Digital Twins: a New Real-Time Data Analytics Technique that Boosts Disaster Response


When a natural disaster strikes, personnel and supplies must quickly be deployed for an effective recovery, creating the daunting challenge to track thousands of data sources. Dr. William Bain, CEO of ScaleOut Software explores how a new software technique for streaming analytics, called ‘real-time digital twins,” helps to immediately identify and handle critical issues.

In the midst of this COVID-19 pandemic and the wake of other past natural disasters, government response organizations, healthcare providers and logistics companiesOpens a new window alike are tasked with pouring over data to determine how they could have better-identified issues in the moment and made more informed decisions faster.

One of the biggest challenges for response teams is managing and analyzing the sheer volume of streaming dataOpens a new window coming in from thousands of sources to make sense of it all in real-time. Sifting through this aggregated torrent of data to see where the most urgent problems are and how to address them can be an overwhelming task.

To make matters worse, with most streaming analytics Opens a new window approaches, there is a real lag between when critical data insights are needed by key organizations and when they are actually obtained. This issue arises because most streaming analytics platforms can only provide minimal introspection to extract patterns of concern as data flows by. Most messages are sent to databases or data lakes for later offline analysis and visualization with big data tools like Spark, rather than providing insights in real-time when they are most needed.

But what if there was a way to gain real-time data insights to answer the most vital disaster-response questions at the moment? How can real-time streaming data analyticsOpens a new window be harnessed to better understand where the biggest needs are and how situations are developing on the ground and across regions? A new software technique promises to do just that.

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The Eye of a Data Storm: Hurricane Maria

Let’s take Hurricane Maria in Puerto Rico in September 2017 as an example. When this tragedy hit, the response was very difficult to track with real-time data. Emergency workers didn’t know where shipments of water, food, or medical supplies were located in order to deliver resources to those who needed them most. Electricity and phone service were out across the island, but they didn’t know which power poles and cell towers to fix first.

These challenges persisted for months and have since become infamous in the news media. In fact, according to The New England Journal of MedicineOpens a new window , households went an average of 84 days without electricity, 68 days without water, and 41 days without cell service.

One of the other major challenges in delivering an effective emergency response is minimizing the time it takes to build a capable data analytics system to manage it. It can take days, weeks or even months to design, build and test a conventional data management systemOpens a new window that is able to answer vital questions and respond to needs. To be useful during an emergency response, streaming analytics platforms need to be agile, easy to build quickly, and able to handle real-time information at scale. These systems need to filter and distill real-time data from thousands of sources into critical intelligence so that immediate actions can be taken.

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A New Analytics Technique for Better Disaster Response

To better manage future disaster responses, it will be critical to prebuild a streaming analytics system before a disaster strikes that can combine real-time telemetry from all needed data sources. These inputs should include smart warehouses that know where supplies are stored, IoT tracking data for shipping information, healthcare clinic data, and telemetry from smart power poles. With this system, response teams can gain needed insights in the moment, and emergency management teams and aid organizations can maximize the effectiveness of their response.

Rather than following the conventional approach of pulling insights from a single, aggregated stream of data, an innovative new software technique for streaming analytics, called “real-time digital twins,” allows messages from each data sourceOpens a new window to be separately analyzed and responded to in real-time. By maintaining dynamic information about each data source, it enables deeper introspection and more effective responses than previously possible. Using the processing speed of in-memory computing, it also incorporates real-time aggregate analytics that quickly pinpoints emerging issues, answer key questions, and help to maintain overall situational awareness.

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During an emergency response, real-time digital twins can track the key parameters of each individual asset, such as a shipment of food, water, medical supplies, or a smart power pole, and update these parameters in milliseconds as messages flow in from systems and personnel in the field. Individual insights can then be rolled up using continuous aggregate analytics to show trends, identify emergency response issues, and help prioritize them.

While big data platforms focus on deep and often lengthy offline analysis to make future projections, real-time digital twins can provide quick answers to pressing questions using the most current data. This difference creates a crucial opportunity for decision-makers that maximizes their situational awareness in rapidly evolving situations.

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We cannot predict when the next disaster will hit, but we can prepare our data management systems now to make better use of streaming data and obtain insights in the moment. This will assist all stakeholders, including government organizations, logistics and healthcare providers, and utility companies. By harnessing this new software technique for real-time streaming data analytics, we will be ready to make better informed and more timely decisions to weather whatever future storms come our way.

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