How WhizAI Supports Decision Analytics

Decision analytics solutions identify trends in data sets and provide that information to analytics consumers to help them determine the next best steps. In the life science domain, basing decisions on data is key to improving patient outcomes, maintaining competitiveness, and capitalizing on top opportunities. 
A hurdle that life science companies face, however, is getting insights to the right people at the right time. WhizAI’s augmented consumer platform for life sciences makes decision analytics accessible to everyone throughout an organization, even if they don’t have “data scientist” in their titles.

How Decision Analytics from WhizAI Benefit Life Sciences Teams

  • Field Sales:
    Decision analytics can inform sales reps about a healthcare provider’s prescribing pattern and the materials from the company that caught the prescriber’s attention. With these insights, the rep can personalize conversations and positively influence prescription rates.
  • Sales Managers:
    WhizAI makes it easy for sales managers to take a big-picture view of sales in their regions as well as drill down to specific healthcare provider activity and individual sales rep performance.
  • Market Access:
    Decision analytics from WhizAI provides intelligence about competitor activity, current and historical market activity, and the company’s market share. This information will help a market access team improve formulary or patient enrollments.
  • Patient Services:
    Using patient data from diagnosis to recovery, the patient services team to improve adherence and patient outcomes.

How WhizAI Overcomes Life Science Data Challenges

WhizAI is designed to overcome a prevalent challenge among life sciences companies: How to achieve data ROI. 
Companies invest millions into data acquisition and analytics but often fail to move the needle on business performance. When companies use traditional BI dashboard solutions for decision analytics, the answer that life science teams need can take weeks, decreasing agility. Additionally, most employees aren’t data scientists, so they rely heavily on their data or IT teams for analytics and assistance using dashboards. 
WhizAI changes the unit of work from a dashboard to a question. WhizAI’s hybrid natural language processing (NLP) engine understands how life science employees across lines of business communicate. Users don’t have to phrase questions in specific ways or use keywords. They can ask questions naturally, and WhizAI responds with relevant, contextual insights and automatically chooses the optimal visualization to present information. Furthermore, if users need deeper insights, all they need to do is ask a follow-up question, and WhizAI responds with insights that take their roles, the regions in which they work, and their product focus into account.  
As a result, companies using WhizAI report a decrease in end-to-end development time, increased efficiency, cost savings, and faster time from data acquisition to insights. WhizAI also contributes to greater job satisfaction among employees. 

WhizAI Decision Analytics Features

Pre-Trained for Life Sciences

WhizAI is pre-trained for the life sciences domain, so it can analyze life sciences data from primary and secondary sources right out of the box. In addition, because it’s pre-trained, deployment is faster than with other AI analytics platforms, reducing the time from months to weeks.

High Scalability

BI dashboards analyze a limited number of data sources and data volumes. But WhizAI scales to an unlimited number of data sources, can base decision analytics on petabytes of data, and still provides insights in less than a second.


WhizAI is designed for enterprise use. Enterprise features include integration with the business applications that life sciences teams commonly use, such as Veeva, Microsoft Teams, and Salesforce. It also has enterprise-grade security and access control via multifactor authentication (MFA) and multilanguage capabilities.


WhizAI’s microservices architecture provides uptime and flexibility to ensure that users always have access to decision analytics when they need them, regardless of a question’s complexity. WhizAI also monitors security intelligence resources to stay on top of vulnerabilities and keep the platform secure.

Focus on Autonomy

WhizAI is structured to support the augmented life sciences analytics consumer, delivering insights with minimal assistance from the data or IT team and encouraging user adoption. When users – from lines of business to the C-suite – see how easy it is to build decision analytics into their workflows and the impact it makes on their job performance, they make WhizAI a part of day-to-day processes. Companies that implement WhizAI see user adoption as high as 100%.

To learn how decision analytics via WhizAI will benefit your life science company

Can data scientists use WhizAI?
WhizAI can become the central repository of a life sciences company’s data and streamline the process of deciding which data sets to use for analysis, eliminating the time and resources to write SQL queries.
How does WhizAI present data insights?
WhizAI can answer diverse types of questions and deliver answers in a wide range of visualizations and responses. WhizAI automatically chooses the best visualization based on the data and the user.
How much training does WhizAI require?
WhizAI is so intuitive that most users only take a few minutes to familiarize themselves with it to begin accessing data insights. There’s no need for users to learn keywords or phrases. They can ask questions naturally.
Does WhizAI replace BI dashboards?
WhizAI can provide the decision analytics that life science consumers need as a standalone solution. Companies cans also choose to use WhizAI as a complement to BI dashboard solutions and reduce the number of dashboards their data teams maintain. On average, companies that deploy WhizAI reduce the number of dashboards from 20+ to about 5.
Where do life science employees use WhizAI?
WhizAI’s interfaces allow life science teams to use WhizAI when working at their desks on a PC or laptop, as well as accessing data insights using mobile devices on the road, preparing for meetings with healthcare providers or working remotely.


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