From regulatory compliance to invoicing dilemmas, businesses are dealing with some major headaches – and turning to blockchain for a cure.  Among these businesses are automakers harnessing blockchain technology.

In May 2021, French automaker Renault announced it had created a new blockchain solution. This ensures European auto industry components comply with new regulations affecting the entire automotive supply chain. Known as Compliance End-to-End Distributed (XCEED), the solution tracks compliance of thousands of vehicle components. It does this as they are assembled in real time, Just Auto reported. XCEED was developed by a consortium encompassing automotive suppliers Faurecia, Knauf Industries, Simoldes, and Coskunöz, in association with IBM.

These are not the only European automakers harnessing blockchain. Mercedes-Benz plans to develop a blockchain solution that will help trace harmful emissions. Volvo is using blockchain to track cobalt in its battery supply chain for use in electric vehicles. Additionally, BMW will work with Amazon (AWS) on solutions to boost operational performance and supply chain transparency. The company’s PartChain Platform uses certain AWS services as well as open-source blockchain management tools to better track parts and raw materials across the supply chain.

Blockchain Helps Walmart Canada Shrink Invoice Discrepancies from 70% to 1%

Retail giant Walmart Canada is now using blockchain technology to create an automated system. This new system manages invoices from and payments to its 70 third-party freight carriers to prevent payment disputes.

Formerly, the retailer depended on numerous information systems between itself and its carriers. Not only did this hinder communication, it also meant that the company needed to manually handle dispute resolutions. This problem wasted time and labor. 

The company’s blockchain solution, DL Freight, regularly collects data along every point of the network. From the carrier’s tender offer to the proof of delivery and payment approval, all of the data is automatically processed in real-time. It is easily accessible to all involved parties. With this new blockchain solution in place, no more than 1% of the company’s invoices contain discrepancies now. Previously, 70% contained discrepancies. 

DL Freight’s success is largely due to how Walmart Canada implemented the technology. According to Harvard Business Review, some essential lessons from the company’s blockchain adoption are:

1. It obtained views from all major stakeholders:
   Getting perspective from all parties involved ensured the solution worked for everyone.
2. It considered all pros and cons of adopting private vs public blockchain:
   Whereas public blockchain networks can provide a more streamlined solution without the need for restrictions and intermediaries, a private network can be better for businesses. This is because it provides more privacy and access restrictions, making the network more secure. 
3. It ensured all parties involved were in agreement over rules:
   Since every company has its own unique “fixed and variable processes and costs,” it was vital that all parties agree prior to implementation.
4. It embedded automated “checks and balances” into the blockchain:
   This prevented mistakes and helped the company spot areas where it could make improvements.

As supply chains and regulations become more stringent and complex, more organizations will turn to blockchain as a solution. Is your organization ready to adopt this revolutionary technology? 

Designing Blockchain Solutions

Get practical guidance for how to design a blockchain solution with the IEEE five-course program, A Step-by-Step Approach to Designing Blockchain Solutions. Developed by experts, this course program recaps the basics of the technology. It covers the expected benefits of a blockchain solution and how a solution would benefit a prospect company, and more.

Contact an IEEE Account Specialist to learn more about how this program can benefit your organization.

Interested in getting access for yourself? Visit the IEEE Learning Network (ILN) today!

Reference

Vitasek, Kate, Bayliss, John, Owen, Loudon, and Srivastava, Neeraj. (5 January 2022). How Walmart Canada Uses Blockchain to Solve Supply-Chain Challenges. Harvard Business Review.

Duke, Sam. (6 May 2021). OEMs look to blockchain solutions for compliance and parts performance. Just Auto.

In less than a month, 2022 is expected to usher in new automation trends for the digital transformation era. According to Enterprise Talk, a few major trends to look out for include the following.

• As organizations continue to transition to cloud technologies, they will shift from robotic process automation (RPA) to an application programming interface (API) approach. While both RPA and API allow applications to “talk” to each other, API is often popular for its backend capabilities and cost savings. 
• Hyper automation will unleash greater business potential, as automation will go from simply being a project feature to “the driving force for today’s digital enterprise,” in which businesses decentralize. 
• Organizations will embrace an “automation mindset” that enables “siloed combinations of line-of-business (LOB) teams” to improve the platforms and data they are already using. 

How to Make Your Digital Transformation A Success

A successful digital transformation is not the result of new technology, but rather, of employees adopting new technical skills and behaviors in order to become “a data-driven organization,” according to Tomas Chamorro-Premuzic, Chief Innovation Officer at ManpowerGroup, a professor of business psychology at University College London and Columbia University, and an associate at Harvard’s Entrepreneurial Finance Lab. Writing in Harvard Business Review, Chamorro-Premuzic says there are “five essential components” to digital transformation, including:

People:

Organizations are often focused on data, but may forget that humans are behind that data— be they customers, clients, or employees. Small businesses tend to have more personal relationships with the people they work with and serve. However, as a large/complex organization, being able to form more personal relationships with the humans behind your data is often more difficult. 

Data:

For large organizations to achieve successful relationships with the people they serve, they need to leverage recorded data based on interactions with customers, employees, and clients in a way that helps the organization understand what they want. This is ‘digitization,’ the process of datafying human behavior. Chamorro-Premuzic defines this process as useful because it focuses on capturing valuable data rather than creating cheaper systems. “It is useful to remember this, because the real benefits from technology are not ‘hard’ (i.e., cheaper systems or infrastructure), but ‘soft’ (i.e., capturing valuable data).”

Insights:

Data by itself is often too messy to understand. In order to be leveraged successfully, systems and processes need to be in place to clean, organize, and refine the raw data in order to turn it into analytics. This will allow you to create predictive models that will help you better understand what changes your organization needs to implement. 

Action:

Once you have your insights, you need a way to turn your insights into action. For example, if your insights predict that some of your customers are likely to leave you for a competitor, what plan will you make to retain them? “AI can make predictions, and data can give us insights, but the ‘so what’ part requires actions, and these actions need the relevant skills, processes, and change management,” Chamorro-Premuzic writes. “This is why talent plays such a critical role in unlocking (or indeed blocking) your digital transformation.”

Results:

Once you’ve gone through all these stages, you can examine the results before going back to the data. “The results themselves become part of the new, richer, dataset, which will be augmented and improved with the findings of the process,” he advises. 

As the new year approaches, organizations should prepare for an accelerated shift to digital transformation. Understanding the technical trends is only a small part of this. The human component is equally, if not more important, to ensuring your transition is successful. 

Prepare Your Organization for Digital Transformation

Get your organization ready for digital transformation. The IEEE five-course program, Digital Transformation: Moving Toward a Digital Society, aims to foster a discussion around how digital transformation can transform various industries and provide the background knowledge needed to smartly implement digital tools into organizations.

Contact an IEEE Account Specialist to get access for your organization.

Interested in the course for yourself? Check out the course program on the IEEE Learning Network.

Resources

Muktewar, Vishal. (25 November 2021). Top Four Digital Transformation Trends in 2022 (part I). Enterprise Talk.

Chamorro-Premuzic, Tomas. (23 November 2021). The Essential Components of Digital Transformation. Harvard Business Review.

Bowman, Dylan. (9 January 2020). RPA vs API Integration: How to Choose Your Automation Technologies. UiPath.

Machine learning models often rely on the simple features of a dataset to make decisions. Known as “shortcuts,” these types of decisions can lead to serious errors. For example, these shortcuts can cause models to make inaccurate medical diagnoses. However, a recent study from MIT poses a possible solution. By removing the simple characteristics of a dataset, the researchers forced the model to examine the more complex features of a dataset.

“It is still difficult to tell why deep networks make the decisions that they do. In particular, which parts of the data these networks choose to focus upon when making a decision,” Joshua Robinson, a PhD student in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and lead author of the paper, told MIT News. “If we can understand how shortcuts work in further detail, we can go even farther. We aim to answer some of the fundamental but very practical questions that are really important to people who are trying to deploy these networks.”

How To Avoid Shortcuts in Machine Learning

As MIT News reports, the new research centers on a type of self-supervised machine learning known as contrastive learning. Self-supervised models are trained on raw data that don’t have any label descriptions. In contrastive learning models, an encoder algorithm is trained to distinguish between pairs of similar inputs. It also distinguishes pairs of dissimilar inputs, which encodes complex data, such as images, in a way the model can decipher. While this makes decision making more effective, the researchers found that these models also tended to fall victim to making shortcuts. They fixate on the simplest features of an image to determine which pairs of inputs are similar and which are not. To solve this, the researchers made it more difficult for the model to differentiate similar and dissimilar pairs. This altered the features the encoder used to make a decision.

“If you make the task of discriminating between similar and dissimilar items harder and harder, then your system is forced to learn more meaningful information in the data, because without learning that it cannot solve the task,” Stefanie Jegelka, one of the researchers, told MIT News.

However, this caused the encoder to get worse at focusing on some features over others, particularly the simpler ones. To solve this, the researchers required the encoder to discriminate between the pairs using the simpler feature. They also evaluated after the researchers removed the data it already learned. Having the encoder solve the problem both ways simultaneously forced it to make better decisions.

Implicit Feature Modification

Known as “implicit feature modification,” this groundbreaking method does not rely on any input from humans. While it has the potential to help machine learning models avoid shortcuts, the researchers told MIT that it still needs to be refined. It should be tested on other types of self-supervised learning.

Machine learning is still in its infancy. However, innovations such as implicit feature modification have the potential to give artificial intelligence (AI) the ability to learn on its own. Not only will this make AI smarter and more efficient, it can also lead to revolutionary technological and scientific discoveries. Machine learning has the ability to solve complex problems— such as determining protein’s 3D shape— that humans cannot. 

Understand Machine Learning

By providing AI with the ability to learn from its experiences without needing explicit programming, machine learning plays a critical role in developing the technology. Covering machine learning models, algorithms, and platforms, Machine Learning: Predictive Analysis for Business Decisions, is a five-course program from IEEE.

Connect with an IEEE Content Specialist today to learn more about this program and how to get access to it for your organization.

Interested in the program for yourself? Visit the IEEE Learning Network.

Resources

Zewe, Adam. (2 November 2021). Avoiding shortcut solutions in artificial intelligence. MIT News. 

Callaway, Ewen. (30 Nov 2020). ‘It will change everything’: DeepMind’s AI makes gigantic leap in solving protein structures. Nature.

From patient-centric hospital networks to higher quality medicine, blockchain technology is revolutionizing healthcare in a number of ways. As discussed in previous posts, blockchain is a decentralized digital ledger of transactions that records data in a way that prevents hacking and altering of data by duplicating transactions and dispersing them to “nodes” across the network. 

According to Jose Morey, Chief Executive Officer of Ad Astra Media and Chief Health Officer of Ever Medical Technologies, the secure and transparent nature of blockchain means that it has a number of potential applications in the medical industry.  Writing in Forbes, he notes a number of ways that blockchain is already transforming healthcare.

Blockchain Applications That Could Revolutionize Healthcare

Blockchain will allow the medical industry to share and access patient data securely:  The technology will “facilitate finely customizable openness while upholding only the best security standards for true interoperability,” Morey writes. This will allow health information systems to “work together within and across organizational boundaries” to enhance healthcare delivery. Chronicled is one company that is already using blockchain to secure patient data.

Blockchain will improve contract negotiations: In the healthcare industry, contract negotiations often get quite complex, which can take up a lot of time as a result. Blockchain, however, is already providing a solution. A company called Curisium uses the technology “to create a platform for rebate negotiation and contract management,” Morey writes. He adds that the platform streamlines traditional processes by allowing “providers and payers to take part in innovative contracting arrangements.”

Blockchain can foster innovation and connect large hospital networks: For example, a company called Ever integrated a “blockchain, data-driven, patient-centric network” within Thailand’s medical system. The technology connected more than 170 hospitals and 5 million patients. “It enables best-in-class security for all connected data and parties while maintaining close and easy communication with trusted parties— all on a flexible, future-proof, scalable blockchain foundation,” Morey says. 

Blockchain allows organizations to create both secure and transparent networks: With blockchain technology, hospitals can quickly and easily share patient data in a way that is fully secure. “Protected by state-of-the-art security solutions, attackers would require vast computational capabilities to even attempt targeting a blockchain-powered network, severely limiting the frequency, possibility and effectiveness of attacks,” states Morey. One company utilizing blockchain in this way is Patientory, which develops “patient-centric applications” that provide up-to-date patient histories and data, pandemic tracking and reporting, and secure communication with verified healthcare personnel.

Furthermore, blockchain has the potential to greatly improve the quality of medicine. According to Pharmacy Times, OCEASOFT, a company that makes atmospheric monitors for supply chains, and Chronicled, an Internet of Things- and blockchain-focused supply chain technology business, are working together to implement blockchain for atmospheric monitoring in the drug supply chain. These monitors glean information such as CO2, temperature, and humidity, which is stored in a decentralized ledger. Buyers and sellers can use this secure ledger to monitor the quality of drugs across global supply chains. Such a system would also help prevent fraud and the purchase of expired drugs.  

These blockchain transformations are only the beginning. It’s only a matter of time before more industries are harnessing this revolutionary technology. 

Preparing Your Industry for Blockchain Technology

In addition to the healthcare industry, many fields can benefit from distributed ledger technology. Check out Enterprise Blockchain for Healthcare, IoT, Energy, and Supply Chain to learn about highly anticipated use cases.

Developed by leading experts in blockchain technology, this five-course program is ideal for managers, professional engineers, and business leaders.

Contact an IEEE Content Specialist to learn more about how this program can benefit your organization.

Interested in getting access for yourself? Visit the IEEE Learning Network (ILN) today!

Resources

Kenney, Skylar. (28 October 2021). The Case for Leveraging Blockchain to Improve the Global Health Supply Chain. Pharmacy Times. 

Morey, Jose. (25 October 2021). The Future Of Blockchain In Healthcare. Forbes.

As the COVID-19 pandemic continues to rattle industries, many organizations are turning to digital transformation to ease disruptions. While this shift requires business leaders to make a great deal of technological change, digital transformation also requires them to re-examine their attitudes and culture.

“As I’ve gone down this route, I’ve become increasingly aware of how much digital transformation is really about behaviour change within an organisation,” Matthew Reinbold, former director of API & event streaming platform services at Capital One, told Information Age. “It’s not enough to simply do what we did yesterday using new tools. It’s about changing how we behave when given certain problems or certain opportunities. And behaviour change for people is really, really hard.” 

One of the key digital transformation changes organizations will need to implement is adopting a “data-driven decision making culture,” according to Cynthia A. Conway, CEO Peer Advisory Group Chair of Vistage Worldwide, Inc. and Mitch Codkind, president of Initiative Consulting, a division of Initiatives, Inc. To implement such a culture, Conway and Codkind recommend hiring a data scientist who can help you interpret and implement the data that your digitization efforts will rely on, and who can create a “technology architectural plan.”

Writing in Harvard Business Review, they say your plan should outline how to store your company’s data and include:

• technology solutions that allow you to meet your corporate governance requirements 
• analytical capabilities that “connect and share information” throughout your organization
• “a data visualization layer” that helps you see your “prioritized performance metrics quickly and easily” for more efficient decision making

“With this in place, you, your data scientist, and their peers will develop processes across your organization to foster a data-driven decision-making culture for a customer-centric organization — without bias,” Conway and Codkind said.

How To Know Your Digitization Efforts Are Working

Once you’ve implemented digital transformation at your organization, how will you know these efforts are paying off? According to Andrew Butt, co-founder and CEO of Enable, a cloud-based B2B software solution for rebate management, there are five ways to gauge success:

1. Your employee’s productivity has gone up. If staff engagement and productivity have improved and employees are able to dedicate their time to more important activities, you know your digitization efforts are proving effective. To ensure you are meeting this goal, determine a way to track employee productivity.
2. You see a return on your investment. Instead of looking solely at your revenue, compare sales before and after the digitization. Consider variables like project costs and shifting economic and market conditions. 
3. Your employees have been able to fully adapt to your digital transformation. To measure adaptation, you need a system or process in place to determine if and how your employees are using new software. This tool will help you make decisions about what technologies and systems are bringing value to your organization.
4. Your rate of innovation has improved. The amount of innovation that comes out of your digitization efforts is a good indication that these efforts have been successful. This goes beyond providing new digital products and services — it means “making digital experiences as intuitive and streamlined as possible.”
5. You are reaching a greater number of customers. To understand how many customers you are reaching, you should track how they are finding you online using digital tools, and use this data to make improvements and further boost your retention.

Digital transformation requires organizations to undergo an evolution in technology and culture. By implementing a strong data-driven culture and a plan for measuring success, they can reap huge benefits. 

Prepare Your Organization for Digital Transformation

Get your organization ready for digital transformation. The IEEE five-course program, Digital Transformation: Moving Toward a Digital Society, aims to foster a discussion around how digital transformation can transform various industries and provide the background knowledge needed to smartly implement digital tools into organizations.

Contact an IEEE Account Specialist to get access for your organization.

Interested in the course for yourself? Check out the course program on the IEEE Learning Network.

Resources

(2 September 2021). Digital transformation – it’s a people problem. Information Age.

Butt, Andrew. (30 August 2021). Digital transformation metrics: 5 questions to ask. Enterprise Project. 

Conway, Cynthia A. and Codkind, Mitch. (24 August 2021). Where Digital Transformations Go Wrong in Small and Midsize Companies. Harvard Business Review.

Organizations are increasingly adopting machine learning, a type of artificial intelligence that allows software applications to get better at predicting outcomes on their own. While these models can help businesses make better decisions, they have a major weakness. Once the data that support machine learning models become outdated, so too do the models—a problem known as “data drift.”

“The impact on business is profound. To avoid this, you need to keep track of whether or not your models are stale,” Google Cloud’s Dale Markowitz and Craig Wiley write in Forbes. “But knowing which of your models are in use and what they are doing is something many companies struggle with. Consider several features all drifting at the same time. This might seem like simple housekeeping compared to the hard math of building neural networks, but maybe that’s why it’s so often overlooked.”

How Can You Avoid Data Drift?

You can prevent data drift by building machine learning operations (MLOps). As we discussed in a previous post, an appropriate MLOps establishes a governance framework around machine learning that helps organizations make these models work not only technically but also operationally. According to Chida Sadayappan, Lead Specialist for Data Cloud and Machine Learning at Deloitte Consulting, MLOps allows you to more effectively manage the operation of your data.

“MLOps isn’t an algorithm, but it does operationalize the algorithm to simplify the predictive process,” Sadayappan writes in insideBigData. “MLOps enables the appropriate uses of ML algorithms to teach systems how to identify and classify data today and ‘learn’ new, more effective techniques to do so in the future. These decision-making ML algorithms help businesses recognize patterns that predict consumer preferences, identify fraud, monitor financial performance, and reimagine customer experience, to name a few use cases—and become operationalized with MLOps.”

Four Elements of Effective MLOps

There are four basic components for building an effective MLOps framework. According to Sadayappan, these include:

• Versioning the model: Explore various data sets and algorithms that can fix the same business problems. “Reproducibility is critical, and versioning each data set, algorithm, and ingestion pipeline is essential to creating results that can be reproduced.”
• Autoscaling: Your MLOps model should have the ability to quickly scale up or down as necessary. “That’s essential because large organizations may eventually create thousands of data models.”
• Constantly monitor and train your models: Continuously monitoring and training model performance ensures they provide correct results. “That’s because external factors like economic conditions are constantly in flux, which can make obsolete the data used in the initial training process. Monitoring helps evaluate model output and track drift and effectiveness over time.”
• Retraining and redeployment: As model drift happens, “be prepared to retrain the model using new data and then redeploy it.”

Data drift can become a serious problem for any organization looking to improve its business with machine learning. However, building sound machine learning operations with best practices for continuously monitoring and retraining your models on new data can go a long way in avoiding this problem.

Understand Machine Learning

By providing AI with the ability to learn from its experiences without needing explicit programming, machine learning is important to developing the technology. Covering machine learning models, algorithms, and platforms, Machine Learning: Predictive Analysis for Business Decisions, is a five-course program from IEEE.

Connect with an IEEE Content Specialist today to learn more about this program and how to get access to it for your organization.

Interested in the program for yourself? Visit the IEEE Learning Network.

Resources

Sadayappan, Chida. (29 July 2021). Turning Big Data into better data with MLOps. Inside Big Data.

Markowitz, Dale and Wiley, Craig. (19 May 2021). Why MLOps Is Critical To The Future Of Your Business. Forbes.

Cyber crime is on the rise. In the U.S. alone, McDonalds, Colonial Pipeline, SolarWinds, and JBS Foods were all recently forced to pay millions due to ransomware attacks that compromised their data. Former Cisco Systems CEO John Chambers predicts an onslaught of up to 100,000 ransomware attacks this year alone, which could cost organizations an average of $170,000 USD each. 

The absence of cyber security standards contributes to the problem. Almost all organizations invest in security procedures to protect their physical property from intruders. However, the threats to digital property are just as real, with fewer organizations developing adequate protocols to safeguard these assets. It’s not hard to understand why. Cyber security technology is complex and ever-evolving, and so are the threats. Keeping up with these changes is not easy or cheap. 

Voluntary Standards Are Not Enough

Despite the growing wave of cyber attacks, the U.S. requires very few of the sixteen most vital industry sectors to meet minimum cyber security requirements. With threats increasing, 86% of the Cybersecurity 202 Network—a panel of more than 100 cybersecurity experts—said that the government should require organizations in “critical industry sectors” to meet minimum cyber security standards, according to a recent survey from the Washington Post. 

While officials in the past considered voluntary standards good enough, that attitude is quickly changing. The U.S. government may soon require organizations considered critical to the nation’s interest to follow a defined set of cyber security standards. According to CNBC, a recent memo from the White House warned businesses that “the threats are serious and they are increasing.” The memo highlighted a number of best practices organizations can take to protect themselves from ransomware, including backing up data, systems images, and configurations, as well as regular testing and network segmentation. 

“If a company has done proper segmentation, every time the bad guys try to cross a segment you get the opportunity to detect them before they can trigger the malware,” Michael Daniel, president and CEO of the Cyber Threat Alliance, told CNBC. “By employing this practice you make yourself more resilient against having a successful ransomware attack launched against you, and if you do have one you’re usually able to mitigate the damage and recover much more quickly. This is what gives companies a lot more options than believing they have to pay the ransomware.”

A Problem Organizations Must Manage

Because a lack of universal cyber security standards is precisely what criminals are taking advantage of, it’s vital that governments and organizations develop them soon. In the meantime, organizations must grapple with cyber crime on their own. Those with mature cyber security strategies look at it as a threat they must manage rather than a problem to solve after it happens.

“For some risk you employ technology, for some you buy insurance,” Daniel told CNBC. “The point is that a company is actively managing the risk, not just hoping that something bad doesn’t happen to them.”

Among the steps organizations can take to manage cyber security risks are developing a strategy and ensuring employees are properly trained on how to deal with potential threats. 

Cyber Security Considerations for an Effective Cyber Strategy Within Your Workforce

Ideal for technical professionals across all industries who support their company’s IT departments and require up-to-date information on how to protect enterprise networks from potential threats, Cyber Security Tools for Today’s Environment is an 11-course program designed to help businesses improve their security techniques.

Contact an IEEE Account Specialist today to get access to the course program for your organization.

Interested in learning about getting access to the course program for yourself? Visit the IEEE Learning Network to learn more.

Resources

Hum, Thomas. (14 June 2021). Over 65,000 ransomware attacks expected in 2021: former Cisco CEO. Yahoo!finance

Caminiti, Susan. (11 June 2021). Cyber standards are key in battling ransomware attacks. CNBC.

Marks, Joseph. (11 June 2021). The Cybersecurity 202: Our expert network says it’s time for more cybersecurity regulations. Washington Post.

With news that vaccines to control the spread of COVID-19 have been developed and approved, the next step will be the enormous undertaking of administering them to the public. For the current vaccines developed by Moderna and Pfizer-BioNTech to be long-lasting and effective, individuals must take two separate doses three to four weeks apart (the length of time depends on which vaccine is used)—bringing the total to about 15 billion doses. 

“[It’s] a level of undertaking that is just beyond anything we have done as a society,” Mark Treshock, Blockchain Solutions Leader for Healthcare and Life Sciences at IBM, told mobihealthnews. “To confound that, it’s the fact that these vaccines are all different, and they are not interchangeable. So even though they treat or vaccinate against the same virus, they are different vaccines.”

Blockchain Technology

Blockchain technology, a decentralized digital ledger of transactions that records data in a way that prevents hacking and data altercation, may be able to help medical professionals, manufacturers, distributors, and patients stay on top of these vaccines in a secure manner. Not only can blockchain be used to track vaccines over long distances in order to ensure they are temperature controlled and safe for use upon delivery, it can also help medical professionals and patients maintain vaccination records. This could help patients as they may need to prove to authorities that they are safe to travel or to verify that they can be in an indoor office environment. Blockchain can also be used to solidify immunization records about a patient. This process can ensure a patient is receiving the correct pair of COVID-19 vaccines, which they may also need for verification purposes.

“The two-dose challenge,” said Treshock. “Where you need two doses, they need to be within a set time window, let’s say 30 days, and they need to be from the same manufacturer. So, if your first dose is Pfizer, your second dose has to be Pfizer as well. They aren’t interchangeable. When we start administering this vaccine at scale, it is going to be very challenging coordinating that.”

How Can Blockchain Help Manage Health Data?

Blockchain has the power to transform the healthcare industry. Whereas much of our online data is currently in the hands of private companies like Facebook, blockchain can give individuals control over their personal data. Data collected on the Internet is a kind of virtual representation of every user. However, many individuals have no real ownership over their data, which can create problems when it comes to security, access, monetization, privacy, and advocacy.  

“That identity is now yours, but the data that comes from its interaction in the world is owned by someone else,” Carlos Moreira, CEO of WISeKey, told Harvard Business Review. 

Not only is blockchain decentralized, it’s also immutable. This means transactions cannot be changed or undone without approval. Blockchain keeps digital identity safe in a “digital wallet” that gathers and protects all the data, which can include health information. For example, this “digital wallet” could house personal health records or health information captured by a smart watch, and then give an individual control over how that data is used. 

Some organizations are already using blockchain to successfully manage health data, including:

• Canada’s University Health Network (UHN) created a patient control-and-consent platform designed to make clinical research easier. Created in partnership with IBM, UHN uses blockchain to amass and secure patient data throughout the network. It receives and records consent from each patient in order for their data to be shared with researchers.
• MiPasa, an initiative founded by the start-up Hacera, is a platform designed to capture pandemic data on an international scale from the Center for Disease Control, the World Health Organization, licensed private facilities, local public health agencies, and individuals—without identifying them. The platform aggregates data through Hacera’s Unbounded Network, a decentralized blockchain supported by Hyperledger Fabric. It then uses IBM’s blockchain and cloud platforms to stream the data.
• The blockchain startup Shivom is developing an international project that gathers and shares virus host data. The platform uses blockchain to actively maintain patient consent, and to securely and privately share genomic information and data analysis with third parties without offering access to patients’ raw genomic data.

Blockchain has the potential to revolutionize health care, but requires a transformation in the rules for defining and assigning data ownership. 

Understand Enterprise Blockchain for Your Industry

What other industries can benefit from blockchain technology? Get Enterprise Blockchain for Healthcare, IoT, Energy, and Supply Chain, a five-course program from IEEE, to find out. Developed by leading experts in blockchain technology, this advanced program provides business use cases across key industries and sectors. It’s ideal for managers, professional engineers, and business leaders.

Contact an IEEE Content Specialist to learn more about how this program can benefit your organization.

Interested in getting access for yourself? Visit the IEEE Learning Network (ILN) today!

Resources

Goodnough, Abby. Zimmer, Carl. Robbins, Rebecca. Mandavilli, Apoorva. Thomas, Denise Grady Katie. Parker-Pope, Tara. Weiland, Noah. Singer, Natasha, Leonhardt., David, Rabin. Roni Caryn. Bosman, Julie. Abelson, Reed. Pérez-Peña, Richard. (14 December 2020). Answers to Your Questions About the New Covid Vaccines in the U.S. New York Times.

Lovett, Laura. (25 November 2020). Blockchain could be the key to vaccine distribution, says IBM. mobihealthnews.

Tapscott, Don and Tapscott, Alex. (12 June 2020). What Blockchain Could Mean for Your Health Data. Harvard Business Review.

Edge computing for business can increase the speed of data processing and analysis. The Internet of Things (IoT) is expected to grow significantly, predicted to reach about $1.6 trillion USD by 2025. Edge technology can help process the copious amounts of data that this surge in IoT-enabled devices will produce.

Because edge computing processes data at the location where the data is being generated, it stores, processes, analyzes and informs actions of users instantaneously. The benefits of edge computing over cloud computing is the speed at which data is analyzed and acted on. See a few ways it can transform a business in the next year.

Real-Time Data Analysis

Data is normally sent to one central location so that it can be analyzed in order to take proper action. However, edge computing allows for the data analysis to take place near the area where it is created.  With edge technology, the data can be kept close to its origin point, which is optimal for nearly real-time decision making.

Augmented Reality

Edge computing has the chance to improve augmented reality. Users will gain a more vivid and realistic augmented reality (AR) experience. By taking advantage of this technology early on, technology firms can be one of the first to provide this upgraded experience to their customers.

Smart Manufacturing

Manufacturing companies can improve their production floors with edge technology. With almost real-time data analysis, it helps improve efficiency and margins. Companies can help avoid line shutdowns by identifying problems while edge computing allows analyzes the collected data.

Security Systems

Large organizations need fast and accurate security systems to help keep their information and buildings safe. Edge computing makes security systems more efficient when operating at a lower bandwidth. Data from security cameras are frequently collected and stored in the cloud through a signal. Edge computing allows each device to have an internal computer that is able to transfer footage to the cloud when it is needed.

Lowered Operational Costs

Because edge computing helps collect data, it does not require a central server to determine what action should be taken. This helps reduce operational costs by needing less storage to hold the information.

Get Close to the Edge with Customized Solutions

Not many organizations know what edge computing means or what impact it can have on their business. For one company, it could mean installing on-site servers that are capable of nearly real-time IoT data analysis. For another company, it could mean reducing organizational costs by using smaller deployments. One key benefit to edge computing it that is can be customized to meet the company’s needs.

Prepare your organization for edge computing integration. Designed to train your entire team to support edge computing, IEEE Introduction to Edge Computing is an online five-course program. The on-demand courses included in this program are:

• Overview of Edge Computing
• Practical Applications of Edge Computing
• Research Challenges in Edge Computing
• Designing Security Solutions for Edge, Cloud, and IoT
• Tools and Software for Edge Computing Applications

To learn more about getting access to these courses for your organization, connect with an IEEE Content Specialist today.

Interested in the course for yourself? Visit the IEEE Learning Network (ILN) to learn more.

 

 

Resources

(23 December 2019). 13 Ways Edge Computing Can Benefit Businesses. Forbes.

Lital, Marom. (13 December 2019.) Enter A New Era Of Edge Computing. Forbes.