Accelerate clinical trials

Productize and harmonize your clinical trials data to increase your chances of clinical success. For Clinical Trials

Rapidly analyze existing and emerging data types

Advanced self-service analysis to optimize your clinical trials. Improve your chances of clinical success by assessing the safety and efficacy of your drug candidates as often and as quickly as possible.

Instant access to analysis

Promptly translate your findings across all clinical trial phases, from phase I to phase IV, using a streamlined approach to statistical analysis.’s data products offer self-guided, no-code analysis apps that enable you to instantly ask and answer your research questions. Below are some examples of how our apps can be used to evaluate your study details, such as biomarkers, endpoints, compounds, and study arms, to increase the chances of your drug candidates’ success:

Perform unsupervised clustering analysis on multimodal clinical data to identify subtypes in heterogeneous disease populations cluster analysis

Create cohorts — using the point-and-click function — to compare the clinical outcomes of your drug candidate against the standard of care cohort builder

Generate a cox survival analysis report, via R or Python scripts, on your defined subgroups via biomarker status and treatment arm cox survival analysis

Access the outputs of your ML/AI models ​​to identify study sites, healthcare professionals, and participants for your studies pathway analysis

See analysis app demo data product - Overview data product - Data engineers data product - Data scientists data product - Researchers

Single Cell Gene Expression

In this demo, we’re going to show you how you can use a no-code analysis app to look for genes that are differentially expressed in cancer cells.

How Top Organizations Are Using

A Top 5 Global Pharma is working towards comprehensive harmonization of all past & future trials — starting with 10 clinical trials in translational oncology.

A Top 5 Global Pharma is using data products to cross querying proprietary, public & annotation data relating to autoimmunity.

Productizing your data

Your data analysis process can be dramatically streamlined when you productize your data. By turning your clinical trials data into analyzable data products, you gain fast access into crucial information that will accelerate your clinical trial planning, such as biomarker identification, protocol design, and site selection. Furthermore, the data products will be readily available throughout your drug development lifecycle for quick references.

Curious what productizing your clinical trials data looks like? Below are two examples.

Pfizer’s Trial Phase III - JAVELIN 101

This data product contains data on avelumab with axitinib versus sunitinib in advanced renal cell cancer. Analysis app examples: Explorer

This data product contains data from to assist in discovering clinical trials. Analysis app examples:

Types of data that can be turned into data products’s data product framework will integrate any combination of datasets into a simplified, flexible model – perfect for use within apps or as a dataframe to export to other tools.

Some examples of potential data sources, types, and formats:

Data type examples
  • DNA-Seq (VCF, MAF)
  • RNA-Seq (bulk and single-cell, spatial transcriptomics)
  • Proteomics
  • Flow cytometry
  • Compound screening
  • Immune repertoire
  • Clinical trials (outcomes and biomarkers)
  • Longitudinal studies
  • Annotation, ontology, pathways
  • Machine behavior and maintenance
  • Drug response & pKa studies
  • Biomanufacturing yields
  • Knockdown studies (RNAi, CRISPR)
  • Meta genomics
  • Gene expression
  • DNA methylation
  • Somatic mutations
  • Germline variants
  • High content screening
  • Patient-reported outcomes
  • Patient apps
  • CTMS
  • mBio
Data source examples
  • Siloed data
  • Data warehouses
  • Data lakes
  • Data products
  • cBioPortal
  • TCGA
  • dbGaP
  • GEO & ArrayExpress
  • Clinical trials
  • UK Biobank
  • CROs
Data format examples
  • CSV
  • JSON
  • XML
  • SQL
  • CDISC (SDTM, ADaM, etc.)
  • Any form of tabular data (CSV/TSV)

Talk To Our Experts

If you don’t see the data types or sources you’re looking for in the lists, chances are we can accommodate them.

Talk to our experts to discover how to turn your clinical trials data into data products!

Get A Demo

Harmonize your clinical trials data using data mesh

Build a data mesh of reusable data products to promote data harmonization and bring fragmented data together across clinical trials and therapeutic areas.

Seamlessly integrate both historical and emerging data, such as biomarkers and clinical trial phase III, to inform and enhance your clinical trial planning

Compare your proprietary data against public datasets so that you can identify scientific trends and correlations across multiple studies

Quickly perform exploratory and confirmatory analysis to reduce trial failures and improve clinical development and operations

Data mesh in a box offers an out-of-the-box solution to help you accelerate your data mesh implementation. Talk to our experts to learn more about our offerings!

Promote cross-organizational collaboration

Access your data mesh of clinical trials data products and collaborate efficiently using’s two-sided analysis environment. - Analysis Platform and Developer Studio

Analysis Platform offers a customizable Analysis Platform to support your unique business and research needs. Leverage the enterprise features to drive collaboration and innovation.

For researchers

  • Ask and answer your research questions with confidence – by using self-guided, no-code analysis apps within each data products to perform advanced analytics, such as clustering analysis and generating R reports
  • Promptly identify, store, and share your useful data artifacts (UDATs), such as biomarkers and clinical outcomes, with your fellow team members

For data scientists and bioinformaticians

  • Demonstrate the value of your data science in clinical trials – by making your work accessible to researchers and other domain experts across the organization
  • Save hours from performing one-off analyses – by repurposing your work as reusable analysis apps across multiple data products

For leadership

  • Promote a consistent view of reliable, harmonized clinical trials data – by providing a platform that presents a single source of truth
  • Boost efficiency and drive innovation – by streamlining the data analysis process and making data science accessible across your organization

For strategic IT

  • Simplify your data governance process as your clinical trials data grows – by streamlining your data management and access
  • Stay compliant with regulatory and legal requirements without compromising data use

Developer Studio

To help you build high quality data products, offers a Developer Studio that uses a familiar, Jupyter notebook-based setting. Leverage the pre-built templates to streamline your data product creation.

For data scientists

  • Access reliable and harmonized clinical trials data from data products for your R & Python algorithms and ML/AI models
  • Collaborate more efficiently when your fellow data scientists and data engineers use the same tool to access data and build data products

For data engineers

  • Deliver quality and harmonized clinical trials data to data scientists and promote data reusability – by standardizing how nomenclatures get mapped into the data products
  • Advance data literacy when you use terminologies that researchers understand – by partnering with domain experts to standardize the terminologies being mapped into the data products

For quality controlled analysts

  • Rapidly reproduce any reports and analyses to support regulatory submissions and publications – with instant access to all the versioned data, software packages, and analysis methods
  • Streamline ​​the auditing procedures when you gain transparency into the data mapping process and source code

For leadership

  • Set a lasting foundation that promotes consistent quality controlled outputs as your research trial data grows and new data types emerge
  • Significantly free up your resources time so that they can focus on what they do best – by streamlining and automating manual repetitive work

More ways to use helps you overcome data challenges across your drug development process. For Basic Research
Basic Research

To identify novel targets, pathways, biomarkers, and signatures. For Preclinical

To evaluate potential success of a treatment, using data from cell lines and model organisms. For Regulatory Review
Regulatory Review

To produce quality controlled reports for reviews. For RWE/RWD
Real World evidence

To connect clinical endpoint biomarkers with real world data. Data Security In Clinical Research Environment

Data Security in Clinical Research Environment is hosted entirely within your secure network and/or your secure cloud. Source data storage and access is tightly controlled within your network, and the platform is compliant with standards such as GxP, GDPR and HIPAA.

Learn More About Security

Explore Resources For Life Sciences for clinical data management and analysis ebook

Data Harmonization for Translational Research

A practical guide to help you resolve clinical trial data challenges using the data mesh approach.

Promote transparency in the data analysis process

Promote Transparency In The Data Analysis Process

Gain visibility into peer-reviewed work. Demonstrate integration of python-based machine learning algorithm into the platform.

Iterative Analysis Allows Faster Discoveries

In the course of one evening in 2019, Dr. Radovich found three significant results from his own dataset in Thymoma (TCGA).
Maximize your chances of clinical success

From a 30-minute demo to an inquiry about our data mesh in a box offering, we are here to answer all of your questions!