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Use Case:

'Data as a Service' Product

Seven Bridges Genomics connects healthcare and life sciences to reveal the true promise of precision medicine — a continuous flow of knowledge between researchers, scientists, and clinicians worldwide, creating insights that radically improve human health.

Seven Bridges Genomics Data as a Service Hero

Problem Space & Opportunity


The Seven Bridges Genomics research and analytics platform products are highly effective in leveraging and processing petabytes of health, clinical, and multiomic data for millions of patients worldwide. However, our current data delivery system is fragmented, with many manual processes that can be traced back to a historical precedent of responding to client needs with customized solutions. While this approach has helped us accelerate data discovery and analysis, it has also created several problems for the business; namely,

  • Frequent reworking of systems and data creates additional costs and loss of profit since we have to start over each time we receive a new or modified dataset.
  • Manual processes are
    • prone to data quality issues due to the lack of version control and number of handoffs. 
    • labor-intensive and hinder scaling of the ingestion, curation, and modeling processes.
  • Limited ability to harmonize multiomic and real world health data.

Additionally, the current system makes it difficult for product managers to understand what data is being examined by which client or end-user, which functions they prefer to use, or how often they access it. This lack of transparency inhibits product innovation and development because we are lacking important usage data that would drive confidence in new feature enhancements.


To address these challenges successfully, we need to streamline our data systems and processes by investing in Data as a Service infrastructure that supports the flow of new data over time while automating key processes where possible. By doing so, we can increase efficiency while reducing costs associated with manual workarounds. Moreover, implementing robust version control mechanisms will help ensure that data quality and integrity is intact at all times and increase confidence, compliance, and security in the handling of sensitive data. The result is a service that delivers analytic-ready data that can be consumed by our downstream products to product business value.


By forming a Data Insights shared services function, we can produce analytic ready, data frameworks which incorporate known ingestion and data governance best practices to enable repeatable and configurable data pipelines. The Data Insights teams will scale and accelerate data ingestion via configurable, reusable, traceable and reliable tools & templates. Their mission was to drive insights and analytics through continuous process improvement that give our customers, products, and professional services teams the opportunity to accelerate biomedical research and improve health outcomes.

The Goal:

Partner with existing Seven Bridges Genomics customer-facing product teams to create a Data as a Service roadmap that tackles existing business problems we face today. Use this partnership as a means to build out the templates and automation frameworks that form the foundation of our Data as a Service function. Deliver quick wins for these products by accelerating time to ingest, reducing manual touchpoints, and improving quality standards. Begin to implement the follow components:

  • Data Intelligence
    • Data Provenance
    • Dataset and Data File Discovery
    • Data File Catalog
  • Data Enrichment
    • Data Profile
    • Data Quality & Integrity
    • Data Transfer (Standards and Rules)
  • Data Modeling
    • Reference & Code Sets
    • Data Model
    • Data Product
    • Data Dictionary


  • Solution-layer:
    1-3 months
    • 1 scrum teams dedicated to the pipeline product delivery.
    • 1 scrum team dedicated to establishing templates & frameworks.
  • Program-Layer:
    3-18 months
    • Product core team including Principal Product Manager, Scientific Liaison, Architect, and Principal Product Designer
  • Portfolio-Layer:
    1-5 years
    • SVP Scientific Strategy, VP Product & Design, Chief Architect, Chief Technology Officer, Director, Product Infrastructure


  • Stakeholder Interviews

  • Technical Team Audit

  • Service Blueprint Design

  • Storytelling & Value Prop

  • Integrations and Automations

  • Template & Framework Design

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