Virtual ExperienceJune 21, 2022

Virtual twin experiences powered by data science

Virtual twins, AI and embedded analytics are the key to leveraging an abundance of data and contextualizing it for purposeful and strategic decision-making to improve the current and future business.
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Pierre Leroux

To enable fast, confident data-driven decisions across their organization, enterprises in every industries are investing massively in analytics and big data technologies. IDC estimates businesses spent almost $216 billion on big data and business analytics in 2021. With a forecasted compound annual growth rate of 12.8% between 2021 and 2025, this investment trend will continue unabated. But what’s next for enterprises already taking advantage of confident decision making powered by data? Could they capitalize further on the available data and use it as a strategic instrument to drive sustainable innovation and competitive advantage? In this post, we will explore how virtual twin experienceand data science play a fundamental role in the success of current and future industry leaders.

Beyond decision making: Your data as a strategic instrument for innovation

Your organization already ingests and enriches data – whether internal or external, structured or unstructured, simple or complex – using a common, trusted referential to break down silos. It also builds analytics experiences that democratize information consumption across the enterprise and act as the cornerstone of decision-making. So how can you make your data even more powerful? The answer: Virtual twin experience.

Virtual twin experience is a representation of an entity – like a jigsaw, an assembly line, or even an entire city – that brings virtual and real worlds together. The central idea is to create a science-based representation of an entity based on 3D models and combine it with simulation and data science capabilities such as predictive analytics. The result is a strategic instrument driving the innovation of products and operations that cuts across functional siloes.

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How does an enterprise bring virtual twin experience to life? First, you bring all relevant data into one single unified environment. This environment includes everything related to the entity: 3D models, engineering and manufacturing bills of materials, operational data, etc. In short, all the information generated throughout the lifecycle of the real-world entity. A good example is an aircraft. Its virtual twin experience includes all the design information like 3D models, all documents related to the models, the maintenance records, operational data, etc.

Short- and long-term innovation opportunities

Once you have all this information available in a single immersive experience, it becomes easier to visualize your data in the right context. Virtual twin experience organizes all the relevant data and, combined with data science capabilities, generates insights relevant to short-term innovation such as solving an operational issue. For example, the virtual twin of an industrial equipment could show a 3D visualization and call attention to a part exhibiting premature wear based on operational thresholds, include historical wear experienced in the past. The failing part could be identified as coming from a recent batch of parts sourced from an alternative supplier.

Virtual twin experience powered by data science also drives data-driven collaboration processes, made possible because the analytics and collaboration processes are not siloed or supported by separate systems. Stakeholders do not have to leave the analytics system in favor of collaborative tools. The virtual twin experience powered by data science creates visualizations, contextualizes actionable insights and triggers efficient collaboration – communications and tasks – ensuring direct accountability from a single unified environment. In our example, the premature wear issue would trigger a contextualized action in the form of an immediate alert and assignment to an expert. The visualization, the wear data, and the supplier data would all be available to the expert without having to leave the virtual twin experience.

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Virtual twin experience is also critical for long-term collaborative innovation. The representation of the real-life entity and all its operational data is a great foundation for ongoing development because it generates accurate, science-based simulations to help assess the impact of potential ideas. You gather the data, the experiments and the results, and instantly share all that information in a community without having to leave the unified environment. It is easier and less costly to experiment using virtual twin experience. You reduce physical resources devoted to testing and you get to explore a far greater number of accurate science-based scenarios than what is possible in real-life conditions; and even some that would be very difficult to reproduce in the real world, like systems failures. The combination of virtual twins and data science opens unlimited horizons to new virtual experimentation.

NETVIBES roles enrich virtual twin experiences

NETVIBES is the Dassault Systèmes data intelligence brand that facilitates enriching virtual twin experiences on the 3DEXPERIENCE platform for better-informed decisions. Its portfolio of data science experience solutions, powered by artificial intelligence and big data analytics, includes the following collaborative roles on the cloud:

Data Analyst:  For exploring and analyzing enterprise data to create data experiences based on business needs.

  • Reveal and discover meaningful information from your enterprise data using a powerful query system.
  • Gain perspectives by presenting any data analysis using diverse tables and charts.
  • Publish ready-to-use applications for actionable insights.

Data Viewer:  For navigating and interacting with your company data.

  • Analyze KPIs to gain insights into your business, collaborate and manage your business processes.
  • Track KPIs on a common referential:  the virtual twin of your product. 

Data Engineer: For collecting and preparing data to provide ready-to-use datasets.

  • Collect raw data and configure data ingestion from multiple sources.
  • Set up pipelines to automate different stages of data acquisition, from extraction to storage.
  • Orchestrate data processing, including data normalization and cleansing to provide datasets to Data Analysts.

Data Steward: For organizing your company knowledge and ensuring governance of your data.

  • Define and manage your company ontologies and datasets, ensuring a single source of truth.
  • Define, develop and validate your ontologies through dedicated user interfaces for authoring classes and properties.
  • Better visualize company data and its history via the cataloging application.

Conclusion

Companies already taking advantage of confident decision making powered by data can gain further advantage by leveraging virtual twin experience for continuous improvement. Virtual twins, AI and embedded analytics are the key to leveraging an abundance of data and contextualizing it for purposeful and strategic decision-making that can improve the current and predict the future state of the business.

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