How a digital twin can help manage complex data needs

Data is growing exponentially, powered by the adoption of 5G, IoT, and the widespread use of devices, from wearables to smartphones. Meanwhile, businesses are adopting new digital capabilities to align IT services and accelerate digital transformation.

The ASEAN region is betting big on cloud because of its enormous opportunity to deliver improved citizen services. According to data from IDC, Vietnam’s cloud market is growing at a CAGR of 32% from 2018 to 2023. Indonesia, Philippines, Thailand, Malaysia, and Singapore are also intensifying their cloud capabilities as large business enterprises scale operations.

This rapid digitalisation puts enormous pressure on modern IT systems to streamline massive amounts of data. As it is highly complex and involves vast networks of interlinked devices, navigating data through conventional table-based databases can be challenging.

This is where graph technology and digital twins come to the rescue. A digital twin helps organisations make sense of vast amounts of data and draw meaningful, real-time business insights. A digital twin is a virtual representation or a digital counterpart of a physical object or process. It creates highly detailed reproductions of real-world assets in software, which behave identically to their original counterpart.

Digital twins have many applications and use cases, with enormous potential to ease complexities for ASEAN organisations in the new normal. They can help teams make more informed decisions – whether designing, manufacturing or operating.

Below are four applications and use cases for a digital twin:

Cybersecurity

System security is unforgivingly complex and costs ASEAN businesses millions of dollars every year, but it doesn’t have to. Organisations can effectively use digital twins to track weaknesses and calculate gaps in security standards, ensuring they are always a step ahead of cyberthreats. Graph technology and a digital twin can detect conspicuous movements and events in real time. Chains and rings of people can be visually identified, equipping organisations with better cyber defence capabilities.

As ASEAN organisations speed up their digital ambitions, ensuring cyber hygiene will be critical in the journey towards security and resilience. This is where a digital twin can help embed security across the entire organisation. Businesses can zero in on the biggest vulnerabilities before a breach by prioritising their responses because they already know where the attacks will come from.

Supply chain

A supply chain digital twin replicates an actual supply chain which helps organisations stress test their supply network using real-time data. By building a digital twin, logistics companies can mirror production lines and simulate journeys in highly complex global fulfilment networks. A digital twin is a critical component of a robust supply chain that can withstand setbacks and recover quickly from disruptions – whether staff shortages, blockades, climate-related slowdowns or geopolitical tensions.

A digital twin can substantially reduce response time by creating a robust contingency plan with clear alternatives to get businesses back up and running. Think of it as having a ready playbook to navigate foreseen and unforeseen events that could stall the entire supply chain and severely impact the business.

Construction

Digital twins can be used in construction to create replicas of real-world spaces. Architects and developers may sometimes find their creativity curtailed because they need to know if their designs are safe and practical in a real-world scenario. Testing them may be impossible because of the enormous amount of time, money, and resources required.

However, with digital twins, developers and project teams can reconstruct every metric from a physical structure in a digital environment and evaluate expected outcomes before making any changes to a structure. A digital twin can improve the analytical capabilities of Building Information Modelling (BIM) and provide real-time inventory status, working conditions, and resources for more efficient cost estimation, contractor financing, better material management, improved bidding, and so on.

Customer experience

Digital twins can also transform how businesses deliver customer experiences by contextualising data to understand what customers need. This is critical for organisations to keep up with evolving customer behaviour, especially after the pandemic. Customer behaviour can be simulated and anticipated, with knowledge graphs providing a much deeper understanding of the customer. This rich context enhances the accuracy of ethical decision-making and drives better engagement.

ASEAN organisations that adopt a digital twin will massively reduce operational costs. Digital twins use online and physical interactions to simulate the customer experience accurately, so organisations can better predict system and machine maintenance, and improve production workflows. They can also provide context and help predict future consumer behaviour.

Competitive business advantage

A comprehensive digital twin can help organisations weather disruptive events, optimise product performance, and improve profitability. Its use cases will be particularly valuable in the ASEAN region, which has enjoyed remarkable economic growth in the last 20 years, despite some disparity in life expectancy, job productivity, and quality of education. A digital revolution is propelling the region to a new era of growth, but a few barriers impede a robust digital ecosystem – including the lack of a single digital market.

Harnessing the potential of a digital twin will steer ASEAN economies to successfully navigate the fourth industrial revolution – and strengthen key industries like manufacturing, electronics, textiles and automotive. By turning data complexities into competitive business advantage, digital twins will nudge key sectors forward as the region prepares to become the fourth-largest economy in the world.

By Nik Vora, Vice President, Asia-Pacific and Japan at Neo4j

This article was first published by Frontier Enterprise