Risk-Based Data Science

  • powered by QbD
  • Your Data, Our Expertise, Collaborative Success!

Risk-based data science supports the proactive identification, assessment, monitoring, and mitigation of risks in clinical trials using Quality by Design (QbD) principle.

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Risk Based Data Science Enablers
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Benefits of Risk-Based Data Science with QbD
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Our Deep Expertise
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Our flexible Engagement

Risk Based Data Science Enabler

Approach Technique Tool/Aid
Quality By Design (QbD)
  • Early Pre-Risk Assessment (EPRA)
  • Risk Assessment & Mitigation Planning
  • Protocol Data Model
  1. QbD-EPRA Starter Pack
  2. Risk Inventory
  3. Data Flow Mapping & Standards
  4. Data Governance and Acquisition
  5. Integrated Data Review Plan
Risk Based Data Science
  • Central Data Surveillance Toolkit
  • Action & Decision Insights
  1. Master Analysis & Metrics
  2. Data Cleaning Checks
  3. Issue & Decision Log
  4. Risk Report

    Benefits of Risk-Based Data Science with QbD

    By following the QbD approach, you gain greater control over your clinical trials and ensure safety and efficiency. Here are some of the key benefits:

    • Improved data quality: By proactively managing risks and ensuring all relevant data is captured, the overall quality of the data improves.
    • Reduced risk of potential issues: Early identification and mitigation of risks prevent problems from arising later in the trial.
    • Increased process efficiency: Standardized data management and continuous monitoring streamline the clinical trial process.
    • Proactive Risk management reduces time & cost: Addressing risks early saves time and money that would otherwise be spent fixing problems later.
    • Enhanced regulatory compliance: Following QbD principles ensures your data meets regulatory requirements for submission and reporting.

    In essence, Risk-Based Data Science powered by QbD is a smarter way to conduct clinical trials. It leverages data to proactively manage risk and ensure the quality of the data you collect. This leads to more efficient trials, better quality data, and, ultimately, the development of safer and more effective products.

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    Our Deep Expertise

    Deep Expertise in Clinical Research:

    • 30+ Cumulative Years of experience leading risk-based implementations, clinical operations, and data management
    • Advanced data skills, including programming (SAS, Python, R), analytics, and data science.
    • Proficient in data review tools: Spotfire, Tableau, PowerBI, Cluepoints, Medidata Detect, TRI-OPRA, Comprehend.
    • Product Management experience to ensure successful project delivery.
    • AI Solutions Consulting expertise to bring innovative solutions to clinical research.
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    Our flexible Engagement

    Stay Long (more than 3 Months)

    Long-term Engagement: Delve profoundly into your projects with our team accompanying you for an extended span, ensuring sustained success and growth.

    Stay short (1 to 3 Months)

    Short-term Engagement: Our short-term Engagement is meticulously designed to tackle specific challenges efficiently and effectively for urgent and precise problem-solving. This tailored approach ensures that your immediate needs are met precisely and quickly.

    Stay Quick (Less than a Month)

    Rapid response Engagement: When time sensitivity is paramount, our rapid response team promptly initiates action, delivering solutions quickly and accurately

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Mukesh Babu

Chief Data Scientist

With over 16 years of experience in the clinical research industry, Mukesh has worked with companies that provide next-generation technology solutions for RBQM, Data Management, and Data Science. Mukesh is a trained pharmacologist with expertise in various therapeutic areas and phases of clinical research.

As the Product Owner and SME for Central Monitoring and Clinical Technologies, Mukesh has extensive experience in the implementation and optimization of RBQM tools and processes, such as Central Statistical Monitoring, Quality Tolerance Limits, and Risk Indicators. His mission is to drive innovation and excellence in clinical research with data and technology, and to collaborate with internal and external stakeholders to ensure quality, compliance, and efficiency.

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