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Big Data Analytics for the Pharmaceutical Industry
and Clinical Trials

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Finding the cure for inaccessible data

What happens when the data you need is hidden in silos, or when billions of dollars are riding on drug testing data you can’t access? How do you see a long-term view of 10 billion records to understand biological response to drugs? Researchers in the pharmaceutical industry turn to Hortonworks for advanced big data analytics on integrated translational data and to gain a holistic view of their pharmaceutical data.

Desbloquear el poder de los datos farmacéuticos.

Big Data integration, pharmaceutical big data analytics, internal and external collaboration, portfolio decision support, more efficient clinical trials, faster time to market, improved yields, improved safety - these are just a few of the benefits pharmaceutical companies around the world achieve by tapping into the full power of their pharma big data.

Use Cases

Merck optimiza los rendimientos de las vacunas: luchando por el "lote de oro"

Merck optimiza su rendimiento de vacunas mediante el análisis de los datos de fabricación para aislar las variables predictoras más importantes para un "lote de oro". Los líderes de Merck pendían del tiempo de fabricación para aumentar los volúmenes y reducir los costes, pero se convirtió en cada vez más difícil descubrir maneras adicionales de mejorar los rendimientos. Utilizaron Open Enterprise Hadoop para las nuevas ideas que podían reducir más los costes y mejorar los rendimientos. Merck se dirigió a Hortonworks para el descubrimiento de datos en registros de 255 lotes de una vacuna que se remontan a 10 años. Esos datos se han distribuido a través de 16 sistemas de mantenimiento y de gestión de edificios e incluía datos de los sensores precisos en configuración de calificaciones, presión de aire, temperatura y humedad. Después de agrupar todos los datos en Hortonworks Data Platform y procesar 15 mil millones de cálculos, Merck tenía nuevas respuestas a las preguntas que había estado pidiendo durante una década. Entre cientos de variables, el equipo de Merck fue capaz de detectar aquellas que optimizaban el rendimiento. La empresa procedió a aplicar estas lecciones a sus otra vacunas, con un enfoque en el suministro de fármacos de calidad al precio más bajo posible. Vea la entrevista de InformationWeek de Dough Henschen con George Llado de Merck.


Minimizar los residuos en todo el proceso de fabricación de medicamentos

One Hortonworks pharmaceutical customer uses HDP for a single view of its supply chain and their self-declared “War on Waste”. The operations team added up the ingredients going into making their drugs, and compared that with the physical product they shipped. They found a big gap between the two and launched their War on Waste, using HDP big data analytics to identify where those valuable resources were going. Once it identifies those root causes of waste, real-time alerts in HDP notify the team when they are at risk of exceeding pre-determined thresholds.


Investigación traslacional: convertir estudios científicos en medicina personalizada

The goal of Translational Research is to apply the results of laboratory research towards improving human health. Hadoop empowers researchers, clinicians, and analysts to unlock insights from translational data to drive evidence-based medicine programs. The data sources for translational research are complex and typically locked in data siloes, making it difficult for scientists to obtain an integrated, holistic view of their data. Other challenges revolve around data latency (the delay in getting data loaded into traditional data stores), handling unstructured and semi-structured types of data, and bridging lack of collaborative analysis between translation and clinical development groups. Researchers are turning to Open Enterprise Hadoop as a cost-effective, reliable platform for managing big data in clinical trials and performing advanced analytics on integrated translational data. HDP allows translational and clinical groups to combine key data from sources such as: Omics (genomics, proteomics, transcription profiling, etc) Preclinical data Electronic lab notebooks Clinical data warehouses Tissue imaging data Medical devices and sensors File sources (such as Excel and SAS) Medical literature Through Hadoop, analysts can build a holistic view that helps them understand biological response and molecular mechanisms for compounds or drugs. They’re also able to uncover biomarkers for use in R&D and clinical trials. Finally, they can be assured that all data will be stored forever, in its native format, for analysis with multiple future applications.


Secuenciación de próxima generación

IT systems cannot economically store and process next generation sequencing (NGS) data. For example, primary sequencing results are in large image format and are too costly to store over the long term. Point solutions have lacked the flexibility to keep up with changing analytical methodologies, and are often expensive to customize and maintain. Open Enterprise Hadoop overcomes those challenges by helping data scientists and researchers unlock insights from NGS data while preserving the raw results on a reliable, cost-effective platform. NGS scientists are discovering the benefits of large-scale processing and analysis delivered by HDP components such as Apache Spark. Pharmaceutical researchers are using Hadoop to easily ingest diverse data types from external sources of genetic data, such as TCGA , GENBank , and EMBL. Another clear advantage of HDP for NGS is that researchers have access to cutting-edge bioinformatics tools built specifically for Hadoop. These enable analysis of various NGS data formats, sorting of reads, and merging of results. This takes NGS to the next level through: Batch processing of large NGS data sets Integration of internal with publically available external sequence data Permanent data storage for large image files, in their native format Substantial cost savings on data processing and storage.

HDP utiliza los datos del mundo real para entregar evidencia del mundo real

Real-World Evidence (RWE) promises to quantify improvements to health outcomes and treatments, but this data must be available at scale. High data storage and processing costs, challenges with merging structured and unstructured data, and an over-reliance on informatics resources for analysis-ready data have all slowed the evolution of RWE. With Hadoop, RWE groups are combining key data sources, including claims, prescriptions, electronic medical records, HIE, and social media, to obtain a full view of RWE. With big data analytics in the pharmaceutical industry, analysts are unlocking real insights and delivering advanced insights via cost-effective and familiar tools such as SAS® ,R®, TIBCO™ Spotfire®, or Tableau®. RWE through Hadoop delivers value with optimal health resource utilization across different patient cohorts, a holistic view of cost/quality tradeoffs, analysis of treatment pathways, competitive pricing studies, concomitant medication analysis, clinical trial targeting based on geographic & demographic prevalence of disease, prioritization of pipelined drug candidates, metrics for performance-based pricing contracts, drug adherence studies, and permanent data storage for compliance audits.

Acceso perpetuo a datos brutos de investigaciones previas

HDP Uses Real-World Data to Deliver Real-World Evidence
Real-World Evidence (RWE) promises to quantify improvements to health outcomes and treatments, but this data must be available at scale. High data storage and processing costs, challenges with merging structured and unstructured data, and an over-reliance on informatics resources for analysis-ready data have all slowed the evolution of RWE. With Hadoop, RWE groups are combining key data sources, including claims, prescriptions, electronic medical records, HIE, and social media, to obtain a full view of RWE. Analysts are unlocking real insights and delivering advanced analytic insights via cost-effective and familiar tools such as SAS:registered: ,R:registered:, TIBCO:tm: Spotfire:registered:, or Tableau:registered:. RWE through Hadoop delivers value with optimal health resource utilization across different patient cohorts, a holistic view of cost/quality tradeoffs, analysis of treatment pathways, competitive pricing studies, concomitant medication analysis, clinical trial targeting based on geographic & demographic prevalence of disease, prioritization of pipelined drug candidates, metrics for performance-based pricing contracts, drug adherence studies, and permanent data storage for compliance audits.