Skip to content
Advertisements

Posts tagged ‘Cloud Computing’

McKinsey’s State Of Machine Learning And AI, 2017

  • Tech giants including Baidu and Google spent between $20B to $30B on AI in 2016, with 90% of this spent on R&D and deployment, and 10% on AI acquisitions.
  • Artificial Intelligence (AI) investment has turned into a race for patents and intellectual property (IP) among the world’s leading tech companies.
  • U.S.-based companies absorbed 66% of all AI investments in 2016. China was second with 17% and growing fast.
  • By providing better search results, Netflix estimates that it is avoiding canceled subscriptions that would reduce its revenue by $1B annually.

These and other findings are from the McKinsey Global Institute Study, and discussion paper, Artificial Intelligence, The Next Digital Frontier (80 pp., PDF, free, no opt-in) published last month. McKinsey Global Institute published an article summarizing the findings titled   How Artificial Intelligence Can Deliver Real Value To Companies. McKinsey interviewed more than 3,000 senior executives on the use of AI technologies, their companies’ prospects for further deployment, and AI’s impact on markets, governments, and individuals.  McKinsey Analytics was also utilized in the development of this study and discussion paper.

Key takeaways from the study include the following:

  • Tech giants including Baidu and Google spent between $20B to $30B on AI in 2016, with 90% of this spent on R&D and deployment, and 10% on AI acquisitions. The current rate of AI investment is 3X the external investment growth since 2013. McKinsey found that 20% of AI-aware firms are early adopters, concentrated in the high-tech/telecom, automotive/assembly and financial services industries. The graphic below illustrates the trends the study team found during their analysis.

ssddsd

  • AI is turning into a race for patents and intellectual property (IP) among the world’s leading tech companies. McKinsey found that only a small percentage (up to 9%) of Venture Capital (VC), Private Equity (PE), and other external funding. Of all categories that have publically available data, M&A grew the fastest between 2013 And 2016 (85%).The report cites many examples of internal development including Amazon’s investments in robotics and speech recognition, and Salesforce on virtual agents and machine learning. BMW, Tesla, and Toyota lead auto manufacturers in their investments in robotics and machine learning for use in driverless cars. Toyota is planning to invest $1B in establishing a new research institute devoted to AI for robotics and driverless vehicles.

asdagg

  • McKinsey estimates that total annual external investment in AI was between $8B to $12B in 2016, with machine learning attracting nearly 60% of that investment. Robotics and speech recognition are two of the most popular investment areas. Investors are most favoring machine learning startups due to quickness code-based start-ups have at scaling up to include new features fast. Software-based machine learning startups are preferred over their more cost-intensive machine-based robotics counterparts that often don’t have their software counterparts do. As a result of these factors and more, Corporate M&A is soaring in this area with the Compound Annual Growth Rate (CAGR) reaching approximately 80% from 20-13 to 2016. The following graphic illustrates the distribution of external investments by category from the study.

hjgugikug

  • High tech, telecom, and financial services are the leading early adopters of machine learning and AI. These industries are known for their willingness to invest in new technologies to gain competitive and internal process efficiencies. Many start-ups have also had their start by concentrating on the digital challenges of this industries as well. The\ MGI Digitization Index is a GDP-weighted average of Europe and the United States. See Appendix B of the study for a full list of metrics and explanation of methodology. McKinsey also created an overall AI index shown in the first column below that compares key performance indicators (KPIs) across assets, usage, and labor where AI could contribute. The following is a heat map showing the relative level of AI adoption by industry and key area of asset, usage, and labor category.

ashasdsahd

  • McKinsey predicts High Tech, Communications, and Financial Services will be the leading industries to adopt AI in the next three years. The competition for patents and intellectual property (IP) in these three industries is accelerating. Devices, products and services available now and on the roadmaps of leading tech companies will over time reveal the level of innovative activity going on in their R&D labs today. In financial services, for example, there are clear benefits from improved accuracy and speed in AI-optimized fraud-detection systems, forecast to be a $3B market in 2020. The following graphic provides an overview of sectors or industries leading in AI addition today and who intend to grow their investments the most in the next three years.

hhhhi

  • Healthcare, financial services, and professional services are seeing the greatest increase in their profit margins as a result of AI adoption. McKinsey found that companies who benefit from senior management support for AI initiatives have invested in infrastructure to support its scale and have clear business goals achieve 3 to 15% percentage point higher profit margin. Of the over 3,000 business leaders who were interviewed as part of the survey, the majority expect margins to increase by up to 5% points in the next year.

njhikhi8yhu

  • Amazon has achieved impressive results from its $775 million acquisition of Kiva, a robotics company that automates picking and packing according to the McKinsey study. “Click to ship” cycle time, which ranged from 60 to 75 minutes with humans, fell to 15 minutes with Kiva, while inventory capacity increased by 50%. Operating costs fell an estimated 20%, giving a return of close to 40% on the original investment
  • Netflix has also achieved impressive results from the algorithm it uses to personalize recommendations to its 100 million subscribers worldwide. Netflix found that customers, on average, give up 90 seconds after searching for a movie. By improving search results, Netflix projects that they have avoided canceled subscriptions that would reduce its revenue by $1B annually.
Advertisements

73% Are Using Internet Of Things Data To Improve Their Business

afafdea

  • According to the Cisco Visual Networking Index, M2M connections will represent 46% of connected devices by 2020.
  • 95% of execs surveyed plan to launch an IoT business within three years.

These and many other insights are from the recently published Cisco Internet of Things (IoT) study, The Journey to IoT Value: Challenges, Breakthroughs, and Best Practices published on SlideShare last month. The study is based on a survey of 1,845 IT and business decision-makers in the United States, UK, and India. Industries included in the analysis include manufacturing, local government, retail/hospitality/sports, energy (utilities/oil & gas/mining), transportation, and health care. All respondents worked for organizations that are implementing or have completed IoT initiatives. 56% of all respondents are from enterprises.

Key takeaways from the study include the following:

  • 73% Are Using Internet Of Things Data To Improve Their Business. The data and insights gained from IoT are most often used for improving product quality or performance (47%), improving decision-making (46%) and lowering operational costs (45%). Improving or creating new customer relationships (44%) and reducing maintenance or downtime (42%) are also strategic areas where IoT is making a contribution today according to the Cisco study.

gghhiui8

  • IT executives often see IoT initiatives as more successful (35%) than their line-of-business counterparts (15%). With IT concentrating on technologies and line-of-business users focused on strategy and business cases, the potential exists for differences of opinion regarding IoT initiatives’ value. The following graphic provides an overview of how stark these differences are.

asasddef

  • Engaging with the IoT partner ecosystem in every phase of a project or initiative improves the probability of success. The most valuable phases to engage with ecosystem partners include strategic planning (60%), implementation and deployment (58%) and technical consulting or support (58%). The following graphic provides an overview of most and less successful organizations by their level of involvement in the IoT partner ecosystem.

dfdsfdsfds

  • Only 26% of all companies are successful with their IoT initiatives. The three best practices that lead to a successful IoT implementations include collaboration between IT and business, the availability of internal and external partnerships to gain IoT expertise; and a strong technology-focused culture.
  • 60% of companies believe IoT projects look good on paper but prove more complex that expected. This finding underscores how critical it is for IT and line-of-business executives to have the same goals and objectives going into an IoT project. Being selective about which integration, technology, and professional services partners are chosen needs to be a shared priority between both IT and line-of-business executives.

6 Ways Cloud ERP Is Revolutionizing How Services Deliver Results

  • Cloud ERP is the fastest growing sector of the global ERP market with services-based businesses driving the majority of new revenue growth.
  • Legacy Services ERP providers excel at meeting professional & consulting services information needs yet often lack the flexibility and speed to support entirely new services business models.
  • Configure-Price-Quote (CPQ) is quickly emerging as a must-have feature in Services-based Cloud ERP suites.

From globally-based telecommunications providers to small & medium businesses (SMBs) launching new subscription-based services, the intensity to innovate has never been stronger. Legacy Services ERP and Cloud ERP vendors are responding differently to the urgent needs their prospects and customers have with new apps and suites that can help launch new business models and ventures.

Services-based Cloud ERP providers are reacting by accelerating improvements to Professional Services Automation (PSA), Financials, and questioning if their existing Human Capital Management (HCM) suite can scale now and in the future. Vertical industry specialization is a must-have in many services businesses as well.  Factoring all these customer expectations and requirements along with real-time responsiveness into a roadmap deliverable in 12 months or less is daunting.  Making good on the promises of ambitious roadmaps that includes biannual release cycles is how born-in-the-Cloud ERP providers will gain new customers including winning many away from legacy ERP providers who can’t react as fast.

The following key takeaways are based on ongoing discussions with global telecommunications providers, hosters and business & professional services providers actively evaluating Cloud ERP suites:

  • Roadmaps that reflect a biyearly release cadence complete with user experience upgrades are the new normal for Cloud ERP providers. Capitalizing on the strengths of the Salesforce platform makes this much easier to accomplish than attempting to create entirely new releases every six months based on unique code lines. FinancialForceKenandy and Sage have built their Cloud ERP suites on the Salesforce platform specifically for this reason. Of the three, only FinancialForce has provided detailed product roadmaps that specifically call out support for evolving services business models, multiple user interface (UI) refreshes and new features based on customer needs. FinancialForce is also one of the only Cloud ERP providers to publish their Application Programming Interfaces (APIs) already to support their current and next generation user interfaces.
  • Cloud ERP leaders are collaborators in the creation of new APIs with their cloud platform provider with a focus on analytics, integration and real-time application response. Overcoming the challenges of continually improving platform-based applications and suites need to start with strong collaboration around API development. FinancialForce’s decision to hire Tod Nielsen, former Executive Vice President, Platform at Salesforce as their CEO in January of this year reflects how important platform integration and an API-first integration strategy is to compete in the Cloud ERP marketplace today. Look for FinancialForce to have a break-out year in the areas of platform and partner integration.
  • Analytics designed into the platform so customers can create real-time dashboards and support the services opportunity-to-revenue lifecycle. Real-time data is the fuel that gets new service business models off the ground. When a new release of a Cloud ERP app is designed, it has to include real-time Application Programming Interface (API) links to its cloud platform so customers can scale their analytics and reporting to succeed. What’s most important about this from a product standpoint is designing in the scale to flex and support an entire opportunity-to-revenue lifecycle.
  • Having customer & partner councils involved in key phases of development including roadmap reviews, User Acceptance Testing (UAT) and API beta testing are becoming common.  There’s a noticeable difference in Cloud ERP apps and suites that have gone through UAT and API beta testing outside of engineering.  Customers find areas where speed and responsiveness can be improved and steps saved in getting workflows done. Beta testing APIs with partners and customers forces them to mature faster and scale further than if they had been tested in isolation, away from the market. FinancialForce in services and IQMS in manufacturing are two ERP providers who are excelling in this area today and their apps and suites show it.
  • New features added to the roadmap are prioritized by revenue potential for customers first with billing, subscriptions, and pricing being the most urgent. Building Cloud ERP apps and suites on a platform free up development time to solve challenging, complex customer problems. Billing, subscriptions, and pricing are the frameworks many services businesses are relying on to start new business models and fine-tune existing ones. Cloud ERP vendors who prioritize these have a clear view of what matters most to prospects and customers.
  • Live and build apps by the mantra “own the process, own the market”. Configure-Price-Quote (CPQ) and Quote-to-Cash (QTC) are two selling processes services and manufacturing companies rely on for revenue daily and struggle with. Born-in-the-cloud CPQ and QTC competitors on the Salesforce platform have the fastest moving roadmaps and release cadences of any across the platform’s broad ecosystem. The most innovative Services-focused Cloud ERP providers look to own opportunity-to-revenue with the same depth and expertise as the CPQ and QTC competitors do.

Artificial Intelligence Will Enable 38% Profit Gains By 2035

sedff

  • By 2035 AI technologies have the potential to increase productivity 40% or more.
  • AI will increase economic growth an average of 1.7% across 16 industries by 2035.
  • Information and Communication, Manufacturing and Financial Services will be the top three industries that gain economic growth in 2035 from AI’s benefits.
  • AI will have the most positive effect on Education, Accommodation and Food Services and Construction industry profitability in 2035.

Today Accenture Research and Frontier Economics published How AI Boosts Industry Profits and Innovation. The report is downloadable here (28 pp., PDF, no opt-in).The research compares the economic growth rates of 16 industries, projecting the impact of Artifical Intelligence (AI) on global economic growth through 2035. Using Gross Value Added (GVA) as a close approximation of Gross Domestic Product (GDP), the study found that the more integrated AI is into economic processes, the greater potential for economic growth.  One of the reports’ noteworthy findings is that AI has the potential to increase economic growth rates by a weighted average of 1.7% across all industries through 2035. Information and Communication (4.8%), Manufacturing (4.4%) and Financial Services (4.3%) are the three sectors that will see the highest annual GVA growth rates driven by AI in 2035. The bottom line is that AI has the potential to boost profitability an average of 38% by 2035 and lead to an economic boost of $14T across 16 industries in 12 economies by 2035.

Key takeaways from the study include the following:

  • AI will increase economic growth by an average of 1.7% across 16 industries by 2035 with Information and Communication, manufacturing and financial services leading all industries. Accenture Research found that the Information and Communication industry has the greatest potential for economic growth from AI. Integrating AI into legacy information and communications systems will deliver significant cost, time and process-related savings quickly. Accenture predicts the time, cost and labor savings will generate up to $4.7T in GVA value in 2035. High growth areas within this industry are cloud, network, and systems security including defining enterprise-wide cloud security strategies.

awfdasdf

  • AI will most increase profitability in Education, Accommodation and Food Services and Construction industries in 2035. Personalized learning programs and automating mundane, routine tasks to free up colleges, universities, and trade school instructors to teach new learning frameworks will accelerate profitability in the education through 2035.  Accommodation & Food Services and Construction are industries with manually-intensive, often isolated processes that will benefit from the increased insights and contextual intelligence from AI throughout the forecast period.

qwjhjh

  • Manufacturing’s adoption of Industrial Internet of Things (IIoT), smart factories and comparable initiatives are powerful catalysts driving AI adoption. Based on the proliferation of Industrial Internet of Things (IIoT) devices and the networks and terabytes of data they generate, Accenture predicts AI will contribute an additional $3.76T GVA to manufacturing by 2035. Supply chain management, forecasting, inventory optimization and production scheduling are all areas AI can make immediate contributions to this industry’s profits and long-term economic

asdfsda

  • Financial Services’ greatest gains from AI will come automating and reducing the errors in mundane, manually-intensive tasks including credit scoring and first-level customer inquiries. Accenture forecasts financial services will benefit $1.2T in additional GVA in 2035 from AI. Follow-on areas of automation in Financial Services include automating market research queries through intelligent bots, and scoring and reviewing mortgages.

ujhhuuhkj

  • By 2035 AI technologies could increase labor productivity 40% or more, doubling economic growth in 12 developed nations. Accenture finds that AI’s immediate impact on profitability is improving individual efficiency and productivity. The economies of the U.S. and Finland are projected to see the greatest economic gains from AI through 2035, with each attaining 2% higher GVA growth.The following graphic compares the 12 nations included in the first phase of the research.

eterwtreert

Sources:

How Artificial Intelligence Is Revolutionizing Enterprise Software In 2017

future-of-artificial-intelligence-and-big-data

  • 81% of IT leaders are currently investing in or planning to invest in Artificial Intelligence (AI).
  • Cowen predicts AI will drive user productivity to materially higher levels, with Microsoft at the forefront.
  • Digital Marketing/Marketing Automation, Salesforce Automation (CRM) and Data Analytics are the top three areas ripe for AI/ML adoption.
  • According to angel.co, there are 2,200+ Artificial Intelligence start-ups, and well over 50% have emerged in just the last two years.
  • Cowen sees Salesforce ($CRM), Adobe ($ADBE) and ServiceNow ($NOW) as well-positioned to deliver and monetize new AI-based application services.

These and many other fascinating insights are from the Cowen and Company Multi-Sector Equity Research study, Artificial Intelligence: Entering A Golden Age For Data Science (142 pp., PDF, client access reqd). The study is based on interviews with 146 leading AI researchers, entrepreneurs and VC executives globally who are involved in the field of artificial intelligence and related technologies. Please see the Appendix of the study for a thorough overview of the methodology. This study isn’t representative of global AI, data engineering and machine learning (ML) adoption trends. It does, however, provide a glimpse into the current and future direction of AI, data engineering, and machine learning.  Cowen finds the market is still nascent, with CIOs eager to invest in new AI-related initiatives. Time-to-market, customer messaging, product positioning and the value proposition of AI solutions will be critical factors for winning over new project investments.

Key takeaways from the study include the following:

  • Digital Marketing/Marketing Automation, Salesforce Automation (CRM) and Data Analytics are the top three areas ripe for AI/ML adoption. Customer self-service, Enterprise Resource Planning (ERP), Human Resource Management (HRM) and E-Commerce are additional areas that have upside potential for AI/ML adoption. The following graphic provides an overview of the areas in software that Cowen found the greater potential for AI/ML investment.

Artificial Intelligence: Entering A Golden Age For Data Science

  • 81% of IT leaders are currently investing in or planning to invest in Artificial Intelligence (AI). Based on the study, CIOs have a new mandate to integrate AI into IT technology stacks. The study found that 43% are evaluating and doing a Proof of Concept (POC) and 38% are already live and planning to invest more.  The following graphic provides an overview of company readiness for machine learning and AI projects.

How Artificial Intelligence Is Revolutionizing Enterprise Software In 2017

  • Market forecasts vary, but all consistently predict explosive growth. IDC predicts that the Cognitive Systems and AI market (including hardware & services) will grow from $8B in 2016 to $47B in 2020, attaining a Compound Annual Growth Rate (CAGR) of 55%. This forecast includes $18B in software applications, $5B in software platforms, and $24B in services and hardware. IBM claims that Cognitive Computing is a $2T market, including $200B in healthcare/life sciences alone. Tractica forecasts direct and indirect applications of AI software to grow from $1.4B in 2016 to $59.8B by 2025, a 52% CAGR.

Artificial Intelligence: Entering A Golden Age For Data Science

  • According to CBInsights, the number of financing transactions to AI start-ups increased 10x over the last six years, from 67 in 2011 to 698 in 2016. Accenture states that the total number of AI start-ups has increased 20-fold since 2011. The top verticals include FinTech, Healthcare, Transportation and Retail/e-Commerce. The following graphic provides an overview of the AI annual funding history from 2011 to 2016.

Artificial Intelligence: Entering A Golden Age For Data Science

  • Algorithmic trading, image recognition/tagging, and patient data processing are predicted to the b top AI uses cases by 2025. Tractica forecasts predictive maintenance and content distribution on social media will be the fourth and fifth highest revenue producing AI uses cases over the next eight years. The following graphic compares the top 10 uses cases by projected global revenue.

ai-use-cases

  • Machine Learning is predicted to generate the most revenue and is attracting the most venture capital investment in all areas of AI. Venture Scanner found that ML raised $3.5B to date (from 400+ companies), far ahead of the next category, Natural Language Processing, which has seen just over $1Bn raised to date (from 200+ companies). Venture Scanner believes that Machine Learning Applications and Machine Learning Platforms are two relatively early stage markets that stand to have some of the greatest market disruptions.

Artificial Intelligence: Entering A Golden Age For Data Science

  • Cowen predicts that an Intelligent App Stack will gain rapid adoption in enterprises as IT departments shift from system-of-record to system-of-intelligence apps, platforms, and priorities. The future of enterprise software is being defined by increasingly intelligent applications today, and this will accelerate in the future. Cowen predicts it will be commonplace for enterprise apps to have machine learning algorithms that can provide predictive insights across a broad base of scenarios encompassing a company’s entire value chain. The potential exists for enterprise apps to change selling and buying behavior, tailoring specific responses based on real-time data to optimize discounting, pricing, proposal and quoting decisions.

Artificial Intelligence: Entering A Golden Age For Data Science

  • According to angel.co, there are 2,200+ Artificial Intelligence start-ups, and well over 50% have emerged in just the last two years. Machine Learning-based Applications and Deep Learning Neural Networks are experiencing the largest and widest amount of investment attention in the enterprise.
  • Accenture leverages machine learning in 40% of active Analytics engagements, and nearly 80% of proposed Analytics opportunities today. Cowen found that Accenture’s view is that they are in the early stages of AI technology adoption with their enterprise clients.  Accenture sees the AI market growing exponentially, reaching $400B in spending by 2020. Their customers have moved on from piloting and testing AI to reinventing their business strategies and models.

How AWS And Azure Competing Is Improving Public Cloud Adoption

Global Cloud

  • Public Cloud spending is predicted to grow at quickly, attaining 16% year-over-year growth in 2017.
  • Cowen’s AWS segment model is predicting Revenue and EBITDA to grow 25% and 26.8% annually from 2017 to 2022.
  • Microsoft Azure is viewed as the platform that customers would most likely purchase or renew going forward (28% of total vs. AWS at 22%, GCP at 15%, and IBM at 10%).

These and many other fascinating insights are from Cowen’s study published this week, Public Cloud V: AWS And Azure Still Leading The Pack (58 pp., PDF, client access reqd.). Cowen partnered with Altman Vilandrie & Company to complete the study. The study relies on a survey sample of 551 respondents distributed across small, medium and enterprises who are using Public Cloud platforms and services today.  For purposes of the survey, small businesses have less than 500 employees, medium-sized businesses as 500 to 4,999 employees, and enterprises as more than 5,000 employees. The study provides insight on a range of topics including cloud spending trends, workload migration dynamics, and vendor positioning. Please see pages 5,6 & 7 for additional details regarding the methodology.

The more AWS and Azure compete to win customers, the greater the innovation and growth in public cloud adoption as the following key takeaways illustrate:

  • Existing Public Cloud customers predict spending will grow 16% year-over-year in 2017. Existing mid-market Public Cloud customers predict spending will increase 18% this year. SMBs who have already adopted Public Cloud predict a 17% increase in spending in 2017, and enterprises, 13%. Public Cloud providers are the most successful upselling and cross-selling mid-market companies this year as many are relying on the cloud to scale their global operations to support growth.

Public Cloud Spending, 2017

  • AWS dominates awareness levels with SMBs who have existing Public Cloud deployments, with Microsoft Azure the most known and considered in enterprises. Consistent with many other surveys of Public Cloud adoption, IBM SoftLayer scored better in enterprises than any other segment including SMBs (71% vs. 58%). Google Cloud Platform has its strongest awareness levels in SMBs, attributable to the adoption of their many cloud-based applications in this market segment. They trail AWS, Azure, and SoftLayer in the enterprise, however. Across all existing companies who have adopted Public Cloud, the majority are most aware of AWS and Microsoft Azure. The second graphic provides an overview of awareness across the entire respondent base.

test

  • Microsoft is the most-used Public Cloud and the most likely to be purchased or renewed by 28% of all respondents. While AWS is the most reviewed Public Cloud across all respondents, Microsoft Azure is the most used. When asked which Public Cloud provider they are likely to purchase or renew, the majority of respondents said Microsoft Azure (28%), followed by AWS (22%), Google Cloud Platform (15%) and IBM SoftLayer (10%). The following graphic compares awareness, reviewed and use levels by Public Cloud platform.

Comparative Analysis Of Most Used Public Cloud Provider

  • Only 37% of current Azure users expect to add or replace their Public Cloud provider, compared to 53% of current AWS users and 50% of GCP users. The study found that approximately 40% of respondents expect to add or replace their cloud provider in the next two years, compared to 43% who predicted that last year. Companies who have adopted Microsoft Azure are least likely to replace/add other vendors, as only 37% of current Azure users expect to add or replace, compared to 53% of current AWS users and 50% of GCP users.

substitute

  • AWS and Azure dominate all seven facets of user experience included in the survey. AWS has the best User Interface, API Complexity, and Reporting & Billing. Microsoft Azure leads all Public Cloud providers globally in the areas of Management & Monitoring, Software & Data Integration, Technical Support and Training &   Google Cloud Platform is 3rd on all seven facts of user experience.

user

  • 18% of workloads are supported by Public Cloud today with SMBs and mid-market companies slightly leading enterprises (16%). Overall, 38% of all workloads are supported with on-premise infrastructure and platforms, increasing to 43% for enterprises. The following graphic illustrates the percentage of workloads supported by each infrastructure type.

Infrastructure

  • 77% of existing Public Cloud adopters are either likely or very likely to add a SaaS workload in the next two years, led by mid-market companies (81%). SMBs (76%) and enterprises (73%) are also likely/very likely to add SaaS workloads in the next two years. The majority of these new SaaS workloads will be in the areas of Testing & Development, Web Hosting, and e-mail and communications.

Comparing

  • Cowen’s AWS segment model is predicting Revenue and EBITDA to have a five-year Compound Annual Growth Rate (CAGR) of 25% and 26.8% from 2017 to 2022. AWS Net Income is predicted to increase from $2.7B in 2017 to $8.2B in 2022, attaining a projected 24.5% CAGR from 2017 to 2022. Revenue is predicted to soar from an estimated $16.8B in 2017 to $51.5B in 2022, driving a 25% CAGR in the forecast period.

Roundup Of Cloud Computing Forecasts, 2017

  • Cloud computing is projected to increase from $67B in 2015 to $162B in 2020 attaining a compound annual growth rate (CAGR) of 19%.
  • Gartner predicts the worldwide public cloud services market will grow 18% in 2017 to $246.8B, up from $209.2B in 2016.
  • 74% of Tech Chief Financial Officers (CFOs) say cloud computing will have the most measurable impact on their business in 2017.

Cloud platforms are enabling new, complex business models and orchestrating more globally-based integration networks in 2017 than many analyst and advisory firms predicted. Combined with Cloud Services adoption increasing in the mid-tier and small & medium businesses (SMB), leading researchers including Forrester are adjusting their forecasts upward. The best check of any forecast is revenue.  Amazon’s latest quarterly results released two days ago show Amazon Web Services (AWS) attained 43% year-over-year growth, contributing 10% of consolidated revenue and 89% of consolidated operating income.

Additional key takeaways from the roundup include the following:

  • Wikibon is predicting enterprise cloud spending is growing at a 16% compound annual growth (CAGR) run rate between 2016 and 2026. The research firm also predicts that by 2022, Amazon Web Services (AWS) will reach $43B in revenue, and be 8.2% of all cloud spending. Source: Wikibon report preview: How big can Amazon Web Services get?
Wikibon Worldwide Enterprise IT Projection By Vendor Revenue

Wikibon Worldwide Enterprise IT Projection By Vendor Revenue

Rapid Growth of Cloud Computing, 2015–2020

Rapid Growth of Cloud Computing, 2015–2020

Worldwide Public Cloud Services Forecast (Millions of Dollars)

Worldwide Public Cloud Services Forecast (Millions of Dollars)

  • By the end of 2018, spending on IT-as-a-Service for data centers, software and services will be $547B. Deloitte Global predicts that procurement of IT technologies will accelerate in the next 2.5 years from $361B to $547B. At this pace, IT-as-a-Service will represent more than half of IT spending by the 2021/2022 timeframe. Source: Deloitte Technology, Media and Telecommunications Predictions, 2017 (PDF, 80 pp., no opt-in).
Deloitte IT-as-a-Service Forecast

Deloitte IT-as-a-Service Forecast

  • Total spending on IT infrastructure products (server, enterprise storage, and Ethernet switches) for deployment in cloud environments will increase 15.3% year over year in 2017 to $41.7B. IDC predicts that public cloud data centers will account for the majority of this spending ( 60.5%) while off-premises private cloud environments will represent 14.9% of spending. On-premises private clouds will account for 62.3% of spending on private cloud IT infrastructure and will grow 13.1% year over year in 2017. Source: Spending on IT Infrastructure for Public Cloud Deployments Will Return to Double-Digit Growth in 2017, According to IDC.
Worldwide Cloud IT Infrastructure Market Forecast

Worldwide Cloud IT Infrastructure Market Forecast

  • Platform-as-a-Service (PaaS) adoption is predicted to be the fastest-growing sector of cloud platforms according to KPMG, growing from 32% in 2017 to 56% adoption in 2020. Results from the 2016 Harvey Nash / KPMG CIO Survey indicate that cloud adoption is now mainstream and accelerating as enterprises shift data-intensive operations to the cloud.  Source: Journey to the Cloud, The Creative CIO Agenda, KPMG (PDF, no opt-in, 14 pp.)
Cloud investment by type today and in three years

Cloud investment by type today and in three years

AWS Segment Financial Comparison

AWS Segment Financial Comparison

  • In Q1, 2017 AWS generated 10% of consolidated revenue and 89% of consolidated operating income. Net sales increased 23% to $35.7 billion in the first quarter, compared with $29.1 billion in first quarter 2016. Source: Cloud Business Drives Amazon’s Profits.
Comparing AWS' Revenue and Income Contributions

Comparing AWS’ Revenue and Income Contributions

  • RightScale’s 2017 survey found that Microsoft Azure adoption surged from 26% to 43% with AWS adoption increasing from 56% to 59%. Overall Azure adoption grew from 20% to 34% percent of respondents to reduce the AWS lead, with Azure now reaching 60% of the market penetration of AWS. Google also increased adoption from 10% to 15%. AWS continues to lead in public cloud adoption (57% of respondents currently run applications in AWS), this number has stayed flat since both 2016 and 2015. Source: RightScale 2017 State of the Cloud Report (PDF, 38 pp., no opt-in)
Public Cloud Adoption, 2017 versus 2016

Public Cloud Adoption, 2017 versus 2016

  • Global Cloud IT market revenue is predicted to increase from $180B in 2015 to $390B in 2020, attaining a Compound Annual Growth Rate (CAGR) of 17%. In the same period, SaaS-based apps are predicted to grow at an 18% CAGR, and IaaS/PaaS is predicted to increase at a 27% CAGR. Source: Bain & Company research brief The Changing Faces of the Cloud (PDF, no opt-in).
60% of IT Market Growth Is Being Driven By The Cloud

60% of IT Market Growth Is Being Driven By The Cloud

  • 74% of Tech Chief Financial Officers (CFOs) say cloud computing will have the most measurable impact on their business in 2017. Additional technologies that will have a significant financial impact in 2017 include the Internet of Things, Artificial Intelligence (AI) (16%) and 3D printing and virtual reality (14% each). Source: 2017 BDO Technology Outlook Survey (PDF), no opt-in).
CFOs say cloud investments deliver the greatest measurable impact

CFOs say cloud investments deliver the greatest measurable impact

Cloud investments are fueling new job throughout Canada

Cloud investments are fueling new job throughout Canada

  • APIs are enabling persona-based user experiences in a diverse base of cloud enterprise As of today there are 17,422 APIs listed on the Programmable Web, with many enterprise cloud apps concentrating on subscription, distributed order management, and pricing workflows.  Sources: Bessemer Venture Partners State of the Cloud 2017 and 2017 Is Quickly Becoming The Year Of The API Economy. The following graphic from the latest Bessemer Venture Partners report illustrates how APIs are now the background of enterprise software.
APIs are fueling a revolution in cloud enterprise apps

APIs are fueling a revolution in cloud enterprise apps

Additional Resources:

Five Ways CPQ Is Revolutionizing Selling Today

CPQ, Salesforce CPQ, enosiX SAP to Salesforce Integration Configure-Price-Quote (CPQ) continues to be one of the hottest enterprise apps today, fueled by the relentless need all companies have to increase sales while delivering customized orders profitably and accurately. Here are a few of the many results CPQ strategies are delivering today:

  • Companies relying on CPQ are growing profit margins at a 57% greater rate year-over-year compared to non-adopters.
  • 89% improvement in turning Special Pricing Requests (SPRs) into sales by automating them using a cloud-based CPQ system.
  • 67% reduction in reworked orders at a leading specialty vehicle manufacturer due to quotes reflecting exactly what customers wanted to buy.
  • 23% improvement in upsell and cross-sell revenue by having the CPQ system intelligently recommend the optimal product or service that has the highest probability of purchase and best possible gross margin.
  • CPQ strategies excel when they are designed to reach challenging selling, pricing, revenue and operational performance goals versus automating existing selling workflows.

Another factor fueling CPQs’ rapid growth is how quickly results of a pilot can be measured and used for launching a successful company-wide launch.  Pilots often concentrate on quote creation time, quoting accuracy, sales cycle reduction, automating Special Pricing Requests (SPRs), up-sells and cross-sells, perfect order performance, margin improvements and best of all, winning new customers. These are the baseline metrics many companies use to measure their CPQ performance. Throughout 2017 these metrics across industries are accelerating. There is a revolution going on in selling today.

5 Ways CPQ Is Revolutionizing Selling Today

Cloud- and SaaS-based CPQ solutions are quicker to implement, easier to customize to customers’ requirements, and available 24/7 on any Internet-enabled device, anytime. Many are designed to integrate into Salesforce, further accelerating adoption seamlessly.  The following five factors are the primary catalysts revolutionizing selling today:

  1. Designing in excellent user experiences (UX) is the new normal for CPQ apps – CPQ vendors are competing with the quality of user experiences they deliver in 2017, moving beyond packing every feature possible into app releases. This is having a corresponding impact on adoption, increasing the number of sales representatives and entire teams who can get up and running fast with a new CPQ app. The net result is reduced sales cycles, growing pipelines, and more sales reps actively using CPQ apps to increase their selling effectiveness.
  2. Integrating with legacy CRM, ERP and pricing systems in real-time are using service-oriented frameworks gives sales teams what they need to close deals faster – Legacy CPQ systems in the past often had very precise field mappings to 3rd party legacy CRM, ERP and pricing systems. They were brittle and would break very easily, slowing down sales cycles and making sales reps resort to manually-based approaches from decades before. In 2017 there are service-oriented frameworks that make brittle, easily broken mappings thankfully an integration practice in the past. With a loosely coupled service framework, real-time integration between CRM and ERP systems can be quickly be implemented and sales teams can get out and close more deals. Leaders in the area include enosiX, who are enabling their customers’ sales forces to enter sales orders into SAP directly from Salesforce, saving valuable selling time and increasing order accuracy.
  3. Competing for deals using Artificial Intelligence (AI), machine learning and Intelligent Agents are force multipliers driving greater salesSalesforce’s Einstein is an example of the latest generation of AI applications that are enabling sales reps and teams to gain insights that weren’t available before. Combining customer data with these advanced predictive data analytics technologies yields insights into how selling strategies for different accounts can customize to specific prospect needs. Selling strategies are more effective and focused when AI, machine learning, and Intelligent Agents are designed in to guide quoting, pricing and product configuration in real-time.
  4. CPQ apps optimized for mobile devices are enabling sales reps to drastically reduce quote creation times, sales cycles and increase sales win rates – For many companies whose sales teams are in the field calling on accounts the majority of the time, mobile-based CPQ apps are how they get the majority of their work done. Salesforce’s Force.com is one of the leading platforms CPQ software companies are relying on to create mobile apps, further capitalizing on the already-established levels of familiarity sales teams have with the Salesforce platform.
  5. The vision many companies have of synchronizing multichannel and omnichannel selling as part of their CPQ strategies is now attainable – One of the greatest challenges of expanding sales channels is ensuring a consistently high-quality customer experience across each. With on-premise CPQ, CRM and ERP selling systems, this is very challenging as there are often multiple database systems supporting each. This is a breakout year for omnichannel selling as cloud-based CPQ systems and the platforms they are built on can securely scale across all selling channels a company chooses to launch. Being able to track which CPQ deals emanated from which marketing program, and which channels are the most effective in closing sales is now possible.

CIO’s Guide To The New Economics Of Real-Time Integration

CEOs’ decisions today to pursue digital-first strategies for greater revenue growth are defining their company’s competitive strengths in the future. CIOs and their teams are being challenged to drive a larger percentage of revenue growth in 2017 than ever before by providing IT-based insights daily.

  • Enabling faster revenue growth, improving products and replacing obsolete technologies are the top three CEO priorities have for CIOs in 2017.
  • 42% of CIOs say “digital first” is their company’s go-forward strategy for IT investments in 2017 and beyond.
  • 33% of CIOs consider revenue growth as their primary metric for measuring success with their digital business strategies.

The New Economics Of Real-Time Integration

IT teams are taking on the challenge by concentrating on those areas that can scale the quickest and deliver measurable revenue results. They’re finding that the integration approaches taken in the past don’t match the speed that customers, sales, suppliers and senior management need today. A key takeaway from CIOs’ initial efforts includes the finding that making small improvements in data latency can increase sales win rates in 90 days or less while improving cost controls.  Improving data latency is one of the key factors driving the new economics of real-time integration, which is defined below.

  • Integrations’ Inflection Point Has Arrived – Digital-first initiatives for defining new channel, selling and product strategies require more speed than batch-oriented integration can deliver. Customers now expect real-time response across all sales and support channels on a 24/7 basis. The pressure to drive greater revenue through digital channels and deliver a consistently great customer experience are forcing an inflection point of integration technologies today.
  • Batch-oriented approaches to integration fit well in an era of transaction-centric IT. Asynchronous, tightly-coupled, and relying on ETL for moving data around an enterprise network, these approaches were better suited for more predictable revenue strategies.  In contrast, going after new digital channels is unpredictable and requires real-time integration to deliver excellent customer experiences. Service-oriented frameworks that support synchronous data consumption and have low latency are emerging as a better choice for digital-first revenue strategies. Based on loosely-coupled integration points, these frameworks are capable of quickly adapting to new business requirements. Companies including enosiX are revolutionizing services-oriented frameworks by removing the roadblocks legacy integration approaches created.  The following graphic illustrates integrations’ inflection point and how past approaches to integration are giving way to more synchronous, loosely- coupled service-oriented frameworks capable of scaling faster to drive greater revenue.

  • And it’s fueling faster development cycles, reducing time-to-market and improving app and web services quality. The apps, web services, and APIs needed to launch a digital-first strategy don’t exist off-the-shelf, ready to be deployed for the majority of companies. Every company needs to create customizations to existing apps and web services, or create entirely new ones to support digital revenue strategies. Availability of real-time data through service-oriented frameworks is revolutionizing how apps, web services, and customizations get built. With real-time data designed in, it’s possible to test new apps across more use cases and ensure higher quality too.
  • While also enabling IT teams to exceed stakeholder expectations and their goals for digital-first strategies. Integrations’ inflection point is the most visible in how CIOs are now considered more responsible for revenue than ever before. From the initial revenue strategy definition through project managing apps and web services to delivery and producing revenue, CIOs and their teams who see themselves as business strategists excel in their roles. IT teams and the CIOs who lead them are seeing signs of integration’s inflection point every day. They’re seeing just how urgent the inflection point is, and how it’s redefining the economics of how they orchestrate systems together to attain revenue growth.  The insights and expertise CEOs, VPs of Channel Strategy, Marketing, Cloud & IT Infrastructure, and other senior management team members have needed to get quickly translated into apps, web services and digital first strategies that capitalize fast on new opportunities. Only through the use of service-oriented frameworks that can scale to support new revenue processes can any company compete in 2017 and beyond.

 

Business Intelligence And Analytics In The Cloud, 2017

  • 78% are planning to increase the use of cloud for BI and data management in the next twelve months.
  • 46% of organizations prefer public cloud platforms for cloud BI, analytics and data management deployments.
  • Cloud BI adoption increased in respondent companies from 29% to 43% from 2013 to 2016.
  • Almost half of organizations using cloud BI (46%) use a public cloud for BI and data management compared to less than a third (30%) for hybrid cloud and 24% for private cloud.

These and many other insights are from the BARC Research and Eckerson Group Study, BI and Data Management in the Cloud: Issues and Trends published January 2017 (39 pp., PDF, no opt-in). Business Application Research Center (BARC) is a research and consulting firm that concentrates on enterprise software including business intelligence (BI), analytics and data management. Eckerson Group is a research and consulting firm focused on serving the needs of business intelligence (BI) and analytic leaders in Fortune 2000 organizations worldwide. The study is based on interviews completed in September and October 2016. 370 respondents participated in the survey globally. Given the size of the sample, the results aren’t representative of the global BI and analytics user base. The study’s results provide an interesting glimpse into analytics and BI adoption today, however. For a description of the methodology, please see page 31 of the study.

Key insights from the study include the following:

  • Public cloud is the most preferred deployment platform for cloud BI and analytics, and the larger the organization toe more likely they are using private clouds. 46% of organizations selected public cloud platforms as their preferred infrastructure for supporting their BI, analytics, and data management initiatives in 2016. 30% are relying on a hybrid cloud platform and 24%, private clouds. With public cloud platforms becoming more commonplace in BI and analytics deployments, the need for greater PaaS- and IaaS-level orchestration becomes a priority. The larger the organization, the more likely they are using private clouds (33%). Companies with between 250 to 2,500 employees are the least likely to be using private clouds (16%).

grouped-bi-cloud-platform-graphic

  • Dashboard-based reporting (76%), ad-hoc analysis and exploration (57%) and dashboard authoring (55%) are the top three Cloud BI use cases. Respondents are most interested in adding advanced and predictive analytics (53%), operational planning and forecasting (44%), strategic planning and simulation (44%) in the next year. The following graphic compares primary use cases and planned investments in the next twelve months. SelectHub has created a useful Business Intelligence Tools Comparison here that provides insights into this area.

cloud-bi-use-cases

  • Power users dominate the use of cloud BI and analytics solutions, driving more complex use cases that include ad-hoc analysis (57%) and advanced report and dashboard creation (55%). Casual users are 20% of all cloud BI and analytics, with their most common use being for reporting and dashboards (76%). Customers and suppliers are an emerging group of cloud BI and analytics users as more respondent companies create self-service web-based apps to streamline external reporting.

cloud-bi-power-users

  • Data integration between cloud applications/databases (51%) and providing data warehouses and data marts (50%) are the two most common data management strategies in use to support BI and analytics solutions today. Respondent organizations are using the cloud to integration cloud applications with each other and with on-premises applications (46%).  The study also found that as more organizations move to the cloud, there’s a corresponding need to support hybrid cloud architectures. Cloud-based data warehouses are primarily being built to support net new applications versus existing apps on-premise. Data integration is essential for the ongoing operations of cloud-based and on-premise ERP systems. A useful comparison of ERP systems can be found here.

cloud-data-integration

  • Data integration between on-premises and cloud applications dominates use cases across all company sizes, with 48% of enterprises leading in adoption. Enterprises are also prioritizing providing data warehouses and data marts (48%), the pre-processing of data (38%) and data integration between cloud applications and databases (38%). The smaller a company is the more critical data integration becomes. 63% of small companies with less than 250 employees are prioritizing data integration between cloud applications and databases (63%).

use-cases-of-cloud-management-by-company-size

  • Tools for data exploration (visual discovery) adopted grew the fastest in the last three years, increasing from 20% adoption in 2013 to 49% in 2016. BI tools increased slightly from 55% to 62% and BI servers dropped from 56% to 51%. Approximately one in five respondent organizations (22%) added analytical applications in 2016.

bi-tools-growth

  • The main reasons for adopting cloud BI and analytics differ by size of the company, with cost (57%) being the most important for mid-sized businesses between 250 to 2.5K employees. Consistent with previous studies, small companies’ main reason for adopting cloud BI and analytics include flexibility (46%), reduced maintenance of hardware and software (43%), and cost (38%). Enterprises with more than 2.5K employees are adopting cloud BI and analytics for greater scalability (48%), cost (40%) and reduced maintenance of hardware and software (38%). The following graphic compares the most important reason for adopting cloud BI, analytics and data management by the size of the company.

most-important-reason-for-adopting-cloud-bi-and-data-management

%d bloggers like this: