What are the Key Components of Next-Gen Augmented Analytics Tools?

Life science teams can do their jobs more effectively when data-driven decision-making is a part of their workflows. With access to data insights, field sales can tailor presentations with physician-level details. The market access team can identify hurdles to adherence, and clinical development can streamline trial processes. However, making data insights readily available to these life science professionals has been a challenge. Traditional analytics and business intelligence tools require data science expertise and time to build dashboards and create visualizations. With those tools, it’s not possible to quickly identify a new sales opportunity, solve an urgent issue with access to treatment, or decrease the time it takes to enroll patients in a trial. But augmented analytics tools give business users autonomy.

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What Are Augmented Analytics Tools?

Augmented analytics tools use advanced technologies, such as artificial intelligence (AI), machine learning (ML), deep learning, natural language processing (NLP), and low-code/no-code capabilities, to make analytics accessible to all business users. Augmented analytics makes it easier for humans to interact with data, even if they don’t have data science expertise, and to get answers to their questions and analytics insights on demand. 

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Features of Optimal Augmented Analytics Tools for Life Sciences 

Several components and techniques work together to make data insights more accessible to life science teams: 

• Domain-specific training

AI performance is tied to its training. Augmented analytics platforms for life science organizations should be trained with life science data. While this gives companies the advantage of faster implementation, it also provides accurate, contextual answers immediately, building user trust more quickly and encouraging adoption. 

• Scalability

A platform intended for use in life sciences needs an architecture that enables limitless scalability. Life science analyses often require multiple data sources and large volumes of data. A highly-scalable analytics tool enables deep, granular insights that are truly valuable to users. 

• Automatic data identification

AI enables the automatic detection of data from specific fields, such as location, a physician’s name, or brand. With this capability, the platform can provide users with insights relevant to their specific markets. For example, a field sales rep can receive information at physician level and tailor a presentation to be personalized, impactful, and more effective. 

• Large language model (LLM)

Configuring or manipulating a traditional business intelligence (BI) dashboard takes training and time, and, unfortunately, life science employees turn to their data teams for help, adding to their workloads. On the other hand, an augmented analytics solution leveraging an LLM allows the users to interact with the platform conversationally in a “ChatGPT-style” manner, with no intervention from the data or IT team required. 

• Low-code/no-code environment 

Platforms that require users to code to configure their interfaces or perform other functions are difficult, if not impossible, for all members of commercial teams or other life science employees to use. Businesses should be able to interact with the platform without writing code. 

• Anomaly detection

Keeping up with changes in the complex and dynamic life sciences space is a challenge. Automated anomaly detection enables the augmented analytics tool to deliver proactive alerts when a deviation from set ranges occurs. For example, the platform could alert users when prescriptions vary from forecast ranges. This feature keeps teams informed so that they can take corrective action more quickly. 

• Key driver analysis 

Augmented analytics tools have added value when they can not only provide information on what happened but also why it happened and prioritize corrective action, such as increasing sample drops or advertising. Or, if a team’s performance has been exceptional, the tool can show the activities that contributed to it so they can double down on what works. 

• Data governance capabilities 

When business users have easy access to a data analytics platform, they may also have access to protected data. Augmented analytics tools should give administrators the ability to control access to protected health information (PHI) and other sensitive data, preferably at the column or field level. This enables deep analysis while complying with data privacy regulations and policies. 

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Benefit from the Next Generation of Analytics 

Life sciences organizations that transition from traditional BI solutions to augmented analytics tools will see a fast return on their investment. It starts with adoption. Organizations that have implemented WhizAI, for example, see up to 100% user adoption of our easy-to-use platform. 

Additionally, giving business users analytics autonomy decreases their dependence on the company’s data and IT teams by 40-60%. Furthermore, by simplifying processes, the total cost of ownership of data and data analytics decreases by as much as 50%. 

However, those benefits depend on choosing the right augmented analytics tool for life sciences. The tool should give all business users autonomy so they can build data-driven decision-making into their processes, deliver accurate, contextual insights, provide proactive alerts, identify key drivers, and include features that keep data secure. Contact WhizAI to learn more about a platform designed specifically for life sciences that does it all.

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