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Posts tagged ‘Cloud Computing’

Seven Ways Microsoft Redefined Azure For The Enterprise And Emerged A Leader

  • cloud startupsAs of Q2, 2016 Microsoft Azure has achieved 100% year-over-year revenue growth and now has the 2nd largest market share of the Cloud Infrastructure Services market according to Synergy Research.
  • Microsoft’s FY16 Q4 earnings show that Azure attained 102% revenue growth in the latest fiscal year and computing usage more than doubling year-over-year.
  • 451 Research predicts critical enterprise workload categories including data, analytics, and business applications will more than double from 7% to 16% for data workloads and 4% to 9% for business applications.
  • Cloud-first workload deployments in enterprises are becoming more common with 38% of respondents to a recent 451Research survey stating their enterprises are prioritizing cloud over on-premise.

451 Research’s latest study of cloud computing adoption in the enterprise, The Voice of the Enterprise: Cloud Transformation – Workloads and Key Projects provides insights into how enterprises are changing their adoption of public, private and hybrid cloud for specific workloads and applications. The research was conducted in May and June 2016 with more than 1,200 IT professionals worldwide. The study illustrates how quickly enterprises are adopting cloud-first deployment strategies to accelerate time-to-market of new apps while reducing IT costs and launch new business models that are by nature cloud-intensive. Add to this the need all enterprises have to forecast and track cloud usage, costs and virtual machine (VM) usage and value, and it becomes clear why Amazon Web Services (AWS) and Microsoft Azure are now leaders in the enterprise. The following graphic from Synergy Research Group’s latest study of the Cloud Infrastructure Services provides a comparison of AWS, Microsoft Azure, IBM, Google, and others.

Cloud Infrastructure Services

Seven Ways Microsoft Is Redefining Azure For The Enterprise

Being able to innovate faster by building, deploying and managing applications globally on a single cloud platform is what many enterprises are after today. And with over 100 potential apps on their cloud roadmaps, development teams are evaluating cloud platforms based on their potential contributions to new app development and business models first.

AWS and Microsoft Azure haven proven their ability to support new app development and deployment and are the two most-evaluated cloud platforms with dev teams I’ve talked with today. Of the two, Microsoft Azure is gaining momentum in the enterprise.

Here are the seven ways Microsoft is making this happen:

  • Re-orienting Microsoft Azure Cloud Services strategies so enterprise accounts can be collaborators in new app creation. Only Microsoft is coming at selling Cloud Services in the enterprise from the standpoint of how they can help do what senior management teams at their customers want most, which is make their app roadmap a reality. AWS is excellent at ISV and developer support, setting a standard in this area.
  • Giving enterprises the option of using existing relational SQL databases, noSQL data stores, and analytics services when building new cloud apps. All four dominant cloud platforms (AWS, Azure, Google, and IBM) support architectures, frameworks, tools and programming languages that enable varying levels of compatibility with databases, data stores, and analytics. Enterprises that have a significant amount of their legacy app inventory in .NET are choosing Azure for cloud app development. Microsoft’s support for Node.js, PHP, Python and other development languages is at parity with other cloud platforms. Why Microsoft Azure is winning in this area is the designed-in support for legacy Microsoft architectures that enterprises standardized their IT infrastructure on years before. Microsoft is selling a migration strategy here and is providing the APIs, web services, and programming tools to enable enterprises to deliver cloud app roadmaps faster as a result. Like AWS, Microsoft also has created a global development community that is developing and launching apps specifically aimed at enterprise cloud migration.  Due to all of these factors, both AWS and Microsoft are often considered more open cloud platforms by enterprises than others. In contrast, Salesforce platforms are becoming viewed as proprietary, charging premium prices at renewal time. An example of this strategy is the extra 20% Salesforce charges for Lightning experience at renewal time according to Gartner in their recent report, Salesforce Lightning Sales Cloud and Service Cloud Unilaterally Replaced Older Editions; Negotiate Now to Avoid Price Increases and Shelfware Published 31 May 2016, written by analysts Jo Liversidge, Adnan Zijadic.
  • Simplifying cloud usage monitoring, consolidated views of cloud fees and costs including cost predictions and working with enterprises to create greater cloud standardization and automation. AWS’ extensive partner community has solutions that address each of these areas, and AWS’ roadmap reflects this is a core focus of current and future development. The AWS platform has standardization and automation as design objectives for the platform. Enterprises evaluating Azure are running pilots to test the Azure Usage API, which allows subscribing services to pull usage data. This API supports reporting to the hourly level, resource metadata information, and supports Showback and Chargeback models. Azure deployments in production and pilots I’ve seen are using the API to build web services and dashboards to measure and predict usage and costs.
  • Openly addressing Total Cost of Ownership (TCO) concerns and providing APIs and Web services to avoid vendor lock-in. The question of data independence and TCO dominates sustainability and expansion of all cloud decisions. From the CIOs, CFOs and design teams I’ve spoken with, Microsoft and Amazon are providing enterprises assistance in defining long-term cost models and are willing to pass along the savings from economies of scale achieved on their platforms. Microsoft Azure is also accelerating in the enterprise due to the pervasive adoption of the many cloud-based subscriptions of Office365, which enables enterprises to begin moving their workloads to the cloud.
  • Having customer, channel, and services all on a single, unified global platform to gain greater insights into customers and deliver new apps faster. Without exception, every enterprise I’ve spoken with regarding their cloud platform strategy has multichannel and omnichannel apps on their roadmap. Streamlining and simplifying the customer experience and providing them with real-time responsiveness drive the use cases of the new apps under development today. Salesforce has been successful using their platform to replace legacy CRM systems and build the largest community of CRM and sell-side partners globally today.
  • Enabling enterprise cloud platforms and apps to globally scale. Nearly every enterprise looking at cloud initiatives today needs a global strategy and scale. From a leading telecom provider based in Russia looking to scale throughout Asia to financial services firms in London looking to address Brexit issues, each of these firms’ cloud apps roadmaps is based on global scalability and regional requirements. Microsoft has 108 data centers globally, and AWS operates 35 Availability Zones within 13 geographic Regions around the world, with 9 more Availability Zones and 4 more Regions coming online throughout the next year. To expand globally, Salesforce chose AWS as their preferred cloud infrastructure provider. Salesforce is not putting their IOT and earlier Heroku apps on Amazon. Salesforces’ decision to standardize on AWS for global expansion and Microsoft’s globally distributed data centers show that these two platforms have achieved global scale.
  • Enterprises are demanding more control over their security infrastructure, network, data protection, identity and access control strategies, and are looking for cloud platforms that provide that flexibility. Designing, deploying and maintaining enterprise cloud security models is one of the most challenging aspects of standardizing on a cloud platform. AWS, Azure, Google and IBM all are prioritizing research and development (R&D) spending in this area. Of the enterprises I’ve spoken with, there is an urgent need for being able to securely connect virtual machines (VMs) within a cloud instance to on-premise data centers. AWS, Azure, Google, and IBM can all protect VMs and their network traffic from on-premise to cloud locations. AWS and Azure are competitive to the other two cloud platforms in this area and have enterprises running millions of VMs concurrently in this configuration and often use that as a proof point to new customers evaluating their platforms.

Bottom line: Amazon AWS and Microsoft Azure are the first cloud platforms proving they can scale globally to support enterprises’ vision of world-class cloud app portfolio development.


451 Research: The Voice of the Enterprise: Cloud Transformation – Workloads and Key Projects

Gartner Magic Quadrant for Cloud Infrastructure as a Service, Worldwide 2016 Reprint

Microsoft Earnings Release FY16 Q4 – Azure revenue grows 102% year-over-year

Synergy Research Group’s latest study of the Cloud Infrastructure Services


5 Ways Brexit Is Accelerating AWS And Public Cloud Adoption

  • London sykline duskDeutsche Bank estimates AWS derives about 15% of its total revenue mix or has attained a $1.5B revenue run rate in Europe.
  • AWS is now approximately 6x the size of Microsoft Azure globally according to Deutsche Bank.

These and other insights are from the research note published earlier this month by Deutsche Bank Markets Research titled AWS/Cloud Adoption in Europe and the Brexit Impact written by Karl Keirstead, Alex Tout, Ross Sandler, Taylor McGinnis and Jobin Mathew.  The research note is based on discussions the research team had with 20 Amazon Web Services (AWS) customers and partners at the recent AWS user conference held in London earlier this month, combined with their accumulated research on public cloud adoption globally.

These are the five ways Brexit will accelerate AWS and public cloud adoption:

  • The proliferation of European-based data centers is bringing public cloud stability to regions experiencing political instability. AWS currently has active regions in Dublin and Frankfurt, with the former often being used by AWS’ European customers due to the broader base of services offered there. An AWS Region is a physical geographic location where there is a cluster of data centers. Each region is made up of isolated locations known as availability zones. AWS is adding a third European Union (EU) region in the UK with a go-live date of late 2016 or early 2017. Microsoft has 2 of its 26 global regions in Europe, with two more planned in the UK.  Google’s Cloud Platform (GCP) has just one region active in Europe. The following Data Center Map provides an overview of data centers AWS, Microsoft Azure and GCP have in Europe today and planned for the future.

Data Center Map

  • Brexit is making data sovereignty king. European-based enterprises have long been cautious about using cloud platforms to store their many forms of data. Brexit is accelerating the needs European enterprises have for greater control over their data, especially those based in the UK.  Amazon’s planned third EU region based in London scheduled to go live in late 2016 or early 2017 is well-timed to capitalize on this trend.
  • Up-front costs of utilizing AWS are much lower and increasingly trusted relative to more expensive on-premise  IT platforms. Brexit is having the immediate effect of slowing down sales cycles for managed hosting, enterprise-wide hardware and software maintenance agreements. The research team found that the uncertainty of just how significant the economic impact Brexit will have on the European economies is making companies tighten capital expense (CAPEX) budgets and trim expensive maintenance agreements.  UK enterprises are reverting to OPEX spending that is already budgeted.
  • CEOs are pushing CIOs to get out of high-cost hardware and on-premise software agreements to better predict operating costs faster thanks to Brexit. The continual pressure on CIOs to reduce the high hardware and software maintenance costs is accelerating thanks to Brexit. Because no one can quantify with precision just how Brexit will impact European economies, CEOs, and senior management teams want to minimize downside risk now. Because of this, the cloud is becoming a more viable option according to Deutsche Bank. One reseller said that public cloud computing platforms are a great answer to a recession, and their clients see Brexit as a catalyst to move more workloads to the cloud.
  • Brexit will impact AWS Enterprise Discount Program (EDP) revenues, forcing a greater focus on incentives for low-end and mid-tier services. Deutsche Bank Markets Research team reports that AWS has this special program in place for its very largest customers. Under an EDP, AWS will give price discounts to large customers that commit to a full year (or more) and pay upfront, in many cases with minimum volume increases. One AWS partner told Deutsche Bank that they’re aware of one EDP payment of $25 million. In the event of a recession in Europe, it’s possible that such payments could be at risk. These market dynamics will drive AWS to promote further low- and mid-tier services to attract new business to balance out these larger deals.

10 Ways Machine Learning Is Revolutionizing Manufacturing

machine learningBottom line: Every manufacturer has the potential to integrate machine learning into their operations and become more competitive by gaining predictive insights into production.

Machine learning’s core technologies align well with the complex problems manufacturers face daily. From striving to keep supply chains operating efficiently to producing customized, built- to-order products on time, machine learning algorithms have the potential to bring greater predictive accuracy to every phase of production. Many of the algorithms being developed are iterative, designed to learn continually and seek optimized outcomes. These algorithms iterate in milliseconds, enabling manufacturers to seek optimized outcomes in minutes versus months.

The ten ways machine learning is revolutionizing manufacturing include the following:

  • Increasing production capacity up to 20% while lowering material consumption rates by 4%. Smart manufacturing systems designed to capitalize on predictive data analytics and machine learning have the potential to improve yield rates at the machine, production cell, and plant levels. The following graphic from General Electric and cited in a National Institute of Standards (NIST) provides a summary of benefits that are being gained using predictive analytics and machine learning in manufacturing today.

typical production improvemensSource: Focus Group: Big Data Analytics for Smart Manufacturing Systems

  • Providing more relevant data so finance, operations, and supply chain teams can better manage factory and demand-side constraints. In many manufacturing companies, IT systems aren’t integrated, which makes it difficult for cross-functional teams to accomplish shared goals. Machine learning has the potential to bring an entirely new level of insight and intelligence into these teams, making their goals of optimizing production workflows, inventory, Work In Process (WIP), and value chain decisions possible.

factory and demand analytics

Source:  GE Global Research Stifel 2015 Industrials Conference

  • Improving preventative maintenance and Maintenance, Repair and Overhaul (MRO) performance with greater predictive accuracy to the component and part-level. Integrating machine learning databases, apps, and algorithms into cloud platforms are becoming pervasive, as evidenced by announcements from Amazon, Google, and Microsoft. The following graphic illustrates how machine learning is integrated into the Azure platform. Microsoft is enabling Krones to attain their Industrie 4.0 objectives by automating aspects of their manufacturing operations on Microsoft Azure.

Azure IOT Services

Source: Enabling Manufacturing Transformation in a Connected World John Shewchuk Technical Fellow DX, Microsoft

  • Enabling condition monitoring processes that provide manufacturers with the scale to manage Overall Equipment Effectiveness (OEE) at the plant level increasing OEE performance from 65% to 85%. An automotive OEM partnered with Tata Consultancy Services to improve their production processes that had seen Overall Equipment Effectiveness (OEE) of the press line reach a low of 65 percent, with the breakdown time ranging from 17-20 percent.  By integrating sensor data on 15 operating parameters (such as oil pressure, oil temperature, oil viscosity, oil leakage, and air pressure) collected from the equipment every 15 seconds for 12 months. The components of the solution are shown

OEE Graphic

Source: Using Big Data for Machine Learning Analytics in Manufacturing

  • Machine learning is revolutionizing relationship intelligence and Salesforce is quickly emerging as the leader. The series of acquisitions Salesforce is making positions them to be the global leader in machine learning and artificial intelligence (AI). The following table from the Cowen and Company research note, Salesforce: Initiating At Outperform; Growth Engine Is Well Greased published June 23, 2016, summarizes Salesforce’s series of machine learning and AI acquisitions, followed by an analysis of new product releases and estimated revenue contributions. Salesforce’s recent acquisition of e-commerce provider Demandware for $2.8B is analyzed by Alex Konrad is his recent post,     Salesforce Will Acquire Demandware For $2.8 Billion In Move Into Digital Commerce. Cowen & Company predicts Commerce Cloud will contribute $325M in revenue by FY18, with Demandware sales being a significant contributor.

Salesforce AI Acquisitions

Salesforce revenue sources

  • Revolutionizing product and service quality with machine learning algorithms that determine which factors most and least impact quality company-wide. Manufacturers often are challenged with making product and service quality to the workflow level a core part of their companies. Often quality is isolated. Machine learning is revolutionizing product and service quality by determining which internal processes, workflows, and factors contribute most and least to quality objectives being met. Using machine learning manufacturers will be able to attain much greater manufacturing intelligence by predicting how their quality and sourcing decisions contribute to greater Six Sigma performance within the Define, Measure, Analyze, Improve, and Control (DMAIC) framework.
  • Increasing production yields by the optimizing of team, machine, supplier and customer requirements are already happening with machine learning. Machine learning is making a difference on the shop floor daily in aerospace & defense, discrete, industrial and high-tech manufacturers today. Manufacturers are turning to more complex, customized products to use more of their production capacity, and machine learning help to optimize the best possible selection of machines, trained staffs, and suppliers.
  • The vision of Manufacturing-as-a-Service will become a reality thanks to machine learning enabling subscription models for production services. Manufacturers whose production processes are designed to support rapid, highly customized production runs are well positioning to launch new businesses that provide a subscription rate for services and scale globally. Consumer Packaged Goods (CPG), electronics providers and retailers whose manufacturing costs have skyrocketed will have the potential to subscribe to a manufacturing service and invest more in branding, marketing, and selling.
  • Machine learning is ideally suited for optimizing supply chains and creating greater economies of scale.  For many complex manufacturers, over 70% of their products are sourced from suppliers that are making trade-offs of which buyer they will fulfill orders for first. Using machine learning, buyers and suppliers could collaborate more effectively and reduce stock-outs, improve forecast accuracy and met or beat more customer delivery dates.
  • Knowing the right price to charge a given customer at the right time to get the most margin and closed sale will be commonplace with machine learning.   Machine learning is extending what enterprise-level price optimization apps provide today.  One of the most significant differences is going to be just how optimizing pricing along with suggested strategies to close deals accelerate sales cycles.

Additional reading:

Cisco Blog: Deus Ex Machina: Machine Learning Acts to Create New Business Outcomes

Enabling Manufacturing Transformation in a Connected World John Shewchuk Technical Fellow DX, Microsoft 

Focus Group: Big Data Analytics for Smart Manufacturing Systems

GE Predix: The Industrial Internet Platform

IDC Manufacturing Insights reprint courtesy of Cisco: Designing and Implementing the Factory of the Future at Mahindra Vehicle Manufacturers

Machine Learning: What It Is And Why It Matters

McKinsey & Company, An Executive’s Guide to Machine Learning

MIT Sloan Management Review, Sales Gets a Machine-Learning Makeover

Stanford University CS 229 Machine Learning Course Materials
The Economist Feature On Machine Learning

UC Berkeley CS 194-10, Fall 2011: Introduction to Machine Learning
Lecture slides, notes

University of Washington CSE 446 – Machine Learning – Winter 2014


Lee, J. H., & Ha, S. H. (2009). Recognizing yield patterns through hybrid applications of machine learning techniques. Information Sciences, 179(6), 844-850.

Mackenzie, A. (2015). The production of prediction: What does machine learning want?. European Journal of Cultural Studies, 18(4-5), 429-445.

Pham, D. T., & Afify, A. A. (2005, July). Applications of machine learning in manufacturing. In Intelligent Production Machines and Systems, 1st I* PROMS Virtual International Conference (pp. 225-230).

Priore, P., de la Fuente, D., Puente, J., & Parreño, J. (2006). A comparison of machine-learning algorithms for dynamic scheduling of flexible manufacturing systems. Engineering Applications of Artificial Intelligence, 19(3), 247-255.

Internet of Things, Machine Learning & Robotics Are High Priorities For Developers In 2016

  • 200213603-00156.4% of developers are building robotics apps today.
  • 45% of developers say that Internet of Things (IoT) development is critical to their overall digital strategy.
  • 27.4% of all developers are building apps in the cloud today.
  • 24.7% are using machine learning for development projects.

These and many other insights are from the Evans Data Corporation Global Development Survey, Volume 1 (PDF, client access) published earlier this month. The methodology was based on interviews with developers actively creating new applications with the latest technologies. The Evans Data Corporation (EDC), International Panel of Developers, were sent invitations to participate and complete the survey online. 1,441 developers completed the survey globally. Please see page 17 of the study for additional details on the methodology.

Key takeaways from the study include the following:

  • Big Data analytics developers are spending the majority of their time creating Internet of Things (IoT).  The second-most popular Big Data analytics applications are in professional, scientific and technical services (10%), telecommunications (10%), and manufacturing (non-computer related) (9.6%). The following graphic provides an overview of where Big Data analytics developers are investing their time building new applications.

Best Describes App

  • Robotics (56.4%), Arts, Entertainment and Recreation (56.3%), and Automotive (52.9%) are the three most popular industries data mining app developers are focusing on today. Additional high priority industries include telecommunications (48.3%), Internet of Things (47.1%) and manufacturing (46.7%). A graphic from the study is shown below for reference.

Data Mining adoption

  • Nearly one-third (27.4%) of all app developers globally are planning to build new apps on the cloud. 66.9% expect to have a new cloud app within 12 months. Overall, 81.3% of all developers surveyed are building cloud apps today. The following graphic compares developers’ predicted timeframes for cloud app development over the next two years.

Plans for Apps In the Clouds

  • Better security (51.9%), more reliability (42%) and better user experience (41%) are the top three areas that motivate developers to move to new cloud platforms. Additional considerations include a better breadth of services (39.4%), networking and data center speed (37.8%), better pricing options (37.5%), better licensing structures (34.6%) and completeness of vision (30.9%). The following graphic compares the key factors that most motivate developers to switch cloud platforms.

key factors

  • 45% of developers say that Internet of Things (IoT) development is very important to their overall digital strategy. 7% say that IoT is somewhat important to their digital strategy. The study also found that 29.5% of all developers are creating Internet of Things (IoT) apps today. The following graphic illustrates the relative level of importance of IoT to developers’ digital strategies.

importance of IoT strategy

  • 41% say that cognitive computing and artificial intelligence (AI) are very important to their digital strategies. In speaking with senior executives at services firms, the opportunity to provide artificial intelligence-based services using a subscription model is gaining momentum, with many beginning to fund development projects to accomplish this on a global scale.

AI Importance

  • Most frequently created machine learning apps include those for the Internet of Things (11.4%), Professional, Scientific and Technical Services (10%), and Manufacturing (9.4%) industries.  Additional industries include telecommunications (8.3%), utilities/energy (8.1%), robotics (7.2%) and finance or insurance (6.8%). The following graphic breaks out the industries where machine learning app development is happening today.

Machine learning industries final

  • The majority of developers (84.2%) say that analytics is important for enabling their organizations to operate today. Of that group, 45.7% say that analytics are very important for their organizations to attain their goals.

2015 Gartner CRM Market Share Update

  • Worldwide customer relationship management (CRM) software totaled $26.3B in 2015, up 12.3% from $23.4B in 2014.
  • SaaS revenue grew 27% yr-over-yr, more than double overall CRM market growth in 2015.
  • Asia/Pacific grew the fastest of all regions globally, increasing 9% 2015, closely followed by greater China with 18.4% growth.

These and many other insights into the current state of the global CRM market are from Gartner’s Market Share Analysis: Customer Relationship Management Software, Worldwide, 2015 (PDF, client access) published earlier this month.  The top five CRM vendors accounted for 45% of the total market in 2015. Salesforce dominated in 2015, with a 21.1% annual growth rate and absolute growth of over $902M in CRM revenue, more than the next ten providers combined. Gartner found that Salesforce leads in revenue in the sales and customer service and support (CSS) segments of CRM, and is now third in revenue in the marketing segment. Gartner doesn’t address how analytics are fundamentally redefining CRM today, which is an area nearly every C-level and revenue team leader I’ve spoken with this year is prioritizing for investment. The following graphic and table compare 2015 worldwide CRM market shares.

CRM Market Share 2015

table 1

Adobe, Microsoft, and Salesforce Are Growing Faster Than The Market

Adobe grew the fastest between 2014 and 2015, increasing worldwide sales 26.9%. Salesforce continues to grow well above the worldwide CRM market average, increasing sales 21.1%. Microsoft increased sales 20% in the last year.  The worldwide CRM market grew 12.3% between 2014 and 2015.

Spending by vendor 2015

 Analytics, Machine Learning, and Artifical Intelligence Are The Future Of CRM

Advanced analytics, machine learning and artificial intelligence (AI) will revolutionize CRM in the next three years. Look to the five market leaders in 2015 to invest heavily in these areas with the goal of building patent portfolios and increasing the amount of intellectual property they own. Cloud-based analytics platforms offer the scale, speed of deployment, agility, and ability to rapidly prototype analytics workflows that support the next generation of CRM workflows. My recent post on SelectHub, Selecting The Best Cloud Analytics Platform: Trends To Watch In 2016, provides insights into how companies with investments in CRM systems are making decisions on cloud platforms today. Based on insights gained from discussions with senior management teams, I’ve put together an Intelligent Cloud Maturity Model that underscores why scalability of a cloud-based analytics platform is a must-have for any company.

Sources:  Gartner Says Customer Relationship Management Software Market Grew 12.3 Percent

The Era Of The Intelligent Cloud Has Arrived

intelligent cloudBottom line: Enterprises are impatient to translate their investments in cloud apps and the insight they provide into business outcomes and solid results today.

The following insights are based on a series of discussions with C-level executives and revenue team leaders across several industries regarding their need for an Intelligent Cloud:

  • In the enterprise, the cloud versus on-premise war is over, and the cloud has won. Nearly all are embracing a hybrid cloud strategy to break down the barriers that held them back from accomplishing more.
  • None of the C-level executives I’ve spoken with recently are satisfied with just measuring cloud adoption. All are saying the want to measure business outcomes and gain greater insights into how they can better manage revenue and sales cycles.
  • Gaining access to every available legacy and 3rd party system using hybrid cloud strategies is the new normal. Having data that provides enterprise-wide visibility gives enterprises greater control over every aspect of their selling and revenue management processes. And when that’s accomplished, the insights gained from the Intelligent Cloud can quickly be turned into results.

Welcome to the Era of the Intelligent Cloud

The more enterprises seek out insights to drive greater business outcomes, the more it becomes evident the era of the Intelligent Cloud has arrived. C-level execs are looking to scale beyond descriptive analytics that defines past performance patterns.  What many are after is an entirely new level of insights that are prescriptive and cognitive. Getting greater insight that leads to more favorable business outcomes is what the Intelligent Cloud is all about. The following Intelligent Cloud Maturity Model summarizes the maturity levels of enterprises attempting to gain greater insights and drive more profitable business outcomes.

maturity model


Why The Intelligent Cloud Now?  

Line-of-business leaders across all industries want more from their cloud apps than they are getting today. They want the ability to gain greater insights with prescriptive and cognitive analytics. They’re also asking for new apps that give them the flexibility of changing selling behaviors quickly.  In short, everyone wants to get to the orchestration layer of the maturity model, and many are stuck staring into a figurative rearview mirror, using just descriptive data to plan future strategies.  The future of enterprise cloud computing is all about being able to deliver prescriptive and cognitive intelligence.
Consider the following takeaways:


Who Is Delivering The Intelligent Cloud Today?

Just how far advanced the era of the Intelligent Cloud is became apparent during the Microsoft Build Developer Conference last week in San Francisco.  A fascinating area discussed was Microsoft Cognitive Services and their implications on the Cortana Intelligence Suite. Microsoft is offering a test drive of Cognitive Services here. Combining Cognitive Services and the Cortana Intelligence Suite, Microsoft has created a framework for delivering the Intelligent Cloud. The graphic below shows the Cortana Analytics Suite.
Cortana suite


The Best Cloud Computing Companies And CEOs To Work For In 2016

careeer startEmployees would most recommend Zerto, FusionOps, Google, OutSystems, AppDirect, Sumo Logic, Cloudera, HyTrust, Tableau Software and Domo to their friends looking for a cloud computing company to work for in 2016. These and other insights are from an analysis completed today to determine the best cloud computing firms and CEOs to work for this year.

To keep the rankings and analysis completely impartial and fair, the latest Computer Reseller News list, The 100 Coolest Cloud Computing Vendors Of 2016 is the basis of the rankings. Cloud computing companies are among the most competitive there are about salaries, performance and sign-on bonuses and a myriad of perks and benefits. They are also attracting senior management teams that have strong leadership skills, many of whom are striving to create distinctive company cultures. The most popular request from Forbes readers are for recommendations of the best cloud computing companies to work for, and that’s what led to this analysis.

Using the 2016 CRN list as a baseline to compare the scores of the (%) of employees who would recommend this company to a friend and (%) of employees who approve of the CEO, the table below is provided. You can find the original data set here. There are many companies listed on the CRN list that doesn’t have than many or any entries on Glassdoor, and they are excluded from the rankings shown below but are in the original data set. If the image below is not visible in your browser, you can view the rankings here.

best cloud computing companies to work for in 2016 large

The highest rated CEOs on Glassdoor as of February 3rd, 2016 include the following:

  • Ziv Kedem, Zerto, 100%
  • Gary Meyers, FusionOps, 100%
  • Christian Chabot, Tableau Software, 100%
  • John Burton, Nintex, 100%
  • Rob Mee, Pivotal, 100%
  • Rajiv Gupta, Skyhigh Networks, 100%
  • Ken Shaw Jr., Infrascale, 100%
  • Beau Vrolyk, Engine Yard, 100%
  • Ramin Sayar, Sumo Logic, 99%
  • Sundar Pichai, Google, 98%
  • Lew Cirne, New Relic, 97%
  • Daniel Saks, AppDirect, 96%
  • James M. Whitehurst, Red Hat, 96%
  • Marc Benioff, Salesforce, 96%
  • Tom Kemp, Centrify, 95%
  • Jeremy Roche, FinancialForce, 95%

Five Key Take-Aways From North Bridge’s Future Of Cloud Computing Survey, 2015  

  • bostonSaaS is the most pervasive cloud technology used today with a presence in 77.3% of all organizations, an increase of 9% since 2014.
  • IT is moving significant processing to the cloud with 85.9% of web content management, 82.7% of communications, 80% of app development and 78.9% of disaster recovery now cloud-based.
  • Seeking simple and clear relationships, over 50% of enterprises opt for online purchasing or direct to provider purchasing of cloud services. Online buying is projected to increase over the next two years up to 56%.
  • Vendor leadership/consolidation continues to take hold with 75% of enterprises using fewer than ten

These and many other insights are from North Bridge Growth Equity and Venture Partners’ Future of Cloud Computing Survey published on December 15th. North Bridge and Wikibon collaborated on the study, interviewing 952 companies across 38 different nations, with 65% being from the vendor community and 35% of enterprises evaluating and using cloud technologies in their operations  The slide deck is accessible on SlideShare here:

Key takeaways from the study include the following:

  1. Wikibon forecasts the SaaS is worth $53B market today and will grow at an 18% Compound Annual Growth Rate (CAGR) from 2014 to 2026. By 2026, the SaaS market will be worth $298.4B according to the Wikibon forecast. The fastest growing cloud technology segment is Platform-as-a-Service (PaaS), which is valued at $2.3B today, growing at a CAGR of 38% from 2014 to 2026.  Infrastructure-as-a-Service (IaaS) has a market value of $25B and is growing at a 19% CAGR in the forecast period.  Please see the graphic from the report below and a table from Wikibon’s excellent study, Public Cloud Market Forecast 2015-2026 by Ralph Finos published in August.

SaaS Graphic from North Bridge study


Public Cloud Vendor Revenue Projection

  1. Cloud-based applications are becoming more engrained in core business processes across enterprises. The study found that enterprises are migrating significant processing, systems of engagement and systems of insight to the cloud beyond adoption levels of the past.  81.3% of sales and marketing, 79.9% of business analytics, 79.1% of customer service and 73.5% of HR & Payroll activities have transitioned to the cloud. The impact on HR is particularly noteworthy as in 2011; it was the third least likely sector to be disrupted by cloud computing.
  1. 78% of enterprises expect their SaaS investments to deliver a positive Return on Investment (ROI) in less than three months. 58% of those enterprises who have invested in Platform-as-a-Service (PaaS) expect a positive ROI in less than three months.
  1. Top inhibitors to cloud adoption are security (45.2%), regulatory/compliance (36%), privacy (28.7%), lock-in (25.8%) and complexity (23.1%). Concerns regarding interoperability and reliability have fallen off significantly since 2011 (15.7% and 9.9% respectively in 2015).
  1. Total private financing for cloud and SaaS startup has increased 4X over the last five years. North Bridge and Wikibon found that average deal size rose 1.8X in the same period. The following graphic provides an overview of cloud and SaaS finance trends from 2010 to present.

cloud and saas financing


5 Insights & Predictions On Disruptive Tech From KPMG’s 2015 Global Innovation Survey

  • cloud computing survey 215% of U.S. tech leaders see biotech/digital health/healthcare IT as the most disruptive consumer-driven technology in the next three years.
  • 13% of U.S. tech leaders predict data and analytics will be the most disruptive enterprise technology in three years.
  • Global tech leaders predict cloud computing (11%), mobile platforms and apps (9%), Internet of Things (IoT)/machine-to-machine (M2M) (9%) and data and analytics (9%) will be the most disruptive technologies over the next three years.

These and many other insights are from the fourth annual 2015 Global Technology Innovation Survey released via webcast by KPMG last month. KPMG surveyed 832 technology industry business leaders globally, with the majority of being C-level executives (87%). Respondents were selected from a broad spectrum of businesses including tech industry startups, mid- and large-scale enterprises, angel investors and venture capital firms. For an in-depth explanation of the survey methodology, please see slides 6 and 7 of the webinar presentation. The goals of the survey include spotting disruptive technologies, identifying tech innovation barriers and opportunities, and tracking emerging tech innovation hubs.

The five insights and predictions from the report include the following:

  • Global tech leaders predict cloud computing (11%), mobile platforms and apps (9%), Internet of Things (IoT)/M2M (9%) and data and analytics (9%) will be the most disruptive technologies over the next three years.  U.S. tech leaders predict biotech/digital health/healthcare IT (15%), data and analytics (14%) and cloud computing (14%) will be the three most disruptive technologies over the next three years.  Chinese tech leaders predict artificial intelligence/cognitive computing (15%) will be the most disruptive technology impacting the global business-to-consumer (B2C) marketplace.

tech driving consumer technologies

  • The three most disruptive technologies predicted to drive business transformation in enterprises over the next three years in the U.S. include cloud computing (13%), data and analytics (13%), and cyber security (10%). Japanese tech leaders predict artificial intelligence/cognitive computing will have the greatest effect (23%), and 14% of Chinese tech leaders predict the Internet of Things/M2M (14%) will have the greatest impact on business transformation in their country.  The following table compares global tech leader’s predictions of which technologies will disrupt enterprises the most and drive business transformation over the next three years.

business transformation

  • Improving business efficiencies/higher productivity, and faster innovation cycles (both 20%) are top benefits tech leaders globally are pursuing with IoT strategies. The point was made on the webinar that in Asia, consumers are driving greater adoption of IoT-based devices to a richer contextual customer experience. Greatest challenges globally to adopting IoT is technology complexity (22%), lack of experience in the new technology or business model (16%), and both displacement of the existing tech roadmap and security (both 13%).       

IoT in the enteprrise

  • Analytics are most often adopted to gain faster innovation cycles (25%), improved business efficiencies and higher productivity (17%) and more effective R&D (13%).  The greatest challenges are technology complexity (20%) and lack of experience in the new technology or business model (19%),

data and analytics KPMG Survey

  • Tech leaders predict the greatest potential revenue growth for IoT in the next three years is in consumer and retail markets (22%).  IoT/M2M is also expected to see significant revenue growth in technology industries (13%), aerospace and defense (10%), and education (9%).  The following graphic compares tech leader’s predictions of the industries with the greatest potential revenue growth (or monetization potential) in the next three years.

Emerging Tech IoT monetization



Tech Innovation Global Webcast presenting the findings of KPMG’s 2015 Global Technology Innovation Survey

KPMG Survey: Top Disruptive Consumer Tech – AI In China, Healthtech In U.S., 3-D Printing In EMEA


Salesforce On The State Of Analytics, 2015

  • analytics predictions 2015Between 2015 and 2020, the number of data sources analyzed by enterprises will jump 83%.
  • 9 out of 10 enterprise leaders believe analytics is absolutely essential or very important to their overall business strategies and operational outcomes.
  • 54% of marketers say marketing analytics is absolutely critical or very important to creating a cohesive customer journey.
  • High performing enterprises are 5.4x more likely than underperformers to primarily use analytics tools to gain strategic insights from Big Data.

These and many other interesting insights are from the 2015 State of Analytics study from Salesforce Research. Salesforce conducted the study in mid-2015, generating 2,091 responses from business leaders from enterprises (not limited to Salesforce customers). Geographies included in the study include the U.S., Canada, Brazil, U.K., France, Germany, Japan, and Australia.  While Salesforce is a leading provider of analytics, the report strives to deliver useful insights beyond just endorsing their product direction.

10 insights and predictions on the state of analytics include the following:

  • Between 2015 and 2020, the number of data sources analyzed will jump 83%. Salesforce Research found that the number of data sources actively analyzed by businesses has grown just 20% in the last five years. This is projected to accelerate rapidly, attaining a compound annual growth rate of 120% in the 10-year forecast period. High performing enterprises will be relying on a projected 50 different data sources by 2020, leading all performance categories tracked in the study.

data explosion

  • Relying on manual processes to get all the data in one view (53%) is one of the greatest challenges enterprises face today. Additional factors driving enterprises to integrate more data sources into their analytics applications include finding that too much data is left unanalyzed (53%), spending too much time updating spreadsheets (52%), and analysis is performance by business analysts, not end users of the data (50%).  All of these factors and those shown in the graphic below form the catalyst that is driving greater legacy, 3rd party and broader enterprise data integration into analytics applications.

lack of automation

  • 9 out of 10 enterprise leaders believe analytics is absolutely essential or very important to their overall business strategies and operational outcomes. In addition, 84% of high performers are projecting that the importance of analytics will increase substantially or somewhat in the next two years. 65% of all business leaders surveyed are predicting that the importance of analytics will increase substantially or somewhat in the next two years.

analytics is critical to driving business strategy

  • High performing enterprises are 4.6x more likely than underperformers to agree that data is driving their business decisions. In addition, 60% of high performing enterprises’ leaders agree with the statement that their organizations have moved beyond numbers keeping score to data driving business decisions. Salesforce Research also found that 43% of high performers rely on empirical data, developing hypotheses and then experimenting and observing the outcomes before making a decision.

data drives decisions

  • Driving operational efficiencies and facilitating growth (both 37%) are the two areas enterprises are initially focused on with analytics today.  Once analytics apps are delivering insights and are part of daily workflows, enterprises expand their use into optimizing operational processes (35%), identifying new revenue streams (33%) and predicting customer behavior (32%). The following graphic provides a comparison of the top ten use cases.

analytics every corner

  • High performance enterprises consistently analyze more than 17 different kinds of data across their analytics apps.  In contrast, underperforming organizations only analyze 10 different data sources, and moderate performers, 15. The following graphic provides an overview of the top ten most-used sources of data.

companies track a wide variety of data

  • High performers are 3.5x more likely than underperformers to extensively use mobile reporting tools to analyze data wherever they are. 55% of high performing enterprises are more likely to be extensively using mobile reporting tools to analyze data.  The following graphic compares mobile analytics adoption across high, medium and low performing enterprises.

top teams tap mobile analytics

  • Speed of deployment (68%), ease of use for business users (65%) and self-service and data discovery tools (61%) are the three top three priorities leaders place on selecting new analytics apps.  Mobile capabilities to explore and share data (56%) and cloud deployment (54%) are the fourth and fifth factors leaders mentioned.  The following graphic compares the decision factors that go into selecting an analytics app.

decision factor analytics app

  • Industries who have the greater analytics adoption today (over 50% of users active on apps and tools) include high tech (36%) and financial services (32%). Automotive (30%) and media & communications (30%) also have attained significant adoption.


  • High performing enterprises are 5.4x more likely than underperformers to primarily use analytics tools to gain strategic insights from Big Data. Leaders in high performance enterprises see the value of Big Data (76%) to a much greater extent than their lower performing counterparts (14%).   High performing enterprises are 3.1x more likely than underperformers to be confident in ability to manage data from internal systems, customers, and third parties.
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