Foundational Differentiation: CE Requires Efficient Operationalization of Data

Published November 5, 2024

What is data operationalization, and why is it important in life sciences?

The ability to integrate insights from your data into your business workflows is commonly referred to as data operationalization.

Throughout life sciences organizations, functional needs and behaviors have evolved, making data operationalization at scale a vital source of competitive advantage for life sciences.

It is no longer enough to derive business strategy from data to ensure a competitive advantage. Executives now expect real-time updates from the market—and patients and prescribers expect seamless omnichannel experiences. The differentiator is in connecting petabytes of data from multiple sources quickly to optimize tactics.

Today’s customer engagement activities are more targeted and personalized, requiring precision and frequent refinement

The Data Foundation

Historical

Today

Field & Sales Execution

Field & Sales Execution

Static target lists based on market deciles

Dynamic targeting and segmentation

Marketing

Marketing

One-time campaign planning and few messages

Continuous refinement and flexible journeys

Executive Leadership

Executive Leadership

Weekly and monthly reports

Daily tracking and forward-looking insights

Patient Services & Market Access

Patient Services &
Market Access

Siloed and reactionary

Collaborative and prospective

Finance

Finance

Scorekeeper and gatekeeper

Partner to business optimizing channel and payer contracts

Where should life sciences organizations focus as they operationalize data?

To support the increased need for nuanced, targeted insights that are continually refining, data operationalization should be developed with these things in mind:

• Data contextualization—When integrating third-party and first-party datasets, incorporating nuanced business rules improves relevance of the data and drives better understanding.
• Alignment on performance metrics and definitions—Cohesive goals and reporting encourage collaboration and clarity.
• Automated data ingestion and quality checks—Speed and accuracy are lost if the organization relies on manual processes or human intervention.

Setting up an efficient, scalable data foundation is integral to success in customer engagement. A robust and operational data foundation will enable both strategic and operational impact. Savvy companies will also be vigilant for new ways to augment their existing data to bring more actionable customer, channel and content intelligence.

Authors: Nancy Phelan, Paramita Kar & Vishnu Prashanth Veerasamy

This blog is part of the One Chapter Ahead blog series, a Customer Engagement-focused thought leadership initiative to bring an informed, inspired point of view.  Our intent is to bring a fresh perspective, driven by data, benchmarks, insights and expertise.

Other blog posts in this series:

• What Does “Good” Look Like in Customer Engagement? 
• Get Ready to Orchestrate a Better Customer Experience
• Integrated Data + Hyper-local + Cross‑channel = Customer Engagement
• Key Takeaways and Learnings from HLTH 2024 
• Is Medical Omnichannel Poised for Growth?
• Are Pharma Brands Really Transformational?
• Culture and Collaboration: Keys to Customer Engagement Success
• Medical Affairs Customer Engagement: What’s Next?

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*Gartner, Hype Cycle for Life Sciences Commercial Operations, Animesh Gandhi, published 13 August 2024.

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