“We all have to look into the Future”: How to bridge data science talent gap to embrace Pharma fieldforce transformation

Despite the alleged ‘sexiness’ of data science as a profession—at least according to the Harvard Business Review—shortages of data scientists continue to be an issue across all sectors. Data scientists are in the happy position of being able to pick and choose their employers and industries. Meanwhile, organizations are increasingly frustrated that they cannot access the expertise that they need to get value from increasing quantities of data.

The pandemic and associated ‘Great Resignation’ have certainly not helped this position. Like many others, data scientists are also moving on—and their average tenure of just two years was fairly short anyway. There is a growing recognition that perhaps organizations have to find other ways to fill the data science gap.

Data science skills alone will not be sufficient

Fran?ois Aerts is Senior Director, Head of Commercial Excellence EUCAN at Idorsia Pharmaceuticals Ltd. Idorsia is a young company, founded in 2017. Francois is challenging the way the industry is looking at the data.

“We have to use analytics in quite a modern way; we have now to look at the future. We have to establish new KPIs to drive our business, and not only traditional Call coverage. It’s all about helping the company to be more proactive, to initiate discussions, to go into exploration and to improve its focus on the outside.”?

Leveraging his experience on different size of organization and different level of decision (global, EMEA), he recognizes that agility is key.

“We need to be responsive, and make sure that we have processes that will allow us to react quickly and connect the data. The real value is in getting information quickly, especially on the link between what we do, and the results.”

Fran?ois recognizes that there is a global shortage of data science skills. However, he suggests that this is maybe not the main challenge.

“I’m not sure we actually need pure data science skills. The kind of person we need is actually more someone with a strong knowledge of both the business and the data, who can develop the right questions. It’s actually easier to find someone who can do the analysis than who has that expertise in data itself”

Linking business and data

Fran?ois notes that data scientists may have challenges providing insights without support from the business.

“I feel like there’s more and more of a disconnect between the business functions and the data. You collect data so that you can use it to guide the future, and that means that you have to be able to identify the gaps in your knowledge. This can only be done by people who can understand and use data, but also know about the business.”

This is indeed consistent with conversations we have with larger pharmaceutical companies too. With analytics maturity comes the need for composite skills.

“Data scientists tend to need other people to identify the questions before they can provide answers. That’s perfectly reasonable, but it doesn’t really help us. Ideally, I need people who are more proactive: who can identify the questions themselves, and then use data to find the answers. What I really need is people who understand data and can manage it—but are within the business.”

In such a complex and competitive environment, Fran?ois stresses that management has to work on this issue from both sides, by helping as well data scientists to improve their business knowledge.

“We have to take action to make sure that data scientists are part of the business discussions. They might attend the steering committee or the operational committee, so that they hear the discussions, and this can inform their analysis. Overall, I think the ideal balance is that they spend about 80% of their time working with data, and the other 20% connecting with the business. It’s a big investment of their time, but it’s worth it.”

The value of collaboration

Fran?ois stresses that not every data scientist needs to be involved in every meeting. However, he clearly values collaboration and cooperation between its analysts and business units.

“There are different levels of interaction. For example, there are levels of meetings which are useful for collaborators to attend regularly. This enables them to be creative on their own. I think this is what creativity is all about: finding the right analysis to answer the right questions.”

He suggests that companies should be inspired by start-ups.

“It’s the Steve Jobs approach: you don’t want to recruit smart people and then just tell them what to do. Instead, we want them to tell us what to do. In a start-up, people have the opportunity to be more proactive, and that’s where things get interesting for both them and the organisation.”

I wanted to thank Fran?ois, that I know since we both started our career at Roche. My takeaway from our discussion is that even if young companies could be more digitally driven, they are facing the same challenges than Big Pharma. Business Leaders, such as Fran?ois, still do have, on a day to day base, to ensure people do have data in their DNA, are collaborating between multi-disciplinary teams to collect new data and extract innovative insights from it. To do so Fran?ois and his Industry peers do have to empower their teams with modern solution that ease data manipulation & data exploration.

For all information on 2022 SAS Expert Study and to go back to the results from last year's interviews please visit?Innovation at Scale: Expert Study | SAS

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