Category: Data Science

Digital Twins

Digital Twins Q&A

Michael Hoard Leave a comment

A digital twin (DT) is a virtual representation of an object, system or process that spans its lifecycle, is updated from real-time data, and uses simulation, machine learning and reasoning to help decision-making. Digital twins can be used to help answer what-if AI-analytics questions, yield insights on business objectives and make recommendations on how to control or improve outcomes.

It’s a fascinating technology that the SNIA Cloud Storage Technologies Initiative (CSTI) discussed at our live webcast “Journey to the Center of Massive Data: Digital Twins.” If you missed the presentation, you can watch it on-demand and access a PDF of the slides at the SNIA Educational Library. Our audience asked several interesting questions which are answered here in this blog.

Q. Will a digital twin make the physical twin more or less secure?

 A. It depends on the implementation.If DTs are developed with security in mind,a DT can help augment the physical twin. Example, if the physical and digital twins are connected via an encrypted tunnel that carries all the control, management, and configuration traffic, then a firmware update of a simple sensor or actuator can include multi-factor authentication of the admin or strong authentication of the control application via features running in the DT, which augments the constrained environment of the physical twin. However, because DTs are usually hosted on systems that are connected to the internet, ill-protected servers could expose a physical twin to a remote intruder. Therefore, security must be designed from the start.

Q. What are some of the challenges of deploying digital twins?

A. Without AI frameworks and real-time interconnected pipelines in place digital twins’ value is limited.

Q. How do you see digital twins evolving in the future?

A. Here are a series of evolutionary steps:

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Genomics Compute, Storage & Data Management Q&A

Alex McDonald Leave a comment

Everyone knows data is growing at exponential rates. In fact, the numbers can be mind-numbing. That’s certainly the case when it comes to genomic data where 40,000PB of storage each year will be needed by 2025. Understanding, managing and storing this massive amount of data was the topic at our SNIA Cloud Storage Technologies Initiative webcast “Moving Genomics to the Cloud: Compute and Storage Considerations.” If you missed the live presentation, it’s available on-demand along with presentation slides.

Our live audience asked many interesting questions during the webcast, but we did not have time to answer them all. As promised, our experts, Michael McManus, Torben Kling Petersen and Christopher Davidson have answered them all here.


Q. Human genomes differ only by 1% or so, there’s an immediate 100x improvement in terms of data compression, 2743EB could become 27430PB, that’s 2.743M HDDs of 10TB each. We have ~200 countries for the 7.8B people, and each country could have 10 sequencing centers on average, each center would need a mere 1.4K HDDs, is there really a big challenge here?

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Moving Genomics to the Cloud

Alex McDonald Leave a comment

The study of genomics in modern biology has revolutionized the discovery of medicines and the COVID pandemic response has quickened genetic research and driven the rapid development of vaccines. Genomics, however, requires a significant amount of compute power and data storage to make new discoveries possible. Making sure compute and storage are not a roadblock for genomics innovations will be the topic of discussion at the SNIA Cloud Storage Technologies Initiative live webcast “Moving Genomics to the Cloud: Compute and Storage Considerations.”

This session will feature expert viewpoints from both bioinformatics and technology perspectives with a focus on some of the compute and data storage challenges for genomics workflows. 

We will discuss:

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A New Wave of Video Analytics

Jim Fister Leave a comment

Adoption of cognitive services based on video and image analytics is on the rise. It’s an intriguing topic that the SNIA Cloud Storage Technologies Initiative will dive into on December 2, 2020 at our live webcast, “How Video Analytics is Changing the Way We Store Video.” In this webcast, we will look at some of the benefits and factors driving this adoption, as well as explore compelling projects and required components for a successful video-based cognitive service. This includes some great work in the open source community to provide methods and frameworks, some standards that are being worked on to unify the ecosystem and allow interoperability with models and architectures. Finally, we’ll cover the data required to train such models, the data source and how it needs to be treated.

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A Q&A on Data Literacy

Jim Fister Leave a comment

The SNIA Cloud Storage Technologies Initiative (CSTI) recently hosted a conversation with Glyn Bowden from HPE that I moderated on “Using Data Literacy to Drive Insight.”  In a wide-ranging conversation just over 45 minutes, we had a great discussion on a variety of topics related to ensuring the accuracy of data in order to draw the right conclusions using current examples of data from the COVID-19 pandemic as well as law enforcement. In the process of the dialog, some questions and comments arose, and we’re collecting them in this blog. 

Q. So who really needs Data Literacy skills?

A: Really, everyone does.  We all make decisions in our daily life, and it helps to understand the provenance of the information being presented.  It’s also important to find ways to the source material for the data when necessary in order to make the best decisions. Everyone can benefit from knowing more about data.  We all need to interpret the information offered to us by people, press, journals, educators, colleagues, friends.

Q. What’s an example of “everyone” who needs data literacy?

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Achieving Data Literacy

Jim Fister Leave a comment

We’re all spending our days living in the pandemic and understanding the cultural changes on a personal level.  That keening wail you hear is not some outside siren, it’s you staring out the window at the world that used to be.  But with all that, have you thought about the insight that you could be applying to your business?

If the pandemic has taught data professionals one essential thing, it’s this:  Data is like water when it escapes; it reaches every aspect of the community it inhabits. This fact becomes apparent when the general public has access to statistics, assessments, analysis and even medical journals related to the pandemic, at a scale never seen before.

But having access to data does not automatically grant the reader knowledge of how to interpret that data or the ability to derive insight. In fact, it can be quite challenging to judge the accuracy or value in that data.

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The Power of Data Aggregation during a Pandemic

Glyn Bowden Leave a comment

The new coronavirus that has been ravaging countries and sending us all into lockdown is the most observed pandemic we’ve ever experienced. Data about the virus itself and perhaps more appropriately, the nations upon which it is having an impact have been shared from multiple sources. These include academic institutions such as John Hopkins University, national governments and international organisations such as the World Health Organisation. The data has been made available in many formats, from programmatically accessible APIs to downloadable comma delimited files to prepared data visualisations. We’ve never been more informed about the current status of anything.

Data Aggregation

What this newfound wealth of data has also brought to light is the true power of data aggregation. There is really only a limited number of conclusions that can be drawn from the number of active and resolved cases per nation and region. Over time, this can show us a trend and it also gives a very real snapshot of where we stand today. However, if we layer on additional data such as when actions were taken, we can see clear pictures of the impact of that strategy over time. With each nation taking differing approaches based on their own perceived position, mixed with culture and other socio-economic factors, we end up with a good side-by-side comparison of the strategies and their effectiveness. This is helping organisations and governments make decisions going forward, but data scientists globally are urging caution. In fact, the data we are producing today by processing all of these feeds may turn out to be far more valuable for the next pandemic, than it will for this one. It will be the analysis that helps create the “new normal.”

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