Edge Computing, the discipline of getting actionable data and information as close to the source of their availability as possible, is no longer simply a talking point for massive industries. The quickly evolving landscape of information itself has dictated that edge computing become a necessity for most business’ digital infrastructure in the foreseeable future. Let’s examine some of the remarkable steps, directions, and pressing concerns likely to be faced in 2022 when it comes to the edge.    
#1: THE DIRECTION OF EDGE, SIMPLY PUT, IS MORE
Organizations within the digital business arena have significantly more considerations as each year passes. The growth in the sheer amount of usable data has been so utterly fast-tracked by the ubiquity of smart devices that the ceiling on volume doesn’t even appear to exist.
With this massive amount of data, there is a greater strain on the traditional model of doing digital business and its processes. For example, processes such as streaming information to the cloud and/or data processing centers becomes difficult when there are large amounts to stream (though I doubt neither the cloud nor data processing centers will ever become a thing of the past). The advantages of computing as closely as possible to the original source - without the proverbial middleman of cloud computing and data processing – makes edge a vital implementation for all industries. While edge computing was once considered a necessity for only elite Fortune 100 companies, it is now becoming essential for all companies, both large and small.
How quickly though? Gartner predicts edge computing will grow 75% by 2025. My own prediction would be an increase of 25% or more just in 2022 alone. That jump is linked notably to the new pandemic paradigm shift of employees working from home. That number is also inextricably related to the increase in efficiency due to access to data without third parties.
Like the aforementioned volume of data and its theoretically non-existent ceiling, the room for expansion in edge computing strikes a similar pose; major cities are quickly dubbing and even branding themselves as “smart cities” as they implement hubs and cabinets for real-time information transfer via something as commonplace as a stoplight.
Continue reading: https://www.thefastmode.com/services-and-innovations/22374-edge-computing-in-2022-predictions-and-analysis

Closed-loop manufacturing is central to Manufacturing 4.0 automation, but it's also been in place on production floors for years. But can it be automated to work with little or no human intervention? Or should it be?
A closed-loop system on a production floor is a set of machines utilized in manufacturing that communicate and coordinate with each other to get certain processes done. The only catch is when something goes wrong and an alert is issued. At that point, a human has to step in to resolve the issue.
What the Manufacturing 4.0 (also known as Industry 4.0) movement hopes to achieve is total automation of these closed-loop systems. It proposes to do this by using artificial intelligence and machine learning in an overarching manufacturing operating system that runs each closed-loop system deployed on the floor. 
Binghampton University Professor Sang Won Yoon explained this in detail: "With the rapid technology development, such as the Industrial Internet of Things, big data analysis, cloud computing, artificial intelligence, many manufacturing processes can be more intelligent, and Industry 4.0 can then be realized in the near future  … . Data-driven solutions, such as AI and machine-learning algorithms, can be applied to diagnose abnormal defects and adjust optimal machine parameters in response to unexpected changes/situations during production. Smart manufacturing adopts real-time decision-making based on operational and inspectional data and integrates the entire manufacturing process as a 'unified framework.'"
This is the Manufacturing 4.0 vision for closed-loop systems—and it presents several interesting implications for edge computing.
1. AI-directed closed-loop systems can benefit edge deployments
Imagine a series of closed-loop systems distributed at the enterprise edge that can "run themselves" in a closed environment, much like a mini-network. This could reduce present resource stressors, like challenges in managing and paying for large data payloads that continuously stream over communications lines to data centers and clouds.
Instead, IoT data can be processed and stored locally within each closed-loop system. Outside communications loads and costs are reduced, with the exception of batched data and transactions that are required for centralized storage over time.
Closed-loop systems also provide additional resilience if a data center or cloud outage occurs because these systems can sustain themselves. 
2. Edge architecture may need to be revisited
Current edge data collection and forwarding is orchestrated around clouds, data centers and the edge. With self-contained and fully automated closed-loop manufacturing, it would be likely that the volume of data transmissions and storage to and into the cloud or data center would be reduced, resulting in an edge architecture that becomes even more distributed in data processing and storage than it is today. 
Continue reading: https://www.techrepublic.com/article/ai-in-closed-loop-manufacturing-can-benefit-edge-computing-systems-4-things-to-consider-in-iiot/

While the cybersecurity landscape is bound to undergo multiple changes throughout 2022, much of what will happen over the next 12 months will be based on a series of decisions and security incidents that occurred in 2021.
In May, as part of a response to the 2020 disclosure that nation-state actors had targeted SolarWinds and customers of the company’s Orion network monitoring platform, President Joe Biden signed a sweeping presidential executive order related to cybersecurity. This order will fundamentally alter how the federal government approaches security, as well as how departments evaluate and purchase software and other technologies from third-party suppliers.
Besides SolarWinds, a series of high-profile ransomware attacks has spurred Congress to consider several bills that seek to strengthen the rules of how and when organizations should report these attacks. At the same time, lawmakers have pushed to implement greater privacy protections for citizens’ data at both the state and federal level.
During all this, the stubborn COVID-19 pandemic has remained, with variants (first Delta and now Omicron) continuing to cause concern among employers—and guaranteeing that remote and hybrid work is likely to remain a fixture well into 2022. This also means the security and IT challenges of the last 24 months will continue into the new year.
“Then 2021—and reality—set in: the Delta variant spread, lockdowns reappeared, and employees flirted with heading back to the office … only to join remote meetings from home just like before,” according to a recent Forrester analysis that looked at how cybersecurity issues are developing for the new year. “Relationships, collaboration, and trust will dominate 2022, and gaps in those areas will have outsized impacts on firms’ relationships with their colleagues, partners, and suppliers.”
With this evolving security landscape, the next 12 months are expected to bring additional changes for organizations’ cybersecurity practices, especially as better technologies and more modern practices become standard. Here are four trends that IT and security pros should watch as 2022 comes into focus.
Zero Trust Comes of Age
Several security analysts believe that 2022 is the year when more organizations will apply the principles of zero trust to their security plans as a way to reinforce principles of least privilege and defense-in-depth. This, in turn, can limit the number of breaches and reduce lateral movement by attackers if they do manage to bypass initial security tools.
The Biden executive order is also pushing federal agencies to adopt zero trust architecture as well to counter ransomware and attacks by nation-state groups looking to conduct espionage or steal data.
Continue reading: https://insights.dice.com/2022/01/06/cybersecurity-trends-for-2022-4-issues-it-and-security-pros-should-know/

As the popularity of the Internet of Things (IoT) continues to grow, businesses must note the latest technologies in the landscape. An effective device management platform (DMP) solution is a vital component of any successful small or large IoT implementation project. Such a platform allows businesses to manage their internet-connected devices remotely. It entails the process of setting up an IT infrastructure and authentication of the configured devices. You can also perform remote configuration, data collection, reporting, real-time monitoring, and over-the-air (OTA) software deployments through these platforms. You can also handle updates or patches on the various connected objects through DMPs. In a nutshell, a DMP offers the glue required to tie together the assets and devices consisting of the physical layer of an IoT platform.
How Do DMPs Work?
An IoT device management platform serves as a central hub, which provides remote access, configuration control, and security for your connected devices.
When selecting an appropriate DMP, it is essential to look at factors such as robust APIs that enable custom scripting on deployed devices. A customizable dashboard with group controls and edge device access allows you to manage groups of smart sensors.
Critical Criteria to Take Into Account for Building IoT Device Management Solutions
IoT platforms help automate management functions and add another layer of security measures. They control vulnerabilities of IoT products and minimize breaches.
The decision-making process is complex and needs to be evaluated through the lens of several requirements and limitations. To do this successfully, some technical criteria need to be taken into account:

• Security
• Scalability
• Interoperability
• Stability
• Time-to-market
• Platform compatibility
• Platform life expectancy

With the IoT DMP client library, your application can securely and seamlessly communicate with the host app. The IoT DMP allows you to create applications for managing millions of devices connected to different platforms.
This includes the likes of Windows 10 or third-party ones like Arduino using an easy-to-use cloud service interface. You can avail IoT app development services without worrying about adding another layer of authorization for device management.
The arrangement makes the connection process easier and faster than before, and you can automate the life cycles of the connected assets, improving the resilience and security of the IoT device estates.
Continue reading: https://www.iotforall.com/benefits-and-challenges-of-iot-device-management-platforms

Stewardship of fiscal performance has always been a numbers game; now, big data and automation are allowing financial leaders to take their key performance indicators to a new level.
While the data deluge has created ample opportunity to improve financial KPIs, managing that data and transforming it into actionable insights is proving to be a challenge — an issue explored in a panel discussion at  the 19th annual MIT Sloan CFO Summit.
The challenges are especially acute in companies with a data estate that is spread across different systems and is punctuated by silos, data gaps, and inconsistencies in the type and quality of data stored, chief financial officers agreed.
To fully capitalize on data-driven performance, financial organizations need to start back at step one: Getting enterprise data in order to ensure the right data is captured and that data initiatives are aligned with core fiscal strategy and business goals. Key to that effort is a renewed focus on data governance and working through the question of who owns the data model.
“Before we can even think about getting insights and value out of the data, we [need] a good way of getting that data into our systems and a good way of governing it so that it can be usable for us,” said panel moderator Peter Irwin, a partner at KPMG Lighthouse, which specializes in data analytics, automation, and artificial intelligence.
Here are key takeaways from the discussion:
Finance needs an ownership stake in the data model
The data explosion is a doubled-edged sword. There’s so much potential in leveraging data and advanced analytics to boost fiscal performance, yet without a single source of truth, organizations can be stuck in a cycle of never-ending reconciliations and questionable data integrity that diminishes data’s value to the business.
Historically, the information technology department has had sole responsibility for the data model, but without complete understanding of what’s required for fiscal KPIs, that can lead to a lot of redundant and unproductive work. Aligning goals and responsibilities is central to data governance; as part of that process, finance should have some ownership stake in the data model, along with IT.
“Finance has a deep understanding of the calculations, the sources, and the definitions and mapping” of financial data, said Kae Arima, vice president of finance at Workday, which has an ownership stake in the data model at the provider of cloud-based finance and human resources software.
Continue reading: https://mitsloan.mit.edu/ideas-made-to-matter/how-chief-financial-officers-optimize-kpis-data-automation

Everyone is interested in getting more data. Few consider that more data is not always better if the quality is low. Quality assurance is an enormous problem, plaguing numerous organizations. In fact, Gartner’s 2020 research showed that poor data quality causes an average of $12.8 million losses each year for those surveyed.
Risks associated with poor quality data can be nearly invisible as they can only become apparent after a considerable amount of time. However, it can often lead to lost business opportunities, increased operating costs and lower decision-making accuracy. Data governance is the only way to protect organizations from the appearance of low-quality data.
What Is Data Governance?
You would be hard-pressed to find one definition of data governance. Everyone understands the process slightly differently. However, the goal is always the same — to ensure that all data across the organization is of high quality and is processed according to rules and guidelines.
As a result, data governance not only mitigates numerous associated risks (such as the ones outlined above) but also provides a wide array of benefits. These can range from simple ones like increased decision-making confidence and better data use to the ability to meet regulatory requirements, increased profitability and staff productivity.
However, while immensely useful, proper data governance isn’t easy. It’s a multifaceted process that requires the involvement of everyone in the organization. While getting everyone on board with data governance might be a strenuous process, it’s a necessity as data governance works only when it is a universally accepted framework.
Data governance can be separated into several parts. More pieces can be added along the way, however, the foundational parts are:
People and organizational bodies. These will be the people who are directly responsible for the implementation of data governance. Usually, this includes responsibility for data quality assurance, adherence to governance guidelines and the creation of new rules.
Rules and rules of engagement. Rules define how data has to be handled. Access rights and methods, data ownership and management should be clearly stated. Data pipelines should also have owners, handling practices and crisis action plans assigned. Finally, adherence to the rules should be measured through evaluation processes such as minimum data quality requirements. In the end, the goal of all rules is to ensure data quality and security while improving the speed of changes.
Continue reading: https://www.forbes.com/sites/forbesbusinessdevelopmentcouncil/2022/01/07/how-to-approach-data-governance-to-avoid-poor-data-quality/?sh=6c4781ad11a2

The quickest way for hackers to harm a healthcare provider organization is to target patient information, and many of them focus on databases that support electronic health records.
The Internet of Things has amplified the number of attack vectors to target the functioning of hospitals, physician practices, outpatient centers and other facilities. But it also creates a direct risk to patient care.
Phones, tablets, connected medical devices and other technologies provide a side door for hackers to infiltrate networks. With many devices using outmoded operating systems, patients face a unique vulnerability, because a hacker could interfere with treatment.
Many devices, such as pacemakers or implantable devices that provide micro-shocks to the brain to treat Parkinson's disease or other neurological disorders, are controlled by mobile apps that allow doctors to adjust treatment without resorting to surgery. The convenience trades off the risk of surgery against the risk of a hacker tampering with treatment.
Upgrading the security of these devices could require an entirely new FDA approval, a lengthy and expensive process. Some of these organizations are taking a wait-and-see approach to security, but that also reflects wishful thinking about vulnerabilities and potentially huge liabilities.
To help CISOs, CIOs and other health security leaders tackle these issues, Healthcare IT News interviewed Edward L. Goings, national pillar lead of cyber response services and global incident response lead at KPMG Global. Goings discussed the risks inherent in the Internet of Things, whether hackers can get in through implantable and similar devices, and what needs to happen to ensure security is maintained.
Continue reading: https://www.healthcareitnews.com/news/cybersecurity-and-internet-things-dangers-and-solutions

The Internet of things (IoT) is a system of interconnected devices that can transfer data over a network. These devices can be anything that can communicate with each other without requiring human-to-human or human-to-computer interaction.
The Internet of things can be an array of smart devices with sensors that can work by themselves and connect to other devices. For example, think of sensors in your vehicle that tell you about another car that users can also control through an app.
How does IoT work?
IoT devices use their inbuilt systems, such as processors, sensors, and communication hardware, to collect data. These web-enabled smart devices then transfer the data to other devices to provide better services and address specific needs. These devices do all this without any human intervention. However, they can be given additional instructions/parameters by an individual.
Continue reading: https://fossbytes.com/what-is-the-internet-of-things-iot/

There is no denying the pandemic has brought unprecedented challenges to the global workforce. Overnight it eradicated millions of jobs, causing a drop in employment that was 14 times bigger than the financial crisis of 2008. Its impacts were particularly devastating for women. On International Women’s Day 2021, the labor participation rate for women in the United States was 55.8%, the lowest it has been since 1987. However, the issues that drove these numbers were not new, just frustrated by the challenges of moving to the world of remote work in March 2020. A 2008 study featured in Harvard Business Review reported that the attrition rate for women in technology was more than twice the rate for men. Well before the pandemic, women were leaving the workforce for a variety of reasons, but family responsibilities and a need for a more flexible work-life balance were large factors.  
However, the pandemic also proved that the construct of work, and the world of employees, can fundamentally shift overnight. In fact, the pandemic gave employees an opportunity to re-evaluate the drivers of the workplace and culture trends. Throughout the pandemic, women distinguished themselves in business. Research by Forbes shows that women are perceived to have outperformed their male counterparts in many ways. For example, they rated higher in competencies valued in a crisis, including taking the initiative, acting with resilience, practicing self-development, and displaying integrity.  
Long before the pandemic, women proved the broad impact of their strengths in areas key to technology leadership. These areas included employee development, cultivating relationships and creative thinking. However, women also faced the challenging clichéd assumptions that they underperformed in areas historically seen as male strengths, such as taking the initiative and driving for results. While women left the workforce in droves, in 2021, all S&P 500 firms have at least one female board member. As companies strive for diversity and employees are looking for meaningful opportunities that support them holistically, there is an opportunity for a more meaningful conversation about the issues that have faced women for decades.  
Continue reading: https://www.toolbox.com/hr/culture/guest-article/3-ways-the-pandemic-can-change-the-conversation-for-women-in-tech/

As leaders in technology gather in person and virtually this week at CES to witness the latest innovations, women in tech should be top of mind—especially given the continuing stresses of the global pandemic. Why? Consider the following:
 

• Only 38% of women in technology feel their organization’s commitment to supporting them during the pandemic has been sufficient, and just 30% say their employer increased their access to flexible work. 
  [*]51% of women in technology feel less optimistic about their career prospects now than before the pandemic, and 57% expect to leave their employer for a new role within 2 years—citing lack of work-life balance as the biggest reason. [ii]
  

 
It’s sentiments like these and their impact on the sector that has made the importance of supporting women in tech one of the key trends in Deloitte’s 2022 Predictions.  If technology companies want to succeed in the coming year and beyond, they need to renew their commitment to advancing gender diversity in tech—especially as COVID-19 moves from pandemic to endemic.
Continue reading: https://www.forbes.com/sites/deloitte/2022/01/05/women-in-tech-the-key-to-success-post-pandemic/?sh=1552429030d2

New Delhi, TickerPlant, a subsidiary of 63 Moons technologies limited, today announced its collaboration with Google Cloud for the development of the CryptoWire eco-system, catering to all stakeholders of crypto and the blockchain industry on a common platform. As an exchange neutral global platform, CryptoWire aims to simplify the digital asset class, blockchain technology, its industrial application and empower enthusiasts and professionals to make informed business decisions by offer deep insight and leading-edge knowledge. CryptoWire's state-of-the-art knowledge portals, CryptoTV and Crypto University will provide technology and knowledge-intensive global intervention in crypto asset and blockchain ecosystem to enable seamless operations and convergence of all applications for participants to make informed investment decisions and enable industry usage of the blockchain.
Continue reading: https://economictimes.indiatimes.com/markets/cryptocurrency/google-cloud-partners-with-cryptowire-to-develop-blockchain-crypto-ecosystem/articleshow/88731636.cms

To the uninitiated, cryptocurrencies can raise thoughts of unbelievable volatility and a currency type that offers safe harbor to the kind of people who would otherwise reject oversight of their financial dealings.
Those ideas are quickly fading away however as cryptocurrencies and their various offshoot concepts work their way into the mainstream. Stereotypes are being replaced by communities of investors who got in early on one of the most significant changes to the world’s financial system yet seen.
And for marketers who take the time to look a little deeper, they will find an audience of technologically savvy, cashed up investors who defy many of the norms and behaviors of traditional demographic groups.
The value of crypto
Cryptocurrencies are virtual currencies that use blockchain technology to keep an immutable ledger of ownership. Most are not issued by a central authority, although that is changing as central banks come to see their value for cross border transactions and other use cases. While Bitcoin’s wild valuations have tended to see that currency gain most of the attention, it represents only the tip of the iceberg in terms of available cryptocurrencies are and what they can do.
The Bank of America estimates the global aggregate market value of the digital asset ecosystem to be approximately US$2.1 trillion, with about 221 million people globally as of June 2021 having traded a cryptocurrency or used a blockchain-based application. It’s a massive rise from the 66 million users estimated at the end of May 2020.
Continue reading: https://www.cmo.com.au/article/694295/explainer-what-marketers-need-know-about-cryptocurrency/

It’s a bit difficult to pin down exactly what people in the biotechnology industry mean by “artificial intelligence” (AI). In general, they seem content with a working definition, one that describes AI as a computer program that can learn and predict outcomes based on the data sets it receives.
Given that the working definition of AI is vague, it follows that the status of AI in the biotechnology industry is vague, too. For example, it is unclear whether AI is to be regarded as something new and revolutionary. Are news stories in the popular press any guide? These include breathless reports of how AlphaFold, an AI system developed by Google’s DeepMind, accurately predicted the structure of hundreds of thousands of proteins.
Although AlphaFold is a groundbreaking technology, it isn’t the be-all and end-all of AI in biotechnology. AI-related spadework in biotechnology is occurring in several fields of endeavor. Indeed, biotechnology companies far and wide have been implementing AI in their pipelines.
If we are to clarify AI’s status, we should begin by recognizing that AI in biotechnology hasn’t suddenly become mainstream. In fact, it is already mainstream. Moreover, it is diverse and ready to produce results. In the biotechnology industry, AI that is accurate, predictive, and productive is within reach. Such AI will be worth a hundred times its weight in bench lab scientists.
Continue reading: https://www.genengnews.com/artificial-intelligence/the-future-of-biotech-in-an-artificially-intelligent-world/

Thinking involves manipulating symbols and reasoning consists of computation according to Thomas Hobbes, the philosophical grandfather of artificial intelligence (AI). Machines have the ability to interpret symbols and find new meaning through their manipulation -- a process called symbolic AI. In contrast to machine learning (ML) and some other AI approaches, symbolic AI provides complete transparency by allowing for the creation of clear and explainable rules that guide its reasoning.
Commonly used for segments of AI called natural language processing (NLP) and natural language understanding (NLU), symbolic AI follows an IF-THEN logic structure. When an IF linguistic condition is met, a THEN output is generated. Symbolic AI works best when rules are straightforward. By using the IF-THEN structure, you can avoid the "black box" problems typical of ML where the steps the computer is using to solve a problem are obscured and non-transparent.
Originating in the 1950s, symbolic AI was the original approach to AI, such that it received the nickname "Good Old-Fashioned AI (GOFAI)" in the 1980s book, Artificial Intelligence: The Very Idea by John Haugeland.
Continue reading: https://tdwi.org/articles/2022/01/06/adv-all-how-symbolic-ai-yields-cost-savings-and-business-results.aspx

The USS Abraham Lincoln deployed this week from San Diego under the command of Capt. Amy Bauernschmidt, the first woman to lead a nuclear carrier in U.S. Navy history. Bauernschmidt, who previously served as the Abraham Lincoln's executive officer from 2016 to 2019, took over command from Capt. Walt Slaughter during a ceremony last August, CBS 8 in San Diego reported.
Source: https://www.youtube.com/watch?v=c8y1Vvs9KEE

The Internet of Things (IoT) is a global network specifically for digital devices, rather than humans, allowing a wide range of devices to communicate with each other, while all is managed by AI. From household appliances to commercial aircraft and everything in between will be on this global grid, which will be powered by 5G and this will change many industries for the better.
Artificial Intelligence
Machine learning might be in its infancy, but we have already made great progress and large groups of computers use complex algorithms to learn from data that is input by humans and the more raw data it receives, the smarter it gets. AI offers us unlimited potential across every industry and will likely revolutionize the way we live.
Remote Working
The year 2019 saw remote working become very popular due to the pandemic and the IoT has 10x speeds of 4G and that means everyone can work remotely. Assignments are sent by email and employees can chat with their boss via a Zoom call, while online collaboration is easy with VoIP solutions.
Continue reading: https://newsaffinity.com/what-is-the-internet-of-things-and-how-might-it-affect-my-business/

Weave the web of connectivity — with 5G, with all manner of mobile devices — and the Internet of Things takes shape. Add platforms into the mix, and the rise of a truly seamless continuum of services takes shape, with a single point of entry and flow.
The infrastructure, of course, must come first, must be ready, to house and host and, yes, promote a range of activities that span everything from mobile order ahead to voice-activated commerce.
Amazon, of course, that juggernaut of electronic life, has striven mightily to craft the IoT.
To that end, and as announced Tuesday (Jan. 4), IoT carrier 1NCE and Amazon Web Services (AWS) said they would expand their existing joint efforts to extend the 1NCE IoT platform on a global scale.
The collaboration, according to the Tuesday statement, “further aims to strengthen ties between the two companies and to develop the Next Level IoT Software speeding up global deployment of IoT projects.” In terms of mechanics, 1NCE has said that its software running on AWS that enables IoT developers to “quickly integrate” cellular IoT connectivity.
Continue reading: https://www.pymnts.com/internet-of-things/2022/iot-platforms-aim-to-speed-connected-economys-evolution/

Today’s Woman in Tech: Lilac Mohr, Interim VP of Engineering, Flow, Pluralsight
Lilac Mohr leads the Flow engineering group at Pluralsight. She has a passion for helping individual contributors and teams use data to frame the human stories behind their engineering work. Lilac has 25 years of experience in the tech industry and holds a B.S. degree in Computer Information Systems and an M.S. degree in Statistics. She is also the author of two middle-grade math adventure novels that encourage girls to pursue STEM fields.
When did you become interested in technology? What first got you interested in tech?
I took my first programming class in 9th grade, and I was the only girl in the room. While my classmates jumped right into the DOS prompt and started hacking away, I struggled with finding the right keys on the keyboard since I’d never worked on a PC before. The boys sitting behind me were placing bets on how long it would be before I dropped out of the class. Despite a rough start, I fell in love with coding. By the end of the year, I had written a functioning Tetris game in GW Basic and had earned the respect of my peers.
How did you end up in your career path? What obstacles did you have to overcome?
Finding tech jobs was never challenging for me, but the male-dominated tech industry didn’t cultivate a healthy sense of belonging. I felt that as a woman I had to work harder to prove my worth. This often led to loneliness and burnout.
Continue reading: https://jaxenter.com/women-in-tech-mohr-176298.html

Blockchain explained
First things first, let’s recap what blockchain actually is. Blockchain is a kind of database that gathers data in groups called blocks. All of these blocks have a fixed storage capacity, and, when filled, are chained onto an existing block to create a chain— i.e., the term blockchain.
A key feature of this is that, once a block has been filled, it is given an exact timestamp of when it was added to the blockchain. Every event that happens on it is recorded on a public ledger, which is essentially a record-keeping database that ensures the participants’ identities are kept secure and pseudo-anonymous. They can only be identified by private keys, which are strings of letters and numbers needed to make a blockchain transaction.
An example: Bitcoin
To demonstrate blockchain in action, it makes sense to look at the most famous example of a technology that uses one: Bitcoin. The cryptocurrency exists on a blockchain across thousands of computers worldwide, all operated by different groups of people. These computers are called ‘nodes’, each of which has a record of every transaction that has taken place on it.
This has many benefits, with perhaps the main one being that, if one node has an error in its data caused by a fraud attempt, the blockchain can reference the other nodes to correct the database. Consequently, every transaction is accountable, secure and irreversible, with no de-centralized organization being able to control things either.
Continue reading: https://www.finance-monthly.com/2022/01/how-blockchain-is-shaking-the-global-payments-system/

Early Black crypto advocates want to maintain a seat at the table they helped to set, as Wall Street eyes big opportunities in the digital currency sector. People who did some of the earliest work spreading cryptocurrency’s message to the Black community are being increasingly pushed out of the space. With Just under one in four, about 23%, of Black people owning this caché currency, compared to 11% of White Americans, and 17% of Hispanics, how important is it for the Black community to be included as more legacy financial institutions increasingly enter the arena?
The Breakdown You Need To Know:
About 2 in 5 Black adults are likely to purchase or invest in Bitcoin, compared to roughly 3 in 10 adults overall, according to Morning Consult data. CultureBanx reported that many people question whether or not cryptocurrency is helping to democratize finance and the economy, or is it a volatile asset that puts Black communities at particularly high risk. Especially now that Large financial institutions that once looked down upon digital currencies, are now investing huge amounts of money to meet the demand for customers. 
In some ways cryptocurrencies offer a new decentralized financial model allowing Black communities to grow their own wealth, after being ostracized for so long from traditional banking institutions. The number of crypto investors is on track to double as a new generation of Black traders emerge, with 44% of cryptocurrency traders being investors of color.
Continue reading: https://www.forbes.com/sites/korihale/2022/01/04/black-main-street-crypto-investors-want-their-wall-street-respect/?sh=5c6894871388

Jonas Lundqvist, CEO at Haidrun, explores how private blockchain technology is helping to fight fraud in the supply chain by delivering trust and confidence using cryptographic security, a shared source of the truth and tamper-evident transaction history 
Every enterprise has a supply chain. They can’t exist without them and effective management of the supply chain is at the heart of any successful business. Controlling the flow of data and funds related to suppliers and partners for components, parts, raw materials, work in progress and finished goods from the point of origin to the point of consumption has always been a complex procedure. Global events have made this even more challenging and with the increasing risk of cyber attacks, counterfeiting and the drive for sustainability, it is no wonder that supply chain fraud poses a bigger threat to organizations now than ever before.
Supply chain fraud affects organizations of all sizes in all industries and can occur at any step in a supply chain and routinely including bribes offered during supplier selection and forged checks for financing to fraudulent payments and guarantees. However, a growing and more challenging trend is the corruption in supply chain data. It is a significant management challenge that can take a wide variety of forms including manipulation of provenance data, item quantity and payments information, as well as the introduction of fake and counterfeit goods into the system.
Identifying such compromises in the process is obscured by the lack of consistency and integration across today’s broad spectrum of technologies supporting the links in the chain: from legacy analogue comes through to cutting edge integrated digital systems. People and processes connect in a combination of ways from non-secure, low-tech phone calls, emails, apps and even faxes to advanced technologies such as IoT-enabled devices, artificial intelligence (AI) and machine learning (ML). This lack of uniformity and security across the modes of communication creates risk exposure in areas such as authenticity, payments and reporting.
Continue reading: https://www.information-age.com/fighting-fraud-supply-chain-with-blockchain-123498278/

When I speak with clients about their data management architecture — and the accessibility, availability and flow of their business data, both internally and externally — the conversation often turns to data hubs, data lakes, data marts and data warehouses.
The problem with this type of discussion is it detracts from the main issue regarding clients’ most prominent data pain point: the lack of real-time accessibility and flow of data among multiple systems in response to business or customer events or transactions.
After all, one of the cornerstones of a digital business is making data available to those who need it at the right time. Modern businesses are driven by real-time business insights, decisions and reporting.
The real conversation
What we should be talking about is the role an integration data hub can play in addressing most of the real-time data challenges clients experience. That’s because very often, these challenges are the result of using traditional point-to-point batch-mode integration among systems for operational data. The issue with point-to-point integration is that it gets complex quickly: If a company has 10 systems that data needs to be moved or exchanged with, there would be up to 90 bi-directional integration lines, according to the n (n-1) connection rule.
The result: complex and costly IT maintenance, challenges with managing multiple copies of data and databases, security risks, and inconsistent data definitions across the organization. Business agility and innovation also suffer.
From point-to-point to hub-and-spoke
Using an integration data hub, however, businesses can quickly and easily streamline access to operational data from systems of record. A centralized data hub is not a technology per se but a method for sharing and communicating data and connecting core IT systems in a real-time, event-driven, hub-and-spoke pattern instead of the traditional point-to-point integration approach.
A data hub enables information sharing by connecting data producers with data consumers. Systems of record publish their data in real-time to the integration data hub so applications can access, consume and use the data in real-time. The hub provides a point of mediation, governance and visibility to how data flows across the enterprise. It defines data-level access, as well as policies on how long the data is kept.
Integration data hubs come to life
Many healthcare, retail and insurance clients have achieved a high degree of success creating an integration data hub. For example, we helped a health payer make claims data from core systems available to upper-stream processes and customer-facing applications within 20 seconds of update or change in status. We also assisted a retail client to provide accurate multi-site inventory updates to its point-of-sale system within 12 seconds.
In all of these cases, the integration data hub helped streamline operational data access easily and quickly, publishing data to upper-stream operational systems and historical and analytical ones. Both are important:
 

• Upper-stream systems need the data to complete business transactions that cut across multiple IT systems, such as issuing payment for a processed claim or displaying real-time updates to a member portal or app. Both of these capabilities positively impact customer service and satisfaction.
• Analytics platforms use the data to create real-time business insights and reports or to train artificial intelligence/machine learning (AI/ML) models. Doing so leads to real-time, data-driven business decisioning and visibility.

 
Continue reading: https://www.forbes.com/sites/cognizant/2022/01/04/dont-overlook-integration-data-hubs-when-modernizing-it/?sh=bfb41de11298

In the Foreword to this year’s survey, NewVantage Partners CEO Randy Bean, and Thomas H. Davenport, a Fellow with the firm, write “The ten years of the survey provide a useful measure of progress—or the lack thereof in some respects—in how companies are managing these important initiatives. From 2012 to 2022 the survey has assessed the initiatives that large companies are focused on, where they are investing and the returns they are getting, the roles assigned to manage data, and the issues that cause significant challenges.”
The state of data and AI initiatives
Investment in data and AI initiatives continues to grow as efforts deliver measurable results
Investment in data and AI initiatives continues to grow – the 2022 survey indicates that 97% of participating organizations are investing in data initiatives and that 91% are investing in AI activities. This year, 92.1% of organizations report that they are realizing measurable business benefits, up from just 48.4% in 2017 and 70.3% in 2020.
Achieving data-driven leadership remains an elusive aspiration for most organizations
Organizations still face a potentially long road ahead of them in their efforts to become data driven. Less than half of respondents replied that they were competing on data and analytics – 47.4%; only 39.7% reported that they were managing data as an enterprise business asset; barely over a quarter – 26.5% — report that they have created a data-driven organization; and just 19.3% indicate that they have established a data culture.
Continue reading: https://www.helpnetsecurity.com/2022/01/05/data-ai-initiatives/

In today’s digital age, artificial intelligence (AI) and machine learning (ML) are emerging everywhere: facial recognition algorithms, pandemic outbreak detection and mitigation, access to credit, and healthcare are just a few examples. But, do these technologies that mirror human intelligence and predict real-life outcomes build a consensus with human ethics? Can we create regulatory practices and new norms when it comes to AI? Beyond everything, how can we take out the best of AI and mitigate the potential ill effects? We are in hot pursuit of the answers.
AI/ML technologies come with their share of challenges. Globally leading brands such as Amazon, Apple, Google, and Facebook have been accused of bias in their AI algorithms. For instance, when Apple introduced Apple Card, its users noticed that women were offered smaller lines of credit than men. This bias seriously affected the global reputation of Apple.
In an extreme case with serious repercussions, U.S. judicial systems use AI algorithms to declare prison sentences and parole terms. Unfortunately, these AI systems are built on historically biased crime data, amplifying and perpetuating embedded biases in AI systems. Ultimately, this leads to questioning the fairness offered by the ML algorithms in the criminal justice system.
Continue reading: https://www.itbusinessedge.com/applications/whats-next-for-ethical-ai/

Researchers at Cal State Fullerton and the University of Southern California are studying if and how artificial intelligence can help guide and mentor more college students from underrepresented communities.
The project, “CareerFair.ai: Increasing Connections to Fast-Growing STEM Careers,” aims to increase interest and engagement in science, technology, engineering and mathematics, or STEM, fields. CareerFair.ai centers on an online platform that can answer students’ academic and career questions using AI-based virtual agents.
Virtual agents use prerecorded videos from graduate students and professionals who are real-life mentors in STEM and STEM-adjacent fields. These mentors also belong to such underrepresented groups as first-generation college students, women and historically marginalized racial and ethnic groups.
“Our AI-based technology helps simulate a real-life conversation, as if students are talking to these professionals in real time,” said Cal State Fullerton assistant professor of psychology Yuko Okado, one of the project’s principal investigators.
Continue reading: https://news.fullerton.edu/2022/01/can-artificial-intelligence-help-increase-diversity-in-stem/