Foundational Differentiation: CE Requires Efficient Operationalization of Data

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.

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

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