Data analytics in life sciences: Unlocking faster trials, smarter launches, and safer outcomes

Why data has emerged as the lifeblood of life sciences

Drug discovery is taking longer and costing more. Regulatory requirements are only going to go higher. And patient expectations? Lightyears beyond what most pharma brands can manage today. Amid this high-stakes game, executives in the life sciences sector are relying increasingly on one potent lever: data.

But not any data. Structured, secure, real-time, and actionable data. That’s where data analytics in life sciences is starting to have a game-changing impact. Whether it’s enhancing clinical trial design or streamlining market access strategies, data is no longer an IT asset. It’s a C-suite priority.

72% of biopharma executives indicate that their competitive advantage in the next five years will rely on how well they leverage data analytics, according to a Deloitte 2024 survey. The race is on. And those who understand how to translate insights into outcomes will lead the next generation of healthcare breakthroughs.

Data analytics transforming life sciences: Applications in the real world

1. Speeding up drug discovery: AI-fueled breakthroughs

The conventional drug discovery process takes time and money. But life sciences data analytics is changing all that. AI and machine learning enable scientists to scan massive datasets, genomic sequences, chemical libraries, patient records, to sift out potential drug candidates sooner and with greater accuracy.

Real-world example: Insilico Medicine is a good example. The firm employed its AI-driven platform, Pharma.AI, to create and select a new anti-fibrotic molecule. What’s so remarkable about this is the pace: the drug went from early design to Phase I trials in less than 30 months, a timeline nearly unheard of in conventional pharma pipelines. 

Insilico not only forecast the molecule shape but also applied analytics to verify its safety and efficacy prior to clinical trials. This innovation indicates that therapies for rare diseases or emergent conditions in the future may come sooner, with more specific mechanisms of action. 

2. Streamlining clinical trials: Predictive analytics in action

Clinical trials are among the most costly stages of drug development. Failure rates are high, timelines are long, and the stakes are huge. But predictive analytics is mitigating risk and enhancing success rates by improving protocol design, optimizing site selection, and enhancing recruitment and retention.

Real-world example: During the COVID-19 vaccine development, Pfizer used predictive analytics to identify top-performing clinical trial sites. By integrating real-time epidemiological data, site performance histories, and participant demographics, they were able to establish and execute trials in six countries with more than 46,000 participants in a record period. 

Not only did this compress the usual timeline by months, but it also enabled Pfizer to make data-driven adjustments in real time. The lessons learned were crucial in gaining swift approval without compromising safety. 

3. Data-driven commercialization strategies for improved market access 

It takes only half the battle to get a drug approved. Getting to the right patients, meeting payer expectations, and maintaining provider adoption are often the greater challenges. Life sciences data analytics offers the strategic brawn to drive through that complexity.

Real-life example: Amgen’s market access through commercialization of its migraine drug, Aimovig, is a good case in point for analytics-fostered market access. Through the combination of insurance claims information, physician prescribing practice, and real-world evidence on patient outcomes, Amgen was able to create a clear value proposition. 

This enabled them to anticipate payer concerns and develop clinical dossiers countering economic and clinical issues. Consequently, Aimovig achieved good reimbursement positioning, speeding adoption in competitive markets.

4. Simplifying operations: Predictive supply chain management

Operational effectiveness is important to making sure therapies reach patients in a timely manner. From manufacturing schedules to inventory control, predictive analytics can help companies see around corners to predict disruptions and make proactive changes.​

Actual example: Novartis has been a leader in the adoption of AI in its supply chain functions. Through the use of advanced analytics and machine learning, Novartis has improved its demand forecasting and inventory management functions. 

The integration has resulted in better accuracy in forecasting product demand, optimized inventory levels, and fewer stockouts. The digital transformation efforts of the company have not only made its operations more efficient but also ensured timely delivery of medications to patients.

5. Pharmacovigilance: Advancing real-time safety monitoring

After a drug is approved and available in the market, adverse event monitoring becomes essential. Conventional pharmacovigilance systems are slow and usually reactive. But data analytics in life sciences is making it possible to adopt a more proactive, real-time system.

Real-world example: The FDA’s Sentinel Initiative is one of the most ambitious uses of data analytics for public health surveillance. By examining data from more than 100 million patients, it allows constant monitoring of medical products. 

Sentinel can quickly pick up on safety signals from places such as insurance claims and EHRs, and has played a key role in re-reviewing medications with possible adverse reactions. Life sciences firms gain by implementing similar systems internally, allowing them to identify hazards before they become recalls or lawsuits. 

What drives it all: The right data infrastructure 

Beneath it all is a corporate-wide focus on scalable data architecture. Without compliant, connected, and clean data, even superior algorithms don’t work. Executives are investing in:

• Cloud-first infrastructure for scalability and compliance
• Data lakes that consolidate genomic, clinical, and commercial data
• Master data management (MDM) systems to drive consistency
• Metadata and lineage tracking to enhance audit readiness

When Novartis overhauled its data architecture, it reduced data retrieval times by 40% and enhanced analytics adoption across 14 departments. This foundation is no longer a choice. It’s the foundation for reliable and effective data analytics in life sciences.

How Netscribes is empowering life sciences with operational analytics

Organizations within life sciences are discovering the essential contribution operational analytics makes in propelling business excellence. Netscribes assists healthcare and life sciences organizations in unleashing the value of real-time information to maximize operational performance, reduce inefficiency, and support decisive decision-making.

Case study example: Reshaping manufacturing for a health multinational

One of the world’s biggest healthcare multinationals commissioned Netscribes to rationalize its production operations across APAC. The organization was frequently inspecting at its locations for cost-reduction opportunities but were being thwarted by:

• Offline, stand-alone reports
• Data extraction done manually with attendant risk of human errors
• Limited visibility across the geographically spread production centers

Netscribes implemented an integrated operational analytics solution incorporating the following components:

• Uniformed data structure framework to consolidate audit inputs from every location
• Automated data extraction to decrease manual effort and enhance accuracy
• Interactive dashboards supporting drill-down functionality for enhanced understanding

Delivered results:

• Achieved economies of scale by resolving shared problems across sites
• Facilitated trend analysis to detect and anticipate recurring operations inefficiencies
• Developed a centralized knowledge repository to facilitate continuous improvement and strategic decision-making

This example highlights how Netscribes’ data analytics solutions, particularly operational analytics, are assisting life sciences companies in creating more agile, data-driven operations that yield quantifiable business results.

Future directions: New frontiers in life sciences analytics

1. Generative AI and drug repurposing

GenAI is opening up new drug discovery and repurposing routes by predicting protein structures, forecasting compound activity, and designing novel molecule candidates in silico. Significantly, Nvidia and Recursion launched a $50M collaboration in 2024 to train GenAI models on Recursion’s 23 petabytes of biological and chemical data. These models are accelerating discovery for rare and complicated diseases, remodeling what previously took years into months.

2. Digital twins for patient simulation

Leading institutions such as the Mayo Clinic and Mount Sinai are pushing the application of digital twins virtual patient representations developed from real-world and clinical information. These simulations enable simulation of disease development and forecasting patient-specific treatment responses. Companies such as Unlearn.AI are also at the forefront of applying digital twins to substitute control arms in trials, eliminating patient recruitment issues and ethical barriers.

3. Synthetic control arms

The FDA and EMA now welcome synthetic control arms derived from real-world data to back clinical trials, especially in orphan diseases. These virtual arms reduce the necessity for placebo groups and contribute to enhancing trial efficiency. Flatiron Health and Tempus are investing heavily in oncology data platforms that facilitate this transition, enabling trials to be designed around historical patient outcomes.

4. AI governance

With algorithms driving regulatory submissions and patient treatment flows, AI governance is shifting from nice-to-have to must-have. In 2024, Roche set up an enterprise AI governance committee to oversee risk around explainability, bias, and reproducibility. Other executives are putting together cross-functional boards with compliance, legal, medical affairs, and IT representation to make sure that AI is being used ethically and transparently.

These investments point to a clear future: one where analytics is not merely applied to speed up processes, but to redefine the way life sciences discovers, tests, and delivers value.

Getting started: Strategies for C-suite success

Converting data into a strategic advantage doesn’t happen in a vacuum,  it requires intentional leadership, cross-functional alignment, and a culture that values insight over instinct. For life sciences organizations willing to infuse analytics at the core of their strategy, here’s how the C-suite can lead the way:

Lead data initiatives at the board level: Hold data analytics to the boardroom level, rather than just the IT or innovation agenda. The senior leadership needs to establish tone by investing, sponsoring pilot projects, and making analytics part of enterprise KPIs.

Align commercial, R&D, and IT functions around common data objectives: Analytics projects fail because they’re isolated. C-suite executives need to create a common purpose that brings research, product development, regulatory, and go-to-market functions together. When functions agree on the “why” and “what” of using data, execution is simplified.

Invest in humans: Recruit and enable “data translators” – individuals with scientific know-how, data linguistics, and ability to verbalize insights business teams can turn into action. These are the usual missing middle men between algorithms and results.

Start small, scale fast: Start with a specific use case where data analytics can create tangible value, such as optimizing trial recruitment or inventory forecasting. Leverage those early wins to gain momentum and credibility before scaling up across departments and geographies.

Prioritize governance: With regulatory oversight increasing, robust data governance structures are not optional. Establish clear ownership, maintain data lineage and quality controls, and have robust access protocols in place. This is particularly important in environments that deal with sensitive patient and clinical data.

Establish a data-literate culture: Training and enablement is not just about data teams. When scientists, marketers, compliance officers, and operations leads learn how to deal with data, organizations become agile and resilient. Leadership needs to foster curiosity, reward insight-driven decisions, and make experimentation a norm.

Life sciences data analytics isn’t a future vision, it’s a present imperative. But without bold leadership, even the most hopeful technologies fail to deliver change. C-suite engagement is the difference between isolated pilots and enterprise-wide transformation.

Conclusion: Making data a strategic asset

For life sciences leaders navigating the intersection of innovation, regulation, and market pressure, data is the differentiator. Not raw numbers, but refined insight that drives faster trials, safer drugs, smarter launches, and more connected patient care. 

Analytics is no longer a back-office capability. It’s a boardroom discussion. Our data analytics tools help you build the infrastructure, empower their teams, and embed data into every decision.

Ready to make your data a competitive advantage? Let’s begin the conversation.