiveness and criticality of advanced analytics and AI grows, the chief data scientist can play an essential role in providing the dedicated, specialised attention that these systems need to deliver long-term business value.

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