5 Interesting Things About Refurbished Phones You Must Know

These days, high-end smartphones are getting more and more expensive, unless you wait for Black Friday sales or promotions. Even the latest iPhone XS Max is priced over £1400, which is quite a lot of money. This is the reason that so many smartphone users are opting for refurbished mobile phones to save some of their hard earned money.

As refurbished mobile phones are actually quite common these days, in this article let’s discuss some of the top interesting facts about refurbished phones. And don’t forget to read the 3^rd fact. It will entice you to buy a refurbished phone right now. But first of all, let us understand the definition of a refurbished phone.

What is a Refurbished Phone?

Refurbished phones, commonly known as refurb phones are those phones which, after their functionality defect, are returned back to the manufacturer. The manufacturer then repairs these phones and send them back to the market for re-selling. You might also see refurbished phones labelled as ‘pre-owned or reconditioned’ phones. Most of the times there is nothing wrong with refurbished phones even before they get their repairing. The owner might send back the device to buy a new one.

When it comes to a new phone vs refurbished phone, there is not much difference except that the refurbished phones are carefully and lightly used before coming in your hands. In simple words, a refurbished phone is just like a new phone, though technically it is not a ‘brand new’ phone. That is why people like to buy refurbished phones because they are basically new. And you can get a refurbished phone at a discounted price as well.

Interesting Things About Refurbished Phones

Here are some of the interesting things about a Refurbished phone. After knowing these things, we are sure that you will never hesitate while buying a refurbished phone.

1. Almost New Phones at a Lower Price

Saving money is one of the utmost desires of a common human being. And Refurbished phones fulfil this desire, as you can get an almost like new phone at a huge discounted price. These refurbished phones work like new smartphones. The discounted prices open new possibilities for you while selecting a new phone.

If a brand new phone costs you around 1000 pounds, the same phone of the same brand in a refurbished form will costs you 50% less or even more than original pricing. So, the ‘low cost’ factor is one of the most important things about refurbished phones.

2. Refurbished Phone Stays Longer Than People Think

You should avoid buying phones with short lifespan. But as far as refurbished phones are concerned, you can buy them without any hesitation. Refurb phones work the same as new ones. But these phones are always repaired and tested by a skilled technician. When a phone is installed with replacement parts like a new battery, its lifecycle automatically increases and you get a chance to stick with your favourite phone a bit longer. That is why refurbished iPhone 5s is still common in mobile phone users.

3. Refurbished Phone Comes With Extensive Guarantee

Because refurbished phones get repaired and tested by professionals, so they come up with a long guarantee. Normally the guarantee time period is between 6 to 12 months. For comparison: if you buy a refurbished iPhone from Apple, you will get a guarantee of maximum 3 months, and if you buy a refurbished phone from a trusted online mobile phone marketplace then you will get a guarantee of up to 12 months.

Alpha SmartPhones is one such company who provides 12-month long and extensive guarantee with its high quality refurbished phones. If mobile phone malfunctions in that time, you can return it without any hassle and get your cash back. And this condition applies to all mobile phones available at Alpha SmartPhones.

4. Refurbished Phone – An Environment-Friendly Choice

Buying a refurbished phone is more environment-friendly gesture than buying a new phone. Millions of smartphones end up at landfills every year. These phones are continuously polluting our environment due to the toxic materials inside them. So it is better to recycle these phones instead of throwing them. Refurbished phones contain good condition parts of these recycled phones. Thus, reusing refurbished phones is a great service towards protecting the environment.

5. Risk-Free Money Back Guarantee With Refurbished Phones

Usually, people get shy while buying a refurbished phone online for the first time. However, there is no such risk involved when you buy a refurbished phone from an online mobile phone marketplace. You can buy your favourite phone in a refurb form online with free home delivery. By the way, Alpha SmartPhones does provide such service to its customers with 30 days money back guarantee as well. So you can easily claim your money back if the mobile phone doesn’t meet your expectations.

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10 top data science and analytics education programs of 2018

These universities and institutes offer the best programs for professionals interested in a career in big data and analytics.

Data science and analytics

Data science and analytics- There’s never been a better time to begin a career in data science: By 2020 the number of annual job openings for all data-savvy professionals in the US will increase to 2.7 million, IBM predicted. And those with data science skills can command an average salary of $139,840 in the US, according to Glassdoor. The massive growth in data has changed the way enterprises operate, and data science has become crucial for making smart business decisions.

But how do you gain this coveted skillset? Analytics Insight Magazine has named the 10 best analytics and data science institutes of 2018, naming the top programs with experienced global faculties that offer real-world experience to their students, according to a press release.

“The featured institutions offer a comprehensive curriculum in Big Data and Data Science, delivered by top-class faculties along with extraordinary industry exposure,” Ashish Sukhadeve, founder and editor-in-chief of Analytics Insight, said in the release. “We congratulate all the ten institutes for providing world-class analytical education and building impactful data professionals.”

Here are 10 analytics and data science institutes that are at the forefront of education in the field.

1. Carnegie Mellon University’s Heinz College of Information Systems and Public Policy

Carnegie Mellon offers a Master’s program in Business Intelligence and Data Analytics (BIDA), and deploys the latest in analytical education, according to the release.

2. Cornell University

Cornell offers a unique flagship program in Master of Professional Studies in Applied Statistics (M.P.S.). It also offers undergraduate degrees in statistics and data science, as well as M.S and Ph.D. degrees in these fields.

3. Great Lakes Institute of Management

Great Lakes Institute of Management is a leading business school in India, which offers a one-year Post Graduate Program in Management (PGPM), a regular two-year Post Graduate Diploma in Management (PGDM), along with weekend and executive programs in analytics, for professionals who want to gain these skills while working.

4. International School of Engineering

The International School of Engineering (INSOFE) in India is an applied engineering school with a focus on data science and big data analytics education, according to the release. The school offers courses in big data analytics, teaching the latest machine learning and deep learning techniques to solve real-world problems.

5. New York University Stern School of Business

The NYU Stern School offers a Masters of Science in Business Analytics degree that aims to help executives understand the role of data in decision-making. The program focuses on domain-specific areas such as analytics strategy, marketing, and optimization, according to the release.

6. Penn State University

Penn State University offers Master in Data Analytics, preparing students to work in positions that require the design and maintenance of big data and data analytics systems with exposure to real-world datasets.

7. Praxis Business School

India’s Praxis Business School offers a one-year post-graduate program in data science with machine learning and artificial intelligence (AI) capabilities aimed at equipping students with the tools, techniques, and skills to enter the analytics field, the release said.

8. Saint Mary’s College, Notre Dame

Notre Dame offers a Master of Science in Data Science program provides students with a deep dive into the mathematical and computational skills needed to take on complex data challenges.

9. University of Chicago Graham School

The University of Chicago offers a Master of Science in Analytics (MScA) program to help students analyze complex datasets and and solve real-world problems, the release noted.

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Article Credit: TechRepublic

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Top five business analytics intelligence trends for 2019

From explainable AI to natural language humanising data analytics, James Eiloart from Tableau gives his take on the top trends in business analytics intelligence as we head into 2019.

Business analytics trends

Business analytics trends- Business analytics intelligence prediction number one: After the hype, the rise of explainable AI

AI promises to enhance human understanding by automating decision-making. But, as organisations rely on AI and machine learning for data-driven decision-making, we’re seeing a rise in human hesitation about the trustworthiness of model-driven recommendations. Many machine learning applications don’t offer a transparent way to see the algorithms or logic behind decisions and recommendations. This need for transparency will drive growth of explainable AI in 2019. If you can question humans, why not have the same option with machine learning when making decisions?

Business leaders will put greater pressure on data science teams to use models that are more explainable and reveal how models are constructed. AI has to be trusted to make the strongest impact, and the generated conclusions must be intelligible, simple and dynamically answer questions to help humans understand their data.

Explainable AI : The margins of accountability

How much can anyone trust a recommendation from an AI? Yaroslav Kuflinski, from Iflexion gives an explanation of explainable AI

Business analytics intelligence prediction number two: Natural language humanises data analytics

Natural language processing (NLP) helps computers understand the meaning of human language. BI vendors will incorporate natural language into their platforms, offering a natural language interface to visualisations. At the same time, natural language is evolving to support analytical conversation—defined as a human having a conversation with the system about their data. The system leverages context within the conversation to understand the user’s intent behind a query and further the dialogue, creating a more natural, conversational experience. That means when a person has a follow-up question of their data, they don’t have to rephrase the question to dig deeper or clarify an ambiguity. Natural language will be a paradigm shift in how people ask questions of their data. When people can interact with a visualisation as they would a person, it allows more people of all skill sets to ask deeper questions of their data. As natural language evolves within the BI industry, it will break down barriers to analytics adoption and help transform workplaces into data-driven, self-service operations.

Business analytics intelligence prediction number three: Actionable analytics put data into context

Data workers need to access their data and take action—all in the same workflow. In 2019, expect more organisations to use data analytics exactly where it’s needed and not in isolation. Organisations will truly reap the benefits of how BI platform vendors are offering capabilities like mobile analytics, embedded analytics, dashboard extensions, and APIs. Embedded analytics puts data and insights where people are already working so they don’t have to navigate to another application or shared server, while dashboard extensions bring access to other systems right into the dashboard. And mobile analytics put data directly into the hands of people in the field. These advancements are equally powerful as they meet the needs of different business teams and verticals by empowering new audiences with on-demand data in context.

Organisations across the globe lack data analytics maturity, says study

Harvard Business Review Analytic Services report reveals that only five per cent believe that their organisations are very effective at implementing modern data sharing, while 67% want to move towards that approach, showing that data sharing is very much on the agenda of organisations in the year ahead

Business analytics intelligence prediction number four: Enterprises get smarter about analytics

Business intelligence initiatives often have a well-defined start and end date and it’s not uncommon for them to be considered “complete” after they are rolled out to users. But merely providing access to business intelligence solutions isn’t the same as adoption. Chief data officers, primarily, are re-evaluating how BI adoption plays a part in a strategic shift towards modernisation, because true value isn’t measured by the solution you deploy, but how your workforce uses the solution to impact the business.  The assumption that everyone is getting value out of a BI platform just because they have access to it can actually be an inhibitor to real progress with analytics.

As these internal communities on-board workers onto a BI platform, organisations can start to delegate analytical responsibilities and create new user champions. This will ultimately reduce the heavy lifting for maintenance and reporting, traditionally reserved for IT.  More internal champions will start to emerge, acting as subject matter experts who socialise best practices and align people on data definitions. Inevitably, all of these movements will lead to more people using and getting value out of BI software. And most importantly, your workforce will become more efficient and your organisation more competitive.

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Making the most out of Customer Data Analytics

Customer Data Analytics

Customer Data Analytics- Being a successful entrepreneur isn’t easy. A viable idea, a profitable niche, a target demographic and something of value to sell are the prime requirements to run a business. Aside from these customer relations also ranks high on the list. A customer responds better to what is done for them. An entrepreneur should catch his/her customer base, their preferences and purchasing behavior. For attracting and retaining profitable customers a systematic examination of customer information is needed.

Customer data analytics is not just collecting numbers on product sale. It can lead to an accurate prediction about the future and provide actionable roadmaps for achieving desired goals. The process involves examining and capturing customer behavioral data. Descriptive analytics, Prescriptive analytics, and predictive analytics are three types of customer data analytics. The foremost one describes ‘what happened?’. This is the standard type of customer analytics, in which summarizing of raw data is done. They provide a clue into the past. Prescriptive analytics provide insight into a fruitful outcome. It is more like providing a solution. Predictive analysis try to answer the question ‘what could happen?’. It provides information on what can be expected in the future. Together it becomes the backbone for all marketing activities.

The key features of an effective customer insight are consumer research, data quality, database marketing, and analytics team. Customer’s perception of their behavior could not be neglected. Customer data management also is vital in data analytics. The predictive customer insight is proportional to the quality of data gathered. Database marketing is a robust way of testing hypothesis, a scientific method to know customer insight. A proficient team also is required for strategic marketing.

There are many benefits to using customer data analytics. Primarily they help in improving business operations. It is used extensively to create promotion and campaigns. Provision into a better understanding of the target group helps in reducing campaign cost by picking up customers who are likely to respond. In future, this customer data can provide insight in designing new products too.

Customers expect a faster customer service and instant gratification today. Relevant knowledge to address these pain points will nurture customer relationships which in effect will help in the business itself.

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IT monitoring tops uses for machine data analytics

Advanced analytics is being deployed to make sense of the masses of machine data in IT operation, but complex environments is making this harder

Machine data analytics

Machine data analytics- A survey of 250 executives across the UK, Sweden, the Netherlands and Germany whose companies use machine data reported that IT operations remains the top use case for machine data analytics.

The 451 Research report for SumoLogic, Using machine data analytics to gain advantage in the analytics economy, found that machine data analytics was being used in IT operations in 56% of the organisations surveyed. Data scientists were the second biggest users (45%), while security analysts (37%) was the third highest user group.

Nancy Gohring, senior analyst at 451 Research and author of the report, wrote: “Although IT operations, the typical use case for machine data analytics, still tops the list in terms of ownership and usage of these tools, we were surprised at the wide array of employees in other roles who commonly use machine data analytics – a result that again emphasises that companies do recognise the many ways that machine data can drive intelligence across the business.”

451 Research also reported that security, monitoring and big data insights top the list of application areas for machine learning analytics. The study found that more than half of respondents felt new cloud and container IT architectures did not hamper their ability to get data fast decision-making.

However, 451 Research found that in organisations using continuous integration and continuous deployment tools, respondents were more likely to say that the adoption of those technologies made it more difficult to get the data they needed for fast decision making.

“We have seen tools evolve to collect the granular data required in application environments built on technologies such as containers and microservices,” Gohring wrote in the report.

However, the study also found that 47% of survey respondents said the adoption of modern technologies makes it harder to get the data they need for speedy decision making, indicating that a notable portion of the market is still underserved in this regard, according to Gohring.

Colin Fernandes, director of product marketing at Sumo Logic, said: “Getting effective oversight across systems and users with machine data makes delivering better services easier alongside improving security and operations. It’s gratifying to see that European organisations already understand the value in using machine data analytics for security purposes.”

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Can Blockchain Fix Cybersecurity?

The technology holds a lot of promise for security, but there’s still a long way to go before it sees serious enterprise adoption.

Cybersecurity Blockchain

Cybersecurity Blockchain- Blockchain is one of the biggest tech buzzwords of the last few years, and the technology is marketed as a cure for everything that ails you, including cybersecurity. In practice, at least as far as security is concerned, blockchain might actually cause more problems than it solves.

The basic idea behind blockchain is that you’ve got a list of items, or a ledger, that you’re sharing with your peers. A clever bit of encryption keeps you from changing the previous elements on that list, unless the majority of your peers sign off on the change.

It’s pitched as being better than having one trusted central party keep track of the list and make corrections when needed, because the trusted central party usually charges money for the service.

So, for example, banks can get together and move money from one to another without any centralized gatekeeper.

Security experts seem to agree that the technology has a lot of potential in their space.

“Blockchain holds great promise,” Phil Quade, CISO at Fortinet, the Sunnyvale, California-based cybersecurity firm, said.

One example is its potential to improve efficiency of key and certificate distribution, David Cook, CISO at Databricks, the San Francisco-based data analytics firm, told us. “I think there’s some business value to it,” Cook said.

The downside is that when there’s a problem with a transaction, instead of having that central entity step in and resolve the dispute and correct the ledger, you have to negotiate with everyone else in the system.

This happens a lot with cryptocurrencies, which are currently the biggest and best-known implementations of blockchain. And those implementations haven’t been without problems.

For example, more than $500 million worth of the Ethereum cryptocurrency has been lost because people accidentally left a payment destination address field blank.

“In a traditional [system] you have the ability to roll back the transactions,” said Cook. “With blockchain, it’s permanent.”

Another $500 million of the Ripple cryptocurrency was recently lost when its billionaire owner died, since he was the only one who had access to that currency wallet.

Hackers typically don’t go after the core blockchain encryption technology. Instead, they go after poorly implemented wallets, attack currency exchanges, and launch man-in-the-middle attacks to intercept money transfers. Without a central authority, there’s nobody to complain to when things go wrong.

In the first six months of this year alone, hackers stole $1.1 billion worth of cryptocurrencies, according to security researchers at Carbon Black.

Besides hacks and reversibility issues, there are the practical problems of adapting business processes and technology platforms to blockchain.

“In my prior position, I ran operations for data centers, and based on the legacy code in the infrastructure, I would say we are far from actually implementing it,” Cook said.

As a result, data center operators haven’t yet started deploying blockchain technology to any noticeable degree, he said. “In my dealings with other CISOs, nobody is using it.”

Cook said he also wants to see major vendor support and mainstream acceptance before considering using blockchain. “I would probably wait until one of the bigger companies, like Google or Microsoft, starts to adopt this,” he said. “There are a lot of questions about this technology. On the surface, it seems super secure, but I do feel that it’s going to take a while to adopt based on what I see with my infrastructure.”

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Article Credit: DCK

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What The Cybersecurity Of Our Critical Infrastructure Can Learn From The Climate Change Debate

Cybersecurity Debate

Cybersecurity Debate- Even calling climate change a debate stirs emotions in advocates on both sides of the political spectrum. The deniers on one side consider the repeated warnings from scientific authorities and government agencies to be no more than hype. Changes in climate are a cyclical feature of the long arc of human history. The believers on the other side consider complacency in the face of climate change warnings to be nearly criminal.

In some senses, the original believer was the English philosopher Thomas Malthus. One of his most controversial beliefs was that the rising population and inevitable food shortage would lead to the loss of human life as part of a type of natural population control. He believed our natural ecosystem performed “Malthusian Checks” to bring the population level down to a number that our planet could support. Malthus held a pretty grim view of the world that was eventually discredited by technological advancements in food production, globalization and reports that environmental disasters are linked to changes in climate.

How Technology Infrastructure And The Environment Are Linked

Rather than wading into this debate or evaluating Malthus’ place in history, consider the parallels between changes in our environment and changes in the security of the technology that powers our global infrastructure. The critical systems that run our lives — power, water, waste — are enabled by technology that has for decades been isolated to support their primary function. These systems are commonly known as operational technology (OT) or, specifically, industrial control systems (ICS).

These are systems built to work, every time, for a very long time, with minimal deviation or error. These services are deemed critical because of the importance of their output to our lives. In the developed world, without any one of these core functions, our modern lives would be turned upside down. In the developing world, several, if not all, of these services are absent, leading to a lack of opportunity or substandard living conditions at worst. We have come to rely on these services as part of normal, daily life, and their presence gives us a sense of security. Without them, our economic productivity would be greatly affected, and we would be left in a perpetual state of insecurity.

Why OT Used To Be All Right

Like evaluating natural disasters in the Malthus era, considering the security of critical infrastructure before the internet was largely unscientific. The systems that powered our critical infrastructure were built in an era when physical security was the primary concern. The attack surface was clear. Critical sites were geographically segregated, with strong perimeter defenses. Staff were trained to follow strict procedures, governed by policies often dictated by government agencies. Barring occasional human error, environmental impact or military threat to the physical sites, time wore on with operations that went mostly undisturbed.

Enter The Internet

For companies and governments that began to operate OT, they rightfully focused on segregating the networks of critical infrastructure from their corporate operations, which were rapidly disrupted through digital transformation. As software ate the modern corporation, OT remained logically and physically separate from those internet-connected networks. Productivity benefits from internet-connected software and services would eventually spread to OT. Only in the last several years has the imperative to connect OT to the internet become strong enough to warrant a new approach to security.

So, What’s The Strategy?

Much of the OT world still maintains the segregation approach, and for good reason. Our essential services run like clockwork — for the most part. And the disruption to modern OT is still in its infancy. But preparing for a new connected OT future requires careful planning and flawless execution to ensure the consistent delivery of the services that we deem critical.

In the same way that the awareness of climate change is amplified by the frequency and severity of natural disasters, sadly, the security world only stirs from its slumber when a significant hack or service disruption occurs. Those monumental, headline-worthy breaches are the Malthusian Checks of the 21st century. Thankfully, the watershed data breach that impacts OT environments and therefore our critical infrastructure has yet to take place. This is due to a combination of luck, traditional OT segregation controls and the early stages of internet disruption in OT.

Maybe Malthus Was Right When It Comes To OT

The clear parallel between environmental changes and technology innovation is that awareness is tied to visible public events with grand philosophical theories to explain them. While it’s clear that natural disasters are not a population control mechanism, per se, we can understand the science behind how our living ecosystem reacts to stimuli. Similarly, OT networks that run our critical infrastructure must be carefully assessed and secured in light of the ongoing load that internet disruption brings to them. Some tools already exist to approach OT security in a meaningful and differentiated manner, like the SANS Institute’s Industrial Control System Cyber Kill Chain and the NIST Cybersecurity for IoT Program.

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Article Credit: Forbes

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60 Cybersecurity Predictions For 2019

Cybersecurity Predictions

Cybersecurity Predictions- I’ve always been a loner, avoiding crowds as much as possible, but last Friday I found myself in the company of 500 million people. The breach of the personal accounts of Marriott and Starwood customers forced us to join the 34% of U.S. consumers who experienced a compromise of their personal information over the last year. Viewed another way, there were 2,216 data breaches and more than 53,000 cybersecurity incidents reported in 65 countries in the 12 months ending in March 2018.

How many data breaches we will see in 2019 and how big are they going to be?

No one has a crystal ball this accurate and it’s difficult to make predictions, especially about the future. Still, I made a brilliant, contrarian, and very accurate prediction last year, stating unequivocally that “there will be more spectacular data breaches” in 2018.

Just like last year, this year’s 60 predictions reveal the state-of-mind of key participants in the cybersecurity industry (on the defense team, of course) and cover all that’s hot today. Topics include the use and misuse of data; artificial intelligence (AI) and machine learning as a double-edge sword helping both attackers and defenders; whether we are going to finally “get over privacy” or see our data finally being treated as a private and protected asset; how the cloud changes everything and how connected and moving devices add numerous security risks; the emerging global cyber war conducted by terrorists, criminals, and countries; and the changing skills and landscape of cybersecurity.

It’s the data, stupid

“While data has created an explosion of opportunities for the enterprise, the ability to collaborate on sensitive data and take full advance of artificial intelligence opportunities to generate insights is currently inhibited by privacy risks, compliance and regulation controls. The security challenge of ‘data in use’ will be overcome by applying the most universal truth of all-time—mathematics—to facilitate data collaboration without the need for trust from either side. For example, ‘zero-knowledge proof’ allows proof of a claim without revealing any other information beyond what is claimed. Software that is beyond trust and based on math will propel this trend forward”—Nadav Zafrir, CEO,Team8

“IT security in 2019 is no longer going to simply be about protecting sensitive data and keeping hackers out of our systems. In this day and age of big data and artificial intelligence—where cooperation on data can lead to enormous business opportunities and scientific and medical breakthroughs—security is also going have to focus on enabling organizations to leverage, collaborate on and monetize their data without being exposed to privacy breaches, giving up their intellectual property or having their data misused. Cybersecurity alone is not going to be enough to secure our most sensitive data or our privacy. Data must be protected and enforced by technology itself, not just by cyber or regulation. The very technology compromising our privacy must itself be leveraged to bring real privacy to this data-driven age”—Rina Shainski, Co-founder and Chairwoman, Duality Technologies

AI is a dual-use technology

“AI-driven chatbots will go rogue. In 2019, cyber criminals and black hat hackers will create malicious chatbots that try to socially engineer victims into clicking links, downloading files or sharing private information. A hijacked chatbot could misdirect victims to nefarious links rather than legitimate ones. Attackers could also leverage web application flaws in legitimate websites to insert a malicious chatbot into a site that doesn’t have one. In short, next year attackers will start to experiment with malicious chatbots to socially engineer victims. They will start with basic text-based bots, but in the future, they could use human speech bots to socially engineer victims over the phone or other voice connections”—Corey Nachreiner, CTO, WatchGuard Technologies

“While next-gen technology like Artificial Intelligence (AI) and Machine Learning (ML) are transforming many enterprises for the better, they’ve also given rise to a new breed of ‘smart’ attacks. The ability to scale and carry out attacks is extremely enticing to cybercriminals, including use of intelligent malware. The rise in next-gen threats means that security professionals must be extra vigilant with detection and training against these threats, while also adopting new methods of automated prevention methods”—John Samuel, Senior Vice President and Global Chief Information Officer, CGS

“Cyber defenders have been researching and working on their machine learning/AI/deep Learning for a long time. We expect over the next 5 years that these technologies will also empower adversaries to create more powerful and elusive attacks through a new generation of tools, tactics and procedures. While AI/ML-savvy offensive cybercriminals are in their infancy, this is like any other business. They will invest in whatever provides them the greatest return. Unlike defenders, those on the offense are willing to collaborate and share innovation freely, which could increase rapid development and innovation”—David Capuano, CMO and VP Sales, BluVector

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Article Credit: Forbes

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Cyber security: Hackers step out of the shadows with bigger, bolder attacks

How AI is Revolutionising the Finance Industry for the Better

Image: Pixabay

Since John McCarthy first coined the term artificial intelligence (AI) back in 1956, things have come an awful long way. AI is the intelligence and problem-solving attributes demonstrated by machines rather than humans. These machines utilise their cognitive functions to pinpoint and overcome issues. Today, AI is helping to undertake tasks in the financial industry far better than most humans. It is helping to change the face of the financial sector, expanding the boundaries of what is possible today and in the years to come.

Fraud detection

In all types of financial businesses, organisations are seeking to minimise the risks involved; that goes for even the largest high-street banks too. For example, high-street banks and financial institutions are increasingly looking to AI when it comes to enhancing their identification of fraudulent activity within client accounts. Financial fraud can result in a hefty hit to a bank’s reputation and integrity, so many are investing in AI fraud detection technologies that review past spending and detect transactions that are beyond the norm.

The beauty of AI systems such as these is that they are always learning. In the event an AI fraud detection system highlights a potentially fraudulent cash withdrawal and it is later deemed legitimate, the system learns from the scenario to allow it to make more accurate decisions in the future.

Risk assessments

Image: Pixabay

The very essence of AI is that it learns from past data to make more informed, accurate decisions in the future. It’s therefore unsurprising that AI now plays a major role in the risk assessment of credit applications. Lenders now prefer the bespoke risk assessment functionality that AI affords them. When dealing with new credit applications, lenders can use AI to assess the applicant’s repayment history, the number of active loans and place this alongside their annual salary to design a purpose-built lending plan that suits all parties. There is even talk of AI replacing credit scores, reshaping how consumers obtain loans.

AI has also helped to add a new dimension to the world of customer service in financial services. The advent of conversational AI, also known as ChatBots, has helped banks, lenders and other financial institutions to greatly improve their levels of customer satisfaction by providing swift, accurate answers to common questions and tapping in to vast amounts of data to provide customers with fast solutions that are much-needed in a society where everyone wants answers yesterday.

Financial trading

Finally, AI is also helping to improve the efficiency and automation of all types of financial trading, from forex and options trading through to futures trading and government bonds. Some of the largest hedge funds are now utilising AI to accurately predict market moves based on past patterns. Furthermore, they are also using AI to automate the execution of trades.

High-frequency trading is removing the emotion of placing trades in the financial markets, minimising the prospect of human trader error. The AI systems are being designed to execute trades in markets when the predefined market conditions are met. This can range from a set price or time, or even a predefined percentage loss or gain on an asset. AI does not have feelings or emotions and can execute trades reliably 24/7.

There is no doubt that AI is the future for the financial sector. In fact, it is already the present. AI is allowing many financial industries to cut costs and allow them to focus their investments on providing the best possible service to their clients.

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Could there be a Brexit dividend for the supply chain?

Currency fluctuations caused by worldwide geo-political events like Brexit and changing US trade policy may be being exploited by companies seeking to save money in their supply chain, a new report has found.

The Q3 2018 Global Supply Chain Risk Report, published today (4th December, 2018) by Cranfield School of Management and Dun & Bradstreet, investigates the level of perceived supply chain risk faced by European companies with international supplier relationships.

The report found foreign exchange risk* increased 4% across the Finance, Manufacturing, Services, Infrastructure, and Wholesale sectors during Q3, suggesting that buyers may be taking advantage of the opportunity to pay their international suppliers in different currencies to exploit currency exchange fluctuations and make cost savings.

The Euro to Pound Sterling exchange rate fluctuated more during Q3 than it has in 2018, between a peak of 0.91 and a low of 0.88 Pounds per Euro. The Euro to US Dollar exchange rate also experienced significant fluctuations in the last quarter between a peak of 1.18 and a low of 1.13 US Dollars per Euro.

Dr Heather Skipworth, senior lecturer in logistics, procurement and supply chain management at Cranfield, said: “Larger companies often hold the power in the buyer/supplier relationship, and decide whether to pay suppliers in local or foreign currency to maximise financial benefit.

“During the past quarter, exchange rates across the world have been in a state of fluctuation, partly due to trade issues – not just Brexit, but also the U.S. trade war with China, for example. It seems that buyers may increasingly be seeing an opportunity to use these fluctuations for their own ends.”

The Q3 report suggests companies in the financial services sector have increased their appetite for risk compared to those in manufacturing, infrastructure and wholesale.

Chris Laws, Head of Global Product Development – Supply & Compliance at Dun & Bradstreet, said: “The analysis of Dun & Bradstreet data shows that the biggest increase in risk exposure during Q3 was amongst financial services companies, with three out of the four risk metrics increasing, a trend that has been building throughout 2018.” 

Both global sourcing risk and foreign exchange risk increased by 8% during Q3 for Financial Services companies, and by 31% and 13% respectively since the start of the year. Supplier financial risk increased by 5% over the past three quarters, remaining at a steady high of 26.4% during Q3.

Meanwhile, the Wholesale Trade sector experienced the greatest reduction in risk exposure during Q3, with supplier criticality down by 9%, supplier financial risk by 6% and global sourcing risk by 6%. However, foreign exchange risk increased by 9% during Q3 to the highest level of all sectors, and by 24% since the start of the year.

The quarterly Global Supply Chain Risk Report uses four key metrics – supplier criticality, supplier financial risk, global sourcing risk and foreign exchange risk – to assess overall supply chain risk and provides businesses with a view of trends within their industry sector and the wider economy. By analysing trends by sector, the report highlights areas for monitoring and consideration in procurement decisions.

Analysis in the Q3 2018 report was carried out using proprietary commercial data supplied by Dun & Bradstreet, which included around 120,000 anonymous transactions between European buying companies and their suppliers located in more than 150 countries worldwide.

Notes:

*Foreign exchange risk is the percentage of unique buyer-supplier relationships where the buyer’s currency in the transaction is different from the supplier’s currency. A higher percentage indicates higher exposure to foreign exchange fluctuations.

Global sourcing risk is the percentage of unique buyer-supplier relationships where the supplier is in a country with country risk higher than 4 using the Dun & Bradstreet Country Risk scale, which ranks countries from 1 to 7 in terms of risk, where 1 is the lowest risk and 7 the highest.

Supplier financial risk is the percentage of unique buyer-supplier relationships where the supplier has a risk rating of 3 or 4 (higher-than-average risk or high risk) according to Dun & Bradstreet financial risk scales. This provides an overall indicator of risk from suppliers.

Supplier criticality is the percentage of unique buyer-supplier relationships where the buyer categorises the suppliers as critical or key. A larger number represents a greater perceived exposure to risks from the supply base.

The report authors Dr Heather Skipworth and Professor Emel Aktas from Cranfield School of Management, and Chris Laws, supply and compliance business expert from Dun & Bradstreet, are available for interview.

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Why Trust And Transparency Will Drive Success In Our Big Data And AI Fuelled World

A word of warning to those that infiltrate the content pipeline with information that’s not factual, because there’s heightened demand for new methods to distill the mountains of information we are presented with daily down to the unadulterated facts. People crave a way to cut through the opinions, marketing speak and propaganda to get to the truth. And technology just might be the solution we need to become data-driven decision-makers who objectively understand the information.

Big Data And AI

Big Data And AI- There are reasons why we struggle under the weight of fake or worthless content. Every 60 seconds, 160 million emails are sent, 98,000 tweets are shared on Twitter, 600 videos are uploaded to YouTube, and 1,500 blog entries are created. Nobody but a machine could keep up with it all.

Not only do we struggle to determine if politicians are telling us the truth, but marketers try to hook us up with all kinds of products that are just what we need because they are the better than the competition, the safest, the only one that will get you your desired results. The hyperbole is exhausting.

We have never experienced such a time when we have so much information and so many opinions thrown at us from so many angles. In response to our struggles, fact-checking organizations that are dedicated to dissect and analyze statements made by politicians and public figures now exist and are becoming increasingly visible.

As data continues to explode, the ability to rummage through it to find the truth required in a situation is essential. Consumers won’t be patient either. They want to find out anything they seek to know, and they want to know it now. Brands will have to respond with truth and transparency if they hope to remain competitive.

Businesses are beginning to respond to their customers’ demands for facts. The big data-driven, machine-learning tech that is rolling out gives customers the raw material needed to measure and quantify absolute, objective facts and then act based on those findings, rather than rely on opinions and gut instincts so common today.

New Model for Media

When Ev Williams, co-founder of Twitter and co-creator of Blogger, developed Medium it was to give the world an alternative communication platform where “anyone can express themselves.”  Anyone can also earn influence on Medium, and it’s a force for good where everybody can speak freely and exchange information and ideas.

Facebook’s Got Our Backs

After the 2016 U.S. presidential election opened up concerns regarding the proliferation of fake news that may have impacted the outcome of the race, Facebook has responded by working with four independent fact-checking organizations—Snopes, Politifact, ABC News, and FactCheck.org—to verify the truthfulness of viral stories. New tools that are designed to avert the spread of misinformation will notify Facebook users when they try to share a story that has been bookmarked as false by these “independent fact-checkers.”

Transparency of Reds and Whites

Alit Wine is leading the industry to “shine a light on the places that the wine industry doesn’t talk about,” founder Mark Tarlov says. One of those things that’s typically hush-hush in the industry is the how much each element of the winemaking process costs. But, not Alit Wine. The company sells wine directly to consumers, and they detail exactly how much each step of production costs for the wines they sell.

Big Brother in Reverse

Usually, we’re concerned about the scrutiny of the government into our own affairs. But, Contratobook helps citizens scrutinize the work of government and public officials. Launched in Mexico in 2016 by a group of anonymous hackers, the organization is an open-source platform that allows people to search, filter and comment on more than 1.6 million government bids and contracts dating back to 2002. For those citizens with a desire to do so, they can look at each entry’s details including contact values, involved parties and start date to detect irregular or inaccurate expenses.

Those brands and companies who build trust with their customer base via transparency and factual information that can be verified with data are expected to have the competitive edge in a world that has grown weary of the widespread dishonesty and misinformation that permeates our culture.

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Article Credit: Forbes

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Nine do’s and don’ts of big data

Do’s and don’ts of big data

Do’s and don’ts of big data- As AI becomes mainstream, big data is being applied to every aspect of business – but how do organisations extract value from it? How do you use it for key executive management decisions in the real world – and what are the pitfalls to avoid?

Don’t leave it to the data analysts

The days when ‘technology’could be left to the chief information officer are long gone. You can’t simply give the analytics team the information and ask them to tell you what the business should do next. Maybe, in the next iteration of AI, the analytics will be able to tell you what to sell, who to hire, how to build the website, how to market to your customers and even how to micro-manage the business – but they still won’t be able to tell you what business to be in and why. And, regardless how much data the CIO may have at her or his disposal, they may be no better than the guy at the bus stop when it comes to spotting external threats to the business and new opportunities, let alone how to respond to them. That means the CEO and senior management have to stay on top of what the data is telling them, lead the analytics team in addressing real business concerns, and work constantly to make sure all the data is being targeted at intelligent investment.

Don’t get lost in translation

Many companies now have teams of software engineers pulling the data in from ever-expanding sources, including social media, smartphones, sensors, payment systems and cameras. The question is what to do with it. How do you interpret it so you can put it to positive use, rather than just dig the database ever deeper? You need business analysts who can read the information and see how to use it to spot market opportunities, identify problems, come up with solutions and lead change. In other words, you need business ‘translators’ who may not be fully-fledged data scientists, but who are sufficiently proficient in analytics to take the numbers and know how to apply them for the benefit of the business. And there aren’t many around just yet, so it will likely mean identifying the people in the company who understand the global picture of the business (and who may also have a quantitative background) and getting them up to speed on the analytics side. You also need to invest in training them in terms of leadership skills, so they feel confident in championing change throughout the organisation.

Don’t drown in a sea of data

There is a natural urge to want to capture every atom of the business’s legacy data, then cast around wandering what to do it. Resist the urge – identify how the data was acquired (is it from sales, operations, social media, ‘open’ sources or elsewhere?), have specific business applications in mind, and make sure your data strategy connects directly to the analytics. This means, above all, avoiding the temptation to build complex models from the get-go. Instead of trying to capture all historic information, decide the business priorities – or even one overriding priority – then identify what data is likely to be useful in addressing it, and add to it gradually. This will enable you to develop a sound process and practice – in effect, good data governance – which can then be augmented and refined by linking new and different datasets to yield new insights.

Know where to start

Following on from this, begin by identifying the most promising sources of value to the business. That means developing an organic view of opportunities and pinpointing those components of the value chain with the greatest potential. Is it inventory optimisation or product development? The next step is to identify as many use cases as possible and look at how you can apply new data and techniques to them to generate new insights. Decide your order of priority, based on potential financial impact, suitability to the business, and likely speed of implementation.

Democratise that data

One of the most common reasons for lack of uptake of data analytics is that the people who can put it to best use lack meaningful access to it. Avoiding this pitfall requires a three-step strategy. First, it means making sure that the data is accessible to as many people as possible, so dispense with any organisational hierarchies that may impede access. Second, you need to drive consensus on the validity of the data, so there is agreement that it is, in effect, a single source of ‘truth’ for the business. Third, building on equal access, you need to develop an egalitarian culture whereby everyone is allowed to ‘play’ with the data without fear or favour and try to generate new ideas – or kill established company nostrums that are no longer fit for purpose – in such a way that their ideas, however counterintuitive,  will be given equal airtime when it comes to decision making.

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Article Credit: Qrius

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How blockchain can make data secure for companies

Analysing and handling massive amounts of data is a tedious task but blockchain can help add another layer of protection to the big data analytics process.

Blockchain data

Blockchain data- We have already started living in a Big Data generation. Every day, massive volumes of data are generated by different networks in different industries. This large amount of data includes the records, messages sent and received, uploaded videos, GPS signals, online data transactions, and many other sources. For some top valued industries such as healthcare, educational and banking, high-quality security and maintenance should be provided to protect this data from malware or spy. Regular analysis of this large data leads to more innovation and growth for every economic aspect. Organisations analyse the data for an optimised client engagement and individual decision-making process, which allows them to predict future trends among the others.

Analysing and handling such massive amounts of data could be tedious. This is where blockchain comes into play. Blockchain helps to add another layer of protection to the big data analytics process. Blockchain-generated big data is more secure and valuable due to the network architecture by making it a perfect match for further analysis. Blockchain technology supports the bitcoins, but it can also lead to some great solutions for big data.

Blockchain can be applied to any industry no matter the domain concerned with the data. Organisations hold a ledger for the records of transactions made out and so the blockchain helps form a distributed database to reach through consensus mechanisms. The blockchain is immutable, verifiable and traceable as it makes use of the hash algorithm and timestamp for transforming the low-quality data. Blockchain technology eliminates garbage data, giving higher quality and increments security too. Therefore, if applied correctly, blockchain can be the catalyst for better data resulting in better outcomes.

Data analytics and blockchain

The first obvious space to apply blockchain to big data is finance, as every transaction offers a level of anonymity and data is not kept private. It is possible to find different patterns for transactions for Bitcoin transactions by linking those to people. Multiple organisations have started working on such solutions, and it has helped avoid crime. But beyond finance, Big Data is a digital database technology that can be applied to any industry.

Today, e-commerce businesses have started to implement blockchain by storing granular transaction data to understand its interactions with producers and consumers. Retail giants like Walmart are also using blockchain to increase food safety by increasing traceability and managing daily data to ensure its reliability. Blockchain also has immense potential in the healthcare industry where the bulk of data is generated by the reports and case studies that are private and confidential. Google DeepMind and NHS are joining hands in the UK to use blockchain for storing encryption-free patient data. It will be used to create a verifiable data audit ensuring that the data used for research purpose has appropriate permissions.

How blockchain is improving Big Data

Estonia

Despite the fact that blockchain has just turned into a hot innovation as of late, Estonia has been trying the blockchain innovation since 2008. Since 2012, blockchain has been underway used in Estonia’s information vaults, for example, the national wellbeing, legal, administrative, security and business code frameworks, with plans to stretch out its utilization to different circles, Estonia intends to be the most advanced digital society in the world based on blockchain specially designed for government officials. It is assumed to be using the Keyless Signature Infrastructure (KSI) to store public data in a secure manner. KSI helps to monitor changes in the database hence ensuring that the data is transparent, which helps tracking down tampered or duplicate records. Also, blockchain makes it difficult for any third party to tamper with the data internally, which ultimately reduces fraud and corruption and increasing efficiency

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Article Credit: YS

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Using big data to predict the future

Advances with machine learning and big data analytics are helping researchers, and ultimately companies, to make predictions about future trends by analysing patterns. A new application looks at medicine.

Big data future

Big data future- Researchers from the University of Córdoba have been exploring how large volumes of data can be organized, analyzed and cross referenced to predict certain patterns. This forms part of the process commonly referred to as ‘big data analytics’. The focus of the review was on predicting the response to specific examples, such as medical treatments, operational improvements for smart buildings, and even the behavior of the Sun. Each prediction process is based on the input of key variables.

In assessing how effective big data analysis is, the researchers set out to improve models that are intended to predict several variables simultaneously based on the same set of input variables. The aim here was to find ways to reduce the size of data necessary for the forecast to be exact, thereby speeding up the data analysis process.

Central to the optimization process is with filtering out background ‘noise’ and eliminating those variables which are not significant to the overall assessment of the purpose of the analysis.

The researchers developed a new technique that can inform the person responsible for the analysis which examples are required so that any forecast made is not only reliable but can be enhanced to deliver the most accurate result.

The technique was a type of multi-output regression model. These are categorized as problem transformation and algorithm adaptation methods. Multiple-output regression models require estimating multiple parameters, one for each output.

As a consideration, the research group looked at a method that predicts several parameters connected to soil quality. This was based on a set of data variables such as the types of crops planted, tillage (preparation of the land) and the use and types of pesticides. By applying the new model, the amount of data inputs required to deliver a prediction about crop growth was reduced.

In all eighteen different databases were examined, and by applying the new approach the researchers were able to reduce the amount of information by 80 percent without affecting the predictive performance. This led to less than half the original data being used, and far faster responses being gathered.

Commenting on the study, lead researcher Dr. Sebastian Ventura said: “When you are dealing with a large volume of data, there are two solutions. You either increase computer performance, which is very expensive, or you reduce the quantity of information needed for the process to be done properly.”

The research has been published in the journal Integrated Computer-Aided Engineering. The research paper is called “An ensemble-based method for the selection of instances in the multi-target regression problem.”

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Article Credit: Digital Journal

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Smart Home Facts and Stats

Smart homes are on the rise and the technology and products that make them are becoming more popular and available worldwide. What exactly is a smart home? A smart home is a residence that uses devices that are connected to the internet that allow us to monitor and manage appliances and systems, such as heating, lighting and home security systems. The word “SMART” doesn’t come from the fact that this technology makes our homes more intelligent (although that is the intention), but actually stands for Self-Monitoring Analysis and Reporting Technology, and was originally developed by IBM.

Smart homes are not a new concept, in fact the first smart home device was product in the late 1960s. The device was a kitchen computer called the ECHO IV, which was able to compute shopping lists, control the home’s temperature and turn other appliances on and off. Although the ECHO IV was never commercially sold, it set the foundation for the technology to follow. In the 1980s programmable thermostats and home computing were created by Roy Mason. You could monitor your health, cooking, weather and more with his products. By the late 1980’s and early 2000s, smart products were making their way onto store shelves which has lead us to our current way of living. As of 2015, the most popular smart home product in the US, was wireless speaker systems, with 17% of people owning one or more of these.

Today our smart homes are sustainable and are ensuring that we aren’t wasting too much unnecessary energy which has a direct impact on the environment. There are many devices and appliances freely available in most stores for us to use in our smart homes, these may include: Wireless speaker systems, thermostats, domestic robots, laundry machines, water detectors, home energy use monitors and door locks.

The year 2018 has marked the rise in all-in-one devices that combine smart speakers, camera’s, hubs, lighting and WiFi. Toshiba, Honeywell, Amazon Echo Plus and Hogar Controls are some of the leading products that you can find in stores for your smart home today. These products and devices not only make our lives more convenient, but also helps to decrease the amount of money we spend on energy and on other appliances.

There are some great benefits that come with having a smart home, one of the top ones being home security. Safety and security affects every person across the world, having a smart home ensures that you have added peace of mind whether you are inside your home, or away on vacation. It is estimated that home security will account for over $100 Billion of the total security market by 2020, and that 62% of Americans agree that security is the top benefit of smart homes. Home security systems include cameras that you can monitor via your smart device, smoke and carbon monoxide detectors, as well as locks and alarms. 3 out of 5 Americans are said to buy smart home products in order to monitor their home via their smartphone.

There are many impacts that a smart home can have on our lives. There are already 47% of Americans who own smart devices, while 70% of people who already own smart products say that they are planning to buy another one. 76% of home products are already being controlled or monitored by smartphones. The impact that smart devices can have is huge when it comes to money, energy and time saving. It is estimated that more than half of the American population say that smart products save them at least 30 minutes per day which equals to roughly a week and a half per year. Think of all of the extra things you could do with that time everyday.

Smart products is also making an impact on the housing industry. 81% of home buyers say that they would rather buy a home that already has smart products installed, which is massive. If you aren’t on the smart home train, it’s time to think about climbing on soon as this technology grows.

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ACTEBIS: Faster Order Fulfillment at IT and Telecommunications Distributor with SAP Central Process Scheduling

A manufacturer’s guide to thriving in a Hard-Brexit Britain

With Brexit negotiations in full force, British manufacturers are actively planning to maximise the opportunities that lie before us and mitigate the challenges.

Here, Ian Dowd, CMO at SSG Insight, draws on extensive research involving manufacturing executives across the UK to share his thoughts on how the industry can seek to retain a competitive advantage:

1. Understand your supply chain exposure: With almost half of suppliers to UK manufacturing companies coming from countries within the EU, it’s no surprise that one in six (17 per cent) of UK-based manufacturers have identified supply chain disruption as being the single biggest factor that will impact their business post-Brexit. It’s imperative that businesses ensure the necessary precautions have been put in place.
2. Consider broader opportunities overseas: 81 per cent of British manufacturers expect orders from the continent to reduce following Brexit. In attempts to limit the potential blow this may cause, the clear majority (83 per cent) have confirmed that they are now actively forging new relationships with ‘rest of the world’ territories.
3. Standards, legislation and trade barriers: The impact of changes to EU subsidies, research and development grants and other financial incentives may be harmful to smaller manufacturing businesses. Entry barriers are also a major concern amongst manufacturers. One in five expect that trade tariffs on imported and exported goods will impact their business.
4. Understand the industry impact: Ahead of the UK leaving the EU and the changes that this will bring, manufacturers have signalled that increased global competition is the biggest challenge that they face today. This may continue to grow as a threat after March 2019, knocking manufacturing off as one of the UK’s top industries.
5. Focus on the bigger picture: Brexit and the critical challenges it brings will arguably be one of the most disruptive points in manufacturing history, however it is not the only major change affecting the sector. Industry 4.0 is arguably a bigger turning point – and our research suggests that over a third of manufacturing in Britain will be automated in the next three years. Whilst staying up-to-date with technological advancements can cause major implementation challenges, if used correctly companies can stay ahead of the curve by using AI and automation technology to achieve ambitious global growth plans and compete more successfully at this scale.
6. Spend wisely: With the economic climate constantly changing and advanced technology becoming more accessible, the key area for investment post-Brexit is expected to be big data analysis, AI and automation. With budgets under pressure, manufacturers should invest in new technology to sustain a competitive advantage against competitors.
7. Know your talent pool: A hard Brexit and new laws surrounding immigration may lead to all industries in Britain suffering from a major skills shortage. More than a third (37 per cent) of decision makers in manufacturing expect recruiting the right talent to be harder after March 2019. Employers should start planning ahead now to ensure that their workforce is digitally trained and can keep up with increased competition – driving the industry forward in this defining digital era.

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