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Posts tagged ‘Cloud Computing’

The Best Cloud Computing Companies And CEOs To Work For In 2019 Based On Glassdoor

  • SysdigFivetranNuxeoCloudianMendixStreamSetsZscalerZohoSAPOutSystemsKony, and Netskope are the most likely to be recommended by their employees to friends looking for a cloud computing company to work for in 2019.
  • Cloud platform and development companies dominate the highest rated cloud businesses when indexed by the percent of employees who would recommend their company to a friend.
  • Taken together, the 12 CEOs leading the top-rated cloud computing companies are approved by 98% of employees as of March 3, 2019, on Glassdoor. CEOs in this group include Thomas Hogan of Kony, Paulo Rosado of OutSystems, Bill McDermott of SAP, and Sridhar Vembu of Zoho.

These and many other insights are from an analysis completed today comparing Computer Reseller News’ 100 Coolest Cloud Computing Vendors of 2019 by their respective Glassdoor scores. The Computer Reseller News annual list of the 100 coolest cloud computing vendors is an impartial, 3rd party benchmark of the fastest-growing and most likely to hire cloud businesses expanding today.  By far the most common request from Forbes readers is which cloud computing companies are the best to work for. The goal of this analysis is to provide readers with insights into which cloud computing companies best fit their skills and at the same time have a strong reputation based on feedback from existing employees.

Indexing the most interesting and fastest growing cloud computing companies by their Glassdoor scores and reputations is a great way to begin defining a long-term career growth strategy. One factor not quantified is how well of a fit an applicant is to company culture. Take every opportunity for in-person interviews, read Glassdoor ratings often and observe as much as possible about daily life in companies of interest to see if they are a good fit for your skills and strengths.

Using the 2019 CRN list as a baseline to compare the Glassdoor 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 dataset here. There are 15 companies on the CRN list that don’t have that many or any entries on Glassdoor, and they are excluded from the rankings shown below. You can find their mention in the original dataset. If the image below is not visible in your browser, you can view the rankings here.

The highest rated CEOs on Glassdoor as of March 3, 2019, include the following. Please click on the graphic and dataset to expand for easier reading.

The original dataset is shown below:

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Which Analytics And BI Technologies Will Be The Highest Priority In 2019?

  • 82% of enterprises are prioritizing analytics and BI as part of their budgets for new technologies and cloud-based services.
  • 54% say AI, Machine Learning and Natural Language Processing (NLP) are also a high investment priority.
  • 50% of enterprises say their stronger focus on metrics and Key Performance Indicators (KPIs) company-wide are a major driver of new investment in analytics and BI.
  • 43%  plan to both build and buy AI and machine learning applications and platforms.
  • 42% are seeking to improve user experiences by automating discovery of data insights and 26% are using AI to provide user recommendations.

These and many other fascinating insights are from the recent TDWI Best Practices Report, BI and Analytics in the Age of AI and Big Data. An executive summary of the study is available online here. The entire study is available for download here (39 PP., PDF, free, opt-in). The study found that enterprises are placing a high priority on augmenting existing systems and replacing older technologies and data platforms with new cloud-based BI and predictive analytics ones. Transforming Data with Intelligence (TDWI) is a global community of AI, analytics, data science and machine learning professionals interested in staying current in these and more technology areas as part of their professional development. Please see page 3 of the study for specifics regarding the methodology.

Key takeaways from the study include the following:

  • 82% of enterprises are prioritizing analytics and BI applications and platforms as part of their budgets for new technologies and cloud-based services. 78% of enterprises are prioritizing advanced analytics, and 76% data preparation. 54% say AI, machine learning and Natural Language Processing (NLP) are also a high investment priority. The following graphic ranks enterprises’ investment priorities for acquiring or subscribing to new technologies and cloud-based services by analytics and BI initiatives or strategies. Please click on the graphic to expand for easier reading.

  • Data warehouse or mart in the cloud (41%), data lake in the cloud (39%) and BI platform in the cloud (38%) are the top three types of technologies enterprises are planning to use. Based on this finding and others in the study, cloud platforms are the new normal in enterprises’ analytics and Bi strategies going into 2019. Cloud data storage (object, file, or block) and data virtualization or federation (both 32%) are the next-most planned for technologies by enterprises when it comes to investing in the analytics and BI initiatives. Please click on the graphic to expand for easier reading.

  • The three most important factors in delivering a positive user experience include good query performance (61%), creating and editing visualizations (60%), and personalizing dashboards and reports (also 60%). The three activities that lead to the least amount of satisfaction are using predictive analytics and forecasting tools (27% dissatisfied), “What if” analysis and deriving new data (25%) and searching across data and reports (24%). Please click on the graphic to expand for easier reading.

  • 82% of enterprises are looking to broaden the base of analytics and BI platforms they rely on for insights and intelligence, not just stay with the solutions they have in place today. Just 18% of enterprises plan to add more instances of existing platforms and systems. Cloud-native platforms (38%), a new analytics platform (35%) and cloud-based data lakes (31%) are the top three system areas enterprises are planning to augment or replace existing BI, analytics, and data warehousing systems in. Please click on the graphic to expand for easier reading.

  • The majority of enterprises plan to both build and buy Artificial Intelligence (AI) and machine learning (ML) solutions so that they can customize them to their specific needs. 43% of enterprises surveyed plan to both build and buy AI and ML applications and platforms, a figure higher than any other recent survey on this aspect of enterprise AI adoption. 13% of responding enterprises say they will exclusively build their own AI and ML applications.

  • Capitalizing on machine learning’s innate strengths of applying algorithms to large volumes of data to find actionable new insights (54%) is what’s most important to the majority of enterprises. 47% of enterprises look to AI and machine learning to improve the accuracy and quality of information. And 42% are configuring AI and machine learning applications and platforms to augment user decision making by giving recommendations. Please click on the graphic to expand for easier reading.

2018 Roundup Of Cloud Computing Forecasts And Market Estimates

Cloud computing platforms and applications are proliferating across enterprises today, serving as the IT infrastructure driving new digital businesses. The following roundup of cloud computing forecasts and market estimates reflect a maturing global market for cloud services, with proven scale, speed and security to support new business models.

CIOs who are creating compelling business cases that rely on cloud platforms as a growth catalyst is the architects enabling these new business initiatives to succeed. The era of CIO strategist has arrived. Key takeaways include the following:

  • Amazon Web Services (AWS) accounted for 55% of the company’s operating profit in Q2, 2018, despite contributing only 12% to the company’s net sales. In Q1, 2018 services accounted for 40% of Amazon’s revenue, up from 26% three years earlier. Source: Cloud Business Drives Amazon’s Profits, Statista, July 27, 2018.

  • 80% of enterprises are both running apps on or experimenting with Amazon Web Services (AWS) as their preferred cloud platform. 67% of enterprises are running apps on (45%) and experimenting on (22%) the Microsoft Azure platform. 18% of enterprises are using Google’s Cloud Platform for applications today, with 23% evaluating the platform for future use. RightScale’s 2018 survey was included in the original data set Statista used to create the comparison. Source: Statista, Current and planned usage of public cloud platform services running applications worldwide in 2018. Please click on the graphic to expand for easier viewing.

  • Enterprise adoption of Microsoft Azure increased significantly from 43% to 58% attaining a 35% CAGR while AWS adoption increased from 59% to 68%. Enterprise respondents with future projects (the combination of experimenting and planning to use) show the most interest in Google (41%). Source: RightScale 2018 State of the Cloud Report. Please click on the graphic to expand for easier viewing.

  • Wikibon projects the True Private Cloud (TPC) worldwide market will experience a compound annual growth rate of 29.2%, reaching $262.4B by 2027. The firm predicts TPC growth will far outpace the infrastructure-as-a-service (IaaS) growth of 15.2% over the same period. A true private cloud is distinguished from a private cloud by the completeness of the integration of all aspects of the offering, including performance characteristics such as price, agility, and service breadth. Please see the source link for additional details on TPC. Source: Wikibon’s 2018 True Private Cloud Forecast and Market Shares. Please click on the graphic to expand for easier viewing.

  • Quality Control, Computer-Aided Engineering, and Manufacturing Execution Systems (MES) are the three most widely adopted systems in the cloud by discrete and process The survey also found that 60% of discrete and process manufacturers say their end users prefer the cloud over on-premise. Source: Amazon Web Services & IDC: Industrial Customers Are Ready For The Cloud – Now (PDF, 13 pp., no opt-in, sponsored by AWS). Please click on the graphic to expand for easier viewing.

  • The Worldwide Public Cloud Services Market is projected to grow by 17.3 3% in 2019 to total $206.2B, up from $175.8B in 2018 according to Gartner. In 2018 the market will grow a healthy 21% up from $145.3B in 2017 according to the research and advisory firm. Infrastructure-as-a-Service (IaaS) will be the fastest-growing segment of the market, forecasted to grow by 27.6% in 2019 to reach $39.5B, up from $31B in 2018. By 2022, Gartner expects that 90% of enterprises purchasing public cloud IaaS will do so from an integrated IaaS and Platform-as-a-Service (PaaS), and will use both the IaaS and PaaS capabilities from that provider. Source: Gartner Forecasts Worldwide Public Cloud Revenue to Grow 17.3 Percent in 2019.

  • More than $1.3T in IT spending will be directly or indirectly affected by the shift to cloud by 2022. 28% of spending within key enterprise IT markets will shift to the cloud by 2022, up from 19% in 2018. The largest cloud shift before 2018 occurred in application software, particularly driven by customer relationship management (CRM) software, with Salesforce dominating as the market leader. CRM has already reached a tipping point where a higher proportion of spending occurs in the cloud than in traditional software. Source: Gartner Says 28 Percent of Spending in Key IT Segments Will Shift to the Cloud by 2022.

  • IDC predicts worldwide Public Cloud Services Spending will reach $180B in 2018, an increase of 23.7% over 2017. According to IDC, the market is expected to achieve a five-year compound annual growth rate (CAGR) of 21.9% with public cloud services spending totaling $277B in 2021. The industries that are forecast to spend the most on public cloud services in 2018 are discrete manufacturing ($19.7B), professional services ($18.1B), and banking ($16.7B). The process manufacturing and retail industries are also expected to spend more than $10B each on public cloud services in 2018. These five industries will remain at the top in 2021 due to their continued investment in public cloud solutions. The industries that will see the fastest spending growth over the five-year forecast period are professional services (24.4% CAGR), telecom (23.3% CAGR), and banking (23.0% CAGR). Source: Worldwide Public Cloud Services Spending Forecast to Reach $160 Billion This Year, According to IDC.
  • Discrete Manufacturing is predicted to lead all industries on public cloud spending of $19.7B in 2018 according to IDC. Additional industries forecast to spend the most on public cloud services this year include Professional Services at $18.1B and Banking at $16.7B. The process manufacturing and retail industries are also expected to spend more than $10B each on public cloud services in 2018. According to IDC, these five industries will remain at the top in 2021 due to their continued investment in public cloud solutions. The industries that will see the fastest spending growth over the five-year forecast period are Professional Services with a 24.4% CAGR, Telecommunications with a 23.3% CAGR, and banking with a 23% CAGR. Source: Worldwide Public Cloud Services Spending Forecast to Reach $160 Billion This Year, According to IDC.

Additional Resources:

Zero Trust Security Is The Growth Catalyst IoT Needs

  • McKinsey predicts the Internet of Things (IoT) market will be worth $581B for ICT-based spend alone, growing at a Compound Annual Growth Rate (CAGR) between 7 and 15% according to their study Internet of Things The IoT opportunity – Are you ready to capture a once-in-a-lifetime value pool?
  • By 2020, Discrete Manufacturing, Transportation & Logistics and Utilities industries are projected to spend $40B each on IoT platforms, systems, and services according to Statista.
  • The Industrial Internet of Things (IIoT) market is predicted to reach $123B in 2021, attaining a CAGR of 7.3% through 2020 according to Accenture.

IoT is forecast to be one of the tech industry’s fastest-growing sectors in the next three to five years, as many market estimates like the ones above illustrate. The one factor that will fuel IoT to rapidly grow to new heights or deflate demand just as quickly is security across the myriad of endpoints.

Zero Trust Security (ZTS) is the force multiplier IoT needs to reach its true potential and must be designed into IoT networks if they are going to flex and scale for every endpoint and protect every threat surface.

IoT Needs A Security Wake-Up Call Now  

Industrial Control Systems (ICS) provides a cautionary tale for anyone who thinks enterprise networks don’t need endpoint security and the ability to control access from any point inside or outside an organization.

Chemical, electricity, food & beverage, gas, healthcare, oil, transportation, water services and other key infrastructure industries have relied on ICS applications and platforms for decades. They were designed to deliver reliability and uptime first with little if any effort put into securing them.

However, the glaring security gaps in ICS provide the following lessons for IoT adoption now and in the future:

  • Only digitally enable an endpoint that can verify if every person or device attempting access is authorized, down to the risk score and device level. ICS endpoints were added as fast as utility companies and manufacturers could enable them with speed of deployment, reliability measurement, and uptime being the highest priorities. Security wasn’t a priority with the results being predictable: now many nations’ power grids are vulnerable to attack due to this oversight. With IoT, utilities need to start designing in security to the sensor level using Next-Gen Access as the foundation, leveraging Identity-as-a-Service (IDaaS), Enterprise Mobility Management (EMM) and Privileged Access Management (PAM) to enable Zero Trust strategies organization-wide. Next-Gen Access calculates a risk score predicated on previous authorized login and resource access patterns for each verified account.  When there is an anomaly in account credentials’ use, users are requested to verify with Multi-Factor Authentication (MFA).
  • An ICS doesn’t learn from security mistakes, while NGA gets smarter with every breach attempt. A typical ICS is designed to make operations more efficient and reliable, not secure. Even with many endpoints of an ICS being digitally-enabled today with device retrofitting common, security still isn’t a priority. Instead of digitally enabling IoT sensors purely for efficiency, Next-Gen Access needs to be designed in at the sensor level to protect entire networks. Zero Trust Security’s four main pillars are to verify the user, validate their device, limit access and privilege, and learn and adapt. Machine learning is relied on for learning and adapting in real-time to access requests and threats.
  • ICS assumes no bad actors exist while NGA knows how to stop them. Bad actors, or those who want to breach a system for financial gain or to harm a business, aren’t just outside. Verizon’s 2017 Data Breach Investigations Report finds that 25% of all breaches are initiated from inside an organization and 75% outside which makes NGA essential for attaining Zero Trust Security on an enterprise level. Of the ICS being protected today, the majority are reliant on trusted and untrusted domains, a security technology over two decades old. When organized crime, state-sponsored hacking organizations or internal employees can quickly compromise privileged credentials, entire utility systems are at risk.
  • Replacing security-obsolete ICS with IoT-based systems that have NGA designed in to flex for every person and device shuts down physical and digital attack vectors organization-wide. The strategic security plan for any IoT-enabled enterprise has to prioritize faster automated discovery, configuration and response if it’s going to survive against highly orchestrated attacks. NGA has proven effective at thwarting unauthorized privileged credential attacks while continually learning from usage patterns of authorized and unauthorized users.

Conclusion

ICS have some of the most porous, incomplete security perimeters of any enterprise systems. 63% of all ICS-related vulnerabilities cause processing plants to lose control of operations, and 71% can obfuscate or block the view of operations immediately according to the Dragos Industrial Control Vulnerabilities 2017 in Review.  ICS needs an overhaul starting with Next-Gen Access, enabling Zero Trust Security across every employee and device that forms an organizations’ security perimeter.

Bain & Company released a study on the price elasticity of IoT-enabled products by security level. They found that 93% of the executives surveyed would pay an average of 22% more for devices with better security. Taken together, Bain estimates that improving security solutions for these devices could grow the IoT cybersecurity market by $9B to $11B.

The speed at which manufacturers are building smart, connected products accentuates the need for Zero Trust Security powered by Next-Gen Access from their inception. Security as an afterthought won’t be effective at the scale and pace of IoT.

Source: Bain Snap Chart, July 98, 2018 Better IoT Security Could Grow Device Market

 

Where Business Intelligence Is Delivering Value In 2018

  • Executive Management, Operations, and Sales are the three primary roles driving Business Intelligence (BI) adoption in 2018.
  • Dashboards, reporting, end-user self-service, advanced visualization, and data warehousing are the top five most important technologies and initiatives strategic to BI in 2018.
  • Small organizations with up to 100 employees have the highest rate of BI penetration or adoption in 2018.
  • Organizations successful with analytics and BI apps define success in business results, while unsuccessful organizations concentrate on adoption rate first.
  • 50% of vendors offer perpetual on-premises licensing in 2018, a notable decline over 2017. The number of vendors offering subscription licensing continues to grow for both on-premises and public cloud models.
  • Fewer than 15% of respondent organizations have a Chief Data Officer, and only about 10% have a Chief Analytics Officer today.

These and many other fascinating insights are from Dresner Advisory Service’s  2018 Wisdom of Crowds® Business Intelligence Market Study. In its ninth annual edition, the study provides a broad assessment of the business intelligence (BI) market and a comprehensive look at key user trends, attitudes, and intentions.  The latest edition of the study adds Information Technology (IT) analytics, sales planning, and GDPR, bringing the total to 36 topics under study.

“The Wisdom of Crowds BI Market Study is the cornerstone of our annual research agenda, providing the most in-depth and data-rich portrait of the state of the BI market,” said Howard Dresner, founder and chief research officer at Dresner Advisory Services. “Drawn from the first-person perspective of users throughout all industries, geographies, and organization sizes, who are involved in varying aspects of BI projects, our report provides a unique look at the drivers of and success with BI.” Survey respondents include IT (28%), followed by Executive Management (22%), and Finance (19%). Sales/Marketing (8%) and the Business Intelligence Competency Center (BICC) (7%). Please see page 15 of the study for specifics on the methodology.

Key takeaways from the study include the following:

  • Executive Management, Operations, and Sales are the three primary roles driving Business Intelligence (BI) adoption in 2018. Executive management teams are taking more of an active ownership role in BI initiatives in 2018, as this group replaced Operations as the leading department driving BI adoption this year. The study found that the greatest percentage change in functional areas driving BI adoption includes Human Resources (7.3%), Marketing (5.9%), BICC (5.1%) and Sales (5%).

  • Making better decisions, improving operational efficiencies, growing revenues and increased competitive advantage are the top four BI objectives organizations have today. Additional goals include enhancing customer service and attaining greater degrees of compliance and risk management. The graph below rank orders the importance of BI objectives in 2018 compared to the percent change in BI objectives between 2017 and 2018. Enhanced customer service is the fastest growing objective enterprises adopt BI to accomplish, followed by growth in revenue (5.4%).

  • Dashboards, reporting, end-user self-service, advanced visualization, and data warehousing are the top five most important technologies and initiatives strategic to BI in 2018. The study found that second-tier initiatives including data discovery, data mining/advanced algorithms, data storytelling, integration with operational processes, and enterprise and sales planning are also critical or very important to enterprises participating in the survey. Technology areas being hyped heavily today including the Internet of Things, cognitive BI, and in-memory analysis are relatively low in the rankings as of today, yet are growing. Edge computing increased 32% as a priority between 2017 and 2018 for example. The results indicate the core aspect of excelling at using BI to drive better business decisions and more revenue still dominate the priorities of most businesses today.
  • Sales & Marketing, Business Intelligence Competency Center (BICC) and   Executive Management have the highest level of interest in dashboards and advanced visualization. Finance has the greatest interest in enterprise planning and budgeting. Operations including manufacturing, supply chain management, and services) leads interest in data mining, data storytelling, integration with operational processes, mobile device support, data catalog and several other technologies and initiatives. It’s understandable that BICC leaders most advocate end-user self-service and attach high importance to many other categories as they are internal service bureaus to all departments in an enterprise. It’s been my experience that BICCs are always looking for ways to scale BI adoption and enable every department to gain greater value from analytics and BI apps. BICCs in the best run companies are knowledge hubs that encourage and educate all departments on how to excel with analytics and BI.

  • Insurance companies most prioritize dashboards, reporting, end-user self-service, data warehousing, data discovery and data mining. Business Services lead the adoption of advanced visualization, data storytelling, and embedded BI. Manufacturing most prioritizes sales planning and enterprise planning but trails in other high-ranking priorities. Technology prioritizes Software-as-a-Service (SaaS) given its scale and speed advantages. The retail & wholesale industry is going through an analytics and customer experience revolution today. Retailers and wholesalers lead all others in data catalog adoption and mobile device support.

  • Insurance, Technology and Business Services vertical industries have the highest rate of BI adoption today. The Insurance industry leads all others in BI adoption, followed by the Technology industry with 40% of organizations having 41% or greater adoption or penetration. Industries whose BI adoption is above average include Business Services and Retail & Wholesale. The following graphic illustrates penetration or adoption of Business Intelligence solutions today by industry.

  • Dashboards, reporting, advanced visualization, and data warehousing are the highest priority investment areas for companies whose budgets increased from 2017 to 2018. Additional high priority areas of investment include advanced visualization and data warehousing. The study found that less well-funded organizations are most likely to lead all others by investing in open source software to reduce costs.

  • Small organizations with up to 100 employees have the highest rate of BI penetration or adoption in 2018. Factors contributing to the high adoption rate for BI in small businesses include business models that need advanced analytics to function and scale, employees with the latest analytics and BI skills being hired to also scale high growth businesses and fewer barriers to adoption compared to larger enterprises. BI adoption tends to be more pervasive in small businesses as a greater percentage of employees are using analytics and BI apps daily.

  • Executive Management is most familiar with the type and number of BI tools in use across the organization. The majority of executive management respondents say their teams are using between one or two BI tools today. Business Intelligence Competency Centers (BICC) consistently report a higher number of BI tools in use than other functional areas given their heavy involvement in all phases of analytics and BI project execution. IT, Sales & Marketing and Finance are likely to have more BI tools in use than Operations.

  • Enterprises rate BI application usability and product quality & reliability at an all-time high in 2018. Other areas of major improvements on the part of vendors include improving ease of implementation, online training, forums and documentation, and completeness of functionality. Dresner’s research team found between 2017 and 2018 integration of components within product dropped, in addition to scalability. The study concludes the drop in integration expertise is due to an increasing number of software company acquisitions aggregating dissimilar products together from different platforms.

By 2020 83% Of Enterprise Workloads Will Be In The Cloud

  • Digitally transforming enterprises (63%) is the leading factor driving greater public cloud engagement or adoption today.
  • 66% of IT professionals say security is their most significant concern in adopting an enterprise cloud computing strategy.
  • 50% of IT professionals believe artificial intelligence and machine learning are playing a role in cloud computing adoption today, growing to 67% by 2020.
  • Artificial Intelligence (AI) and Machine Learning will be the leading catalyst driving greater cloud computing adoption by 2020.

These insights and findings are from LogicMonitor’s Cloud Vision 2020: The Future of the Cloud Study (PDF, free, opt-in, 9 pp.). The survey is based on interviews with approximately 300 influencers LogicMonitor interviewed in November 2017. Respondents include Amazon Web Services AWS re:Invent 2017 attendees, industry analysts, media, consultants and vendor strategists. The study’s primary goal is to explore the landscape for cloud services in 2020. While the study’s findings are not statistically significant, they do provide a fascinating glimpse into current and future enterprise cloud computing strategies.

Key takeaways include the following:

  • 83% Of Enterprise Workloads Will Be In The Cloud By 2020. LogicMonitor’s survey is predicting that 41% of enterprise workloads will be run on public cloud platforms (Amazon AWSGoogle Cloud PlatformIBM CloudMicrosoft Azure and others) by 2020. An additional 20% are predicted to be private-cloud-based followed by another 22% running on hybrid cloud platforms by 2020. On-premise workloads are predicted to shrink from 37% today to 27% of all workloads by 2020.

  • Digitally transforming enterprises (63%) is the leading factor driving greater public cloud engagement or adoption followed by the pursuit of IT agility (62%). LogicMonitor’s survey found that the many challenges enterprises face in digitally transforming their business models are the leading contributing factor to cloud computing adoption. Attaining IT agility (62%), excelling at DevOps (58%), mobility (55%), Artificial Intelligence (AI) and Machine Learning (50%) and the Internet of Things (IoT) adoption (45%) are the top six factors driving cloud adoption today. Artifical Intelligence (AI) and Machine Learning are predicted to be the leading factors driving greater cloud computing adoption by 2020.

  • 66% of IT professionals say security is their greatest concern in adopting an enterprise cloud computing strategy. Cloud platform and service providers will go on a buying spree in 2018 to strengthen and harden their platforms in this area. Verizon (NYSE:VZ) acquiring Niddel this week is just the beginning. Niddel’s Magnet software is a machine learning-based threat-hunting system that will be integrated into Verizon’s enterprise-class cloud services and systems. Additional concerns include attaining governance and compliance goals on cloud-based platforms (60%), overcoming the challenges of having staff that lacks cloud experience (58%), Privacy (57%) and vendor lock-in (47%).

  • Just 27% of respondents predict that by 2022, 95% of all workloads will run in the cloud. One in five respondents believes it will take ten years to reach that level of workload migration. 13% of respondents don’t see this level of workload shift ever occurring. Based on conversations with CIOs and CEOs in manufacturing and financial services industries there will be a mix of workloads between on-premise and cloud for the foreseeable future. C-level executives evaluate shifting workloads based on each systems’ contribution to new business models, cost, and revenue goals in addition to accelerating time-to-market.

  • Microsoft Azure and Google Cloud Platform are predicted to gain market share versus Amazon AWS in the next three years, with AWS staying the clear market leader. The study found 42% of respondents are predicting Microsoft Azure will gain more market share by 2020. Google Cloud Platform is predicted to also gain ground according to 35% of the respondent base. AWS is predicted to extend its market dominance with 52% market share by 2020.

53% Of Companies Are Adopting Big Data Analytics

  • Big data adoption reached 53% in 2017 for all companies interviewed, up from 17% in 2015, with telecom and financial services leading early adopters.
  • Reporting, dashboards, advanced visualization end-user “self-service” and data warehousing are the top five technologies and initiatives strategic to business intelligence.
  • Data warehouse optimization remains the top use case for big data, followed by customer/social analysis and predictive maintenance.
  • Among big data distributions, Cloudera is the most popular, followed by Hortonworks, MAP/R, and Amazon EMR.

These and many other insights are from Dresner Advisory Services’ insightful 2017 Big Data Analytics Market Study (94 pp., PDF, client accessed reqd), which is part of their Wisdom of Crowds® series of research. This 3rd annual report examines end-user trends and intentions surrounding big data analytics, defined as systems that enable end-user access to and analysis of data contained and managed within the Hadoop ecosystem. The 2017 Big Data Analytics Market Study represents a cross-section of data that spans geographies, functions, organization size, and vertical industries. Please see page 10 of the study for additional details regarding the methodology.

“Across the three years of our comprehensive study of big data analytics, we see a significant increase in uptake in usage and a large drop of those with no plans to adopt,” said Howard Dresner, founder and chief research officer at Dresner Advisory Services. “In 2017, IT has emerged as the most typical adopter of big data, although all departments – including finance – are considering future use. This is an indication that big data is becoming less an experimental endeavor and more of a practical pursuit within organizations.”

Key takeaways include the following:

  • Reporting, dashboards, advanced visualization end-user “self-service” and data warehousing are the top five technologies and initiatives strategic to business intelligence.  Big Data ranks 20th across 33 key technologies Dresner Advisory Services currently tracks.  Big Data Analytics is of greater strategic importance than the Internet of Things (IoT), natural language analytics, cognitive Business Intelligence (BI) and Location intelligence.

  • 53% of companies are using big data analytics today, up from 17% in 2015 with Telecom and Financial Services industries fueling the fastest adoption. Telecom and financial services are the most active early adopters, with Technology and Healthcare being the third and fourth industries seeing big data analytics Education has the lowest adoption as 2017 comes to a close, with the majority of institutions in that vertical saying they are evaluating big data analytics for the future. North America (55%) narrowly leads EMEA (53%) in their current levels of big data analytics adoption. Asia-Pacific respondents report 44% current adoption and are most likely to say they “may use big data in the future.”

  • Data warehouse optimization is considered the most important big data analytics use case in 2017, followed by customer/social analysis and predictive maintenance. Data warehouse optimization is considered critical or very important by 70% of all respondents. It’s interesting to note and ironic that the Internet of Things (IoT) is among the lowest priority use cases for big data analytics today.

  • Big data analytics use cases vary significantly by industry with data warehouse optimization dominating Financial Services, Healthcare, and Customer/social analysis is the leading use case in Technology-based companies. Fraud detection use cases also dominate Financial Services and Telecommunications. Using big data for clickstream analytics is most popular in Financial Services.

  • Spark, MapReduce, and Yarn are the three most popular software frameworks today. Over 30% of respondents consider Spark critical to their big data analytics strategies. MapReduce and Yarn are “critical” to more than 20 percent of respondents.

  • The big data access methods most preferred by respondents include Spark SQL, Hive, HDFS and Amazon S3. 73% of the respondents consider Spark SQL critical to their analytics strategies. Over 30% of respondents consider Hive and HDFS critical as well. Amazon S3 is critical to one of five respondents for managing big data access. The following graphic shows the distribution of big data access methods.

  • Machine learning continues to gain more industry support and investment plans with Spark Machine Learning Library (MLib) adoption projected to grow by 60% in the next 12 months. In the next 24 months, MLib will dominate machine learning according to the survey results. MLib is accessible from the Sparklyr R Package and many others, which continues to fuel its growth. The following graphic compares projected two-year adoption rates by machine learning libraries and frameworks.

Cloud Computing Market Projected To Reach $411B By 2020

  • Worldwide public cloud services market revenue is projected to grow 18.5% in 2017 reaching $260.2B, up from $219.6B in 2016.
  • 2016 worldwide SaaS revenue exceeded Gartner’s previous forecast by $48.2B.
  • SaaS revenue is expected to grow 21% in 2017 reaching $58.6B by the end of this year.
  • Infrastructure as a Service (IaaS) is projected to grow 36.6% in 2017 alone, reaching $34.7B this year making this area the fastest growing of all cloud services today.

Gartner’s latest worldwide public cloud services revenue forecast published earlier this month predicts Infrastructure-as-a-Service (IaaS), currently growing at a 23.31% Compound Annual Growth Rate (CAGR), will outpace the overall market growth of 13.38% through 2020. Software-as-a-Service (SaaS) revenue is predicted to grow from $58.6B in 2017 to $99.7B in 2020. Taking into account the entire forecast period of 2016 – 2020, SaaS is on pace to attain 15.65% compound annual growth throughout the forecast period, also outpacing the total cloud market. The following graphic compares revenue growth by cloud services category for the years 2016 through 2020. Please click on the graphic to expand it for easier reading.

Catalysts driving greater adoption and correspondingly higher CAGRs include a shift Gartner sees in infrastructure, middleware, application and business process services spending. In 2016, Gartner estimates approximately 17% of the total market revenue for these areas had shifted to the cloud. Gartner predicts by 2021, 28% of all IT spending will be for cloud-based infrastructure, middleware, application and business process services. Another factor is the adoption of Platform-as-a-Service (PaaS). Gartner notes that enterprises are confident that PaaS can be a secure, scalable application development platform in the future.  The following graphic compares the compound annual growth rates (CAGRs) of each cloud service area including the total market. Please click on the graphic to expand it for easier reading.

Source: Gartner Forecasts Worldwide Public Cloud Services Revenue to Reach $260 Billion in 2017

Gartner’s Top 10 Predictions For IT In 2018 And Beyond

  • In 2020, AI will become a positive net job motivator, creating 2.3M jobs while eliminating only 1.8M jobs.
  • By 2020, IoT technology will be in 95% of electronics for new product designs.
  • By 2021, 40% of IT staff will be versatilists, holding multiple roles, most of which will be business, rather than technology-related.

These and many other insights are being presented earlier this month at the Gartner Symposium/ITxpo 2017 being held in Orlando, Florida. Gartner’s predictions and the series of assumptions supporting them illustrate how CIOs must seek out and excel in the role of business strategist first, technologist second. In 2018 and beyond CIOs will be more accountable than ever for revenue generation, value creation, and the development and launch of new business models using proven and emerging technologies. Gartner’s ten predictions point to the future of CIOs as collaborators in new business creation, selectively using technologies to accomplish that goal.

The following are Gartner’s ten predictions for IT organizations for 2018 and beyond:

  1. By 2021, early adopter brands that redesign their websites to support visual- and voice-search will increase digital commerce revenue by 30%. Gartner has found that voice-based search queries are the fastest growing mobile search type. Voice and visual search are accelerating mobile browser- and mobile app-based transactions and will continue to in 2018 and beyond. Mobile browser and app-based transactions are as much as 50% of all transactions on many e-commerce sites today. Apple, Facebook, Google and Microsoft’s investments in AI and machine learning will be evident in how quickly their visual- and voice-search technologies accelerate in the next two years.
  2. By 2020, five of the top seven digital giants will willfully “self-disrupt” to create their next leadership opportunity. The top digital giants include Alibaba, Amazon, Apple, Baidu, Facebook, Google, Microsoft, and Tencent. Examples of self-disruption include AWS Lambda versus traditional cloud virtual machines, Alexa versus screen-based e-commerce, and Apple Face ID versus Touch ID.
  3. By the end of 2020, the banking industry will derive $1B in business value from the use of blockchain-based cryptocurrencies. Gartner estimates that the current combined value of cryptocurrencies in circulation worldwide is $155B (as of October 2017), and this value has been increasing as tokens continue to proliferate and market interest grows. Cryptocurrencies will represent more than half of worldwide blockchain global business value-add through year-end 2023 according to the Gartner predictions study.
  4. By 2022, most people in mature economies will consume more false information than true information. Gartner warns that while AI is proving to be very effective in creating new information, it is just as effective at distorting data to create false information as well. Gartner predicts that before 2020, untrue information will fuel a major financial fraud made possible through high-quality falsehoods moving the financial markets worldwide. By the same year, no significant internet company will fully succeed in its attempts to mitigate this problem. Within three years a significant country will pass regulations or laws seeking to curb the spread of AI-generated false information.
  5. By 2020, AI-driven creation of “counterfeit reality,” or fake content, will outpace AI’s ability to detect it, fomenting digital distrust. AI and machine learning systems today can categorize the content of images faster and more consistently accurate than humans. Gartner cautions that by 2018, a counterfeit video used in a satirical context will begin a public debate once accepted as real by one or both sides of the political spectrum. In the next year, there will be a 10-fold increase in commercial projects to detect fake news according to the predictions study.
  6. By 2021, more than 50% of enterprises will be spending more per annum on bots and chatbot creations than traditional mobile app developments. Gartner is predicting that by 2020, 55% of all large enterprises will have deployed (used in production) at least one bot or chatbot. Rapid advances in natural-language processing (NLP) make today’s chatbots much better at recognizing the user intent than previous generations. According to Gartner’s predictions study, NLP is used to determine the entry point for the decision tree in a chatbot, but a majority of chatbots still use scripted responses in a decision tree.
  7. By 2021, 40% of IT staff will be versatilists, holding multiple roles, most of which will be business, rather than technology-related. By 2019, IT technical specialist hires will fall by more than 5%. Gartner predicts that 50% of enterprises will formalize IT versatilist profiles and job descriptions. 20% of IT organizations will hire versatilists to scale digital business. IT technical specialist employees will fall to 75% of 2017 levels.
  8. In 2020, AI will become a positive net job motivator, creating 2.3M jobs while eliminating only 1.8M jobs. By 2020, AI-related job creation will cross into positive territory, reaching 2 million net-new jobs in 2025. Global IT services firms will have massive job churn in 2018, adding 100,000 jobs and dropping 80,000. By 2021 Gartner predicts, AI augmentation will generate $2.9T in business value and recover 6.2B hours of worker productivity.
  9. By 2020, IoT technology will be in 95% of electronics for new product designs. Gartner predicts IoT-enabled products with smartphone activation emerging at the beginning of 2019.
  10. Through 2022, half of all security budgets for IoT will go to fault remediation, recalls and safety failures rather than protection. Gartner predicts IoT spending will increase sharply after 2020 following better methods of applying security patterns cross-industry in IoT security architectures, growing at more than 50% compound annual growth rate (CAGR) over current rates.The total IoT security market for products will reach $840.5M by 2020, and a 24% CAGR for IoT security from 2013 through 2020. Combining IoT security services, safety systems, and physical security will lead to a fast-growing global market. Gartner predicts exponential growth in this area, exceeding more than $5B in global spending by year-end 2020.

Gartner has also made an infographic available of the top 10 Strategic Technology Trends for 2018, in addition to an insightful article on Smarter with Gartner.  You can find the article here, at Gartner Top 10 Strategic Technology Trends for 2018.

Sources:

Gartner Reveals Top Predictions for IT Organizations and Users in 2018 and Beyond

Smarter With Gartner, Gartner Top 10 Strategic Technology Trends for 2018

Top Strategic Predictions for 2018 and Beyond: Pace Yourself, for Sanity’s Sake (client access reqd)

How Artificial Intelligence Is Revolutionizing Business In 2017

  • 84% of respondents say AI will enable them to obtain or sustain a competitive advantage.
  • 83% believe AI is a strategic priority for their businesses today.
  • 75% state that AI will allow them to move into new businesses and ventures.

These and many other fascinating insights are from the Boston Consulting Group and MIT Sloan Management Review study published this week, Reshaping Business With Artificial Intelligence. An online summary of the report is available here. The survey is based on interviews with more than 3,000 business executives, managers, and analysts in 112 countries and 21 industries. For additional details regarding the methodology, please see page 4.

The research found significant gaps between companies who have already adopted and understand Artificial Intelligence (AI) and those lagging. AI early adopters invest heavily in analytics expertise and ensuring the quality of algorithms and data can scale across their enterprise-wide information and knowledge needs. The leading companies who excel at using AI to plan new businesses and streamline existing processes all have solid senior management support for each AI initiative.

Key takeaways include the following:

  • 72% of respondents in the technology, media, and telecommunications industry expect AI to have a significant impact on product offerings in the next five years. The technology, media and telecommunications industry has the highest expectations for AI to accelerate new product and service offerings of all industries tracked in the study, projecting a 52% point increase in the next five years. AI-based improvements are expected to deliver Business Process Outsourcing (BPO) gains in the Financial Services and Professional Services industries as well. The following graphic compares expectations for AI’s expected contributions to business offerings and process improvements over the next five years by industry.

  • Customer-facing activities including marketing automation, support, and service in addition to IT and supply chain management are predicted to be the most affected areas by AI in the next five years. Demand management, supply chain optimization, more efficient distributed order management systems, and Enterprise Resource Planning (ERP) systems that can scale to support new business models are a few of the many areas AI will make contributions to the in the next five years. The following graphic provides an overview of operations, IT, customer-facing, and corporate center functions where AI is predicted to contribute.

  • 84% of respondents say AI will enable them to obtain or sustain a competitive advantage. 75% state that AI will allow them to move into new businesses and ventures. The research shows that AI will be the catalyst of entirely new business models and change the competitive landscape of entire industries in the next five years. 69% of respondents expect incumbent competitors in their industry to use AI to gain an advantage. 63% believe the pressure to reduce costs will require their organizations to use AI in the next five years.

  • Despite high expectations for AI, only 23% of respondents have incorporated it into processes and product and service offerings today. An additional 23% have one or more pilots in progress, and 54% have no adoption plans in progress, 22% of which have no current plans. The following graphic provides insights into the current adoption of AI with survey respondents.

  • By completing a cluster analysis of survey respondents based on AI understanding and adoption questions, four distinct maturity groups emerged including Pioneers, Investigators, Experimenters, and Passives. 19% of the respondent base is Pioneers or those organizations who understand and are adopting AI. The study says that “these organizations are on the leading edge of incorporating AI into both their organization’s offerings and internal processes.” Investigators (32%) are organizations that understand AI but are not deploying it beyond the pilot stage. Experimenters (13%) are organizations that are piloting or adopting AI without deep understanding. Passives (36%) are organizations with no adoption or much knowledge of AI.

  • Pioneers and Investigators are finding new ways to use AI to create entirely new sources of business value. Pioneers (91%) and Investigators (90%) are much more likely to report that their organization recognizes how AI affects business value than Experimenters (32%) and Passives (23%). One of the most differentiating aspects of the four maturity clusters is understanding the differences and value of investing in high-quality data and advanced AI algorithms. Compared to Passives, Pioneers are 12 times more likely to understand the process for training algorithms and ten times more likely to comprehend the development costs of AI-based products and services.

  • Organizations in the Pioneer cluster excel at analytics expertise versus competitors and have exceptional data governance processes in place, further accelerating their AI-driven growth. Pioneers are excellent at change management, citing their senior management’s vision and leadership as a foundational strength in accomplishing their AI-based initiative Early adopter Pioneers are also adept at product development, capable of changing existing products and services to take advantage of new technologies.

  • 61% of all organizations interviewed see developing an AI strategy as urgent, yet only 50% have one done today. The research found that regarding company size, the largest companies (those with more than 100K employees) are the most likely to have an AI strategy, but only half (56%) have one. The following graphic compares the percentage of respondents by maturity cluster who say developing a plan for Al is urgent for their organization relative to those that have a strategy in place today.

  • 70% of respondents are personally looking forward to delegating the more mundane, repetitive aspects of their jobs to AI. 84% believe employees will need to change their skill sets to excel at delivering AI-based initiatives and strategies. Taking this approach provides career growth and a chance to become more marketable for many whose jobs that are being increasingly automated. Cautious optimism regarding AI’s effects on employment dominates early adopter organizations, not dire fatalism. The bottom line is that AI is providing opportunities for career growth that will only accelerate in the future. Those that seize the chance to learn and earn more will end up having AI removing the mundane tasks from their jobs, leaving more time for the most challenging and rewarding work.
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