Clare Hopping

As Cybric’s Chief Technology Officer, Mike D. Kail is responsible for the strategic vision and technical direction of the platform. Prior to founding Cybric, Mike was Yahoo’s CIO and SVP of Infrastructure, where he led the IT and Data Center functions for the company. He has more than 24 years of IT Operations experience with a focus on highly-scalable architectures.

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To say it has been a busy few weeks for object storage provider Cloudian would be an understatement. The company has pocketed $125 million in funding, issued its most recent product update, and touted a record year with particular growth in Europe cited.

With the latter in mind, the company today announced it had acquired Infinity Storage. The Milan-based company focuses on software-defined file storage and had previously partnered extensively with Cloudian prior to acquisition. The company’s name may not be obvious to those except long-term industry watchers – “they’re technologists, not marketers and salespeople”, as Cloudian CMO Jon Toor puts it – but their kudos cannot be doubted, with founder Caterina Falchi an inventor of WORM (write once, read many) technology.

Toor explains the rationale behind the move. “Two things became clear,” he tells CloudTech. “One is their technology was completely complementary with ours, zero overlap – there was a very nice point of intersection because they have supported the S3 instance for some years now – it was really clean from that perspective. The other thing was they had a very similar culture. They’re technologists that really strive to product a best in class solution, and very focused on making sure it meets all customer expectations.”

The continued rise of object storage is naturally a subject dear to Toor’s heart – and one which can be expanded on at some length. The benefits – inclusion of metadata, API accessibility, scalability – are known, but it’s what’s on the horizon that piques particular interest.

Multi-cloud, as regular readers of this publication will be aware, is being taken up by more and more companies, and vendors – never ones to miss an opportunity – are putting the term everywhere in their marketing materials. Cloudian is no different; but this one is genuine. Through a single API, users can access storage assets both on-premise and in public clouds, including Microsoft, Google and AWS.

“There’s been a huge interest in this, and it’s driven by all the usual suspects,” explains Toor. “One driver is people just want to have flexibility. Different clouds have different APIs. The second thing is the clouds are competing. They’ve all got different strengths, and people will want to use Microsoft for one thing, Google for something else, Amazon for something else, and the reality is the IT guy inside the organisation doesn’t really have any control over what the people in the company are doing.”

An example Toor gave was around a Cloudian customer in the industrial IoT space. All of the company’s software is written for S3, but there was also a requirement for Azure in certain scenarios. By running a controller in Azure and providing a fully compatible S3 interface into the Azure environment, the problem was solved.

Machine learning, if it’s possible, is even more of a buzzword than multi-cloud. With even the flimsiest chat applications offering ML capabilities according to the willing, it shows the one direction the industry is heading. Having data sat in silos being accessed every so often just won’t cut it anymore, whichever profession you are in. Another customer of Cloudian’s is WGBH, a Boston-based TV station, which is moving its archive from tape and hard disk drives – estimated access time 24-72 hours – to object storage.

Here’s where the benefits of attached metadata come in. “What companies want to do is learn from the data – they want to be able to analyse the data and benefit from it, and it’s very hard to do if that data is sitting on a shelf,” says Toor. “You need it to be sitting there with real-time access – preferably something that is cloud-integrated so you can also use tools in the cloud to help you learn about that data.

“You want to be able to analyse the data and then record the product of that analysis in some way that makes sense.”

The Infinity Storage team has entirely joined Cloudian, and for Toor it marks another step in what he says is a very interesting time for unstructured data. “Traditional file storage, traditional unstructured data storage has been around for 30 years now, and there really hasn’t been a lot of innovation in the space during that 30 years,” he says. “Enterprises are facing a scale problem, collecting massive amounts of unstructured data in the form of videos, image, archiving… across the board. A self-driving car, for example, can generate a petabyte of data every year just on the basis of observing the road conditions around it.

“For Cloudian and our customers, [Infinity] adds files services to our object storage environment. Customers can consolidate all unstructured data types, files and objects to a single limitlessly scalable pool where they can manage everything as one," Toor adds.

René Bostic is the Technical VP of the IBM Cloud Unit in North America. Enjoying her career with IBM during the modern millennial technological era, she is an expert in cloud computing, DevOps and emerging cloud technologies such as Blockchain. Her strengths and core competencies include a proven record of accomplishments in consensus building at all levels to assess, plan, and implement enterprise and cloud computing solutions.

René is a member of the Society of Women Engineers (SWE) and a member of the Society of Information Management (SIM) Atlanta Chapter. She received a Business and Economics degree with a minor in Computer Science from St. Andrews Presbyterian University (Laurinburg, North Carolina). She resides in metro-Atlanta (Georgia).

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Microsoft continues to expand its data centre empire, with new locations in Germany, Switzerland, and the United Arab Emirates planned, the company has announced.

The expansion takes Azure to 50 regions worldwide, with other data centres in the works including South Africa and Australia. The UAE data centres will be the first inroads Microsoft has made into the Middle East, with the company citing its ‘long-standing expertise and deep local relationships’ in the area to accelerate technological innovation.

“By delivering the comprehensive, intelligent Microsoft Cloud from data centres in a given geography, we offer scalable, available and resilient cloud services for companies and organisations while meeting data residency, security and compliance needs,” wrote Jason Zander, Azure corporate vice president, in a blog post.

Alongside this, Microsoft announced the France regions were open for business with general availability of Azure and Office 365, with Dynamics 365 to follow early next year.

“With this milestone, Microsoft is empowering organisations like Naval Energies, a global player in renewable marine engines, Astrimmo, a leading provider of housing services in France, and Ercom, a French company specialising in cybersecurity, with greater scalability, agility, and the opportunity to develop new cloud-based solutions,” Zander added.

Microsoft made a point that it operates more regions than any other cloud provider. Naturally, the nomenclature slightly differs depending on who one talks to. Amazon Web Services (AWS) has 19 regions at present, with five more on the way, but in terms of availability zones – each AWS region has a minimum of two, ideally three – AWS has 54 at present.

Earlier this month, Microsoft announced a greater push towards its government cloud offerings, with government-specific editions of Microsoft 365 and Azure Stack, as well as greater security and compliance in Dynamics 365.

In the 67 years since Alan Turing proposed his Imitation Game – the infamous ‘Turing test’ for artificial intelligence (AI) – people have been confused over the very purpose of AI itself.
At issue: whether the point of AI is to simulate human behavior so seamlessly that it can fool people into thinking they are actually interacting with a human being, rather than a piece of software.

Such deception was never the point of Turing’s exercise, however. Rather, he realized that there was no way to define true intelligence, and thus no way to test for it. So he came up with the game as a substitute – something people could theoretically test for.

Regardless of Turing’s intentions, setting the bar for AI based on its ability to snooker an audience has become fully ingrained in our culture, thanks in large part to Hollywood.

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The latest research report, this time from cloud cost management provider Cloudability, reveals some interesting findings: only four infrastructure services comprise almost 85% of spend among the company’s customers, while changing trends in object storage and containers are becoming apparent.

The study, which features data comprised from anonymous analysis of Cloudability’s analytics platform, found Amazon EC2 was by far the most popular service, accounting for 58.7% of spend, compared with Elastic Block Store (EBS, 9.9%), AWS’ relational database (9.3%), and S3 object storage (6.3%).

Cloudability’s customers are in the main Amazon houses, so the fact this data is AWS-heavy should not be a surprise, nor should it detract from other cloud providers. However the trend here is around ‘lift and shift’ migrations dominating enterprise cloud adoption. Cloudability argues there is still a lot of work to be done and many workloads to move over.

The data also finds that the second quarter of 2017 was the tipping point between legacy and current generation compute instances. May (below) saw cloud overtake legacy among Cloudability’s customers for the first time, and by the end of the year the divide had grown significantly to 70/30.

Organisations aren’t just moving to the cloud in greater numbers; they are also becoming savvier about their options. Object storage is a case in point. The report argues capacity has grown by more than 40% over the past 12 months, with 22% of companies opting for the infrequent access AWS S3 option – a combination of low cost and high performance – up from 10% this time last year. Standard usage has dropped almost in parallel, from 68% to 55%, while usage of Glacier, the super-low-cost ‘cold’ storage option, remained stable.

Perhaps not surprisingly, Kubernetes is the most popular container orchestration tool, steadily gaining its lead over Mesos, Rancher and OpenShift throughout the course of 2017. Kubernetes, which ‘graduated’ out of the Cloud Native Computing Foundation (CNCF) earlier this month, and how its trends were related to AWS were described thus in the report: “With ECS, Fargate and EKS offerings starting to become available and delving further into microservices, we anticipate that container adoption will accelerate.

“As a result of container growth, the management complexity (including cost management) will grow significantly as well, since containers are typically seven to eight times the number of VMs and are more ephemeral in nature.”

According to the recent RightScale State of the Cloud report, AWS continues to see its lead in the overall market shrink at the hands of Microsoft Azure, with enterprise cloud spend – as this report affirms – continuing to rise.

You can find out more about the report here.

W. Edward Deming taught that quality is achieved by measuring as much as possible and reducing variations, and reducing variation is achieved by improving the system, not just pieces. Japan widely adopted Deming’s philosophies in the 1950s and became the 2nd biggest economy in the world. Quality improvement didn’t decrease jobs in Japan, it increased jobs.
AI now has the ability to expand and codify Deming’s philosophies – to take them to the next level. AI can improve and standardize decision making based on logic, rather than the fear of missing objectives, bonuses or losing one’s job. It can continuously monitor for quality against specifications by analyzing streams of real-time data coming from embedded sensors connected to the IIoT, IoT and IoA (internet of agriculture). This means companies that are aggressive early adopters of these digital technologies will have more knowledge, higher quality and significant competitive advantages, which means more demand for their products, sales, customer service, manufacturing, distribution, etc. It also means aggressive adopters will likely generate more jobs.

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• 84% of marketing organizations are implementing or expanding AI and machine learning in 2018.
• 75% of enterprises using AI and machine learning enhance customer satisfaction by more than 10%.
• 3 in 4 organizations implementing AI and machine learning increase sales of new products and services by more than 10% according to Capgemini.

Measuring marketing’s many contributions to revenue growth is becoming more accurate and real-time thanks to analytics and machine learning. Knowing what’s driving more Marketing Qualified Leads (MQLs), Sales Qualified Leads (SQL), how best to optimize marketing campaigns, and improving the precision and profitability of pricing are just a few of the many areas machine learning is revolutionizing marketing.

The best marketers are using machine learning to understand, anticipate and act on the problems their sales prospects are trying to solve faster and with more clarity than any competitor. Having the insight to tailor content while qualifying leads for sales to close quickly is being fueled by machine learning-based apps capable of learning what’s most effective for each prospect and customer. Machine learning is taking contextual content,  marketing automation including cross-channel marketing campaigns and lead scoring, personalization, and sales forecasting to a new level of accuracy and speed.

The strongest marketing departments rely on a robust set of analytics and Key Performance Indicators (KPIs) to measure their progress towards revenue and customer growth goals. With machine learning, marketing departments will be able to deliver even more significant contributions to revenue growth, strengthening customer relationships in the process.

The following are 10 ways machine learning is revolutionizing marketing today and in the future:

57% of enterprise executives believe the most significant growth benefit of AI and machine learning will be improving customer experiences and support

44% believe that AI and machine learning will provide the ability to improve on existing products and services. Marketing departments and the Chief Marketing Officers (CMOs) running them are the leaders devising and launching new strategies to deliver excellent customer experiences and are one of the earliest adopters of machine learning. Orchestrating every aspect of attracting, selling and serving customers is being improved by marketers using machine learning apps to more accurately predict outcomes. Source: Artificial Intelligence: What’s Possible for Enterprises In 2017 (PDF, 16 pp., no opt-in), Forrester, by Mike Gualtieri, November 1, 2016. Courtesy of The Stack.

58% of enterprises are tackling the most challenging marketing problems with AI and machine learning first, prioritizing personalized customer care, new product development

These “need to do” marketing areas have the highest complexity and highest benefit. Marketers haven’t been putting as much emphasis on the “must do” areas of high benefit and low complexity according to Capgemini’s analysis. These application areas include Chatbots and virtual assistants, reducing revenue churn, facial recognition and product and services recommendations. Source:  Turning AI into concrete value: the successful implementers’ toolkit, Capgemini Consulting. 2017. (PDF, 28 pp., no opt-in).

By 2020, real-time personalized advertising across digital platforms and optimized message targeting accuracy, context and precision will accelerate

The combined effect of these marketing technology improvements will increase sales effectiveness in retail and B2C-based channels. Sales Qualified Lead (SQL) lead generation will also increase, potentially reducing sales cycles and increasing win rates. Source: Can Machines be Creative? How Technology is Transforming Marketing Personalization and Relevance, IDC White Paper Sponsored by Gerry Brown, July 2017.

Analyze and significantly reduce customer churn using machine learning to streamline risk prediction and intervention models

Instead of relying on expensive and time-consuming approaches to minimize customer churn, telecommunications companies and those in high-churn industries are turning to machine learning. The following graphic illustrates how defining risk models help determine how actions aimed at averting churn affect churn impact probability and risk. An intervention model allows marketers to consider how the level of intervention could affect the probability of churn and the amount of customer lifetime value (CLV). Source: Analyzing Customer Churn by using Azure Machine Learning.

Price optimization and price elasticity are growing beyond industries with limited inventories including airlines and hotels, proliferating into manufacturing and services

All marketers are increasingly relying on machine learning to define more competitive, contextually relevant pricing. Machine learning apps are scaling price optimization beyond airlines, hotels, and events to encompass product and services pricing scenarios. Machine learning is being used today to determine pricing elasticity by each product, factoring in channel segment, customer segment, sales period and the product’s position in an overall product line pricing strategy. The following example is from Microsoft Azure’s Interactive Pricing Analytics Pre-Configured Solution (PCS). Source: Azure Cortana Interactive Pricing Analytics Pre-Configured Solution.

Improving demand forecasting, assortment efficiency and pricing in retail marketing have the potential to deliver a 2% improvement in Earnings Before Interest & Taxes (EBIT), 20% stock reduction and 2 million fewer product returns a year

In Consumer Packaged Goods (CPQ) and retail marketing organizations, there’s significant potential for AI and machine learning to improve the entire value chain’s performance. McKinsey found that using a concerted approach to applying AI and machine learning across a retailer’s value chains has the potential to deliver a 50% improvement of assortment efficiency and a 30% online sales increase using dynamic pricing. Source:  Artificial Intelligence: The Next Frontier? McKinsey Global Institute (PDF, 80 pp., no opt-in)

Creating and fine-tuning propensity models that guide cross-sell and up-sell strategies by product line, customer segment, and persona

It’s common to find data-driven marketers building and using propensity models to define the products and services with the highest probability of being purchased. Too often propensity models are based on imported data, built in Microsoft Excel, making their ongoing use time-consuming. Machine learning is streamlining creation, fine-tuning and revenue contributions of up-sell and cross-sell strategies by automating the entire progress. The screen below is an example of a propensity model.

Lead scoring accuracy is improving, leading to increased sales that are traceable back to initial marketing campaigns and sales strategies

By using machine learning to qualify the further customer and prospect lists using relevant data from the web, predictive models including machine learning can better predict ideal customer profiles. Each sales lead’s predictive score becomes a better predictor of potential new sales, helping sales prioritize time, sales efforts and selling strategies. The following two slides are from an excellent webinar Mintigo hosted with Sirius Decisions and Sales Hacker. It’s a fascinating look at how machine learning is improving sales effectiveness. Source: Give Your SDRs An Unfair Advantage with Predictive (webinar slides on Slideshare).

Identifying and defining the sales projections of specific customer segments and microsegments using RFM (recency, frequency and monetary) modeling within machine learning apps is becoming pervasive

Using RFM analysis as part of a machine learning initiative can provide accurate definitions of the best customers, most loyal, biggest spenders, almost lost, lost customers and lost cheap customers.

Optimizing the marketing mix by determining which sales offers, incentive and programs are presented to which prospects through which channels is another way machine learning is revolutionizing marketing

Specific sales offers are created supported by contextual content, offers, and incentives. These items are made available to an optimization engine which uses machine learning logic to continually try to predict the best combination of marketing mix elements that will lead to a new sale, up-sell or cross-sell. Amazon’s product recommendation feature is an example of how their e-commerce site is using machine learning to increase up-sell, cross-sell and recommended products revenue.

Data sources on machine learning’s impact on marketing:

4 Ways to Use Machine Learning in Marketing Automation, Medium, March 30, 2017

84 percent of B2C marketing organizations are implementing or expanding AI in 2018. Infographic. Amplero.
AI, Machine Learning, and their Application for Growth, Adelyn Zhou. SlideShare/LinkedIn.  Feb. 8, 2018.

AI: The Next Generation of Marketing Driving Competitive Advantage throughout the Customer Life Cycle (PDF, 10 pp., no opt-in), Forrester, February 2017.

An Executive’s Guide to Machine Learning, McKinsey Quarterly. June 2015.

Artificial Intelligence for Marketers 2018: Finding Value beyond the Hype, eMarketer. (PDF, 20 pp., no opt-in). October 2017

Artificial Intelligence: The Next Frontier? McKinsey Global Institute (PDF, 80 pp., no opt-in)

Artificial Intelligence: The Ultimate Technological Disruption Ascends, Woodside Capital Partners. (PDF, 111 pp., no opt-in). January 2017.

AWS Announces Amazon Machine Learning Solutions Lab, Marketing Technology Insights

B2B Predictive Marketing Analytics Platforms: A Marketer’s Guide, (PDF, 36 pp., no opt-in) Marketing Land Research Report.
Four Use Cases of Machine Learning in Marketing, June 28, 2018, Martech Advisor,
How Artificial Intelligence and Machine Learning Will Reshape Small Businesses, SMB Group (PDF, 8 pp., no opt-in) May 2017.

How Machine Learning Helps Sales Success (PDF, 12 pp., no opt-in) Cognizant

Inside Salesforce Einstein Artificial Intelligence A Look at Salesforce Einstein Capabilities, Use Cases and Challenges, Doug Henschen, Constellation Research, February 15, 2017

Machine Learning for Marketers (PDF, 91 pp., no opt-in) iPullRank

Machine Learning Marketing – Expert Consensus of 51 Executives and Startups, TechEmergence. May 15, 2017.

Marketing & Sales Big Data, Analytics, and the Future of Marketing & Sales, (PDF, 60 pp., no opt-in), McKinsey & Company.

Sizing the prize – What’s the real value of AI for your business and how can you capitalize? (PDF, 32 pp., no opt-in) PwC, 2017.

The New Frontier of Price Optimization, MIT Technology Review. September 07, 2017.

The Power Of Customer Context, Forrester (PDF, 20 pp., no opt-in) Carlton A. Doty, April 14, 2014. Provided courtesy of Pegasystems.

Turning AI into concrete value: the successful implementers’ toolkit, Capgemini Consulting. 2017. (PDF, 28 pp., no opt-in)

Using machine learning for insurance pricing optimization, Google Cloud Big Data and Machine Learning Blog, March 29, 2017

What Marketers Can Expect from AI in 2018, Jacob Shama. Mintigo. January 16, 2018.