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Digital Twin: The Brainchild of Big Data

Picture of Mudassirkhan Pathan

Mudassirkhan Pathan

Co-founder & Head of Engineering

Data has emerged as a key element in every technological innovation with the rapid advancement of technologies such as the Internet of Things (IoT), Web 2.0 and artificial intelligence (AI). It is gathered from various sources, including social, machine, and transactional. This vast spectrum of data acquired from these sources is known as Big Data.


Big Data goes through a cycle of generation, parsing, combining and filtering to gain meaningful insights. Its vast and diverse applications range from predictive analytics and consumer behaviour insights to operational cost reduction, demand prediction, real-time monitoring, and personalised marketing.


However, along with its benefits, Big Data presents challenges for start-ups. Managing data from disparate sources, addressing quality issues, dealing with integration complexities, recruiting and training a competent workforce, and managing the escalating costs of computing resources are some of the hurdles faced by enterprises.


As a result, start-ups must invest in workforce training and recruitment programs. They must also employ consulting and cybersecurity teams to ensure smooth data management across the organisation.


Enter, Digital Twin


One of the innovative offshoots of Big Data is the concept of Digital Twin. It is the virtual replica of a physical asset, system, or process that uses sensors to generate data on performance and usability.


Through Digital Twin technology, start-ups can describe, diagnose, predict, and prescribe their assets’ behavior. Its success relies heavily on the voluminous data underlying it, including data from past records, real-time tracking, and predictive analytics.


Digital Twins are actively used in various sectors to predict product performance, customer modelling, supply chain optimisation, and design and development processes. IoT and Artificial Intelligence remain the driving forces behind Digital Twin’s advancement.


Blurring The Lines


In the automotive sector especially, this concept has emerged as a champion of the mobility race. It is being used for product testing, manufacturing, predictive maintenance, and sales, among other applications. It enables data unification, increased visibility between consumers and service providers, reduced downtime and failures, and predictive maintenance.


Start-ups and researchers are continuously exploring new usage scenarios to leverage the power of Digital Twin, with predictive health monitoring being a primary application in high demand. To obtain analytical insights, this uses data from various vehicle systems, such as the engine, battery, alternator, air intake, fuel tank, gearbox, and ECU.


Significant breakthroughs have been made in predictive maintenance and driver behaviour monitoring, enabling features such as breakdown prevention, safety incident prediction, fuel pilferage detection, identification of bad driving behaviour, and improper gear utilization.


Big Data and Digital Twins are slowly converging the gap between the real and virtual worlds. As supporting pillars of the Industrial Revolution 4.0, they are bound to see rapid growth in the near future. The possibilities are endless, and the progress irresistible; what lies ahead is a highly anticipated tech and data-driven tomorrow.

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