Aspire
Open Framework. Open Possibilities

Nucleus

Breaking the SCE Mold

Aspire is a public cloud clinical analytics framework being built, engineered, and delivered using API componentry.  Aspire accelerates modernizations by sharing non-proprietary code, applications and back-end services. By shortening development and modernization cycles, Aspire helps sponsors reduce license and programming costs, fully-leverage cloud containerizations, and reduce maintenance and validation of legacy systems.

Aspire is a cooperative framework built from partnerships with enterprise sponsors through Acceleration Services engagements. The configurable infrastructure of Aspire leverages transferable technologies that reduce the time and cost associated with customization and reinvention.

Read Article: Reinventing the SCE

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 Nucleus

Aspire Component Development

In collaboration with development partners, our d-wise team of software engineers and IT architects are experimenting with a multitude of open source technologies for exploratory clinical analysis.

  • Pre-validated programs and technology packages
  • Flexible containerized cloud-agnostic deployment
  • Open suite micro-services and APIs
  • Scripted personalized task containers
  • DevOps application for clustering
  • Centralized data injector to trap and define arrangements
  • Hybrid cloud deployment to avoid vendor lock-in

What makes d-wise different?

The statistical application environment is continually changing as a direct result of client partnerships. Test or fully adopt applications within your organization that either enhances or fully replaces your legacy statistical-computing environments, leveraging the value of your data and cloud environment.

  • # 1: Most trusted clinical technologists with 17 years of proven data-science innovation
  • Independent: Vendor, Software, and language independence maximizes value to our clients
  • 100+: Life Science clients
  • 5000+ managed cloud users
“We partnered to move away from customized solutions based on proprietary technology.  With too many static solutions, we recognized the need to connect open tools across the enterprise and purchase smaller proprietary pieces that are both configurable and replaceable.”- Global Pharma, Head Data Analytics & Data Science

  • Competition for Talent

    Top talent looking to use technologies that enable agile data science and not archaic data collection.

  • Proprietary Legacy Licenses

    Managing expensive and convoluted commercial licenses wastes time and money.

  • Unsuitable Visualization Tools

    Monolithic enterprise solutions are inflexible, expensive, and do not meet today's unique data science requirements.