As enterprises are embracing the cloud, SD-WAN is more likely to be on the table for their technological roadmaps.

This is the primary finding from a survey conducted by analyst firm IHS Markit. The WAN Strategies North America study found almost three quarters (74%) of organisations polled conducted SD-WAN lab trials in 2017, with many of these moving into production trials and, eventually, live production, this year.

Investments in wide area networks more generally continue undimmed, with the adoption of the Internet of Things (IoT), company and traffic growth, and greater network control all cited. WAN bandwidth usage is expected to grow over 20% each year according to respondents, while total WAN expenditures rose almost 20%, to $300,000 per respondent, last year.

“As companies shift a greater portion of their IT infrastructure into the cloud and expand their physical presence to go after new markets or be closer to customers and partners, the need for reliable, secure and high-performance WAN and internet connectivity has never been greater,” said Matthias Machowinski, IHS Markit enterprise networks senior research director in a statement.

“However, companies don’t have unlimited budgets to fund growing WAN bandwidth consumption, which is why a majority are planning to deploy software-defined WAN in the next three years, to better control how their WANs are used,” Machowinski added.

The rise of SD-WAN has certainly been noted among industry watchers. Writing for this publication in February, Gayle Levin, director of solutions marketing at Riverbed Technologies, discussed the key steps and benefits of software-defined WAN in a cloud-first strategy.

“At the end of the day, cloud-first strategies are designed to provide the best possible user experience. The network is of limited value if the user experience is poor or inconsistent,” wrote Levin. “Effective SD-WAN tools provide deep visibility into application performance, network flows, congested areas, and which users devices are connected to the network, enabling IT to effectively deploy and manage their applications and the underlying infrastructure.

“Whether using leading IaaS offerings or something from the vast set of SaaS applications, software-defined WANs are essential to corporate agility and security,” Levin added. “Optimising applications by rapidly establishing and tearing down connections simply cannot be done without centralised network management and orchestration.

“Cloud first needs cloud networking.”

Find out more about the IHS Markit report here (client access).

• 80% of the positions open in the U.S. alone were due to attrition. On an average, it costs $5,000 to fill an open position and takes on average of 2 months to find a new employee. Reducing attrition removes a major impediment to any company’s productivity.
• The average employee’s tenure at a cloud-based enterprise software company is 19 months; in the Silicon Valley this trends to 14 months due to intense competition for talent according to C-level executives.
• Eightfold.ai can quantify hiring bias and has found it occurs 35% of the time within in-person interviews and 10% during online or virtual interview sessions.
• Adroll Group launched nurture campaigns leveraging the insights gained using Eightfold.ai for a data scientist open position and attained a 48% open rate, nearly double what they observed from other channels.
• A leading cloud services provider has seen response rates to recruiting campaigns soar from 20% to 50% using AI-based candidate targeting in the company’s community.

The essence of every company’s revenue growth plan is based on how well they attract, nurture, hire, grow and challenge the best employees they can find. Often relying on manual techniques and systems decades old, companies are struggling to find the right employees to help them grow. Anyone who has hired and managed people can appreciate the upside potential of talent management today.

How AI and machine learning are revolutionising talent management

Strip away the hype swirling around AI in talent management and what’s left is the urgent, unmet needs companies have for greater contextual intelligence and knowledge about every phase of talent management. Many CEOs are also making greater diversity and inclusion their highest priority. Using advanced AI and machine learning techniques, a company founded by former Google and Facebook AI Scientists is showing potential in meeting these challenges. Founders Ashutosh Garg and Varun Kacholia have over 6000+ research citations and 80+ search and personalization patents. Together they founded Eightfold.ai as Varun says “to help companies find and match the right person to the right role at the right time and, for the first time, personalize the recommendations at scale.” Varun added that “historically, companies have not been able to recognize people’s core capabilities and have unnecessarily exacerbated the talent crisis,” said Varun Kacholia, CTO, and Co-Founder of Eightfold.ai.

What makes Eightfold.ai noteworthy is that it’s the first AI-based Talent Intelligence Platform that combines analysis of publicly available data, internal data repositories, Human Capital Resource Management (HRM) systems, ATS tools and spreadsheets then creates ontologies based on organization-specific success criteria. Each ontology, or area of talent management interest, is customizable for further queries using the app’s easily understood and navigated user interface.

Based on conversations with customers, its clear integration is one of the company’s core strengths. Eightfold.ai relies on an API-based integration strategy to connect with legacy back-end systems. The company averages between 2 to 3 system integrations per customer and supports 20 unique system integrations today with more planned. The following diagram explains how the Eightfold Talent Intelligence Platform is constructed and how it works.

For all the sophisticated analysis, algorithms, system integration connections, and mathematics powering the Eightfold.ai platform, the company’s founders have done an amazing job creating a simple, easily understood user interface. The elegant simplicity of the Eightfold.ai interface reflects the same precision of the AI and machine learning code powering this platform.

I had a chance to speak with Adroll Group and DigitalOcean regarding their experiences using Eightfold.ai. Both said being able to connect the dots between their candidate communities, diversity and inclusion goals, and end-to-end talent management objectives were important goals that the streamlined user experience was helping enable. The following is a drill-down of a candidate profile, showing the depth of external and internal data integration that provides contextual intelligence throughout the Eightfold.ai platform.

Talent management’s inflection point has arrived 

Every interaction with a candidate, current associate, and high-potential employee is a learning event for the system.

AI and machine learning make it possible to shift focus away from being transactional and more on building relationships. AdRoll Group and DigitalOcean both mentioned how Eightfold.ai’s advanced analytics and machine learning helps them create and fine-tune nurturing campaigns to keep candidates in high-demand fields aware of opportunities in their companies. AdRoll Group used this technique of concentrating on insights to build relationships with potential Data Scientists and ultimately made a hire assisted by the Eightold.ai platform. DigitalOcean is also active using nurturing campaigns to recruit for their most in-demand positions. “As DigitalOcean continues to experience rapid growth, it’s critical we move fast to secure top talent, while taking time to nurture the phenomenal candidates already in our community,” said Olivia Melman, Manager, Recruiting Operations at DigitalOcean. “Eightfold.ai’s platform helps us improve operational efficiencies so we can quickly engage with high quality candidates and match past applicants to new openings.”

In companies of all sizes, talent management reaches its full potential when accountability and collaboration are aligned to a common set of goals. Business strategies and new business models are created and the specific amount of hires by month and quarter are set. Accountability for results is shared between business and talent management organizations, as is the case at AdRoll Group and DigitalOcean, both of which are making solid contributions to the growth of their businesses. When accountability and collaboration are not aligned, there are unpredictable, less than optimal results.

AI makes it possible to scale personalized responses to specific candidates in a company’s candidate community while defining the ideal candidate for each open position. The company’s founders call this aspect of their platform personalization at scale. “Our platform takes a holistic approach to talent management by meaningfully connecting the dots between the individual and the business. At Eightfold.ai, we are going far beyond keyword and Boolean searches to help companies and employees alike make more fulfilling decisions about ‘what’s next", commented Ashutosh Garg, CEO, and Co-Founder of Eightfold.ai.

Every hiring manager knows what excellence looks like in the positions they’re hiring for. Recruiters gather hundreds of resumes and use their best judgment to find close matches to hiring manager needs. Using AI and machine learning, talent management teams save hundreds of hours screening resumes manually and calibrate job requirements to the available candidates in a company’s candidate community. This graphic below shows how the Talent Intelligence Platform (TIP) helps companies calibrate job descriptions. During my test drive, I found that it’s as straightforward as pointing to the profile of ideal candidate and asking TIP to find similar candidates.

Achieving greater equality with a data-driven approach to diversity

Eightfold.ai can quantify hiring bias and has found it occurs 35% of the time within in-person interviews and 10% during online or virtual interview sessions. They’ve also analyzed hiring data and found that women are 11% less like to make it through application reviews, 19% less likely through recruiter screens, 12% through assessments and a shocking 30% from onsite interviews. Conscious and unconscious biases of recruiters and hiring managers often play a more dominant role than a woman’s qualifications in many hiring situations. For the organizations who are enthusiastically endorsing diversity programs yet struggling to make progress, AI and machine learning are helping to accelerate them to the goals they want to accomplish.

AI and machine learning can’t make an impact in this area quickly enough. Imagine the lost brainpower from not having a way to evaluate candidates based on their innate skills and potential to excel in the role and the need for far greater inclusion across the communities companies operate in. AdRoll Group’s CEO is addressing this directly and has made attaining greater diversity and inclusion a top company objective for the year. Daniel Doody, Global Head of Talent at AdRoll Group says “We’re very deliberate in our efforts to uncover and nurture more diverse talent while also identifying individuals who have engaged with our talent brand to include them” he said. Daniel Doody continued, “Eightfold.ai has helped us gain greater precision in our nurturing campaigns designed to bring more diverse talent to Adroll Group globally.”

Kelly O. Kay, Managing Partner, Global Managing Partner, Software & Internet Practice at Heidrick & Struggles agrees. “Eightfold.ai levels the playing field for diversity hiring by using pattern matching based on human behavior, which is fascinating,” Mr. Kay said. He added, “I’m 100% supportive of using AI and machine learning to provide everyone equal footing in pursuing and attaining their career goals.” He added that the Eightfold.ai’s greatest strength is how brilliantly it takes on the challenge of removing unconscious bias from hiring decisions, further ensuring greater diversity in hiring, retention and growth decisions.

Eightfold.ai has a unique approach to presenting potential candidates to recruiters and hiring managers. They can remove any gender-specific identification of a candidate and have them evaluated purely on expertise, experiences, merit, and skills. And the platform also can create gender-neutral job descriptions in seconds too. With these advances in AI and machine learning, long-held biases of tech companies who only want to hire from Cal-Berkeley, Stanford or MIT are being challenged when they see the quality of candidates from just as prestigious Indian, Asian, and European universities as well. Daniel Doody of Adroll Group says the insights gained from the Eightfold.ai platform “are helping to make managers and recruiters more aware of their own hiring biases while at the same time assisting in nurturing potential candidates via less obvious channels.”

How to close the talent gap

Based on conversations with customers, it’s apparent that Eightfold.ai’s Talent Intelligence Platform (TIP) provides enterprises the ability to accelerate time to hire, reduce the cost to hire and increase the quality of hire. Eightfold.ai customers are also seeing how TIP enables their companies to reduce employee attrition, saving on hiring and training costs and minimizing the impact of lost productivity. Today more CEOs and CFOs than ever are making diversity and talent initiatives their highest priority. Based on conversations with Eightfold.ai customers it’s clear their TIP provides the needed insights for C-level executives to reach their goals.

Another aspect of the TIP that customers are just beginning to explore is how to identify employees who are the most likely to leave, and take proactive steps to align their jobs with their aspirations, extending the most valuable employees’ tenure at their companies. At the same time, customers already see good results from using TIP to identify top talent that fits open positions who are likely to join them and put campaigns in place to recruit and hire them before they begin an active job search. Every Eightfold.ai customer spoken with attested to the platform’s ability to help them in their strategic imperatives around talent.

As DevOps methodologies expand their reach across the enterprise, organizations face the daunting challenge of adapting related cloud strategies to ensure optimal alignment, from managing complexity to ensuring proper governance. How can culture, automation, legacy apps and even budget be reexamined to enable this ongoing shift within the modern software factory?

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The United States spends around 17-18% of its GDP on healthcare every year. Translated into dollars, it is a mind-boggling $2.9 trillion. Unfortunately, that spending will grow at a faster rate now due to baby boomers becoming an aging population, and they are the largest demographic in the U.S. Unless the U.S. gets this spiraling healthcare spending under control, in a few short years we will be spending almost 25% of our entire GDP in healthcare trying to fix people’s failing health, instead of spending it somewhere else where it is desperately needed. Obviously, we can’t stop the aging population, but we can make the healthcare system more efficient. In the past, digitization of patient records was just for record keeping purposes. But with the advances in IoT, predictive analytics, cognitive computing and the mighty APIs to connect them all together, things have changed dramatically.

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You want to start your DevOps journey but where do you begin? Do you say DevOps loudly 5 times while looking in the mirror and it suddenly appears? Do you hire someone? Do you upskill your existing team? Here are some tips to help support your DevOps transformation. Conor Delanbanque has been involved with building & scaling teams in the DevOps space globally. He is the Head of DevOps Practice at MThree Consulting, a global technology consultancy. Conor founded the Future of DevOps Thought Leaders Debate. He regularly supports and sponsors Meetup groups such as DevOpsNYC and DockerNYC.

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For decades we have feared the cloud.  During my time working counterintelligence for the FBI, we feared the Internet so much that agency computers functioned solely on an isolated intranet connected via hard cables.

It’s no wonder to me that that government has still not embraced the unlimited processing power cloud computing affords.  But despite the fact that utilisation of the cloud has become ubiquitous – we store our photos and memories, email accounts, business files and our very identities there – many companies fear the cloud: how can I control and secure my information if I give it to someone else?

This concern has made cloud computing one of the more polarising issues for IT professionals.  Many opponents of the cloud point to the fact that not all cloud services are equal in their dedication to security:

• Poor configuration of the cloud can lead to circumvention of internal policies that classify sensitive data and protect access to it
• Not all cloud services offer strong authentication, encryption (both in transit and at rest) and audit logging
• Failure to isolate a user’s data from other tenants in a cloud environment together with privacy controls that are not robust enough to control access
• Failure to maintain and patch to ensure that known flaws are not exploited in the cloud service

According to the 2017 Cost of Data Breach Study: Global Overview (Ponemon Institute, June 2017), the average total cost of a data breach is $3.62 million. The average cost for each lost or stolen record containing sensitive and confidential information is $141.  While these costs decreased overall from 2016 to 2017, the numbers remain astronomical, particularly to small businesses who may be unable to recover from data breach liability.  No industry is safe from cyberattacks and cyberattacks continue to grow, year after year.

Cloud security must grow and evolve to face these threats and provide a bulwark of defence for the consumers that leverage the efficiencies and advantages cloud services provide.  In addition to offsetting the fear highlighted above through good security practices by the cloud security vendor, cloud services can take security one step further.  Cloud services can not only secure data within the cloud, but can leverage the transformative cloud industry to secure the endpoint users that use the service.

Cloud security is the future of cybersecurity

Cyberattacks like the WannaCry/NotPetya pandemic and the extraordinary growth of ransomware are often launched by sophisticated attackers – sometimes state sponsored – that bowl over traditional and legacy security.  The modern attackers are cyber spies that use traditional espionage tactics, together with innovative and disruptive malware to bypass passive, defence-based security measures.  To defeat such attacks, security must transform itself into an active profile that hunts today’s attacks as aggressively as it predicts the threats of tomorrow.

To predict and defeat attacks in real time, cybersecurity must move to the cloud. The cloud can leverage big data and instant analytics over a large swath of end users to instantly address known threats and predict threats that seek to overwhelm security. 

Cloud security must create a collaborative approach that analyses event streams of normal and abnormal activity across all users to build a global threat monitoring system.  Because many different users leverage the same cloud environment, cloud security is particularly suited to building a collaborative environment that instantly predicts threats through a worldwide threat monitoring system and shares threats among all users under the cloud umbrella. 

Cyberattacks continue to disrupt our way of life with innovative new approaches to seeding malware and stealing our data.  Security must in turn actively work to disrupt the cyber spies, attackers and terrorists through a collaborative security approach that leverages the big data and analytics that thrive within the cloud.  We’ve come a long way from my days on the FBI Intranet.  It’s time to fully embrace the future of security.  That future is within the cloud.

The good news is that the future of cloud security is now.   Predictive security in the cloud has innovated security in a manner that will frustrate cyber spies for years to come. This technology collects and analyses unfiltered endpoint data, using the power of the cloud, to make predictions about, and protect against future and as-yet unknown attacks.  This means predictive security in the cloud can identify attacks that other endpoint security products miss, and provides visibility into attacks that evolve over time.  In other words, it gives you the ability to hunt threats before the attacker begins to hunt you.

This new approach to security will not just level the playing field between the attacker and security teams, it will shift the balance in the opposite direction and provide security with an advantage.  Cyberattacks rely on stealth and surprise to disrupt, destroy and steal – the tools of a spy. Predictive security in the cloud works like a counterintelligence agency that hunts the spies before they attack.  This innovative approach is the next generation of security.

Amazon and Microsoft have joined IBM and Google in reporting its quarterly numbers – and the good news just keeps on coming for the hyperscalers and the cloud market in general.

Amazon announced total revenues of $51 billion (£36.9bn) in the first quarter, with Amazon Web Services (AWS) contributing $5.4bn (£3.94bn), or 10.6% of all revenues. This compares favourably with this time last year, AWS hitting $3.66bn of total revenues of $35.7bn in Q117, with AWS passing $5bn for the first time in the previous quarter.

The canned quote attributed to CEO Jeff Bezos, unlike previous quarters, singled out AWS; Bezos saying it ‘had the unusual advantage of a seven-year head start before facing like-minded competition…and the team has never slowed down.’

Fielding a question from an analyst after announcing the results, Brian Olsavsky, Amazon chief financial officer, said AWS revenues, as well as enterprise migrations, were accelerating. “What is driving the growth, we believe… [is] the value that we create for AWS customers,” Olsavsky said. “We have the functionality and pace of innovation that others don’t. We have partner and ecosystem that others don’t, and we have proven operational capability and security expertise that’s highly valued to the AWS customer base.”

Microsoft, which is not quite as explicit as Amazon in its cloud reporting, posted total revenue figures of $26.8 billion. Of its main revenue buckets, Microsoft’s intelligent cloud segment – believed to be related to Azure and server products – increased 17% to $7.9bn (£5.7bn), while productivity and business processes, which focuses more on Office 365, was at $9bn, again with a 17% increase.

While specific revenue figures for Azure were not mentioned, the company said Azure had grown 93% in revenue year over year.

Speaking to analysts after the figures were posted, Microsoft CEO Satya Nadella cited cloud security as an area where the company was a ‘clear leader’, as well as discussing the importance of artificial intelligence (AI) within infrastructure.

“Our architectural advantage of a consistent stack from the cloud to the edge is resonating with customers, with Azure revenue growth of 93% this quarter,” said Nadella. “Recent CIO surveys affirm our leadership position in hybrid, developer productivity, trusted security and compliance and new workloads such as IoT and AI at the edge.”

As this publication reported earlier this week, Google CEO Sundar Pichai said the company’s cloud arm was ‘growing well’ and that larger, more strategic deals were being struck. Again, specific figures are not disclosed, but for Q1 Google’s ‘other’ revenues bucket – of which Google Cloud is a part – hit $4.35bn, a 35% increase on the previous year. For the second consecutive quarter, IBM’s revenues went up – albeit only in constant currency – to $19.1bn of global sales, with cloud revenues rising 20% year over year.

Analysis

Good results all round, therefore; but what is driving the change? It’s all about AI, machine learning and – to a lesser extent – blockchain, fuelling the new ‘cloud wars’ and becoming the next generation of cloud-enabled services.

AWS says ‘tens of thousands’ of customers are using its machine learning services with active users going up by more than 250% over the past year. Those who attended re:Invent in November would have been made more than aware of AWS’ push in the area, including SageMaker, a modular service which aims to help developers build, train, and deploy machine learning models. Dow Jones, NFL, and Expedia – the latter announcing at re:Invent they were all-in on AWS – are all confirmed machine learning customers.

The keynote theme of Google’s report was around how AI was ‘unlocking new opportunities for everyone’. Pichai cited Google Photos, Lens and the Google Assistant as products which were getting better all the time, but again the importance of getting developers onside, this time with TensorFlow, was cited. TensorFlow Hub, Pichai explained, was launched to “make it easier for developers to share and reuse models so that we can work together to tackle even more problems and get to better ideas faster.”

From Microsoft’s side, the company says more than one million developers are already on board with Cognitive Services, the company’s set of APIs and SDKs to make applications more intelligent. IBM, for its part, has introduced several offerings to ‘accelerate client journeys to AI’, as SVP and CFO Jim Kavanaugh put it, including a more conversational, enhanced Watson Assistant.

In terms of where the runners and riders are, AWS continues to trounce the rest of the field, according to analyst firm Synergy Research. The company notes that even though the market has almost tripled in size over the past few years, AWS’ grip on one third of the total cloud infrastructure services market remains. Microsoft’s growth, eating into AWS’ dominance, has slipped somewhat, with the Redmond giant taking 13% of share, with IBM, Google, and Alibaba Cloud making up the top five.

“Cloud growth in the last two quarters really has been quite exceptional,” said John Dinsdale, a chief analyst and research director at Synergy. “Normal market development cycles and the law of large numbers should result in growth rates that slowly diminish – and that is what we saw in late 2016 and through most of 2017. But the growth rate jumped by three percentage points in Q4 and by another five in Q1.

“That is good news for the leading cloud providers, whose historically high levels of capex are helping to ensure that they are the main beneficiaries of that exceptional market growth,” Dinsdale added.

Bobby Hellard

Keumars Afifi-Sabet

Kin Lane recently wrote a couple of blogs about why copyrighting an API is not common. I couldn’t agree more that copyrighting APIs is uncommon. First of all, the API definition is just an interface (It is the implementation detail … Continue reading →

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