The Data Dilemma: Turning Insights into Action

Introduction

Health and Life Science organisations are sitting on mountains of data, yet still can't answer some of the most urgent questions. Why are patients dropping out of treatment? Which interventions prevent hospital readmission? Which markets are ready for launch?

Welcome to the data dilemma: a growing gap between information and impact.

In our last edition, we explored how real-time data enhances outcomes across the patient journey. But even with these capabilities, many organisations still struggle to act on what they learn. This edition zooms out to address the bigger question: how do we turn data into meaningful, measurable change?

The truth is, data without insight is noise. And insight without action? It’s just potential left on the table.

As Thomas Kurian, CEO of Google Cloud, puts it:

Data is not useful until it becomes insight—and insight is not useful until it drives action.

Why Data Alone Isn’t Enough

We’re living in the era of data abundance, but data volume doesn’t equal value. Hospitals, Pharma, Consumer Health companies and wellness platforms are sitting on massive troves of data from EHRs, wearables, lab systems, CRM tools and / or clinical trials. Yet without integration, context, and interpretation, this data remains siloed and underutilised.

Common challenges include:

• Fragmented systems that can’t communicate
• Unstructured formats that hinder analysis
• Low data quality or outdated records
• Lack of internal expertise or strategy to drive insights

Example: a hospital might capture real-time patient vitals through an advanced EHR, but without an analytics layer to identify early signs of deterioration or flag high-risk patients, critical opportunities for intervention are missed.

What Turns Data Into Insight?

To unlock actionable intelligence, healthcare organisations must go beyond data storage and invest in tools, processes and mindsets that transform data into decision power.

Key enablers include:

• Interoperability: systems must talk to each other. Whether it’s EHRs, imaging platforms, lab results or supply chains, seamless data exchange is fundamental to delivering a unified view.
• AI & Machine Learning: these technologies turn vast datasets into patterns, predictions, and personalised recommendations—helping detect anomalies, forecast trends, and automate decisions.
• Real-Time Dashboards & Visualisation: user-friendly dashboards allow decision-makers to track key metrics and respond to real-time alerts, critical in both clinical and operational contexts.
• Data Governance: standardisation, privacy, and quality frameworks ensure that insights are trustworthy, compliant and ethically used.
• Skilled Talent: beyond tools, organisations need teams of data analysts, data scientists, clinicians and domain experts to interpret and apply insights where they matter most.

What Good Looks Like

A data-driven health organisation doesn't just collect information, it knows how to put it to work.

Here's what set mature organisations apart:

• Integrated Data Ecosystem: clinical, operational and business systems connect to deliver a single source of truth.
• Real-Time, Predictive Insights: data is used to anticipate needs, not just report on the past.
• Empowered Teams: clinicians, managers and decision-makers access dashboards tailored to their daily decisions.
• Governance aligned to Strategy: data policies are embedded in core priorities like quality, efficiency and equity.
• Agile, Insight-led culture: strategies evolve in real time, based on evidence, not assumptions.

Benefits of Actionable Intelligence

As healthcare becomes more complex, data only delivers value when it leads to impact. Here's how actionable insights drive meaningful benefits for stakeholders across the health ecosystem:

1. Improves Decision-Making

Real-time analytics support faster, more accurate decisions across clinical, operational and business settings.

Examples:

• Clinicians receive early alerts for early signs of complications.
• Pharma teams refine trial designs with real-world data.
• Health systems develop predictive prevention strategies.

Stakeholder impact: better outcomes, smarter planning, faster response, accelerated innovation.

2. Increases Operational Efficiency

Data improve flow across hospitals, supply chains, care teams and patients.

Examples:

• Predictive analytics optimise bed and staff allocation.
• MedTech firms use usage data to improve logistics or product design.
• Health systems coordinate providers more effectively.

Stakeholder impact: smoother operations, better allocation of resources, less waste, improved team performance.

3. Reduces Costs and Waste

From avoiding readmissions to improving inventory, smarter decisions save money.

Examples:

• Pharmacy automation reduces medication errors and restocking costs.
• AI flags unnecessary procedures.
• Insurers forecast high-risk claims and adjust care programs proactively.

Stakeholder impact: significant savings for hospitals, payers, Pharma, supply chains and national health systems.

4. Empowers Patients

Patients receive real-time feedback and personalised guidance to manage their health.

Examples:

• Wearables or apps, prompt healthier behaviours.
• Remote monitoring enables personalised follow-up care.

Stakeholder impact: greater engagement, adherence and self-care.

5. Enhances Multi-Stakeholder Collaboration

Shared data break silos and drives cross-functional alignment.

Examples:

• Case managers and families access real-time dashboards to coordinate care.
• Industry and clinicians co-develop support programs with clinicians.
• Payers and providers design value-based models.

Stakeholder impact: better coordination, fewer gap in care, stronger partnerships.

6. Accelerates Innovation and Business Agility

Insights fuel faster innovation, product development, targeted marketing and service optimisation.

Examples:

• Industry use real-world evidence to shorten trial cycles and inform post-market activities.
• Medtech improves UX by analysing device interaction.
• Consumer health brands personalise digital experiences and adapt product portfolios.
• Health authorities track emerging trends to allocate resources proactively.

Stakeholder impact: faster time-to-market, greater customer alignment, future-ready strategies.

Challenges in Implementing Actionable Intelligence

While the potential of real-time data and advanced analytics is transformative, implementing data-driven strategies across healthcare remains complex. Multiple stakeholders—providers, Pharma, MedTech, payers and regulators—face unique barriers in turning data into impact.

Here are the key challenges that must be addressed to scale actionable intelligence across the ecosystem:

1. Data Privacy, Security and Compliance

With rising cyber threats and evolving data regulations, protecting sensitive health information is non-negotiable.

Examples:

• Hospitals must comply with GDPR or HIPAA when integrating new monitoring systems.
• Pharma and consumer health platforms need secure consent models for using patient-reported and behavioural data.
• Cloud-based analytics platforms must meet international security certifications.

Stakeholder impact: delays in adoption, reputational risk and high compliance costs.

2. Interoperability and Legacy Systems

Siloed systems and incompatible technologies make it difficult to unify data across organisations or care settings.

Examples:

• EHR systems vary significantly across regions and providers, limiting real-time clinical insights.
• Pharma R&D teams struggle to integrate real-world evidence from health systems due to inconsistent data formats.
• MedTech platforms often operate in closed environments, blocking cross-device analytics.

Stakeholder impact: fragmented insights, reduced data quality, and missed opportunities for coordinated care or research acceleration.

3. Cost, Infrastructure and Scalability

Advanced data platforms and AI require substantial investment in cloud infrastructure, tools and internal capabilities.

Examples:

• Smaller hospitals and community clinics may not have the resources to deploy predictive tools.
• MedTech startups face high R&D costs for integrating AI-driven features in regulatory-compliant devices.
• Public health systems in low-resource settings lack the digital infrastructure to implement real-time monitoring.

Stakeholder impact: uneven access to innovation, slower time-to-value, and widening gaps in digital health maturity.

4. Cultural Resistance and Workflow Disruption

Shifting from instinct-driven to data-driven decision-making can face pushback from professionals across sectors.

Examples:

• Clinicians may distrust AI recommendations if transparency is lacking.
• Data teams and marketing in Pharma may struggle to align on KPIs for real-world evidence.
• Consumer health companies may find it difficult to personalise at scale without clear data governance.

Stakeholder impact: low adoption rates, reduced ROI on technology investments, and missed opportunities to enhance user experience or care quality.

Case Study: Turning Glucose Data into Better Care with Roche

Introduction

Roche has expanded beyond traditional pharmaceuticals by building a connected digital health ecosystem focused on diabetes management. By combining patient-generated data with AI-powered analytics, Roche helps both individuals and healthcare teams make smarter, faster decisions that improve outcomes and quality of life.

Key Innovation

Roche’s digital diabetes platform combines the mySugar app and Accu-Chek devices to provide continuous glucose tracking, trend analysis and intelligent feedback. AI algorithms flag anomalies, recommend behavioural adjustments and support timely treatment optimisation. The data is easily shared with care teams to enable personalised care.

Impact

• 70% of users report improved glycaemic control within 3 months.
• 40% fewer hypoglycaemic events through continuous monitoring and real-time alerts.
• 25% reduction in hospitalisations and emergency visits among high-risk patients.
• 50% increase in treatment adherence with app use.

Why it matters

This model shows how actionable insights can change patient behaviour, improve care collaboration and drive better outcomes. It also enables continuous learning for futre product development and evidence generation.

Closing the Loop: Turning Insights Into Action

Turning insights into action isn’t just about technology, it’s about asking the right questions, building the right connections and enabling the right people.

If your organisation is facing this data dilemma, consider these as starting points:

• Clarify your goals: do we know what we’re trying to solve or improve with our data?
• Break the silos: is our data connected across departments, teams and platforms?
• Build trust and literacy: are non-technical teams empowered to interpret and act on insights?
• Design for action: are dashboards and analytics tools built to guide daily decision-making?
• Close the loop: do we measure whether the insights we generate lead to better outcomes?

Whether you’re leading a hospital transformation, scaling a MedTech product, or rethinking patient engagement, the path forward starts with turning knowledge into action.

Takeaway

In a world overflowing with data, the real competitive edge lies in what we do with it. Health and life science organisations that succeed will be those who don't just analyse the data, but translate them into timely, meaningful actions that improve lives, drive innovation and create real-world value.

As Dr. John Halamka, President of Mayo Clinic Platform, says:

The future of healthcare is not about big data—it’s about relevant data, delivered in real time, to the right person.

Because in the end, it's not about having the most data. It's about delivering the most impact.