How AI is Changing the Definition of an Enterprise BI Platform for Life Sciences

When decision-makers with life sciences companies hear “enterprise BI platforms,” they think of reports and dashboards. Furthermore, they probably think about the limitations of these solutions and their frustrations with them. Business intelligence (BI) dashboard solutions require data expertise to use them. So, most life science employees, whose skills and experience may be in sales, manufacturing, market access, or patient services, need to rely on their IT or data teams for the insights they need.

The data team would run the analysis when a life sciences business team wants to answer a question such as how TRx or NRx for a new product changed in a particular region over its first quarter. It may require building a new dashboard; adding weeks to the process. In the meantime, the sales and marketing team may have moved forward with plans without waiting to consult the data.

Enterprise BI platforms are also notorious for their limited scalability. Data analysts’ hands are tied when it comes to adding more data sources and analyzing greater volumes of data. They overcome these limitations by building more dashboards and piecemealing analyses. This makes accessing insights even more time-consuming and complex for the end users who need to scroll through cascades of screens and reports to try and find the answers they need.

A New Perception of Enterprise BI Platforms

Artificial intelligence (AI) and machine learning (ML) are disrupting the life sciences data analytics space once dominated by enterprise BI platforms. One of the primary reasons is that AI scales. Life sciences companies aren’t limited by the number of data sources and volumes they use in analyses. Additionally, they can perform those analyses much more quickly than with BI dashboards, analyzing billions of data points in a sub-second.

AI is also the key to delivering insights directly to the augmented analytics consumer. Natural language processing (NLP), specifically natural language query (NLQ) and natural language understanding (NLU), allows a machine to understand voice or text data, including the user’s intent. Additionally, natural language generation (NLG) and data visualization will enable an enterprise BI platform to deliver a relevant response that users immediately understand. The user experience is very similar to a human conversation, and instead of a dashboard as the analytics unit of work, it’s simply asking a question.

The Importance of Domain-Specificity

When AI-powered enterprise BI platforms are intended to deliver insights to life science users, they should be pre-trained to understand life sciences data, processes, language, and analytics fully. Machine learning models are most effective when trained for a specific use case. Furthermore, life science has a distinctive language of its own full of abbreviations, acronyms, and data sources. Pretraining helps implement the platform more quickly and gain relevant insights from day one. This may have been a sticking point with life science companies that attempted to use an AI analytics platform created to meet the needs of a horizontal market. These solutions can take months to implement, train, and fine-tune for a business. Users may lose trust in the system during that time, which can negatively impact adoption.

A platform pre-trained for life sciences immediately demonstrates its value and reliability and will inspire enthusiastic adoption by users eager to make data-driven decisions.

Making the Platform Ready for the Enterprise

Any analytics platform intended for enterprise use should be secure, have robust access control features, and integrate with employees’ business applications. Above all, it should remove the friction that stands in the way of business users accessing the data insights they need to do their jobs most effectively. Employees at their desks, on the road, collaborating with their teams, or across lines of business should be able to ask a question and get a contextual, relevant answer.

These benefits of augmented consumer platforms for life sciences analytics will set a new standard for enterprise BI platforms. Visit WhizAI to know more about enterprise analytics solutions.

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