Unleashing the power of AI/ML by upskilling business users

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The emerging role of citizen data scientist in Pharma Commercial Operations

 

Pharma and biotech companies, especially those introducing first-in-class Specialty & Oncology drugs, with added complexity such as novel mechanisms of action and special supply chain considerations, face many challenges to successfully market and sell their products. Mounting competition, as more drugs launch in established disease areas, intensifies the need for pharma marketers to find creative ways to stand out from their competitors and take advantage of innovative engagement strategies.

To overcome this increased complexity, we recently see more and more Artificial Intelligence implementations making their way into life sciences companies to enable advanced analysis of huge sets of data. With the power that AI and machine learning bring, brand teams can leverage near real-time data to optimize planning and execution processes and engage HCPs in a personalized manner.

Well, this is easier said than done. More often than not, there is quite a large gap between those doing self-service analytics as business users and those doing advanced analytics as data scientists. To effectively apply science to operations we must be able to bridge over this gap.

To bridge this gap, business users ought to turn into citizen data scientists, utilizing user-friendly machine learning-powered tools that will help accomplish data discovery and analytics tasks that once were reserved for expert data scientists only.

 

What is a citizen data scientist? Gartner defines a citizen data scientist as “a person who creates or generates models that leverage predictive or prescriptive analytics, but whose primary job function is outside of the field of statistics and analytics” and InformationWeek states that the “defining trait is that statistics and analytics are secondary in the role.”

 

Upskilling brand managers to take on enhanced data functions such as exploratory analysis, visualization, and putting their insights into action, allows for better use of data, which leads to a better understanding of their customers and the market, as well as cost savings through greater efficiency.

 

Does this do away with the need for data scientists? Not at all. However, it does free them to focus on the more complex and specialized analysis of data that they are trained to do, and enables organizations to make significantly better use of the data they already have.

 

 

The following three rules will help unleash the power of citizen data scientists and boost effectiveness in Pharma brand teams:

 

  • Support a dynamic, data-driven culture – Strive to base decisions on the latest, most relevant data. Your messaging ought to be personalized to address the precise needs and concerns of every HCP in your therapeutic universe. Your campaigns ought to target specific HCPs of relevance. Nurture the culture of taking advantage of the latest available data, rather than relying on periodical, stagnant segmentation, targeting, and brand strategy planning
  • Provide the right tools and training – Opening up the business benefits of advanced data analytics requires providing user-friendly technology that will allow marketing and sales managers to explore their therapeutic universe, understand the HCP landscape and evaluate scenarios and tactics. Tools have to be explainable and easy enough to use to encourage business users’ self-reliance.
  • Foster collaboration between brand professionals and data scientists – When brand leaders in marketing and sales and data teams use different tools and separate data sets, it perpetuates ineffective work silos and results in suggestions that are misunderstood, incomplete, or even rejected. Providing access to the same data and tools for brand professionals and data scientists, removes those silos and leads to effective collaboration

 

Selecting powerful AI/ML solutions enables companies to make huge leaps forward in less time. Combined with the right data-driven culture of collaboration and agility, life science companies can significantly accelerate their data strategies and scale the value of data analytics across the business.

 

Learn about Tovana– A comprehensive and user-friendly science-driven platform that connects data and business pharma needs using AI and ML.