‍

Analytics is defined as the systematic analysis of data or statistics. It is used for discovering, interpreting and communicating meaningful patterns in data. And based on these insights, effective decisions are made.

‍

Digital Twins help make effective decisions based on Big data and by facilitating analytics. The concept of Digital Twins first emerged in 1970 at NASA. When an Apollo 13 oxygen tank exploded in full flight in April 1970, a race against time began to rescue the three astronauts. With the rocket 330,000 kilometres high in the sky and no margin for experimentation, it was 15 huge simulators on Earth, with which engineers could recreate scenarios, that offered a way out. Meanwhile, the Digital Twin concept has found its way into various sectors and that is mainly due to technological progress. The computingpower, amount of data and methods to gain insights from data did steeply advance over the last years.

As a business, it is important to use technological improvements to gain insights into the available data to make strategic decisions. Digital Twins have a crucial role as they are defined as a data-driven decision support system consisting of several domains:

• Data Services: Enables data access, ingestion and management across the platform, from the edge to the cloud (eg streaming, ETL, processing, ontology management, storage, cleaning,...)
• Integration: Enables data access across internal and external business systems and applications. Enables communication between different systems and applications.(eg API services, Enterprise system integration, IoT integration, legacy integration,...)
• Intelligence: This provides the services for data combination, basic and advanced analytics, AI, orchestration and other data processing capabilities (e.g. alerts & notification, simulations, business rules, ML, predictions, command & control,...)
• User Experience (UX): Enables the user to process and interpret the data (e.g. basic visualisations, Business Intelligence, AR, VR, Real time monitoring, ...)
• Management: This defines the system and ecosystem management approach (e.g. devicemanagement, system monitoring, event logging, data governance,...)
• Trustworthiness: This defines the reliability parameters of the system (e.g. security, privacy, safety, reliability, resilience, device security,...)  

Data is underutilised and underused today.This is because data users spend 80% of their time searching, understanding and cleaning up data for analysis. This reflects the need for a data strategy, an operational model, a system that speeds up this process and contributes to transparent insights.

A Digital Twin is therefore a catalyst for profitability and sustainability. The system acts as a platform to simulate different scenarios digitally. This helps to assess the impact of decisions without real risks, thus having a positive impact on business results. This ensures effective, efficient decision-making and the system aims to maximising profitability, maintaining reliability and improving sustainability.

With digital twins, organisations can improve a number of KPIs in the domains of cost improvements, operational efficiency, lead times and sustainability. A study by Capgemini shows that organisations realised an average 13% cost reduction across Digital Twins use cases and a 15% increase inoperational efficiency.

This indicates that a Digital Twin contributes to positive business results. But the best analytics are worth nothing with bad data. The system alone is therefore insufficient, because a qualitative decision consists of a number of parameters: data x analytics x IT x people x processes. So the decision value chain for qualitative decisions consists of a number of technical parameters (data, analytics and IT) and a number of business parameters (people & processes). A Digital Twin consolidates these domains as a methodology to reach a data driven organisation and should be at the core of the corporate DNA to fully mine the value of data.

The methodology of D-Cide aims to support you by drafting a data strategy, defining the data governance approach, choosing and implementing a data platform and developing the necessary applications. These pieces of the puzzle need to be integrated on the road towards a data-driven organisation and aim to valorise data to its full potential.

‍