[Revised January 14, 2026]

Big Data in Pharma: Case Studies from Drug Discovery to Marketing

Big data is revolutionizing the pharmaceutical industry, enabling companies to glean insights from massive datasets across the drug lifecycle. With the global pharmaceutical analytics market valued at USD 5.16 billion in 2024 and projected to reach USD 18.49 billion by 2031, and more than 85% of biopharma executives planning to increase investment in data, AI, and digital tools in 2025-2026, the transformation continues to accelerate. Pharma organizations are leveraging cloud computing, AI, and advanced analytics to accelerate drug discovery, optimize clinical trials, enhance pharmacovigilance, streamline manufacturing, and refine marketing strategies. Below, we explore in-depth case studies in each of these domains, highlighting the company involved, the big data technologies used, the specific application and objective, the outcomes achieved, and any challenges encountered.

Drug Discovery and Early Research

Pharmaceutical R&D generates enormous volumes of data from sources like scientific literature, genomic databases, and past experiments. Big data technologies now help researchers sift through these troves to identify new drug candidates or novel uses for existing drugs. One striking example is BenevolentAI, a biotechnology company that applied AI-driven big data analysis to find a treatment for COVID-19. BenevolentAI uses a knowledge graph containing millions of biomedical entities and hundreds of millions of relationships, mined from literature and other sources on the AWS cloud (^[1]). In early 2020, as the pandemic emerged, the company pivoted to search its data for approved drugs that could be repurposed against the novel coronavirus. By running machine-learning models at scale on AWS, BenevolentAI's platform identified the rheumatoid arthritis drug baricitinib as a potential COVID-19 therapy in a matter of days (^[1]). Impressively, the AI system sifted through vast datasets and found this candidate with only ~90 minutes of cloud computing time and under three days of human analysis (^[1]). Within one month, a clinical trial of baricitinib for COVID-19 began, and the drug later proved effective enough to earn emergency use authorization in November 2020, followed by full FDA approval in May 2022 – making baricitinib the first immunomodulatory treatment for COVID-19 to receive FDA approval. The drug is now approved for three indications: rheumatoid arthritis (2018), COVID-19 (2022), and severe alopecia areata (2022). This case showcases how big data and AI dramatically accelerated drug repurposing, providing a viable treatment option much faster than traditional methods. The challenge faced was the urgent need to scour enormous data for a quick answer during a global crisis – a task made feasible by scalable cloud infrastructure and data analytics. BenevolentAI's success highlights the growing role of big data in shortening drug discovery timelines and responding rapidly to emerging health threats (^[1]).

Update (2025-2026): BenevolentAI underwent significant restructuring in late 2024, refocusing on its core technologies for drug discovery and seeking earlier partnerships for internally discovered drugs. In March 2025, the company merged into Osaka Holdings and was delisted from Euronext Amsterdam. Meanwhile, other AI drug discovery leaders have emerged: Insilico Medicine achieved a major milestone with its AI-designed drug rentosertib (ISM001-055), which showed positive Phase 2a results in idiopathic pulmonary fibrosis, improving lung function by 98.4 mL compared to placebo – representing what may be the first peer-reviewed Phase 2a result for a molecule generated entirely by generative AI. Insilico's platform demonstrated remarkable speed: from novel target discovery to Phase 1 in under 30 months, about half the traditional timeline. The company has nominated 22 developmental candidates with 10 reaching clinical stage, and rentosertib is expected to enter Phase 3 trials within 18 months (^[2]).

Clinical Trials and Development

Clinical trials are data-intensive and notoriously time-consuming. Integrating and analyzing data across trials can reveal patterns that improve trial design and speed up development. GlaxoSmithKline (GSK) offers a powerful case study of leveraging big data to make clinical research more efficient. GSK, a 300-year-old pharma company, found itself with over 8 petabytes of trial data spread across 2,100 silos, largely untapped for broader insights (^[3]). To break down these silos, GSK built a unified Big Data platform on a Cloudera Hadoop data lake, with pipelines to ingest and harmonize data from thousands of operational systems (^[3]). They employed tools like StreamSets (for data ingestion bots) and Trifacta (for cleaning messy data), as well as machine learning tools such as Tamr (for mapping data to standard ontologies) and even Google TensorFlow for advanced analytics (^[3]). This homegrown platform enabled researchers to analyze cross-trial data at unprecedented speed. For example, querying clinical trial datasets for a correlation that once took nearly one year now takes about 30 minutes (^[3]). Such a dramatic reduction in data processing time had a "huge impact on researcher productivity" according to GSK's Chief Data Officer (^[3]). GSK has used the platform for initiatives like analyzing genetic data from 500,000 UK Biobank participants to find new drug targets (^[3]). The ultimate objective is to accelerate drug development; GSK hopes that by simulating trials and mining data, it can shrink the typical drug discovery timeline from 5–7 years down to roughly 2 years (^[3]). A key challenge was cultural and technical – consolidating decades of legacy trial data and overcoming organizational silos. By investing in a robust data infrastructure and tools, GSK turned its fragmented data into a strategic asset, improving trial efficiency and informing discovery in ways previously impossible without big data technology (^[3]).

Update (2025-2026): GSK has significantly expanded its AI and data partnerships to accelerate R&D. In early 2026, the company announced a "multimodal approach" to drug discovery through several key collaborations. The expanded partnership with Tempus (initiated in 2022 with a $70 million investment) leverages Tempus's AI-enabled precision medicine platform to improve clinical trial design, speed up enrollment, and identify drug targets. This data-driven approach has enabled intelligent site selection within 60 days and enrollment of first patients within three months of launch. GSK also signed a multi-year collaboration with Helix (January 2026) for access to genomic and longitudinal data through GenoSphere cohorts. Additionally, GSK is using Accenture's Intient platform on Google Cloud, which has reduced certain research tasks "from weeks to minutes" through cloud-based data analytics and AI (^[4]).

Pharmacovigilance (Drug Safety)

After drugs reach the market, pharmaceutical companies must continuously monitor safety data to detect adverse events and ensure patient safety. Pharmacovigilance generates big data from sources like adverse event reports, electronic health records, and social media. A notable case study is a top 10 global pharmaceutical company that transformed its drug safety monitoring by digitizing adverse event (AE) collection. Traditionally, AE reporting was a slow, manual process: a single safety case could pass through many hands (doctor, call center, data entry, etc.), causing delays and potential information loss. In 2012, this pharma company recognized that its legacy AE collection process was not fast or agile enough and sought a better solution (^[5]). Partnering with IQVIA (a healthcare data firm), the company implemented the IQVIA Vigilance Platform – specifically a module called Vigilance Collect – to capture adverse events directly from the source in real time. This cloud-based system uses web and mobile portals to allow healthcare professionals or patients to submit AE reports directly, bypassing the old multi-step transcription process (^[5]). The big data tech here includes a centralized safety data platform that can intake high volumes of reports and integrate them for analysis. The results have been impressive: the company was processing over 120,000 adverse event cases per year through the new system – more than 15% of all its global case intake, achieving 30-40% cost reductions in processing and 100% follow-up response rates compared to 1-2% previously (^[5]). Automating and streamlining data capture led to "substantial cost savings and superior pharmacovigilance outcomes," according to IQVIA (^[5]). Safety teams can now detect potential safety signals faster and focus on analysis rather than paperwork. The challenge of integrating this solution involved changing entrenched workflows and ensuring data quality from direct reporter inputs. Nonetheless, this case demonstrates how big data platforms in pharmacovigilance improve compliance and patient safety by speeding up adverse event reporting and analysis (^[5]).

Update (2025-2026): The IQVIA Vigilance Platform has evolved significantly with AI at its core, now enabling near "touchless" processing of adverse events. The platform uses proprietary AI, machine learning (ML), and natural language processing (NLP) algorithms delivered in a secure SaaS environment. Key enhancements include Vigilance Detect, now powered by GenAI, which automatically detects and extracts drug safety events from emails, audio, documents, and chats using NLP and custom sentiment ontologies in over 50 languages. The platform also includes Vigilance Signal for automated real-time analysis across multiple datasets to proactively detect and track potential signals. IQVIA has developed custom-built AI agents using NVIDIA technology designed to enhance workflows and accelerate insights. The industry conversation has shifted from "if" to "how" AI can augment safety and regulatory professionals, with a focus on operationalizing human-in-the-loop AI without compromising compliance (^[6]).

Manufacturing and Supply Chain

In pharmaceutical manufacturing, maximizing quality and uptime is critical – especially for new drugs and clinical trial supplies. Here, big data and IoT (Internet of Things) sensors can drive smarter, more efficient production. Pfizer provides a real-world example through its collaboration with Amazon Web Services (AWS). In 2021 Pfizer and AWS launched the Pfizer-Amazon Collaboration Team (PACT) to apply cloud analytics and machine learning across Pfizer's product development and clinical manufacturing efforts (^[7]). One focus area was continuous manufacturing of oral solid dose drugs for clinical trials. Pfizer equipped equipment like centrifuges, coating machines, and other production gear with sensors, and AWS helped deploy a predictive maintenance solution to analyze this streaming equipment data (^[7]). The technology stack included AWS's big data and AI services: Amazon SageMaker for building and deploying machine learning models, Amazon Lookout for Equipment for detecting anomalous machine behavior from sensor readings, Amazon Lookout for Metrics for anomaly detection in process metrics, and Amazon QuickSight for data visualization (^[7]). By training ML models on historical sensor data, Pfizer's team developed a system that provides early warnings of equipment issues with minimal false alarms (^[7]). In practice, this means the system can flag subtle changes in vibration, temperature, or pressure that might precede a machine failure. As a result, Pfizer can proactively service equipment before a breakdown occurs, thus reducing unplanned downtime in production (^[7]). Ensuring high equipment uptime is especially vital when producing drugs for clinical trials or launch, where delays can set back R&D timelines. The outcome of this big data initiative is a more reliable manufacturing process that can produce new drugs faster and more reliably for testing (^[7]). One challenge in such projects is integrating diverse legacy equipment and sensor data into a unified platform, as well as retraining staff to trust and use AI-driven maintenance alerts. Pfizer's case illustrates how embracing industrial big data analytics in pharma manufacturing can improve operational efficiency and ultimately get treatments to patients sooner.

Update (2025-2026): The PACT initiative has delivered substantial results. Under PACT, Pfizer has pursued 14 projects, including generative AI and machine learning applications that save scientists up to 16,000 hours of search time annually and cut infrastructure costs by 55%. In 2023, Pfizer implemented its generative AI platform Vox, leveraging Amazon Bedrock and SageMaker to optimize manufacturing processes. The company developed Manufacturing Intelligence Edge (MI Edge), a platform using AI and ML for continuous monitoring of mammalian cell culture bioreactors at global manufacturing sites. Pfizer's mRNA prediction algorithm has contributed to improved vaccine production efficiency, yielding 20,000 more vaccine doses per batch. The company continues to expand ML models to optimize Active Pharmaceutical Ingredients (API) manufacturing, predict/prevent equipment failures, and optimize energy consumption at manufacturing sites. McKinsey reports that analytics in pharmaceutical manufacturing can deliver 5-10% procurement savings, 10-20% improvements in conversion costs, and up to 15% better quality cost performance (^[8]).

Marketing and Commercial Strategy

Pharmaceutical companies also use big data analytics to refine their marketing and sales strategies. By analyzing large datasets on prescriber habits, patient demographics, and market trends, companies can tailor their marketing efforts for greater impact. In one case study, a leading global pharma company worked with consultants to implement an AI-driven "Next Best Action" (NBX) system for its sales and marketing teams. The challenge was to boost prescription rates for its product in a highly competitive market – essentially, to ensure that marketing and sales efforts were as effective as possible in persuading healthcare providers to prescribe their drug. The solution involved integrating a wide variety of data: internal data (sales figures, call logs from sales reps, physician profiles, etc.), external data (market share, claims data, rival product info), and even qualitative research (surveys and interviews with doctors and sales reps). This big dataset was then mined using machine learning to identify which sequences of interactions tend to lead to increased prescriptions. In other words, the company analyzed thousands of past marketing "touchpoints" to find an optimal engagement strategy for each customer. From this analysis, the team derived a data-driven NBX model that would recommend the best next action for each sales representative – for example, whether to send a physician an email with clinical data, invite them to a webinar, or schedule a face-to-face meeting, depending on what approach proved most effective in similar situations. The results were compelling: in a pilot across several markets, clinics that were managed using the NBX recommendations saw 30% higher product sales growth compared to those that weren't (^[9]). Moreover, sales reps who followed the AI-driven suggestions achieved sales about 1.5× higher than their peers who did not (^[9]). These improvements translate into significantly increased revenue and better return on marketing investment. The main challenge here was the "last mile" problem of making analytics actionable – i.e. ensuring the insights reached reps in a convenient, timely way and that the reps trusted the recommendations. By integrating the NBX system into the reps' workflow (for instance, as suggestions in their CRM software) and demonstrating clear gains, the company overcame user skepticism. This case underscores how big data and AI can personalize marketing at scale in pharma – identifying the right message, time, and channel for each healthcare provider to maximize engagement and ultimately improve sales.

Update (2025-2026): The pharmaceutical marketing landscape has undergone significant transformation with AI. According to the MM+M/Publicis Health 2025 Innovation Survey, 40% of respondents say AI technology is "deeply embedded in everyday workflow," and healthcare AI investment grew to $1.4 billion in 2025 – nearly triple the investment from 2024. The key trend is the rise of agentic AI – platforms that not only analyze data but act on it. In December 2025, Veeva announced AI Agents for Vault CRM and PromoMats, representing the shift from analysis to action. NBA systems have become more sophisticated, using AI to predict which physicians are most likely to adopt a new therapy and recommending "the best channel, the best time, and the best type of content" optimized for business goals. Companies implementing AI across their value chain are achieving remarkable results: 25% faster drug discovery, 70% cost reductions in clinical trials, and 20% improvements in marketing effectiveness. However, 42% of AI initiatives that fail to meet ROI targets share common characteristics: rushed implementation without proper data preparation, lack of cross-functional collaboration, and insufficient attention to regulatory compliance. Looking ahead, 2026 is expected to be the year AI's role in pharma shifts from analysis to action, with successful brands treating AI "like an ecosystem and not a tactic" (^[10]).

Comparative Summary of Case Studies

To summarize, the table below compares the key aspects of each case study across the pharma value chain:

Conclusion

These case studies demonstrate the transformative impact of big data in pharma, from R&D to commercialization. By investing in modern data platforms, cloud computing, and AI analytics, pharmaceutical organizations have achieved tangible benefits: faster drug discovery, more efficient clinical trials, stronger safety surveillance, leaner manufacturing, and smarter marketing. Each example also highlights challenges – whether technical (integrating siloed or real-time data) or human (gaining user trust, changing legacy processes) – that IT professionals must navigate. For pharma IT leaders in the U.S. and globally, the success of these initiatives underscores the importance of treating data as a strategic asset. Big data technologies, when applied with clear objectives and executive support, are helping pharma companies bring therapies to market faster, operate more efficiently, and ultimately improve patient outcomes (^[3]) (^[5]). As the industry continues to generate ever-growing data volumes (from genomics, health records, wearables, etc.), the ability to harness big data will remain a key competitive differentiator in delivering innovative medicines to patients. Each of the real-world cases above serves as an inspiring blueprint for leveraging data-driven insights in the pharmaceutical domain.

As of 2026, the pharmaceutical industry stands at an inflection point. With the global pharmaceutical analytics market projected to reach USD 18.49 billion by 2031, and AI investments tripling year-over-year, the pace of transformation continues to accelerate. The emergence of the first AI-designed drugs entering late-stage clinical trials (like Insilico Medicine's rentosertib), the shift toward agentic AI that acts rather than just analyzes, and the maturation of GenAI-powered platforms across the value chain signal that big data is no longer just a competitive advantage – it is becoming a fundamental requirement for pharmaceutical innovation. Organizations that embrace this transformation with proper data governance, cross-functional collaboration, and regulatory compliance will be best positioned to bring life-saving therapies to patients faster and more efficiently than ever before.