Life science companies have long understood the impact data can make on their businesses, but over the past few years, the focus has been on augmented analytics. These platforms are designed for everyone, including analytics consumers without data science or IT expertise. When data insights are easily accessible to all employees throughout the organization, users, from sales, marketing, market access, and patient services to R&D, manufacturing, and the C-suite, can build data-driven decision-making into their workflows.
Unfortunately, life science companies’ investments in BI dashboard solutions haven’t enabled that outcome. Dashboards require IT expertise and weeks to build, delaying insights and, sometimes, resulting in missed opportunities. Moreover, if users need to look into an issue more deeply or ask a different question, it can require new dashboards and more waiting.
How to Deliver Augmented Analytics to Life Science Teams
Artificial intelligence (AI) brings speed and agility to analytics. AI-driven augmented analytics platforms can provide insights to all users quickly and easily. Natural language query (NLQ) allows users to ask questions conversationally. Employees don’t have to learn keywords or phrase questions in a specific way; they can ask as if they were speaking to a colleague. Then, the platform analyzes billions of records from multiple data sources to provide an accurate, contextual answer – and leading platforms can accomplish it in a sub-second. AI also enables the platform to choose the best data visualization so that the information is easy for the user to understand. WhizAI, an augmented analytics platform pre-trained for life sciences deploys faster, understands users’ questions and intent, and provides accurate, reliable insights.
The Key to Effective Augmented Analytics for Life Sciences
Although technology enables machines to perform impressive tasks, they can’t learn independently or think for themselves. Solution builders must train analytics platforms to understand what users are asking and the data sources that provide the answers.
In life sciences, this means training the platform on the industry’s vast vocabulary and wide range of data sources. Solutions pre-trained with life sciences data can deploy within a few weeks, unlike “generic” augmented consumer platforms that can take six months or more to deliver value to life science teams.
Domain-specific training and advanced AI capabilities help the platform better understand a user’s intent, even if a user phrases a question atypically or misspells a word. The platform can also intelligently determine how to present results in charts, graphs, or tables.
An augmented analytics platform development team familiar with the domain can also create a solution that integrates seamlessly into life science workflows. For example, users need a way to save the results of analyses and share them with colleagues, such as creating pinboards in a no-code environment. Solution builders that think beyond the analysis to how employees will use insights boost platform adoption.
However, the most critical element of a solution that employees will use is delivering results they trust. The platform must demonstrate value and reliability immediately, or life science users will reject it. Accurately pretraining an effective solution for the domain is the best way to see fast, enthusiastic adoption.
The Benefits of an Augmented Analytics Platform Trained for Life Sciences
The advantages of life sciences teams that base decisions on data can’t be overstated. WhizAI an effective augmented analytics platform, can identify top opportunities that may otherwise have been overlooked and increase market share. WhizAI, the intelligent platform can alert teams to declining product performance or patients at risk of non-adherence, giving them the agility to intervene. It recognizes patterns, trends, and anomalies faster than legacy methods, keeping teams informed and agile.
WhizAI helps life sciences companies see the return on investment (ROI) from data acquisition with teams that use data to do their jobs better and improve patient outcomes – if the platform is designed specifically for the domain.
