Accelerate data to insights journey with big data technologies

    Big data technologies in life sciences to improve patient outcomes

    BIG data and data lakes with dynamic processing

    Big Data and Data Lake(s) are transforming businesses by providing a centralized data repository for entire organizations, with data sources ranging from structured, unstructured, internal to external data. It enables business analysts and data scientists to readily utilize data and IT to streamline and manage data needs from various clients faster and more efficiently than traditional data warehouses and spreadmarts.

    • Data Sources Assessment and Proposal: Access data and propose suitable big data ecosystems services to suit enterprise-wide consumption.
    • Building Data lake: Design and build on-premises or Hybrid data lake
    • Big Data Administration and Support Services: Manage, report, and control big data investments through efficient and effective administration and support services.
    BIG-DATA-a-challenge-or-an-opportunity (1)

    Teradata – HIVE – Redshift – Azure – Talend – HBASE – Google – Cloudera – MAPR – Hortonworks – Informatica – Oracle

    Big data strategy consulting and architecture

    Evaluate legacy data infrastructure and adapt to new age data requirements

    Feasibility study and portfolio analysis

    Access both from within and outside the organization for data maturity and propose strategies optimum for organization, considering best offers available in Industry.


    Work with different organization stakeholders to prioritize use case and business development for Big data and Cloud adoption. Build a roadmap and define steps for adoption.

    Leading industry solutions

    Leverage deep industry understanding, evaluate leading services in the market, and adapt data infrastructure to suit the organization’s needs.

    Transition-aware architecture

    Provide a big data and Cloud Architecture transition roadmap that creates minimum interruption to the business process.

    Big Data Challenges

    Life sciences organizations often face challenges to manage and derive meaningful insights from big data. They cannot achieve the proper value of big data only with technological implementations, but the Commercial IT and the Data Scientists need to work in tandem to achieve the common goal – better patient outcomes.

    • Icon-Inactive-Data-Assessment (1)-1 Massive number of data sources
    • Group-42253 (2) Complex data environments
    • Group-42268-1 (2) Chaotic and unstructured external data
    • Group-42269 (2) Speed required for data to decisions

    Massive number of data sources


    Massive number of data sources

    Internal data systems, cloud, apps, sensors - no one expected that number of data sources would increase so rapidly and will be so varied​

    Complex data environments


    Complex data environments
    Group-42275@2x (1)

    Multiple systems, slow legacy databases and data warehouse - disparate, custom built, low integration, cumbersome privacy rules & requirements, encryption - all inhibit access​

    Chaotic and unstructured external data


    Chaotic and unstructured external data
    Group-42255@2x (1)

    Syndicated – national, specialty, lab, EHR, pharmacy, etc. - social, web, demographic, geographic, weather, chats, and so on..​

    Speed required for data to decisions


    Speed required for data to decisions
    Group-42278 (1)

    Data not ready for analysis, low automation, processes not streamlined, batch processing, heavy dependency on technical skills​

    The value big data can deliver

    In today’s data-driven economy, every aspect of a business is impacted by how data is leveraged using cloud, technologies, and AI/ML. The organizations that effectively apply the big data:


    Enable pre-processing in the cloud using suitable tools, saving 90% of analysts’ time, allowing them to focus on analysis


    Decrease dependency and usage of expensive and unsuitable tools for pre-processing, saving license costs


    Allow business and data scientists to readily access data securely to provide actionable insights


    Reduce fraudulent use of data


    Customer success stories

    Case Study

    Automated Email Campaigns Support The Patient Assistance Journey For Enhanced Treatment Outcomes

    Case Study

    Patient Flow Analysis For A Global Oncology Leader

    Case Study

    50% Faster Customer Onboarding With Scalable Intelligence On Omnichannel Interactions

    Axtria research hub

    Industry Primer

    Data Visualization


    MedTech as a Service: How Subscription-Based Selling Will Play a Role in the Future of MedTech


    Using big data and AI/ML to make drug development cheaper, faster, and more effective