Cell networks have the advantage of long-range communications, reaching an estimated 90% of the world. But cell networks such as 2G, 3G and LTE consume lots of power and were designed for connecting people. They are not optimized for low- or battery-powered devices or for IoT applications with infrequently transmitted data. Cell IoT modules that support narrow-band IoT and 4G cell networks will enable cell connectivity, device management, and app enablement for low-power wide-area network IoT. By using cell IoT modules, telecom service providers, mobile network operators can more quickly and easily develop and deploy IoT that use cell communications.

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They say multi-cloud is coming, but organizations are leveraging multiple clouds already. According to a study by 451 Research, only 21% of organizations were using a single cloud. If you’ve found yourself unprepared for the barrage of cloud services introduced in your organization, you will need to change your approach to engaging with the business and engaging with vendors. Look at technologies that are on the way and work with the internal players involved to have a plan in place when the inevitable happens and the business begins to look at how these things can help affect your bottom line.

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The hierarchical architecture that distributes «compute» within the network specially at the edge can enable new services by harnessing emerging technologies. But Edge-Compute comes at increased cost that needs to be managed and potentially augmented by creative architecture solutions as there will always a catching-up with the capacity demands. Processing power in smartphones has enhanced YoY and there is increasingly spare compute capacity that can be potentially pooled. Uber has successfully been able to harness the excess capacity of privately owned vehicles and turned into a meaningful business. This concept can be step-functioned to harnessing the spare compute capacity of smartphones that can be orchestrated by MEC to provide cloud service at the edge.

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Clare Hopping

With more than 30 Kubernetes solutions in the marketplace, it’s tempting to think Kubernetes and the vendor ecosystem has solved the problem of operationalizing containers at scale or of automatically managing the elasticity of the underlying infrastructure that these solutions need to be truly scalable. Far from it. There are at least six major pain points that companies experience when they try to deploy and run Kubernetes in their complex environments. In this presentation, the speaker will detail these pain points and explain how cloud can address them.

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The financial sector, while forging ahead in other areas of digital transformation, has been relatively slow to adopt the cloud and there has been good reason for it: banks have to deal with highly sensitive data and sharing data storage and compute resources with others could not even be envisaged, let alone adopted.

However, just under two years ago, the Financial Conduct Authority (FCA) published a new guidance for firms outsourcing to the ‘cloud’ and other third-party IT services which paved the way for banks, insurers and other financial services companies to take advantage of cloud computing services. In this new guidance, the regulator outlined that there was no fundamental reason why cloud services (including public cloud services) couldn’t be implemented, subject to compliance with specific guidance for financial firms outsourcing to the cloud and other third-party IT services.

According to Celent, financial services firms will progressively abandon private data centres and triple the amount of data they upload to the cloud in the next three years. Because of the huge and increasing amounts of data financial services firms need to manage, the scalability of cloud has become an attractive feature – especially considering the fact that the number of daily transactions can stretch into the millions. On top of that, the volume of transactional data is not always predictable, so financial institutions must be able to scale up quickly on demand.

While scalability is an attractive aspect of the cloud for financial firms, it’s important to evaluate scalability in conjunction with other key elements of cloud services including security, cost-effectiveness and transparency.

Combining scalability with security

Security is a key concern for banks as they deal with highly sensitive data and increasing regulations around data privacy, particularly with the EU General Data Protection Regulation (GDPR) coming into force in May, 2018. Even if they are fully responsible for clients’ data security, their cloud services provider (CSP) will maintain the security of the cloud infrastructure their apps and data are hosted on. Therefore, the scalability benefits of cloud must be combined with security features that measure up to on-premises levels of cloud security. The good news is that some cloud providers have significantly improved  security offerings, the best of which have security features such as data encryption, vulnerability scanning, intrusion detection and more baked into the cloud platform and offer full reporting on security and compliance elements which financial services firms increasingly need for auditing purposes.  

What used to discourage the financial sector from adopting the cloud is now what’s appealing to it. Banks who take advantage of cloud computing often actually benefit from stronger security safeguards than they are able to invest in for on-premises IT infrastructure. The cloud is certainly more secure than many legacy platforms, so if financial organisations choose the right cloud services provider, they can actually experience a higher level of security than they would via legacy solutions.

However, there’s no question that we’re seeing a rise of cloud-based malware and, according to Palo Alto Networks, 70% of cybersecurity professionals working in large organisations in the UK say the rush to the cloud is not taking full account of the security risks. Even more worrying, the survey reveals that only 15% of UK security professionals were able to maintain consistent, enterprise-class cyber security across their cloud networks and endpoints. Add to this the fact that financial services companies need to scale up quickly in an increasingly regulated environment and you’ll understand why financial firms need to pay careful attention to cloud security and compliance credentials. Choosing a cloud services provider with advanced security features is vital to financial institutions and can help them to report on the security of all of their workloads in the cloud to pass compliance audits.

Combining scalability with cost-effectiveness

Another essential factor for the financial sector when adopting cloud is of course cost. The annual IT spend for global capital markets keeps increasing and, while cloud computing promises many economic benefits, these can only be realised when there’s a good match between cloud workloads and cloud resource utilisation. Cloud computing has the potential to save the industry billions of pounds, as the volume of transactional data increases and the cost of information security escalates in an increasingly complex threat environment.

Some cloud providers, such as iland, enable customers to scale their reserved cloud resources to exactly the amount of GB required. The billing is then determined based on actual compute usage and so customers only pay for what they use. This ensures that customers always have the cloud capacity available, without having to pay for more than what they need. This is far more cost-efficient than provisioning on-premises equipment for maximum workloads and having it lie idle for much of the time.

Combining scalability with transparency

Financial services firms are also seeking transparency into the policies and processes as well as operations of their cloud provider. In recognition of the flexible and collaborative nature of cloud service providers, the new EBA guidance launched a few months ago sets out the terms and processes under which chain outsourcing – a cloud provider outsourcing an element of its provision to a third party – is acceptable. As with most aspects of the guidance, strong emphasis is placed on ongoing risk management and transparency between the CSP and financial organisation. 

Throughout all aspects of the EBA guidelines, it is abundantly clear that the relationship between financial organisations and their CSPs needs to be extremely close and transparent, and conducted at a senior level. Verifiable trust through certification is the linchpin of the whole relationship and the partnership will be dysfunctional (and potentially inviable) without this cornerstone in place. Fortunately, this kind of transparency and commitment to open partnership has been built into the DNA of some cloud providers from the outset. iland, for example, has a dedicated compliance team that focuses on helping customers provide continuous monitoring and evidence of compliant cloud services to regulators.

Overall, financial services firms should not be tentative in taking a step to the cloud; the investment in time and budget in building and managing IT infrastructure can be dramatically reduced and the on-demand scalability benefits are particularly important in this sector. Cloud providers have significantly developed their security capabilities and can offer dedicated, sector-specific support through cloud migration and management. The publication of guidance from regulators at the FCA and EBA, plus the expertise that CSPs have developed for the financial services sector should  give financial services firms more confidence in the cloud and encourage them to fully embrace its possibilities and benefits.

Daniel Booth

Of the trillions of searches Google processes every year, 15% are brand new. Nobody has ever typed those specific queries before. It’s unlikely they’ll produce fake search results because they’re not common enough to attract the attention of fraudsters. Scammers aim big, aiming to exploit search terms that are typed millions of times every day. To avoid those traps, make sure you never search for any of the following.

Software that’s been discontinued

When companies stop supporting software and remove download links to it, scammers see a chance to con people who still want to use the program. Search for it on Google and you’ll see results promising to provide a download, but it’s often malware.

Sometimes the link does download the genuine program, but charges a fee when previously it was free. That was the tactic used late last year by fraudsters tempting searchers with Windows Movie Maker, which Microsoft stopped offering in October. Click the link in the search result (see screenshot) and you’ll be charged $29.95 to unlock the «full version». It’s a crafty con that preys on people’s natural desire to stick with software they’ve got used to.

Tech support for Microsoft

Many tech-support scammers actively target you over the phone. They claim to be calling from Microsoft, and declare in a doom-laden voice (often with an Indian accent) that your computer is riddled with malware. The ‘miracle’ cure is to give them control of your computer. Once in, they waste no time stealing your sensitive info, such as banking passwords.

Other ne’er-do- wells set a trap on Google, waiting for naive searchers to fall in. Type ‘Microsoft tech support’ and typically one of the top paid-for results is for GuruAid (see screenshot). Its headline sounds promising: ‘Tech Support for Microsoft – Call Now (UK Toll Free) – UK.com‎’. At first glance, that sounds like official support for Microsoft products. Click through to their (very ugly) website and you’re asked to phone a number for «toll-free assistance». But instead of contacting UK experts, you’ll reach workers at an Indian call centre who’ll subject you to the hardest of hard sells, pestering you to cough up hundreds of pounds to fix minor problems.

GuruAid has been around for some time. Back in 2011, someone on the Microsoft Forums asked whether they could trust the company. Complaints last year show it’s as awful as ever. For genuine help from Microsoft, bookmark its support page or ring 0344 800 2400.

Charities’ phone numbers

We’re not saying you should stop donating money over the phone, but searching Google for the correct number is fraught with risk. In November last year, the RSPCA complained to Google and Ofcom about sites appearing in results that were advertising expensive premium-rate phone numbers for the charity. People calling the number are put through to the RSPCA’s National Control Centre, so they may not realise they’ve been scammed.

The RSPCA said it found eight rogue sites in the first 10 pages of results. It’s hard to remove such sites completely from results, but the charity asked Google to make sure they don’t appear too highly.

As this proves, scammers don’t just abuse people’s curiosity and confusion; they also take advantage of their kindness. Only phone charity numbers that you find on the official websites.

The web’s most popular sites

In 2016 Google removed 1.7bn fraudulent adverts from its results (2017 figures haven’t been released yet), but some still sneak through, particularly those for the world’s most popular sites. Amazon was a favourite of criminals last year, and they’re very good at making the scams seem convincing. In February, the top paid-for result when searching for ‘Amazon’ was ‘www.amazon.com/Amazon‘. Cleverly constructed to fool even careful clickers, this link directed people to a Windows-support scam.

The scam reappeared in November, a few days before the Black Friday sale frenzy began. The greedy fraudsters were hoping to steal some of the millions spent by shoppers. Some people also spotted a similar scam using YouTube search results.

Scammers will always exploit the popularity of such sites, so bookmark them if you’re likely to visit them regularly. That applies not just to Amazon and YouTube, but also eBay, Facebook, Wikipedia, Yahoo and Netflix.

Local services on Google Maps

This is the kind of scam that makes you mourn the passing of the Yellow Pages. In April last year Google said it would crack down on criminals listing fake businesses in its Maps service. This scam peaked in 2014-15, when Google detected 100,000 fake listings.

The company said fraudsters were posing as «locksmiths, plumbers, electricians, and other contractors». When customers phone them, they are quoted a very cheap price. But when the work is done, they are charged a much higher fee.

Google now says it detects and disables 85% of fake listings before they even appear on Google Maps. And to check that a new listing is genuine, it sends a postcard to the company’s claimed address. When a business has new owners, Google phones them to verify the change.

While these measures are reassuring, nothing beats word of mouth when booking a local service. A friend’s recommendation can’t be hacked.

Bitcoin investment advice

Scammers love it a fad becomes a hysteria, because they know it makes people less cautious when clicking results in Google. And nothing generates more hysteria at the moment than Bitcoin, the cryptocurrency which rocketed 1,000% in value last year.

There’s nothing wrong with searching Google for more information about Bitcoin, or the Blockchain technology behind it. The safest option is to type a question, such as ‘What is Bitcoin?’ You’ll have a choice of hundreds of safe websites, all explaining how it works and examining (ie, speculating) whether it’s a real investment opportunity or a bubble waiting to burst. Here’s our own take on both Bitcoin and Blockchain, available on our sister site IT Pro. Importantly, nobody really knows – and don’t trust anyone who says they do.

But what you shouldn’t search for is ‘Bitcoin investment advice’, or a similar phrase. This will produce many dubious get-rich-quick schemes guaranteeing a crypto-fortune. Don’t get sucked in.

Cloud Pro

Google is set to acquire enterprise cloud migration provider Velostrata, citing the Israel-based company’s ‘technical strength’ as key alongside potential capabilities for their customers around advanced data analytics and machine learning.

Velostrata’s primary goal is to speed up enterprise’s cloud transitions through what it calls ‘real-time agentless workload streaming’ – in other words, decoupling compute from storage without an effect on performance. Theoretically, workloads can transfer to the public cloud in minutes, whether migrating entire data centres or working in a hybrid cloud environment.

The two companies had worked together previously; as recently as April, Velostrata announced it supported migrations into Google Cloud Platform, promising time savings for GCP customers of up to five hours per server during migrations.

From Google’s perspective, it is yet another sign of the company’s strong cloud bet, with the size and scope of its customers changing. The company’s path has long been charted by this publication, with CEO Sundar Pichai telling analysts last month Google’s cloud arm was securing ‘significantly larger, more strategic deals.’

As organisations pay more for Google’s services – Spotify, for instance, is set to spend €365 million on Google Cloud over three years, as revealed by a share sale document following its IPO – performance is key, as Eyal Manor, VP engineering, explained.

“As more and more enterprises move to the cloud, many need a simple way to migrate from on-premises and adopt the cloud at their own pace,” wrote Manor in a blog post. “With Velostrata, Google Cloud customers obtain two important benefits: they’ll be able to adapt their workloads on-the-fly for cloud execution, and they can decouple their compute from storage without performance degradation.

“This means they can easily and quickly migrate virtual machine-based workloads like large databases, enterprise applications, DevOps, and large batch processing to and from the cloud,” Manor added. “On top of that, customers can control and automate where their data lives at all times – either on-premises or in the cloud – in as little as a few clicks.”

“Over the years, Google Cloud has made significant investments in building a robust global cloud infrastructure that delivers industry-leading availability, reliability and security,” wrote Issy Ben-Shaul, Velostrata CEO and co-founder in a blog post confirming the news. “Google Cloud continues to innovate with advanced compute and services platforms. We are proud to join forces and help pave the way for enterprise customers to transform their most demanding enterprise workloads on Google Cloud Platform.”

Speaking to this publication at the end of last year around 2018 trends, Ben-Shaul noted how enterprises will ramp up their multi-cloud efforts. “The majority of enterprises that will migrate to cloud at scale will employ a multi-cloud strategy,” said Ben-Shaul. “More specifically, they will split their production workloads across more than one public cloud.

“Enterprises don’t want to be locked in,” he added. “If an enterprise can get significant cost reduction on infrastructure, this can mean millions of dollars in savings a year.”

• Hiring companies nationwide miss out on 50% or more of qualified candidates and tech firms incorrectly classify up 80% of candidates due to inaccuracies and shortcomings of existing Applicant Tracking Systems (ATS), illustrating how faulty these systems are for enabling hiring.
• It takes on average 42 days to fill a position, and up to 60 days or longer to fill positions requiring in-demand technical skills and costs an average $5,000 to fill each position.
• Women applicants have a 19% chance of being eliminated from consideration for a job after a recruiter screen and 30% after an onsite interview, leading to a massive loss of brainpower and insight every company needs to grow.

It’s time the hiring process gets smarter, more infused with contextual intelligence, insight, evaluating candidates on their mastery of needed skills rather than judging candidates on resumes that reflect what they’ve achieved in the past. Enriching the hiring process with greater machine learning-based contextual intelligence finds the candidates who are exceptional and have the intellectual skills to contribute beyond hiring managers’ expectations. Machine learning algorithms can also remove any ethic- and gender-specific identification of a candidate and have them evaluated purely on expertise, experiences, merit, and skills.

The hiring process relied on globally today hasn’t changed in over 500 years. From Leonardo da Vinci’s handwritten resume from 1482, which reflects his ability to build bridges and support warfare versus the genius behind Mona Lisa, Last Supper, Vitruvian Man, and a myriad of scientific discoveries and inventions that modernized the world, the approach job seekers take for pursuing new positions has stubbornly defied innovation. ATS apps and platforms classify inbound resumes and provide rankings of candidates based on just a small glimpse of their skills seen on a resume. When what’s needed is an insight into which managerial, leadership and technical skills & strengths any given candidate is attaining mastery of and at what pace.  Machine learning broadens the scope of what hiring companies can see in candidates by moving beyond the barriers of their resumes. Better hiring decisions are being made, and the Return on Investment (ROI) drastically improves by strengthening hiring decisions with greater intelligence. Key metrics including time-to-hire, cost-to-hire, retention rates, and performance all will improve when greater contextual intelligence is relied on.

Look beyond resumes to win the war for talent

Last week I had the opportunity to speak with the Vice President of Human Resources for one of the leading technology think tanks globally. He’s focusing on hundreds of technical professionals his organization needs in six months, 12 months and over a year from now to staff exciting new research projects that will deliver valuable Intellectual Property (IP) including patents and new products.

Their approach begins by seeking to understand the profiles and core strengths of current high performers, then seek out matches with ideal candidates in their community of applicants and the broader technology community. Machine learning algorithms are perfectly suited for completing the needed comparative analysis of high performer’s capabilities and those of candidates, whose entire digital persona is taken into account when comparisons are being completed. The following graphic illustrates the eightfold.ai Talent Intelligence Platform (TIP), illustrating how integrated it is with publicly available data, internal data repositories, Human Capital Resource Management (HRM) systems, ATS tools. Please click on the graphic to expand it for easier reading.

The comparative analysis of high achievers’ characteristics with applicants takes seconds to complete, providing a list of prospects complete with profiles. Machine learning-derived profiles of potential hires meeting the high performers’ characteristics provided greater contextual intelligence than any resume ever could. Taking an integrated approach to creating the Talent Intelligence Platform (TIP) yields insights not available with typical hiring or ATS solutions today. The profile below reflects the contextual intelligence and depth of insight possible when machine learning is applied to an integrated dataset of candidates. Please click on the graphic to expand it for easier reading. Key elements in the profile below include the following:

• Career growth bell curve: Illustrates how a given candidate’s career progressions and performance compares relative to others.
• Social following on public sites: Provides a real-time glimpse into the candidate’s activity on Github, Open Stack, and other sites where technical professionals can share their expertise. This also provides insight into how others perceive their contributions.

• Highlights of background that is relevant to job(s) under review: Provides the most relevant data from the candidate’s history in the profile so recruiters and managers can more easily understand their strengths.

• Recent publications: Publications provide insights into current and previous interests, areas of focus, mindset and learning progression over the last 10 to 15 years or longer.

• Professional overlap that makes it easier to validate achievements chronicled in the resume: Multiple sources of real-time career data validate and provide greater context and insight into resume-listed accomplishments.

The key is understanding the context in which a candidate’s capabilities are being evaluated. And a 2-page resume will never give enough latitude to the candidate to cover all bases. For medium to large companies – doing this accurately and quickly is a daunting task if done manually – across all roles, all the geographies, all the candidates sourced, all the candidates applying online, university recruiting, re-skilling inside the company, internal mobility for existing employees, and across all recruitment channels. This is where machine learning can be an ally to the recruiter, hiring manager, and the candidate.

Five reasons why machine learning needs to make resumes obsolete

Reducing the costs and time-to-hire, increasing the quality of hires and staffing new initiatives with the highest quality talent possible all fuels solid revenue growth. Relying on resumes alone is like being on a bad Skype call where you only hear every tenth word in the conversation. Using machine learning-based approaches brings greater acuity, clarity, and visibility into hiring decisions.

The following are the five reasons why machine learning needs to make resumes obsolete:

Resumes are like rearview mirrors that primarily reflect the past: What needed is more of a focus on where someone is going, why (what motivates them) and what are they fascinated with and learning about on their own. Resumes are rearview mirrors and what’s needed is an intelligent heads-up display of what their future will look like based on present interests and talent.

By relying on a 500+-year-old process, there’s no way of knowing what skills, technologies and training a candidate is gaining momentum in: The depth and extent of mastery in specific areas aren’t reflected in the structure of resumes. By integrating multiple sources of data into a unified view of a candidate, it’s possible to see what areas they are growing the quickest in from a professional development standpoint.

It’s impossible to game a machine learning algorithm that takes into account all digital data available on a candidate, while resumes have a credibility issue: Anyone who has hired subordinates, staff, and been involved in hiring decisions has faced the disappointment of finding out a promising candidate lied on a resume. It’s a huge let-down. Resumes get often gamed with one recruiter saying at least 60% of resumes have exaggerations and in some cases lies on them. Taking all data into account using a platform like TIP shows the true candidate and their actual skills.

It’s time to take a more data-driven approach to diversity that removes unconscious biases: Resumes today immediately carry inherent biases in them. Recruiter, hiring managers and final interview groups of senior managers draw their unconscious biases based on a person’s name, gender, age, appearance, schools they attended and more. It’s more effective to know their skills, strengths, core areas of intelligence, all of which are better predictors of job performance.

Reduces the risk of making a bad hire that will churn out of the organisation fast: Ultimately everyone hires based in part on their best judgment and in part on their often unconscious biases. It’s human nature. With more data the probability of making a bad hire is reduced, reducing the risk of churning through a new hire and costing thousands of dollars to hire then replace them. Having greater contextual intelligence reduces the downside risks of hiring, removes biases by showing with solid data just how much a person is qualified or not for a role, and verifies their background strengths, skills, and achievements. Factors contributing to unconscious biases including gender, race, age or any other factors can be removed from profiles, so candidates are evaluated only on their potential to excel in the roles they are being considered for.

Bottom line: It’s time to revolutionize resumes and hiring processes, moving them into the 21st century by redefining them with greater contextual intelligence and insight enabled by machine learning.