#FutureReadyHealthcare
19 Jun 2025
Introduction
The life sciences industry is at an inflection point, where data has become both its greatest asset and its biggest challenge. The surge in first-, second-, and third-party data sources offers vast potential, yet extracting meaningful insights is increasingly difficult due to overlapping use cases, regulatory demands, and evolving technologies.
Factors such as shifting compliance landscapes, dynamic go-to-market models, industry consolidation, and the rise of GenAI and IoT-enabled real-time data to have further intensified the data challenge. In this environment, a clear, enterprise-wide data strategy is no longer a choice—it is a strategic necessity.
This whitepaper provides a structured five-step framework to help life sciences organizations craft a data strategy that is not only future-ready but also business-driven. And that’s not all—inside, you’ll also find an exclusive Data Strategy Maturity Assessment Tool to help you benchmark your current state and uncover clear, actionable next steps.
Unveiling Top Challenges in Life Sciences Data Management
As life sciences organizations continue to evolve, so do the challenges associated with managing and leveraging data effectively.
Without a comprehensive enterprise data strategy, these challenges will persist and limit innovation and business growth. A data strategy is more than just technology or infrastructure—it demands executive vision, business alignment, and a structured approach to value creation.
This brings us to the next critical question: how can organizations design an enterprise data strategy that drives real business impact?
Decoding Enterprise Data Strategy
An enterprise data strategy is a comprehensive, long-term plan that outlines how an organization will leverage its data assets to achieve its strategic business objectives. This encompasses technology, processes, people, and governance necessary to collect, store, manage, analyze, and utilize data effectively and ethically.
An enterprise data strategy is a comprehensive, long-term plan that outlines how an organization will leverage its data assets to achieve its strategic business objectives. This encompasses technology, processes, people, and governance necessary to collect, store, manage, analyze, and utilize data effectively and ethically.
5 Steps to Creating a Robust Enterprise Data Strategy
We have developed a five-step enterprise data strategy framework to help organizations unlock the full value of their data.
Step 1
Define your North Star
In the first step, establish a clear data vision, identify and prioritize key business drivers, and define your success criteria. At the core of this step are the Chief Data Officer (CDO), Chief Marketing Officer (CMO), and business leaders, who must collectively shape the organization's data vision.
Define data vision
A clear and actionable data vision guides every decision across the organization. It defines data goals, priorities, and long-term objectives aligned with business strategy and culture.
For instance, a biopharma company’s data vision might state: “Leverage data to drive personalized HCP engagements, optimize treatment pathways, and enhance patient outcomes, while ensuring the highest standards of data privacy and regulatory compliance across all operations.”
Effort versus priority mapping for different critical drivers
Step 2
Align and communicate with your stakeholders
Make sure your data strategy aligns with your business goals. Without that alignment, it is harder to demonstrate how data supports transformation efforts, and easier to miss out on partnerships that can drive growth.
Engage key stakeholders
Map decision-makers across market access, patient services, digital and marketing, customer engagement, and brand teams to align efforts with business goals.
Articulate business value and expected outcomes to gain stakeholders’ buy-in. This includes:
- Qualitative outcomes that align with the organization’s vision
- Measurable and quantifiable outcomes that define success
Step 3
Align and communicate with your stakeholders
A data strategy maturity assessment helps evaluate your current capabilities, pinpoint gaps, and prioritize investments to align with business goals. This step provides a clear blueprint for strengthening data governance , technology, processes, and culture—ensuring your strategy is built for long-term success.
Capture the current state
Evaluate where your organization stands across the four categories as described above to identify strengths, limitations, and gaps that impact your capability development.
Understanding the current state and gaps in your data strategy with an example.
| Established competencies | Strategic gaps | ||
|---|---|---|---|
| Critical driver: Drive better patient experience | Specialty pharmacy reporting on dispense and status | Advanced Predictive Reporting on high-risk non-adherent patient groups | |
| Critical driver category | |||
| Data sources | APLD Internal Patient Hub Specialty Hub IoT Sensors and Devices RWD (Lab, Bio-Markers) | Data collection and reporting | Lack of integrateddata set from internal/ RWD and other sata sets including digitial |
| Technology | Snowflake | Good technical knowledge on Snowflake | Cloud ML and next-gen predictive capabilities |
| People | Product owner IT owner | Stakeholder management Backlog management | Cross-functional integration Long-term value creation |
| Process | Governance and compliance Build and deployment methodologies | Established manual deployment process | Lack of approval and automation |
We structure data strategy maturity assessment into four key categories.
Data (based on use case)
- Data sources (first, second, and third-party)
- Data architecture
- KBQs based on data sources and user personas
- Ability to solve integrated data challenges
Technology
- Master data management
- Data and integration engineering
- Metadata management
- Advanced analytics
- Reporting and visualization
- Cloud and GenAI capabilities
Process
- Data governance
- Deployment standards
- Compliance and review process
People
- Organizational structure and roles
- Skills and competencies
- Support systems (vendors or partners)
You can assess your data strategy maturity level by answering a set of questions based on the above four categories and get specific recommendations here.
After conducting the assessment, you can determine your organization’s current standing on the Enterprise Data Strategy Maturity model.
Progressive stages of data strategy maturity
Step 4
Create a solid blueprint for your enterprise data strategy
A strong enterprise data strategy isn’t just about technology—it’s about making data work for your business. This means taking a structured, risk-aware approach and deciding whether to continue, migrate, or upgrade existing data and technology assets to meet enterprise standards.
Build a roadmap for your target state
Determine whether your strategy is offensive or defensive.
Decide on a centralized or decentralized data architecture, such as Data Mesh, Data Fabric, Data as a Product, or Data Lakehouse.
Focus on key technology enhancements, including integrated data sources, data quality and adoption, security and compliance, real-time integration, and hyper-automation with Gen AI.
Adopt a scalable, cloud-first tech stack with microservices, ensuring security and compliance.
Break down data silos by integrating data across functional areas such as patient services, market access, sales transactions, account performance, digital, and engagement.
Step 5
Ensure continual evaluation and improvement
Your enterprise data strategy is not a one-time effort—it needs continuous monitoring, adaptation, and enhancement. To stay ahead, you must adopt next-gen technology, strengthen data literacy, and foster a culture of feedback-driven improvement.
Sustain and monitor outcomes
Establish monitoring and feedback loops with internal and external stakeholders to refine initiatives.
Measure progress across all four readiness quadrants to sustain improvements and address gaps.
A Success Story
A leading global pharmaceutical company sought to enhance its omnichannel data strategy to drive measurable improvements in omnichannel performance and customer engagement. However, it encountered a few challenges.
A slow and manual process for extracting and analyzing omnichannel data from media partners delayed optimizations.
A fragmented data pipeline with multiple hops led to poor data quality and made it difficult to generate reliable insights.
Media partners controlled data rules and processes that limited the company’s visibility and governance over its own measurement framework.
Our approach
We created a clear blueprint and roadmap to bring all processes in-house. This gave the client direct control over media partner relationships and improved efficiency. We implemented a robust data governance and quality framework across all media channels to enhance consistency and trust in the data.
Additionally, we established analytics-ready and reporting-ready datasets to serve as a single source of truth for omnichannel analytics, reporting, and marketing teams.
Outcomes we drove
Improvement in channel classification and standardized data formats made reporting and insights more accurate and actionable. It also cut data lag from four weeks to days, allowing the marketing mix modelling team to execute faster channel optimizations.
Additionally, data adoption improved with consistent metrics across functional teams and the integration of data catalog and lineage tools.
Key Takeaways
A well-executed data strategy helps life sciences organizations break data silos and unlock full value from their assets. With a structured approach—from vision to stakeholder alignment, maturity assessment, and scalable design—data becomes a driver of growth.
By combining real-time, AI-driven analytics with strong governance, companies can improve decisions, streamline operations, and personalize engagement. But lasting success takes more than just technology.
Here are a few takeaways you should consider.
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