• Between 2018 and 2019, organisations that have deployed artificial intelligence (AI) grew from 4% to 14%, according to Gartner’s 2019 CIO Agenda survey
• Conversational AI remains at the top of corporate agendas spurred by the worldwide success of Amazon Alexa, Google Assistant, and others
• Enterprises are making progress with AI as it grows more widespread, and they’re also making more mistakes that contribute to their accelerating learning curve

These and many other new insights are from Gartner Hype Cycle For AI, 2019 published earlier this year and summarised in the recent Gartner blog post, Top Trends on the Gartner Hype Cycle for Artificial Intelligence, 2019.  Gartner’s definition of Hype Cycles includes five phases of a technology’s lifecycle and is explained here. Gartner’s latest Hype Cycle for AI reflects the growing popularity of AutoML, intelligent applications, AI platform as a service or AI cloud services as enterprises ramp up their adoption of AI. The Gartner Hype Cycle for AI, 2019, is shown below:

Details of what’s new in Gartner’s Hype Cycle For AI 2019:

Speech recognition is less than two years to mainstream adoption and is predicted to deliver the most significant transformational benefits of all technologies on the Hype Cycle

Gartner advises its clients to consider including speech recognition on their short-term AI technology roadmaps. Gartner observes, unlike other technologies within the natural-language processing area, speech to text (and text to speech) is a stand-alone commodity where its modules can be plugged into a variety of natural-language workflows. Leading vendors in this technology area Amazon, Baidu, Cedat 85, Google, IBM, Intelligent Voice, Microsoft, NICE, Nuance, and Speechmatics.

Eight new AI-based technologies are included in this year’s Hype Cycle, reflecting Gartner enterprise clients’ plans to scale AI across DevOps and IT while supporting new business models

The latest technologies to be included in the Hype Cycle for AI reflect how enterprises are trying to demystify AI to improve adoption while at the same time, fuel new business models. The new technologies include the following:

• AI cloud services – AI cloud services are hosted services that allow development teams to incorporate the advantages inherent in AI and machine learning
• AutoML – Automated machine learning (AutoML) is the capability of automating the process of building, deploying, and managing machine learning models
• Augmented intelligence – Augmented intelligence is a human-centered partnership model of people and artificial intelligence (AI) working together to enhance cognitive performance, including learning, decision making, and new experiences
• Explainable AI – AI researchers define “explainable AI” as an ensemble of methods that make black-box AI algorithms’ outputs sufficiently understandable
• Edge AI – Edge AI refers to the use of AI techniques embedded in IoT endpoints, gateways, and edge devices, in applications ranging from autonomous vehicles to streaming analytics
• Reinforcement learning – Reinforcement learning has the primary potential for gaming and automation industries and has the potential to lead to significant breakthroughs in robotics, vehicle routing, logistics, and other industrial control scenarios
• Quantum computing – Quantum computing has the potential to make significant contributions to the areas of systems optimisation, machine learning, cryptography, drug discovery, and organic chemistry. Although outside the planning horizon of most enterprises, quantum computing could have strategic impacts in key businesses or operations
• AI marketplaces – Gartner defines an AI marketplace as an easily accessible place supported by a technical infrastructure that facilitates the publication, consumption, and billing of reusable algorithms. Some marketplaces are used within an organisation to support the internal sharing of prebuilt algorithms among data scientist

Gartner considers the following AI technologies to be on the rise and part of the Innovation Trigger phase of the AI Hype Cycle: AI marketplaces, reinforcement learning, decision intelligence, AI cloud services, data labelling, and annotation services, and knowledge graphs are now showing signs of potential technology breakthroughs as evidence by early proof-of-concept stories. Technologies in the Innovation Trigger phase of the Hype Cycle often lack usable, scalable products with commercial viability not yet proven.

Smart robots and AutoML are at the peak of the Hype Cycle in 2019

In contrast to the rapid growth of industrial robotics systems that adopted by manufacturers due to the lack of workers, smart robots are defined by Gartner as having electromechanical form factors that work autonomously in the physical world. They learn in short-term intervals from human-supervised training and demonstrations or by their supervised experiences including taking direction form human voices in a shop floor environment. Whiz Robot from SoftBank Robotics is an example of a SmartRobot that will be sold under the robot-as-a service (RaaS) model and will originally be available only in Japan.

AutoML is one of the most hyped technology in AI this year. Gartner defines automated machine learning (AutoML) as the capability of automating the process of building, deploying, or managing machine learning models. Leading vendors providing AutoML platforms and applications include Amazon SageMaker, Big Squid, dotData, DataRobot, Google Cloud Platform, H2O.ai, KNIME, RapidMiner, and Sky Tree.

Nine technologies were removed or reassigned from this years’ Hype Cycle of AI compared to 2018

Gartner has removed nine technologies, often reassigning them into broader categories. Augmented reality and virtual reality are now part of augmented intelligence, a more general category, and remains on many other Hype Cycles. Commercial UAVs (drones) is now part of edge AI, a more general category. Ensemble learning had already reached the plateau in 2018 and has now graduated from the Hype Cycle.

Human-in-the-loop crowdsourcing has been replaced by data labeling and annotation services, a broader category. Natural language generation is now included as part of NLP. Knowledge management tools have been replaced by insight engines, which are more relevant to AI. Predictive analytics and prescriptive analytics are now part of decision intelligence, a more general category.

Sources:

Hype Cycle for Artificial Intelligence, 2019, Published 25 July 2019, (Client access reqd.)
Top Trends on the Gartner Hype Cycle for Artificial Intelligence, 2019 published September 12, 2019

Interested in hearing industry leaders discuss subjects like this and sharing their experiences and use-cases? Attend the Cyber Security & Cloud Expo World Series with upcoming events in Silicon Valley, London and Amsterdam to learn more.

Cloud Pro

Keumars Afifi-Sabet

Sunshine, sandwiches, scenic views, and not a care in the world besides the occasional wasp. Everyone loves a picnic.

Unfortunately, the same cannot be said for PICNIC, an enduring acronym in IT circles standing for Problem In Chair, Not In Computer. The term, dating back to the 1980s, was first employed by frustrated IT professionals weary of dealing with computer problems arising from user error rather than any actual issues with the technology.

Such challenges still exist today although, increasingly, they are migrating to the cloud. Data breaches resulting from cloud misconfigurations can have dramatic consequences. For example, the Capital One breach earlier this year affected more than 100 million records.

In most cases, these breaches are not the result of a particularly skilled threat actor or any advanced exploits or malware. Instead the door is left wide open through human error. Unfortunately, such cloud security issues are the result of poor data protection practices. For instance, sensitive information may be stored in unencrypted form or access permissions not locked down properly.

The Capital One data breach, first reported in July, is a particularly powerful example of how poor practices can trigger a major security crisis. The incident saw customer data including more than 140,000 social security numbers, one million Canadian social insurance numbers, 80,000 bank account numbers, and an unknown quantity of customer names and addresses accessed by Paige Thompson, a former software engineer at Amazon Web Services. It has been estimated that the breach could cost the company upwards of $150m.

The breach was made possible by a misconfiguration of the web application developed by Capital One, and not the underlying Amazon cloud-based infrastructure. Amazon also stated that the perpetrator’s specialist insider knowledge was not a factor, but rather the breach could have been carried out by anyone who stumbled on the misconfiguration.

Similar breaches have occurred at Fedex and Californian car dealership services provider Dealer Leads. Indeed, cloud-based data breaches caused by user errors are becoming so frequent that the PICNIC acronym might arguably be updated to stand for Problem In Company, Not In Cloud.

How can firms reduce the threat?

Because cloud-related mistakes can potentially expose businesses to huge and costly data breaches, organisations must do much more to mitigate the risks. It’s very difficult to remove the risk of human error entirely. Organisations need to ensure they implement robust policies to protect sensitive data in the cloud, as well as the right tool to manage them.

An audit is a good place to start. It is impossible to keep sensitive data safe if a company isn’t aware of what information it owns or where it is kept. Many organisations have spent years hoarding as much data as possible, and it’s all too easy for them to lose track as they expand and change their infrastructure.

Audits can help the enterprise get its house in order.  Their disciplined approach lets organisations locate and classify any and all sensitive information spread across on-premises and cloud servers. Audits are especially good for identifying data that is governed by regulations such as the GDPR and PCI-DSS.

The challenge with built-in classification capabilities present in many cloud-based solutions is that rule creation and tagging is often a labour-intensive, manual process. For this reason enterprises should look to automate this process as much as possible.

Introducing a least privilege approach

Companies should also adopt the principle of least privilege. The least privilege model dictates that all users should only be able to access assets and resources required for their job role, significantly reducing the risk posed by external threat actors and, equally importantly, unauthorised insiders like Paige Thompson. Organisations that take a least privilege approach for on-premises data, should extend the practice for their cloud data, particularly in hybrid environments.

The situation is not helped by the management capabilities built into certain cloud-based services. Some offer only limited permissions visibility and lack centralised controls for making changes to them. Ideally organisations should have a holistic view of user permissions, and the ability to sync access rights across their environments.

It is also important to involve data owners when reviewing permission rights, but this is something else that is not easily achieved with cloud solutions. Automating the entitlement review and authorisation workflow processes can help to save time and make it easier to keep control of data access.

Ongoing visibility is key

Setting up the right processes and policies is only the beginning. To remain secure, organisations need constant insight into how sensitive assets are being accessed, and by whom. Aside from the vast scale of the data involved, one of the most striking aspects of the Capital One breach is it was only discovered after the perpetrator bragged about it on Slack.

Without tools that give clear visibility of who has accessed what information and when, organisations that make a mistake when configuring a cloud deployment will continue to be vulnerable to breaches of sensitive assets.

The risk of human error is unlikely to go away any time soon. However, organisations that regularly audit their sensitive data, adopt a least-privilege approach and use centralised visibility tools can drastically reduce the risk of PICNIC ruining their cloud security.

Interested in hearing industry leaders discuss subjects like this and sharing their experiences and use-cases? Attend the Cyber Security & Cloud Expo World Series with upcoming events in Silicon Valley, London and Amsterdam to learn more.

Bobby Hellard

Cloud Pro

Bobby Hellard

Analysis Last month, Forbes published its list of America’s 100 most innovative leaders. The report’s methodology was based on four ‘essential leadership qualities of top founders and CEOs’. These were, in no particular order, perception in the media for innovation, social connections, and ‘value creation’, both in terms of track record and investor expectations.

There was, however, one glaring problem: the list of 100 featured only one woman. Barbara Rentler, CEO of retailer Ross Stories, was ranked #75, with a further indignity of having a blank avatar where everyone else’s headshots appeared.

Writing shortly afterwards, Forbes editor Randall Lane noted the ‘data-driven’ methodology behind the ranking was ‘flawed’. Given part of this methodology was focused on media perception, one can see the blame could be spread around. Yet when the Forbes Cloud 100, a list of top privately-held cloud companies, was published later in September, the story remained the same. Only three companies listed – Canva, Darktrace, and Guild Education – had female chief executives.

Questions therefore need to be asked. Is it a case of chicken and egg, or are there broader issues to dig into? More importantly, what can be done about it?

The executive and event builder viewpoint

Guild Education, based in Denver, is a cloud-based education services provider. The company does not offer courses in itself, but instead sees itself as a marketplace for large companies to provide education as a benefit to its employee base. Big ticket clients include Walmart and Disney.

It’s very important to remind yourself that you’re good at what you do, you know why you’re there, and you deserve to be there as much as anyone else in that room

Its heritage of tech for good, diversity at the boardroom level and being female-founded is key, as chief product and analytics officer Bijal Shah explains. “We’re pretty proud that we’re a technology company that is utilising technology for good, and helping these Fortune 1000 employees be able to go back and find educational pathway is really important,” Shah tells CloudTech. “Just getting recognised for the work we’re doing through the Cloud 100 is pretty impactful, both from a technology perspective but also from the perspective of the social impact we’re having.”

While there is pride at having made the list, Shah (left) argues the Cloud 100 shows how ‘there is a lot more work to do’ when it comes to greater female representation at the higher echelons of tech. “I personally believe diversity begets diversity,” says Shah. “When you value it and it becomes part of your culture, then it just naturally happens.”

Getting better female representation in STEM has long been an industry goal. While the success of these initiatives varies depending on where you sit, pushback on failures has been more marked of late. The Forbes leaders’ list is a case in point – justifiable criticism was also levelled at the lack of racial diversity among the group – while an increasing number of tweetstorms around ‘manels’ have forced tech event organisers to think twice.

UK-based Women in Data is in no danger of falling into that trap. The brand started in 2014 as a half-day relatively informal gathering, and is now a series of events with the flagship, in November, expecting up to 1500 attendees.

Rachel Keane, managing consultant at technical recruiter Datatech Analytics, put the event together with fellow co-founder Roisin McCarthy after realising the company had placed fewer women in 2014 for data and analytics roles as they had in 2000. “We thought this was really strange, because we were more profitable than ever, our client bases were growing, and as far as we were concerned we were placing the best people for the job,” Keane tells CloudTech. “We didn’t really give it much consideration. We had clients of each gender, but in terms of building those teams out we just put the right CV with the right skill set and the right attitude towards the job.”

The issues women face – seen and unseen

Both Shah and Keane note the issues women face both in terms of climbing the career ladder in STEM, never mind getting on it in the first place.

While Shah’s career prior to Guild had been a mix of technical and business development, her undergraduate degree in engineering reveals her passion. She admits that she had noted the number of women dwindling in her technical classes, going from school, to college, and eventually to the workplace. “I think it’s very important to remind yourself that you’re good at what you do, you know why you’re there, and you equally deserve to be there as much as anyone else in that room,” says Shah.

Today, Shah’s role is focused a lot more on the management side of the house, although she can still occasionally get her hands dirty in the day-to-day code spending time with different teams on more technical strategies. Keane notes the need for technical expertise even as you move further up the company’s hierarchy.

“There’s always that pull – you love the data, you don’t necessarily always want to leave the code behind,” says Keane. “More so when you move to a manager [or] director role – it is quite important that you have those skills as a coder, it is the fact you’re able to go in there and check that code and mentor your staff… it’s an important part of the role.

There are times where you have to take two steps back to take one step forward – being okay with that and being in control of that is really important

“You get some people that love to code, go up a couple of ranks and then say ‘actually, I’m going to manage clients, products, manage a team’, then you get some people who are I would say more AI and data science-traditional people, people who would normally remain relatively hands on,” adds Keane.

Other issues which affect women specifically have an onus on employers. Returning from maternity leave is never as simple as firing up your machine and getting back to work as though you had never been away, but in some technology fields women who take time out to have children can return significantly out of the loop through no fault of their own.

“Technology evolves so quickly and programming languages are one minute they’re here, one minute they’re gone,” says Keane. “SaaS programming was a massive tool back when I joined [Datatech in 2009], and now that really is a thing of the past. It’s now the Rs, the SQLs, the Pythons, and dare I say it more languages that are coming out.”

Opportunities for progress and change

The truth is that there should be a place for all women, regardless of what they want to do. As data democratises and becomes pivotal to virtually every industry, this has the potential to open things up tremendously.

Keane (left) notes that many soon-to-be school and university leavers still expect to work in finance with their maths qualifications. This is such a concern that Women in Data is in the process of putting together an informational film, with the support of many large companies including Facebook, the BBC and Sainsbury’s, to educate young girls on the many doors which will open for them with their respective degrees.

“They’re unaware of the fact that you can have a job in retail, or technology, or gaming, or any other industry sector,” she explains. “It is still very much related to finance and I’m not so sure that every girl relates herself to finance. Finance is very typically male dominated, as is insurance.”

The rise of soft skills can also be seen as a major opportunity; as data becomes a mandatory language across all business, being able to make sense of it and explain it is a boon.

“One of the things we have found that is quite encouraging is the fact that in the last few years it isn’t just about the numbers anymore,” says Keane. “We’re not looking for what they used to call backroom analysts, people who just crunch numbers. It is essential that a person is able to tell a story, and to listen, and to deliver the insight to make sense of how that business makes impact going forward. Naturally, they are skills that are normally more relative to a girl.”

Building confidence and power

The overriding message is one of confidence: be yourself, don’t take any nonsense, and be proud of everything you can do rather than fret over anything you can’t.

It is, of course, easier said than done. We all may suffer from imposter syndrome at times, but the struggle is more real if the aforementioned barriers, seen or unseen, are erected. “It’s hard to be what you cannot see,” says Shah. “My trajectory and my career are a little bit untraditional – I’ve always had a technical background but I’ve been in strategy roles and consulting roles. When you don’t see a pathway similar to your own, I think that can cause self-doubt.”

Shah is a believer that careers naturally have peaks and troughs, and that understanding this can also help remove self-doubt. This may be Shah’s millennial upbringing coming to the fore, as emerging research on Generation Z argues the most recent additions to the workforce are more likely to stay loyal if their employer repays that loyalty in kind. Yet this mindset can remove the pressure of having to get everything right first time.

“Careers are not linear – they are a path that ebbs and flows in different ways, and there are times where you have to take two steps back to take one step forward,” says Shah. “Being okay with that, and being in control of that, taking that by the reins and embracing it, is really important.”

Women need to have more confidence in the skills they have – celebrate what you have, don’t apologise for what you don’t have

That said, the famous quote about ‘the harder you work, the luckier you get’ still applies. “It’s really important to come into an organisation, put your head down and do a great job – not worry so much about what that next title is or what that next role is you’re going to get right away,” adds Shah. “I think proving your skills and capabilities opens a lot of doors.”

From a decade’s experience in recruitment, Keane believes that men and women tend to look at job specifications differently. If a woman looks at a spec and finds they can do 80% of it, they will be hesitant to apply for fear of the other 20%. Men are traditionally much more confident.

“There isn’t one person in nearly 11 years that I have placed who matches the job spec,” says Keane. “I never match anyone to a job spec – I match people on their technical capability, on the cultural environment, on what I think their unique skills are, and how I feel that company will benefit.

“Women need to have a little bit more confidence in the skills they have,” Keane adds. “Celebrate what you have, don’t apologise for what you don’t have.”

This confidence can traverse other areas as well. Keane recalls one recent candidate who was ‘in bits’ following a disastrous interview. “I just said to her ‘look, you’re really not thinking about this in the right way.’ Yes, you’re being interviewed by them, but surely you’re interviewing them too,” she says. “You don’t have to go and work there – it’s just not the right fit. When you start thinking like that, that’s when you start giving yourself your power and confidence back.”

“Everyone faces adversity in their careers,” adds Shah. “There are challenging things that come up but I don’t think it’s any different for men or women as long as you are willing to have the confidence to say ‘I can get past this’ or ‘I can get through this’. I can learn this new skill to then elevate my ability to move forward.”

Interested in hearing industry leaders discuss subjects like this and sharing their experiences and use-cases? Attend the Cyber Security & Cloud Expo World Series with upcoming events in Silicon Valley, London and Amsterdam to learn more.

The SaaS model has lowered the barriers for budding software entrepreneurs to build and commercialise exciting new products, creating new markets that cater to nearly any vertical or niche you can think of. Gartner Inc. currently estimates the number of SaaS companies in existence at around a quarter of a million and predicts that the worldwide public cloud services market will grow 17.5% in 2019 to $214.3 billion, up from $182.4 billion in 2018, with cloud application services by far the largest market segment.

The availability of new tools and methodologies that help with the building of SaaS products and companies are enabling this accelerated growth. There are a number of core elements that are accelerating the market, changing working practices and making software development more agile.

The emergence of SaaS for SaaS

More than 50% of Forbes’ latest Cloud 100 list is made up of ‘SaaS for SaaS’ companies, i.e. those that provide services for SaaS and Cloud products which enable SaaS startups to accelerate their developments. These include infrastructure options, analytical tools and security services, each of which eases the burden on development teams. They provide tried and tested solutions to common SaaS needs, all ready to be slotted into the product.

Including business management tools in this pushes that percentage up further. Billing, customer support and sales management: these are all activities which out-of-the-box solutions can manage, rather than having a development team work on solutions that don’t directly contribute to the product’s bottom line.

Integration is no different than connecting to one or more of the plethora of SaaS applications out there. There is no need to use already overstretched development resources to build out integrations when it is possible to simply connect to one service which provides access to hundreds more.

Embedded iPaaS has enabled SaaS companies to develop out their own solution further, without having to compromise on connectivity or the experience they want to offer their users.

Internal and external comms

Dedicated platforms keep remote teams in touch and on task, but they also work for internal communications just as effectively. Email has all but been replaced by more agile solutions for performing particular tasks, whether it’s using Slack for internal communications or sprint planning platforms for managing development.

Face-to-face communication doesn’t have to be difficult either. Video chat services, such as Zoom, allow teams to talk things over as if they were together in the same office.

As well as bringing teams closer together, these platforms can also help connect a SaaS company to its users. Embedded chat platforms, like Intercom or Drift, and support platforms like Zendesk, means it has never been easier to speak with clients and leads.

Video chat also opens the door to performing live demos, user interviews and even commercial discussions without either party having to worry about location logistics or travel expenses.

A host of infrastructure options

Achieving a solid uptime is within affordable reach thanks to a number of hosting heavyweights that include AWS, Google Cloud and Azure. There are many flexible pricing options available to help find the perfect balance of performance vs. server costs. Each provider will also have incentive programmes for start-ups, meaning that costs can often be absolutely minimal at the outset.

The top-down view

When faced with the sheer volume of SaaS platforms available, how do buyers decide which one to use? This question has driven the rise in SaaS-focused review websites, including industry staples Capterra, GetApp and G2. These allow potential customers and stakeholders to take a top-down view of a vertical market, enabling them to compare features, pricing and direct experiences through reviews.

For SaaS vendors, there are several opportunities to take advantage of:

• The ability for the startup to stand out in its niche
• A relatively low cost way to attract an audience through PPC offerings
• A way to better understand a market’s landscape in order to carve out a specific niche

By participating on these sites, a SaaS startup can accelerate its exposure to the potential end-market. Product differentiation becomes much more important, however, in order to avoid being lost in the crowd.

Conclusion

The range of tools and services available to help a SaaS startup to prototype, build, get a product to market, and scale its team has never been broader or more comprehensive. Being able to equip a team and product with scalable services allows development teams to focus on what matters: building new and innovative applications and experiences that will help elevate their startup to the next level.

Interested in hearing industry leaders discuss subjects like this and sharing their experiences and use-cases? Attend the Cyber Security & Cloud Expo World Series with upcoming events in Silicon Valley, London and Amsterdam to learn more.

Bobby Hellard