Use these 5 Steps to Navigate the Life Science Augmented Analytics Adoption Journey
Life science commercial teams, including field sales, market access, and patient services, need data insights to perform their jobs most effectively. However, traditional analytics platforms, which require heavy intervention from data and IT teams and lengthy time for analysis, can’t provide on-demand insights. Augmented analytics platforms overcome those barriers, allowing commercial team members to interact with the platform independently, regardless of their technical or data expertise. Furthermore, platforms leveraging artificial intelligence (AI), including natural language processing (NLP), allow users to interact with the platform conversationally, creating a simple, personalized user experience.
That ease of use, as well as accurate, contextual insights, can lead to high user adoption. Data from WhizAI users, for example, shows 95% adoption. Additionally, intervention from the data and IT team decreases, contributing to a 50% lower total cost of ownership (TCO).
A 5-Point Blueprint for Augmented Analytics Adoption
However, life sciences companies must follow a systematic approach to augmented analytics adoption to see the maximum benefits and lower risks.
- Evaluate: The first step is to assess current capabilities, including taking stock of the capabilities of the analytics solution already in use. However, it’s also essential to determine the analytics skill level of all users that need data-based insights to do their jobs successfully and the processes that deliver those insights. Finally, use the information from the assessment to identify gaps that must be addressed.
- Strategize: Based on the needs identified in the first step, step two involves creating or revising the life sciences company’s commercial analytics strategy. The plan should align with business goals, e.g., sales growth, better team alignment, or cost savings. Life sciences companies should also focus on users’ needs, planning ways to deliver timely insights to sales reps, patient services, and market access teams. This is also the stage at which companies should secure management and stakeholder buy-in.
- Procure: With goals clearly defined, a life sciences company can use them as a guide to help choose an augmented analytics tool and the vendor that will provide it. Enterprises should select an augmented analytics platform that provides self-service capabilities for non-technical users. Other criteria for platform selection include integrations, solution architecture (i.e., microservices architecture that creates high scalability), and reusability of certain components across business functions. Life sciences companies should also choose a domain-specific tool. An AI tool trained with life sciences data will answer users with accurate, contextual responses from day one, contributing to fast adoption. Additionally, a company with a team with life sciences expertise will ensure that the platform is designed for life sciences workflows and properly configured and integrated into life sciences workflows.
- Plan: At this point, a company is ready to plan solution implementation. The life sciences company should work with its technology partner to create a unique roadmap with realistic and measurable milestones. At the same time, companies should plan employee training and secure key stakeholders’ buy-in. An effective tactic is identifying “augmented analytics tools champions” that can assist with change management, collaboration among the commercial and IT teams, and tracking implementation progress.
- Execute: The last step is implementation according to the roadmap. The company and its technology partner should test tools to ensure they’re properly integrated and configured. If necessary, adjust the plan to include necessary customizations. The implement-test-adopt cycle should continue until the platform works as intended at scale.
Results of a Successful Augmented Analytics Implementation
Successful augmented analytics implementation will lay the groundwork for high user adoption and lower analytics TCO. However, organizations can also expect other benefits. With simplified access to data insights, commercial teams will build data-driven decision-making into their workflows. Additionally, AI enables the platform to uncover patterns or anomalies that may have gone unnoticed, alerting teams to opportunities or downward trends that need their attention. Overall, an augmented analytics platform, properly implemented, will help teams enhance their performance and, ultimately, increase revenues.
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