Results Data Analyst – RIW


Rapidly transform large data into verifiable decisions with advanced analytics



Results Data Analyst role provides dedicated functionality for data post-processing and collaborative decision support. By leveraging advanced analytics, users can rapidly transform data to verifiable decisions. Virtual and physical testing, by its very nature, generates large amount of data and requires analytics tools to extract its value through post-processing, visualization, and trade-off analysis. Through Results Data Analyst role users can now unlock their data to collaboratively answer common questions, such as “What if?” or “What is my best option?” while retaining decision history and reference material in one place.

Users can easily access and merge data sets, refine data by applying filtering, obtain immediate insights through a customizable ranking engine, and predict better outcomes using predictive analytics. They can then compare alternatives in 3D, leveraging lightweight viewers. Users can also collaborate with decision makers via discussion threads and preference trades to explicitly and transparently evaluate scenarios and verify requirements.



  • Easier Data Access and Preparation
    • Read any PLM Object
    • Automatic Data Merge of multiple data sources based on simulation logic
    • Eliminate timely movement of large data and models
  • Uncover Hidden Patterns in Simulation Results Data
    • Use analytics and advanced visualizations to uncover new and emerging patterns in the data of post-processed simulations
  • Faster, Interactive 3D Visualization
    • Data Compression via Analytics
    • Server Rendering and Binning
    • Embedded 3D Lightweight Viewers
  • Generate Predictive Insights
    • Leverage data investment by generating mathematical models for predictive insights using advanced analytic methods
    • Share and manage predictions via traceable approximation objects, FMUs, or co-efficient file for further design exploration.
  • Fact Based Decisions
    • Rigorously assess large set of alternatives against requirements via ordinal ranking engine using Multi-Criteria Decision Making Techniques
  • Improve Decisions through Collective Intelligence
    • Collaboratively compare alternatives and validate against requirements with stakeholders from anywhere via web application on premise or cloud (FD04)
  • Decision Traceability
  • Retain “the why” of a decision with decision history, discussion threads, and reference material in one place
  • Save and share analysis and decisions with stakeholders via “live report”
  • Tie decisions to designs, project tasks, requirements, and engineering change requests