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Posts from the ‘Louis Columbus’ blog’ Category

Analytics, Data Storage Will Lead Cloud Adoption In 2017

  • cioU.S.-based organizations are budgeting $1.77M for cloud spending in 2017 compared to $1.30M for non-U.S. based organizations.
  • 10% of enterprises with over 1,000 employees are projecting they will spend $10M or more on cloud computing apps and platforms throughout this year.
  • Organizations are using multiple cloud models to meet their business’s needs, including private (62%), public (60%), and hybrid (26%).
  • By 2018 the typical IT department will have the minority of their apps and platforms (40%) residing in on-premise systems.

These and many other insights are from IDG’s Enterprise Cloud Computing Survey, 2016. You can find the 2016 Cloud Computing Executive Summary here and a presentation of the results here.  The study’s methodology is based on interviews with respondents who are reporting they are involved with cloud planning and management across their organizations. The sampling frame includes audiences across six IDG Enterprise brands (CIO, Computerworld, CSO, InfoWorld, ITworld and Network World) representing IT and security decision-makers across eight industries. The survey was fielded online with the objective of understanding organizational adoption, use-cases, and solution needs for cloud computing. A total of 925 respondents were interviewed to complete the study.

Key takeaways include the following:

  • The cloud is the new normal for enterprise apps, with 70% of all organizations having at least one app in the cloud today. 75% of enterprises with greater than 1,000 employees have at least one app or platform running in the cloud today, leading all categories of adoption measured in the survey. 90% of all organizations today either have apps running in the cloud are planning to use cloud apps in the next 12 months, or within 1 to 3 years. The cloud has won the enterprise and will continue to see the variety and breadth of apps adopted accelerating in 2017 and beyond.

use-of-cloud-technology-continuously-expanding

 

  • Business/data analytics and data storage/data management (both 43%) are projected to lead cloud adoption in 2017 and beyond. 22% of organizations surveyed are predicting that business/data analytics will be the leading cloud application area they will migrate to in the next twelve months. 21% are predicting data storage/data management apps are a high priority area for their organizations’ cloud migration plans in 2017.

data-storage-and-analytics-moving-to-the-cloud

 

  • 28% of organizations’ total IT budgets is dedicated to cloud computing next year. Of that, 45% is allocated to SaaS, 30% to IaaS and 19% to PaaS. The average investment organizations will make in cloud computing next year is $1.62M, with enterprises over 1,000 employees projected to spend $3.03M. The average investment in cloud computing remains constant in organizations with $1.62M invested in 2014, $1.56M in 2015 and $1.62M in 2016. 10% of enterprises with over 1,000 employees are projecting they will spend $10M or more on cloud computing apps and platforms throughout this year.

cloud-budget

 

  • CIOs, IT architects and IT networking/management control cloud spending in the enterprise. In contrast, CEOs, CIOs, and CFOs are driving small and medium business (SMB) cloud spending this year. The following graphic compares how influential the following groups and individuals are in the cloud computing purchase process.

cloud-investment

 

  • Just 46% of organizations are using Application Programmer Interfaces (APIs) to integrate with databases, messaging systems, portals or storage components. 40% are using them for creating connections to the application layer of their cloud and the underlying IT infrastructures. The following graphic provides insights into how APIs are being used and which teams see the most value in them.

apis

 

  • In 18 months the majority of organizations’ IT infrastructures will be entirely cloud-based. IDG found that in 18 months nearly one-third (28%) of all organizations interviewed will be relying on private clouds as part of their IT infrastructure. Just over a fifth (22%) will have public cloud as part of their IT infrastructure, and 10% will be using hybrid By 2018 the typical IT department will have the minority of their apps and platforms (40%) residing in on-premise systems.

it-shifts-to-the-cloud

 

  • Concerns about where data is stored (43%), cloud security (41%) and vendor lock-in (21%) are the top three challenges organizations face when adopting public cloud technologies. Private and hybrid cloud adoption in organizations is also facing the challenges of cloud security and vendor lock-in. Private and hybrid cloud adoption are being slowed by a lack of the right skill sets to manage and gain the maximum value from cloud investments.

challenges

6M Developers Are Creating Big Data And Advanced Analytics Apps Today

  • analytics-development2M developers are working on IoT applications, increasing 34% since the last year.
  • Over 50% of the developers working on IoT applications are writing software that utilizes sensors in some capacity.
  • 4M enterprise developers play decision-making roles when it comes to selecting organizational IT development resources. Another 5.2 million hold decision-making authority for selecting IT deployment resources.
  • 4M developers (26% of all developers globally) are using the cloud as a development environment today
  • The APAC region leads the world with approximately 7.4M developers today, followed by EMEA with 7.2M, North America with 4.4M and Latin American with 1.9M.

These and many other fascinating insights are from the Evans Data Corporation Global Developer Population and Demographic Study 2016 (PDF, client access) published earlier this week. The methodology Evans Data has created to produce this report is the most comprehensive developed for aggregating, analyzing and predicting developer populations globally. The study combines Evans Data’s proprietary global developer population modeling with the current results of their semi-annual global developer survey.

Key takeaways from the study include the following:

  • 6M developers (29% of all developers globally) are involved in a Big Data and Advanced Analytics project today. An additional 25% of developers, or 5.3M, are going to begin Big Data and Advanced Analytics projects within the next six 13% or 2.6M of all developers globally are going to start Big Data and Advanced Analytics projects within the next 7 to 12 months.  The following graphic provides an overview of the involvement of 21M developers in Big Data and Advanced Analytics projects today. Please click on the image to expand for easier viewing.

involvement in big data analytics

  • 4M developers (26% of all developers globally) are using the cloud as a development environment today. Developers creating new apps in the cloud had increased 375% since Evans began measuring developer participation in mobile development in 2009 when just slightly more than 1.2M developers were using the cloud as their development platform. 4.5M developers (21% of all global developers) plan on beginning app development on cloud platforms in the next six months, and 3.9M (18% of all global developers) plan on starting development on the cloud in 7 – 12 months. Please click on the image to expand for easier viewing.

plans for cloud development

  • 8M developers in APAC (24% of all developers in the region) are currently developing on cloud platforms. 29% of APAC developers are planning to start cloud-based development in six months, and 20% in 7 – 12 months. The following graphic compares the number of developers currently using the cloud as a development environment today and the number who plan to in the future. Please click on the image to expand for easier viewing.

plans for cloud development by region

  • 34% of all Commercial Independent Software Vendors (ISVs) globally today (1.8M developers) are using the cloud as a development environment. An additional 1.4M are planning to begin cloud development in the next six months.  28% of developers globally creating apps in the cloud are from custom system integrators (SI) and value-added resellers (VARs).  23% or approximately 1.2M are from enterprises.  The following graphic compares the percent of developers by developer segment who are currently creating new apps in cloud environments. Please click on the image to expand for easier viewing.

Plans for cloud development by developer segment

  • 30% of developers (6.2M developers globally) are currently developing software for connected devices or the Internet of Things today, with an additional 26% planning to begin projects in 6 months. Evans Data found that this increased 34% over the last year. Also, 2.1M developers plan to begin development in this area within the next 7 to 12 months. The following graphic compares the number of developers globally by stage of development for creating software for connected devices or the Internet of Things. Please click on the image to expand for easier viewing.

Plans for Internet of Things Development

  • 41% of global developers creating connected device and IoT software today are from 27% are from North America, 24% are from EMEA and 7% from Latin America.  There are 6,072,048 developers currently working on connected device and IoT software today globally.  The following graphic provides an overview of the distribution of developers creating connected device and IoT software by region today. Please click on the image to expand for easier viewing.

Development for Connected Devices By Region

  • 34% of developers actively creating software for connected devices or the Internet of Things work for custom System Integrators (SI) and VARs today. ISVs are the next largest segment of developers working on IoT projects (30%) followed by enterprises (21%). The following graphic provides an overview of the global base of developers creating software for connected devices and IoT. Evans Data found there are 6.1M developers currently creating apps and solutions in this area alone. Please click on the image to expand for easier viewing.

Development for connected devices by developer segment 2

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.

Sources:

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

 

Roundup Of Analytics, Big Data & BI Forecasts And Market Estimates, 2016

  • World map technologyBig Data & business analytics software worldwide revenues will grow from nearly $122B in 2015 to more than $187B in 2019, an increase of more than 50% over the five-year forecast period.
  • The market for prescriptive analytics software is estimated to grow from approximately $415M in 2014 to $1.1B in 2019, attaining a 22% CAGR.
  • By 2020, predictive and prescriptive analytics will attract 40% of enterprises’ net new investment in business intelligence and analytics.

Making enterprises more customer-centric, sharpening focus on key initiatives that lead to entering new markets and creating new business models, and improving operational performance are three dominant factors driving analytics, Big Data, and business intelligence (BI) investments today. Unleashing the insights hidden in unstructured data is providing enterprises with the potential to compete and improve in areas they had limited visibility into before. Examples of these areas include the complexity of B2B selling and service relationships,  healthcare services, and maintenance, repair, and overhaul (MRO) of complex machinery.

Presented below are a roundup of recent analytics and big data forecasts and market estimates:

  • The global big data market will grow from $18.3B in 2014 to $92.2B by 2026, representing a compound annual growth rate of 14.4 percent. Wikibon predicts significant growth in all four sub-segments of big data software through 2026. Data management (14% CAGR), core technologies such as Hadoop, Spark and streaming analytics (24% CAGR), databases (18% CAGR) and big data applications, analytics and tools (23% CAGR) are the four fastest growing sub-segments according to Wikibon. Source: Wikibon forecasts Big Data market to hit $92.2B by 2026.

Wikibon big data forecast 2016

  • In 2015, the Global Analytics and Business Intelligence applications market grew 4% to approach nearly $11.6B in license, maintenance and subscription revenues with SAP maintaining market leadership. SAP led the marketing with 10% market share and $1.2B in Analytics and Business Intelligence (BI) product revenues, riding on a 23% jump in license, maintenance, and subscription revenues. SAS Institute was No. 2 achieving 9% share; IBM was the third at 8%, and Oracle and Microsoft were fourth and fifth place with 7% and 5%, respectively. Source: Apps Run The World: Top 10 Analytics and BI Software Vendors and Market Forecast 2015-2020.

analytics market shares

IDC FutureScape

  • The Total Data market is expected to nearly double in size, growing from $69.6B in revenue in 2015 to $132.3B in 2020. The specific market segments included in 451 Research’s analysis are operational databases, analytic databases, reporting and analytics, data management, performance management, event/stream processing, distributed data grid/cache, Hadoop, and search-based data platforms and analytics. Source: Total Data market expected to reach $132bn by 2020; 451 Research, June 14, 2016.

Worldwide total revenue by segment

overall adoption of big data

  • Improving customer relationships (55%) and making the business more data-focused (53%) are the top two business goals or objectives driving investments in data-driven initiatives today. 78% of enterprises agree that collection and analysis of Big Data have the potential to change fundamentally the way they do business over the next 1 to 3 years. Source: IDG Enterprise 2016 Data & Analytics Research, July 5, 2016.

Data Helps Customer Focused Organizations

  • Venture capital (VC) investment in Big Data accelerated quickly at the beginning of the year with DataDog ($94M), BloomReach ($56M), Qubole ($30M), PlaceIQ ($25M) and others receiving funding. Big Data startups received $6.64B in venture capital investment in 2015, 11% of total tech VC.  M&A activity has remained moderate (FirstMark noted 35 acquisitions since their latest landscape was published last year). Source: Matt Turck’s blog post, Is Big Data Still a Thing? (The 2016 Big Data Landscape).

big data landscape

  • IDC forecasts global spending on cognitive systems will reach nearly $31.3 billion in 2019 with a five-year compound annual growth rate (CAGR) of 55%. More than 40% of all cognitive systems spending throughout the forecast will go to software, which includes both cognitive applications (i.e., text and rich media analytics, tagging, searching, machine learning, categorization, clustering, hypothesis generation, question answering, visualization, filtering, alerting, and navigation). Also included in the forecasts are cognitive software platforms, which enable the development of intelligent, advisory, and cognitively enabled solutions.  Source:  Worldwide Spending on Cognitive Systems Forecast to Soar to More Than $31 Billion in 2019, According to a New IDC Spending Guide.
  • Big Data Analytics & Hadoop Market accounted for $8.48B in 2015 and is expected to reach $99.31B by 2022 growing at a CAGR of 42.1% from 2015 to 2022. The rise of big data analytics and rapid growth in consumer data capture and taxonomy techniques are a few of the many factors fueling market growth. Source: Stratistics Market Research Consulting (PDF, opt-in, payment reqd).

Additional sources of market information: 

Analytics Trends 2016 The Next Evolution, Deloitte.

Big data analytics, Ericsson White Paper Uen 288 23-3211 Rev B | October 2015

Big Data and the Intelligence Economy in Canada Big Data: Big Opportunities to Create Business Value, EMC.

The Forrester Wave™: Big Data Hadoop Distributions, Q1 2016

The Forrester Wave™: Big Data Hadoop Cloud Solutions, Q2 2016

The Forrester Wave™: Big Data Text Analytics Platforms, Q2 2016

The Forrester Wave™: Big Data Streaming Analytics, Q1 2016

The Forrester Wave™: Customer Analytics Solutions, Q1 2016

From Big Data to Better Decisions: The ultimate guide to business intelligence today (Domo)

Gartner Hype Cycle for Business Intelligence and Analytics, 2015

IBM: Extracting business value from the 4 V’s of big data

IDC Worldwide Big Data Technology and Services 2012 – 2015 Forecast

Opportunities in Telecom Sector: Arising from Big Data. Deloitte, November 2015

Who will win as Finance doubles down on analytics?

Internet Of Things Will Replace Mobile Phones As Most Connected Device In 2018

  • abstract, background, banner, telecoms, communication, innovation, concept, design, icon, internet of things, internet, computer, innovate, innovative, ball, circle, sphere, circular, social, data, access, wireless, connection, pattern, global, world map, networking, hexagon, circuit, electric, electronics, microchip, power, gradient, blue, vector, illustration, logo,Internet of Things (IoT) sensors and devices are expected to exceed mobile phones as the largest category of connected devices in 2018, growing at a 23% compound annual growth rate (CAGR) from 2015 to 2021.
  • By 2021 there will be 9B mobile subscriptions, 7.7B mobile broadband subscriptions, and 6.3B smartphone subscriptions.
  • Worldwide smartphone subscriptions will grow at a 10.6% CAGR from 2015 to 2012 with Asia/Pacific (APAC) gaining 1.7B new subscribers alone.

These and other insights are from the 2016 Ericcson Mobility Report (PDF, no opt-in). Ericcson has provided a summary of the findings and a series of interactive graphics here. Ericcson created the subscription and traffic forecast baseline this analysis is based on using historical data from a variety of internal and external sources. Ericcson also validated trending analysis through the use of their planning models. Future development is estimated based on macroeconomic trends, user trends (researched by Ericsson ConsumerLab), market maturity, technology development expectations and documents such as industry analyst reports, on a national or regional level, together with internal assumptions and analysis.In addition, Ericsson regularly performs traffic measurements in over 100 live networks in all major regions of the world. For additional details on the methodology, please see page 30 of the study.

Key takeaways from the 2016 Ericcson Mobility Report include the following:

  • Internet of Things (IoT) sensors and devices are expected to exceed mobile phones as the largest category of connected devices in 2018, growing at a 23% compound annual growth rate (CAGR) from 2015 to 2021. Ericcson predicts there will be a total of approximately 28B connected devices worldwide by 2021, with nearly 16B related to IoT. The following graphic compares cellular IoT, non-cellular IoT, PC/laptop/tablet, mobile phones, and fixed phones connected devices growth from 2015 to 2021.

Internet of Things Forecast

  • 400 million IoT devices with cellular subscriptions were active at the end of 2015, and Cellular IoT is expected to have the highest growth among the different categories of connected devices, reaching 1.5B connections in 2021. Ericcson cites the growth factors of 3GPP standardization of cellular IoT technologies and cellular connections benefitting from enhancements in provisioning, device management, service enablement and security. The forecast for IoT connected devices: cellular and non-cellular (billions) is shown

IoT Connected Devices

  • Global mobile broadband subscriptions will reach 7.7B by 2021, accounting for 85% of all subscriptions. Ericcson is predicting there will be 9B mobile subscriptions, 7.7B mobile broadband subscriptions, and 6.3B smartphone subscriptions by 2021 as well. The following graphic compares mobile subscriptions, mobile broadband, mobile subscribers, fixed broadband subscriptions, and mobile CPs, tablets and mobile routers’ subscription growth.

mobile subscription growth

  • Worldwide smartphone subscriptions will grow at a 10.6% compound annual growth rate (CAGR) from 2015 to 2012. Ericcson predicts that the Asia/Pacific (APAC) region will gain 1.7B new subscribers. The Middle East and Africa will have smartphone subscription rates will increase more than 200% between 2015–2021. The following graphic compares growth by global region.

smartphone subscriptions

  • Mobile subscriptions are growing around 3% year-over-year globally and reached 7.4B in Q1 2016. India is the fastest growing market regarding net additions during the quarter (+21 million), followed by Myanmar (+5 million), Indonesia, (+5 million), the US (+3 million) and Pakistan (+3 million). The following graphic compares mobile subscription growth by global region for Q1, 2016.

Mobile subscriptions Q1

  • 90% of subscriptions in Western Europe and 95% in North America will be for LTE/5G by 2021. The Middle East and Africa will see a dramatic shift from 2G to a market where almost 80% of subscriptions will be for 3G/4G. The following graphic compares mobile subscriptions by region and technology.

Mobile technology by region

  • Mobile video traffic is forecast to grow by around 55% annually through 2021, accounting for nearly 67% of all mobile data traffic. Social networking traffic is predicted to attain a 41% CAGR from 2015 to 2021. The following graphic compared the growth of mobile traffic by application category and projected mobile traffic by application category per month.

mobile video traffic

  • Ericcson also provided mobile subscription, traffic per device, mobile traffic growth forecast, and monthly data traffic per smartphone. The summary table is shown below:

summary table

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

Sources:

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.

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.
cloud-maturity-model

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

Machine Learning Is Redefining The Enterprise In 2016

machine learning imageBottom line: Machine learning is providing the needed algorithms, applications, and frameworks to bring greater predictive accuracy and value to enterprises’ data, leading to diverse company-wide strategies succeeding faster and more profitably than before.

Industries Where Machine Learning Is Making An Impact  

The good news for businesses is that all the data they have been saving for years can now be turned into a competitive advantage and lead to strategic goals being accomplished. Revenue teams are using machine learning to optimize promotions, compensation and rebates drive the desired behavior across selling channels. Predicting propensity to buy across all channels, making personalized recommendations to customers, forecasting long-term customer loyalty and anticipating potential credit risks of suppliers and buyers are Figure 1 provides an overview of machine learning applications by industry.

machine learning industries

Source: Tata Consultancy Services, Using Big Data for Machine Learning Analytics in Manufacturing – TCS

Machine Learning Is Revolutionizing Sales and Marketing  

Unlike advanced analytics techniques that seek out causality first, machine learning techniques are designed to seek out opportunities to optimize decisions based on the predictive value of large-scale data sets. And increasingly data sets are comprised of structured and unstructured data, with the global proliferation of social networks fueling the growth of the latter type of data.  Machine learning is proving to be efficient at handling predictive tasks including defining which behaviors have the highest propensity to drive desired sales and marketing outcomes. Businesses eager to compete and win more customers are applying machine learning to sales and marketing challenges first.  In the MIT Sloan Management Review article, Sales Gets a Machine-Learning Makeover the Accenture Institute for High Performance shared the results of a recent survey of enterprises with at least $500M in sales that are targeting higher sales growth with machine learning. Key takeaways from their study results include the following:

  • 76% say they are targeting higher sales growth with machine learning. Gaining greater predictive accuracy by creating and optimizing propensity models to guide up-sell and cross-sell is where machine learning is making contributions to omnichannel selling strategies today.
  • At least 40% of companies surveyed are already using machine learning to improve sales and marketing performance. Two out of five companies have already implemented machine learning in sales and marketing.
  • 38% credited machine learning for improvements in sales performance metrics. Metrics the study tracked include new leads, upsells, and sales cycle times by a factor of 2 or more while another 41% created improvements by a factor of 5 or more.
  • Several European banks are increasing new product sales by 10% while reducing churn 20%. A recent McKinsey study found that a dozen European banks are replacing statistical modeling techniques with machine learning. The banks are also increasing customer satisfaction scores and customer lifetime value as well.

Why Machine Learning Adoption Is Accelerating

Machine learning’s ability to scale across the broad spectrum of contract management, customer service, finance, legal, sales, quote-to-cash, quality, pricing and production challenges enterprises face is attributable to its ability to continually learn and improve. Machine learning algorithms are iterative in nature, continually learning and seeking to optimize outcomes.  Every time a miscalculation is made, machine learning algorithms correct the error and begin another iteration of the data analysis. These calculations happen in milliseconds which makes machine learning exceptionally efficient at optimizing decisions and predicting outcomes.
The economics of cloud computing, cloud storage, the proliferation of sensors driving Internet of Things (IoT) connected devices growth, pervasive use of mobile devices that consume gigabytes of data in minutes are a few of the several factors accelerating machine learning adoption. Add to these the many challenges of creating context in search engines and the complicated problems companies face in optimizing operations while predicting most likely outcomes, and the perfect conditions exist for machine learning to proliferate.
The following are the key factors enabling machine learning growth today:

  • Exponential data growth with unstructured data being over 80% of the data an enterprise relies on to make decisions daily. Demand forecasts, CRM and ERP transaction data, transportation costs, barcode and inventory management data, historical pricing, service and support costs and accounting standard costing are just a few of the many sources of structured data enterprises make decisions with today.   The exponential growth of unstructured data that includes social media, e-mail records, call logs, customer service and support records, Internet of Things sensing data, competitor and partner pricing and supply chain tracking data frequently has predictive patterns enterprises are completely missing out on today. Enterprises looking to become competitive leaders are going after the insights in these unstructured data sources and turning them into a competitive advantage with machine learning.
  • The Internet of Things (IoT) networks, embedded systems and devices are generating real-time data that is ideal for further optimizing supply chain networks and increasing demand forecast predictive As IoT platforms, systems, applications and sensors permeate value chains of businesses globally, there is an exponential growth of data generated. The availability and intrinsic value of these large-scale datasets are an impetus further driving machine learning adoption.
  • Generating massive data sets through synthetic means including extrapolation and projection of existing historical data to create realistic simulated data. From weather forecasting to optimizing a supply chain network using advanced simulation techniques that generate terabytes of data, the ability to fine-tune forecasts and attain greater optimizing is also driving machine learning adoption. Simulated data sets of product launch and selling strategies is a nascent application today and one that shows promise in developing propensity models that predict purchase levels.
  • The economics of digital storage and cloud computing are combining to put infrastructure costs into freefall, making machine learning more affordable for all businesses. Online storage and public cloud instances can be purchased literally in minutes online with a credit card. Migrating legacy data off of databases where their accessibility is limited compared to cloud platforms is becoming more commonplace as greatest trust in secure cloud storage increases. For many small businesses who lack IT departments, the Cloud provides a scalable, secure platform for managing their data across diverse geographic locations.

Further reading

Companies Are Reimagining Business Processes with Algorithms. Harvard Business Review. February 8, 2016.  H. James Wilson, Allan Alter, Prashant Shukla. Source: https://hbr.org/2016/02/companies-are-reimagining-business-processes-with-algorithms

Domingos, P. (2012). A Few Useful Things to Know About Machine Learning. Communications Of The ACM, 55(10), 78-87.

Pyle, D., & San José, C. (2015). An executive’s guide to machine learning. Mckinsey Quarterly, (3), 44-53. Link: http://www.mckinsey.com/industries/high-tech/our-insights/an-executives-guide-to-machine-learning

Sales Gets A Machine-Learning Makeover.  MIT Sloan Management Review, May 17, 2016. H. James Wilson, Narendra Mulani, Allan Alter. Source: http://sloanreview.mit.edu/article/sales-gets-a-machine-learning-makeover/Sebag, M. (2014).

The Next Wave Of Enterprise Software Powered By Machine Learning.  TechCrunch, July 27, 2015. http://techcrunch.com/2015/07/27/the-next-wave-of-enterprise-software-powered-by-machine-learning/

What Every Manager Should Know About Machine Learning, Harvard Business Review,  July 7, 2015.  Link: https://hbr.org/2015/07/what-every-manager-should-know-about-machine-learning

What Is Machine Learning? Making The Complex Simple.  Mike Ferguson.  IBM Big Data & Analytics Hub. Link: http://www.ibmbigdatahub.com/blog/what-machine-learning

World Economic Forum White Paper Digital Transformation of Industries: In collaboration with Accenture Digital Enterprise, January 2016. Link: http://reports.weforum.org/digital-transformation-of-industries/wp-content/blogs.dir/94/mp/files/pages/files/digital-enterprise-narrative-final-january-2016.pdf

Yan, J., Zhang, C., Zha, H., Gong, M., Sun, C., Huang, J., & Yang, X. (2015, February). On machine learning towards predictive sales pipeline analytics. In Twenty-Ninth AAAI Conference on Artificial Intelligence.  Link: http://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/viewFile/9444/9488

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 Glassdoor.com 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%
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