A Checklist for Choosing a Life Science Enterprise Analytics Solution 

Data analysis in enterprise operations has become common. In fact, the 2021 Gartner Analytics Consumerization-Democratization Survey found that 87% of organizations use analytics and business intelligence. Life sciences organizations, for example, implement enterprise analytics solutions to aid in research and streamline and accelerate clinical processes. Data insights also help commercial teams pinpoint top opportunities and uncover patterns, trends, and anomalies that may otherwise go unnoticed.  

Unlocking data insights can certainly provide life science organizations with cost savings, faster time to market, greater operational efficiency, and a competitive edge. However, advancing from the decision to capitalize on data to enabling company-wide data-driven decision making can be a challenge. Furthermore, many enterprises fail to see the data ROI they expected. This is due in part to data analytics and business intelligence user adoption averaging only 29%, according to the Gartner study. 

Essential Enterprise Analytics Solution Capabilities

To reap data benefits, life science companies must carefully evaluate their enterprise analytics solution options to find the best fit for their organizations. Enterprises should pay particular attention to these areas. 

  • Artificial Intelligence (AI)

AI brings speed and scalability to data analytics. However, the AI used in enterprise analytics solutions isn’t always the same. The most beneficial solutions leverage deep learning to enable forecasting and projections. 

Furthermore, life science organizations will gain the most from augmented analytics platforms that leverage natural language processing (NLP) and deep learning, as well as other forms of AI. With NLP, including natural language query (NLQ), users can ask questions conversationally rather than investing time in learning how to use dashboards to find the answers they need. Additionally, natural language generation (NLG) will give an analytics platform the ability to create easy-to-understand responses. Enterprise analytics platform developers who thoughtfully consider users’ needs –  even those without data science expertise – will create a solution that leads to greater adoption and better outcomes. 

  • Domain-Specificity
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Some enterprise analytics platforms claim to offer value to organizations horizontally across markets. However, life science organizations will benefit more from a platform trained for their industry. It should be capable of analyzing the large data volumes required for thorough analysis. It should also understand the types of internal and external data companies use and recognize how people in the industry communicate. For example, in life sciences, the platform should understand that “sales” equals “prescriptions” and that some data is region- or country-specific. 

Domain expertise will enhance the performance and reliability of an enterprise analytics solution, and it will also make implementation faster, decreasing the time to weeks from months to deploy a solution designed without a specific market in mind. 

  • Content Personalization

Because traditional enterprise analytics solutions are so time- and labor-intensive, some dashboards are built in an attempt to meet the needs of many people across an organization. That means employees in different regions or countries may not have the specific information they need. A platform that provides insights to users based on their roles, markets, or geographic locations will enable relevant insights and enhance outcomes. 

  • Zero-Code Environment 

Life science teams will more enthusiastically adopt enterprise analytics solutions that are easy to use. Platforms that enable users to add analyses to a pinboard, have them update automatically, and choose the visualization they prefer –  all without coding –  will make insights more accessible to users.  

  • Embedded Interfaces

Ease of use includes where an employee will use the platform as well as how they use it. Users can integrate data-driven decision-making into their day-to-day workflows more easily when they can access an analytics platform from within the business applications they routinely use, such as Veeva and Salesforce. Additionally, ensure users can access insights whether they’re at their desks or in the field using smartphones or tablets. 

  • Enterprise-Readiness
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Life science companies will benefit from digging deeper into an enterprise analytics solution’s architecture to ensure it’s reliable, secure, and integrable with its IT environment. 

The Stakes Are High

The life sciences industry is reaching a saturation point with data; virtually every company has access to the same information. Therefore, a competitive edge will come from how the company uses it. The right enterprise analytics solution is key to fast, reliable, contextual insights. 

Leading companies will ensure their analytics solutions check all the boxes necessary to give their employees throughout the organization access to the pivotal answers they need. WhizAI provides the top enterprise analytics solutions for life science leverage AI, including NLP, are domain-specific and are designed to be easy to use for all employees regardless of data science expertise. 

 

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