Benefits of the Best Business Analytics Software for Life Sciences

Business Analytics Examples

The best business analytics software for life sciences is pretrained for the domain, so it can provide contextual insights from day one. With reliable performance, the platform builds trust, encouraging adoption, data-driven decision-making into daily workflows, and improved performance across teams throughout the organization.

• Field Sales:
  Companies can give their field reps an advantage by making granular data insights available to them wherever they are. Simply repeating information about a formulary change won’t capture attention. But a presentation tailored to a specific physician or health system and explains the impact of that change can lead to better performance and business growth.
• Market Access:
  The best business analytics software for life sciences’ market access teams provides users with data, including authorizations, tiers, step therapy restrictions, and quantity limits. With these insights, the market access team can identify business opportunities and overcome barriers to care.
• Patient Services:
  With a growing number of products, including specialty drugs and personalized treatments, patient services teams need a solution that allows them to analyze data from a range of business functions, including marketing and sales, to find the right path from available programs to favorable patient outcomes.
• Clinical Development:
  Fast, accurate data analysis can replace manual processes. The right analytics solution can streamline evaluating patients for trials, creating trial protocols, and quickly alert researchers to adverse effects, allowing them to adapt and improve trial quality. The best business analytics software for life sciences also enables data sharing with all stakeholders, reducing the time it takes to disseminate information on a trial from months to weeks.
• Data:
  Giving business users autonomy means that the data team will spend less time assisting employees with dashboards and reports and more time on complex analyses that can provide greater value to the organization overall. In addition, an LLM can save the data team time, allowing them to replace writing SQL queries with simply asking questions to determine which datasets to use in their analyses.

See What Gartner Has to Say About the Benefits of Natural Language Technologies

In the 2022 Hype Cycle for Natural Language Technologies, Gartner says that because of their speed and ease of use, analytics platforms based on LLMs are only two to five years away from mainstream use.Furthermore, companies are embracing them because employees use them. Traditional analytics platforms require extensive training and support, and companies typically see about a 33% adoption rate with them. However, WhizAI data shows that platforms that give users autonomy, allowing them to build data-based decision-making into their routines and helping them improve their performance can drive adoption as high as 100%..

Features of the Best Business Analytics Software for Life Sciences

Life sciences companies looking for the best business analytics software will find numerous options on the market. So, to narrow the choices, look for solutions designed specifically for life sciences organizations and that are best suited to the needs and challenges of the industry.

Scalability

Traditional BI dashboard solutions have a limit to the number of data sources and the data volumes they can handle. This forces business users to attempt to find the answers they need by piecing together information from various dashboards and reports. The best business analytics software for your business won’t put a limit on the data you can use in analyses.

Speed

The best business analytics software will deliver insights on demand. A platform capable of analyzing billions of data points and responding to a user in less than a second will provide more value than a dashboard solution that takes weeks to build.

Domain-specificity

LLMs trained on the language and data sources of life sciences deploy faster than solutions from business analytics software companies that target a horizontal market. They also allow users to interact with the platform naturally rather than learning keywords or how to query a platform to access the information they need.

Enterprise Readiness

Life sciences companies must consider that the analytics platform they choose won’t operate in a vacuum. The best business analytics software will integrate seamlessly with the business applications life sciences companies use. They’ll also leverage enterprise-grade security for access control and data protection and benefit from multi-language capabilities.

Frequently Asked Questions

What types of technology does WhizAI use?

WhizAI’s platform includes a hybrid natural language processing engine that combines a blend of deep learning and sophisticated linguistic techniques, including natural language understanding (NLU), natural language generation (NLG), and natural language query (NLQ).

How can life science companies update data that WhizAI uses?

WhizAI automatically updates when new data is available and delivers insights based on the most recent information. All users in a life sciences organization have access to a single source of truth, supporting team alignment and collaboration.

Where do users find WhizAI insights?

Users can receive insights via SMS text or by clicking links in the applications they commonly use, such as Microsoft Teams, Salesforce, and Veeva. Users can also access WhizAI from any device, including a PC, laptop, tablet, or smartphone, so they have the insights they need in the office, on the road, or anywhere they’re working.

Does WhizAI replace dashboards?

WhizAI works as a standalone solution; however, some companies choose to use it to complement dashboards and reduce the number they maintain.

Do users need to know how to code to use WhizAI?

WhizAI provides a zero-code environment, so any business user can access information and configure the user interface and use the pinboard without writing code.

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