Why Domain Expertise Is Essential for Life Science Analytics

Domain expertise matters. Consider this: Enterprises choose transportation and logistics companies that have created the specific types of services they need. Businesses seek professional services with experience in their industries and look for consultants with good track records in their markets. However, remember that domain expertise benefits extend to analytics platforms, particularly those deployed for life sciences.  

Gartner® points out in its Market Guide for Augmented Analytics 2022 that by 2025, context-driven analytics and artificial intelligence (AI) models will replace 60% of existing models built on existing data. The guide states, “Augmented analytics tools help decision makers achieve contextualized, connected, and relevant insights by applying and demonstrating domain-centric (vertical industry or business function) information, resources, analytical methods/models and product expertise and service knowledge.” 

WhizAI is proud to be among the “Augmented Analytics Tool Vendors and Products” included in the guide, specifically as a domain-centric analytics platform– the only one developed specifically for life sciences. 

WhizAI is also recognized as a Sample Vendor in several 2022 Garter Hype Cycle™ Reports: 

• Gartner Hype Cycle for Life Science Commercial Operations, 2022
• Gartner Hype Cycle for Natural Language Technologies, 2022
• Gartner Hype Cycle for Analytics and Business Intelligence, 2022
• Gartner Hype Cycle for CRM Sales Technology, 2022
• Gartner Hype Cycle for Life Science Clinical Development, 2022

Faster Time to Insights You Can Count On

In general, augmented analytics aims to solve the problem of giving users data analytics autonomy. WhizAI approaches this challenge by automating analytics workflows and creating a user experience that’s as easy to use as a social media app. Natural language query (NLQ) allows users to ask questions conversationally rather than adapting their behaviors to learn to use dashboards natural language understanding (NLU) and natural language generation (NLG) provide contextual answers. 

Gartner explains that augmented analytics also allows users to discover patterns, trends, or anomalies that may have gone unnoticed, minimizing human bias that may creep into traditional analyses and accelerating time to insight. 

However, the platform needs to understand the data an organization uses. An augmented analytics platform designed by a team with domain expertise delivers relevant, contextual answers sooner. 

It’s challenging for a non-domain-specific model to answer questions like, “What is the 13x13 change for claim counts for our brand and competitor A?” or “What are our sales per region?” It can take months of training before a platform can work effectively for augmented life sciences analytics consumers. But an analytics platform pre-trained for life sciences can begin to deliver relevant, contextual insights from day one when users ask for the information they need to do their jobs effectively. 

The Edge that Augmented Analytics and Domain Expertise Creates

Life science companies that compare an augmented analytics platform with domain expertise to other horizontal platforms on the market will discover several key differences. First, because a domain centric platform is designed for their space, it can handle the data volumes that these companies deal with and deliver insights fast. WhizAI solves the problem of having so much data that the IT team can’t keep up and analyses that are so slow that teams can’t use them. When a team needs new insights, building a new dashboard is not necessary. They just ask a question and get the answer in a fraction of a second. 

The real aha moment comes, however, when life science companies realize that an augmented analytics platform with domain expertise will completely change the way they look at analytics. It’s not necessary to invest weeks of time and resources into answering a question and creating visualizations. Users only have to tell the platform what they want to know. WhizAI does the rest. Domain-centric software brings speed, flexibility, and user autonomy to the process that dashboard solutions and even generic analytics platforms can’t match. 

The Best of the Industry 

Another advantage of augmented analytics with life science domain expertise is that the team take a holistic approach to delivering a tool tailored to the industry. WhizAI’s team is comprised of professionals who have worked in life sciences industry and have helped create a platform specifically for life sciences workflows. It’s easy for life sciences teams, including sales, market access, clinical – and even the data team – to leverage WhizAI to make their jobs easier. It can provide insights to sales reps on the way into meeting with a physician, enable a patient services team to access information while on a call, or help a data team determine which datasets are relevant for a new analysis. 

Furthermore, because users immediately see that WhizAI is designed for them by people with life science expertise and delivers relevant insights, it builds trust, and adoption skyrockets. WhizAI sees its role as helping people get answers and solve problems, and adoption quickly skyrockets –  up to 100% –  when its value becomes apparent. Additionally, WhizAI continually innovates to keep up with the industry, providing a future-proof solution. Organizations that build analytics dashboards or adapt a generic solution have to do the work themselves to stay on the cutting edge of their industry.

Implementing Augmented Analytics is a Journey

The 2021 Gartner Analytics Consumerization-Democratization Survey found that 87% of organizations use analytics and business intelligence, and user adoption stalls at 29% on average. Additionally, the 2022 Gartner Analytics & Business Intelligence Platforms Magic Quadrant Highlights Webinar pointed out that automating insights is one of the most important capabilities emerging as a criterion for selecting a platform.

Gartner, Market Guide for Augmented Analytics, David Pidsley, 11 October 2022

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