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The State Of 3D Printing, 2019

  • Proof of concept and prototyping dominate 3D printing applications in 2019.
  • 80% of enterprises say 3D printing is enabling them to innovate faster.
  • 51% of enterprises are actively using 3D printing in production.

These and many other fascinating insights are from Sculpteo’s 5th edition of their popular study, The State of 3D Printing (29 pp., PDF, opt-in). The study’s methodology is based on interviews with 1,300 respondents coming from Europe (64%), United States (16.6 %) and Asia (20.2%), which is the fastest growing region internationally today as measured by this survey over five years. Eight industries are included in the research design including Industrial Goods (13.6%), High Tech (10.6%), Services (9.9%), Consumer Goods (8.6%), Health & Medical (6.2%), Automotive (5.7%), Aerospace & Defense (5.5%), and Education (4.9%). For additional details on the methodology, please see pages 6 and 7 of the study. Key takeaways from the survey include the following:

  • Proof of concepts and prototyping dominate 3D printing applications in 2019. Manufacturers are increasing their reliance on 3D printing as part of their broader manufacturing strategies, with production use up to 51% of all respondents from 38.7% in 2018. The following compares 2019’s purpose of 3D prints versus the last five years of survey data. Please click on the graphic to expand for easier reading.

  • Accelerating product development continues to be enterprises’ top focus guiding their 3D printing strategies in 2019. Mass customization and support for configure-to-order and engineer-to-order product strategies also continue to be a high priority this year, continued the trend since 2015. Increasing production flexibility is the third area of focus that guides additive manufacturing strategies today. Please click on the graphic to expand for easier reading.

  • Nearly 50% of enterprises say that quality control is their top challenge of using their 3D printers. As enterprises increase their adoption of 3D printing to accelerate their additive manufacturing strategies, quality is becoming increasingly more important. Manufacturers most define their success by the perceived level of quality products they deliver to their customers, which is increasing quality control as a needed benefit of 3D printing. Please click on the graphic to expand for easier reading.

  • Adopting a design-to-manufacturing strategy accelerates new product development and innovation, which is why CAD design leads all other activities today. When responses were asked which areas related to 3D printing and additive manufacturing, they spend the majority of their time, nearly 50% said CAD design. Building prototypes, research, and testing prototypes are also areas those enterprises adopting additive manufacturing are investing in today. Please click on the graphic to expand for easier reading.

  • Additive manufacturing adoption is growing across shop floors globally, evidenced by more than 70% of enterprises finding new applications for 3D printing in 2019 and 60% using CAD, simulation, and reverse engineering internally. The leading indicators of additive manufacturing becoming more pervasively adopted across global shop floors are shown in the following graphic. New uses for 3D printing, experimenting with new materials, extensive CAD Design integration combined with simulation and reverse engineering provide further evidence of how engrained additive manufacturing is becoming in production processes daily. 3D printing is now most commonly used alongside CNC machining, another strong indicator of how essential additive manufacturing is becoming to the production process. Please click on the graphic to expand for easier reading.

  • 3D printings’ innate strengths at producing items with complex geometries at a quick pace or iteration are the leading two benefits of 3D printing in 2019. More than 40% of enterprises say that rapid iterations of prototypes and lead time reductions are the leading benefits followed by mass customization (support for configure-to-order & engineer-to-order product strategies) and cost savings. Please click on the graphic to expand for easier reading.

  • 80% of high tech manufacturing respondents are relying on 3D printing for prototyping, leading all industries in this category. 47% use 3D printing to accelerate product development. High tech manufacturers are above average in their experimenting with new 3D printing materials and technologies, looking for greater competitive strength in their industry. Please click on the graphic to expand for easier reading.

  • North American-based enterprises see the scale to support complex product concepts (complex geometries), speed (quick iterations), scale (mass customizations) and cost savings as the top benefits of 3D printing. Sculpteo’s survey found that North American enterprises are more optimistic about the potential for 3D printing becoming mainstream in a production environment. While budget and physical space are the two most significant barriers enterprises face in adopting 3D printing at scale, their optimistic outlook on the technology’s future is driving greater adoption to the shop floor. Please click on the graphic to expand for easier reading.

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Seven Things You Need To Know About IIoT In Manufacturing

  • Global spending on IIoT Platforms for Manufacturing is predicted to grow from $1.67B in 2018 to $12.44B in 2024, attaining a 40% compound annual growth rate (CAGR) in seven years.
  • IIoT platforms are beginning to replace MES and related applications, including production maintenance, quality, and inventory management, which are a mix of Information Technology (IT) and Operations Technology (OT) technologies.
  • Connected IoT technologies are enabling a new era of smart, connected products that often expand on the long-proven platforms of everyday products. Capgemini estimates that the size of the connected products market will be $519B to $685B by 2020.

These and many other fascinating insights are from IoT Analytics’ study, IIoT Platforms For Manufacturing 2019 – 2024 (155 pp., PDF, client access reqd). IoT Analytics is a leading provider of market insights for the Internet of Things (IoT), M2M, and Industry 4.0. They specialize in providing insights on IoT markets and companies, focused market reports on specific IoT segments and Go-to-Market services for emerging IoT companies. The study’s methodology includes interviews with twenty of the leading IoT platform providers, executive-level IoT experts, and IIoT end users. For additional details on the methodology, please see pages 136 and 137 of the report. IoT Analytics defines the Industrial loT (lloT) as heavy industries including manufacturing, energy, oil and gas, and agriculture in which industrial assets are connected to the internet.

The seven things you need to know about IIoT in manufacturing include the following:

  • IoT Analytics’ technology architecture of the Internet of Things reflects the proliferation of new products, software and services, and the practical needs manufacturers have for proven integration to make the Industrial Internet of Things (IIoT) work. IoT technology architectures are in their nascent phase, showing signs of potential in solving many of manufacturing’s most challenging problems. IoT Analytics’ technology architecture shown below is designed to scale in response to the diverse development across the industry landscape with a modular, standardized approach.

  • IIoT platforms are beginning to replace MES and related applications, including production maintenance, quality, and inventory management, which are a mix of Information Technology (IT) and Operations Technology (OT) technologies. IoT Analytics is seeing IIoT platforms begin to replace existing industrial software systems that had been created to bridge the IT and OT gaps in manufacturing environments. Their research teams are finding that IIoT Platforms are an adjacent technology to these typical industrial software solutions but are now starting to replace some of them in smart connected factory settings. The following graphic explains how IoT Analytics sees the IIoT influence across the broader industrial landscape:

  • Global spending on IIoT Platforms for Manufacturing is predicted to grow from $1.67B in 2018 to $12.44B in 2024, attaining a 40% compound annual growth rate (CAGR) in seven years. IoT Analytics is finding that manufacturing is the largest IoT platform industry segment and will continue to be one of the primary growth catalysts of the market through 2024. For purposes of their analysis, IoT Analytics defines manufacturing as standardized production environments including factories, workshops, in addition to custom production worksites such as mines, offshore oil gas, and construction sites. The lloT platforms for manufacturing segment have experienced growth in the traditionally large manufacturing-base countries such as Japan and China. IoT Analytics relies on econometric modeling to create their forecasts.

  • In 2018, the industrial loT platforms market for manufacturing had an approximate 60%/40% split for within factories/outside factories respectively. IoT Analytics predicts this split is expected to remain mostly unchanged for 2019 and by 2024 within factories will achieve slight gains by a few percentage points. The within factories type (of lloT Platforms for Manufacturing) is estimated to grow from a $1B market in 2018 to a $1.5B market by 2019 driven by an ever-increasing amount of automation (e.g., robots on the factory floor) being introduced to factory settings for increased efficiencies, while the outside factories type is forecast to grow from $665M in 2018 to become a $960M market by 2019.

  • Discrete manufacturing is predicted to be the largest percentage of Industrial IoT platform spending for 2019, growing at a CAGR of 46% from 2018. Discrete manufacturing will outpace batch and process manufacturing, becoming 53% of all IIoT platform spending this year. IoT Analytics sees discrete manufacturers pursuing make-to-stock, make-to-order, and assemble-to-order production strategies that require sophisticated planning, scheduling, and tracking capabilities to improve operations and profitability. The greater the production complexity in discrete manufacturing, the more valuable data becomes. Discrete manufacturing is one of the most data-prolific industries there are, making it an ideal catalyst for IIoT platform’s continual growth.

  • Manufacturers are most relying on IIoT platforms for general process optimization (43.1%), general dashboards & visualization (41.1%) and condition monitoring (32.7%). Batch, discrete, and process manufacturers are prioritizing other use cases such as predictive maintenance, asset tracking, and energy management as all three areas make direct contributions to improving shop floor productivity. Discrete manufacturers are always looking to free up extra time in production schedules so that they can offer short-notice production runs to their customers. Combining IIoT platform use cases to uncover process and workflow inefficiencies so more short-notice production runs can be sold is driving Proof of Concepts (PoC) today in North American manufacturing.

  • IIoT platform early adopters prioritize security as the most important feature, ahead of scalability and usability. Identity and Access Management, multifactor-factor authentication, consistency of security patch updates, and the ability to scale and protect every threat surface across an IIoT network are high priorities for IIoT platform adopters today. Scale and usability are the second and third priorities. The following graphic compares IIoT platform manufacturers’ most important needs:

For more information on the insights presented here, check out IoT Analytics’ report: IIoT Platforms For Manufacturing 2019 – 2024.

How To Improve Privileged User’s Security Experiences With Machine Learning

Bottom Line: One of the primary factors motivating employees to sacrifice security for speed are the many frustrations they face, attempting to re-authenticate who they are so they can get more work done and achieve greater productivity.

How Bad Security Experiences Lead to a Breach

Every business is facing the paradox of hardening security without sacrificing users’ login and system access experiences. Zero Trust Privilege is emerging as a proven framework for thwarting privileged credential abuse by verifying who is requesting access, the context of the request, and the risk of the access environment across every threat surface an organization has.

Centrify’s recent survey Privileged Access Management In The Modern Threatscape found that 74% of data breaches start with privileged credential abuse. Forrester estimates that 80% of data breaches have a connection to compromised privileged credentials, such as passwords, tokens, keys, and certificates. On the Dark Web, privileged access credentials are a best-seller because they provide the intruder with “the keys to the kingdom.” By leveraging a “trusted” identity, a hacker can operate undetected and exfiltrate sensitive data sets without raising any red flags.

Frustrated with wasting time responding to the many account lock-outs, re-authentication procedures, and login errors outmoded Privileged Access Management (PAM) systems require, IT Help Desk teams, IT administrators, and admin users freely share privileged credentials, often resulting in them eventually being offered for sale on the Dark Web.

The Keys to the Kingdom Are In High Demand

18% of healthcare employees are willing to sell confidential data to unauthorized parties for as little as $500 to $1,000, and 24% of employees know of someone who has sold privileged credentials to outsiders, according to a recent Accenture survey. State-sponsored and organized crime organizations offer to pay bounties in bitcoin for privileged credentials for many of the world’s largest financial institutions on the Dark Web. And with the typical U.S.-based enterprise losing on average $7.91M from a breach, more than double the global average of $3.86M according to IBM’s 2018 Data Breach Study, it’s clear that improving admin user experiences to reduce the incidence of privileged credential sharing needs to happen now.

How Machine Learning Improves Admin User Experiences and Thwarts Breaches

Machine learning is making every aspect of security experiences more adaptive, taking into account the risk context of every privileged access attempt across any threat surface, anytime. Machine learning algorithms can continuously learn and generate contextual intelligence that is used to streamline verified privileged user’s access while thwarting many potential threats ― the most common of which is compromised credentials.

The following are a few of the many ways machine learning is improving privileged users’ experiences when they need to log in to secure critical infrastructure resources:

  • Machine learning is making it possible to provide adaptive, personalized login experiences at scale using risk-scoring of every access attempt in real-time, all contributing to improved user experiences. Machine learning is making it possible to implement security strategies that flex or adapt to risk contexts in real-time, assessing every access attempt across every threat surface, and generating a risk score in milliseconds. Being able to respond in milliseconds, or real-time is essential for delivering excellent admin user experiences. The “never trust, always verify, enforce least privilege” approach to security is how many enterprises from a broad base of industries including leading financial services and insurance companies are protecting every threat surface from privileged access abuse. CIOs at these companies say taking a Zero Trust approach with a strong focus on Zero Trust Privilege corporate-wide is redefining the legacy approach to Privileged Access Management by delivering cloud-architected Zero Trust Privilege to secure access to infrastructure, DevOps, cloud, containers, Big Data, and other modern enterprise use cases. Taking a Zero Trust approach to security enables their departments to roll out new services across every threat surface their customers prefer to use without having to customize security strategies for each.
  • Quantify, track and analyze every potential security threat and attempted breach and apply threat analytics to the aggregated data sets in real-time, thwarting data exfiltration attempts before they begin. One of the tenets or cornerstones of Zero Trust Privilege is adaptive control. Machine learning algorithms continually “learn” by continuously analyzing and looking for anomalies in users’ behavior across every threat surface, device, and login attempt. When any users’ behavior appears to be outside the threshold of constraints defined for threat analytics and risk scoring, additional authentication is immediately requested, and access denied to requested resources until an identity can be verified. Machine learning makes adaptive preventative controls possible.
  • When every identity is a new security perimeter, machine learnings’ ability to provide personalization at scale for every access attempt on every threat surface is essential for enabling a company to keep growing. Businesses that are growing the fastest often face the greatest challenges when it comes to improving their privileged users’ experiences. Getting new employees productive quickly needs to be based on four foundational elements. These include verifying the identity of every admin user, knowing the context of their access request, ensuring it’s coming from a clean source, and limiting access as well as privilege. Taken together, these pillars form the foundation of a Zero Trust Privilege.

Conclusion

Organizations don’t have to sacrifice security for speed when they’re relying on machine learning-based approaches for improving the privileged user experience. Today, a majority of IT Help Desk teams, IT administrators, and admin users are freely sharing privileged credentials to be more productive, which often leads to breaches based on privileged access abuse. By taking a machine learning-based approach to validate every access request, the context of the request, and the risk of the access environment, roadblocks in the way of greater privileged user productivity disappear. Privileged credential abuse is greatly minimized.

Industry 4.0’s Potential Needs To Be Proven On The Shop Floor

  • 99% of mid-market manufacturing executives are familiar with Industry 4.0, yet only 5% are currently implementing or have implemented an Industry 4.0 strategy.
  • Investing in upgrading existing machinery, replacing fully depreciated machines with next-generation smart, connected production equipment, and adopting real-time monitoring including Manufacturing Execution Systems (MES) are manufacturers’ top three priorities based on interviews with them.
  • Mid-market manufacturers getting the most value out of Industry 4.0 excel at orchestrating a variety of technologies to find new ways to excel at product quality, improve shop floor productivity, meet delivery dates, and control costs.
  • Real-time monitoring is gaining momentum to improve order cycle times, troubleshoot quality problems, improve schedule accuracy, and support track-and-trace.

These and many other fascinating insights are from Industry 4.0: Defining How Mid-Market Manufacturers Derive and Deliver ValueBDO is a leading provider of assurance, tax, and financial advisory services and is providing the report available for download here (PDF, 36 pp., no opt-in). The survey was conducted by Market Measurement, Inc., an independent market research consulting firm. The survey included 230 executives at U.S. manufacturing companies with annual revenues between $200M and $3B and was conducted in November and December of 2018. Please see page 2 of the study for additional details regarding the methodology. One of the most valuable findings of the study is that mid-market manufacturers need more evidence of Industry 4.0, delivering improved supply chain performance, quality, and shop floor productivity.

Insights from the Shop Floor: Machine Upgrades, Smart Machines, Real-Time Monitoring & MES Lead Investment Plans

In the many conversations I’ve had with mid-tier manufacturers located in North America this year, I’ve learned the following:

  • Their top investment priorities are upgrading existing machinery, replacing fully depreciated machines with next-generation smart, connected production equipment, and adopting real-time monitoring including Manufacturing Execution Systems (MES).
  • Manufacturers growing 10% or more this year over 2018 excel at integrating technologies that improve scheduling to enable more short-notice production runs, reduce order cycle times, and improve supplier quality.

Key Takeaways from BDO’s Industry 4.0 Study

  • Manufacturers are most motivated to evaluate Industry 4.0 technologies based on the potential for growth and business model diversification they offer. Building a business case for any new system or technology that delivers revenue, even during a pilot, is getting the highest priority by manufacturers today. Based on my interviews with manufacturers, I found they were 1.7 times more likely to invest in machine upgrades and smart machines versus spending more on marketing. Manufacturers are very interested in any new technology that enables them to accept short-notice production runs from customers, excel at higher quality standards, improve time-to-market, all the while having better cost visibility and control. All those factors are inherent in the top three goals of business model diversification, improved operational efficiencies, and increased market penetration.

  • For Industry 4.0 technologies to gain more adoption, more use cases are needed to explain how traditional product sales, aftermarket sales, and product-as-a-service benefit from these new technologies. Manufacturers know the ROI of investing in a machinery upgrade, buying a smart, connected machine, or integrating real-time monitoring across their shop floors. What they’re struggling with is how Industry 4.0 makes traditional product sales improve. 84% of upper mid-market manufacturers are generating revenue using Information-as-a-Service today compared to 67% of middle market manufacturers overall.

  • Manufacturers who get the most value out of their Industry 4.0 investments begin with a customer-centric blueprint first, integrating diverse technologies to deliver excellent customer experiences. Manufacturers growing 10% a year or more are relying on roadmaps to guide their technology buying decisions. These roadmaps are focused on how to reduce scrap, improve order cycle times, streamline supplier integration while improving inbound quality levels, and provide real-time order updates to customers. BDOs’ survey results reflect what I’m hearing from manufacturers. They’re more focused than ever before on having an integrated engagement strategy combined with greater flexibility in responding to unique and often urgent production runs.

  • Industry 4.0’s potential to improve supply chains needs greater focus if mid-tier manufacturers are going to adopt the framework fully. Manufacturing executives most often equate Industry 4.0 with shop floor productivity improvements while the greatest gains are waiting in their supply chains. The BDO study found that manufacturers are divided on the metrics they rely on to evaluate their supply chains. Upper middle market manufacturers are aiming to speed up customer order cycle times and are less focused on getting their total delivered costs down. Lower mid-market manufacturers say reducing inventory turnover is their biggest priority. Overall, strengthening customer service increases in importance with the size of the organization.

  • By enabling integration between engineering, supply chain management, Manufacturing Execution Systems (MES) and CRM systems, more manufacturers are achieving product configuration strategies at scale. A key growth strategy for many manufacturers is to scale beyond the limitations of their longstanding Make-to-Stock production strategies. By integrating engineering, supply chains, MES, and CRM, manufacturers can offer more flexibility to their customers while expanding their product strategies to include Configure-to-Order, Make-to-Order, and for highly customized products, Engineer-to-Order. The more Industry 4.0 can be shown to enable design-to-manufacturing at scale, the more it will resonate with senior executives in mid-tier manufacturing.

  • Manufacturers are more likely than ever before to accept cloud-based platforms and systems that help them achieve their business strategies faster and more completely, with analytics being in the early stages of adoption. Manufacturing CEOs and their teams are most concerned about how quickly new applications and platforms can position their businesses for more growth. Whether a given application or platform is cloud-based often becomes secondary to the speed and time-to-market constraints every manufacturing business faces. The fastest-growing mid-tier manufacturers are putting greater effort and intensity into mastering analytics across every area of their business too. BDO found that Artificial Intelligence (AI) leads all other technologies in planned use.

How To Improve Supply Chains With Machine Learning: 10 Proven Ways

Bottom line: Enterprises are attaining double-digit improvements in forecast error rates, demand planning productivity, cost reductions and on-time shipments using machine learning today, revolutionizing supply chain management in the process.

Machine learning algorithms and the models they’re based on excel at finding anomalies, patterns and predictive insights in large data sets. Many supply chain challenges are time, cost and resource constraint-based, making machine learning an ideal technology to solve them. From Amazon’s Kiva robotics relying on machine learning to improve accuracy, speed and scale to DHL relying on AI and machine learning to power their Predictive Network Management system that analyzes 58 different parameters of internal data to identify the top factors influencing shipment delays, machine learning is defining the next generation of supply chain management. Gartner predicts that by 2020, 95% of Supply Chain Planning (SCP) vendors will be relying on supervised and unsupervised machine learning in their solutions. Gartner is also predicting by 2023 intelligent algorithms, and AI techniques will be an embedded or augmented component across 25% of all supply chain technology solutions.

The ten ways that machine learning is revolutionizing supply chain management include:

  • Machine learning-based algorithms are the foundation of the next generation of logistics technologies, with the most significant gains being made with advanced resource scheduling systems. Machine learning and AI-based techniques are the foundation of a broad spectrum of next-generation logistics and supply chain technologies now under development. The most significant gains are being made where machine learning can contribute to solving complex constraint, cost and delivery problems companies face today. McKinsey predicts machine learning’s most significant contributions will be in providing supply chain operators with more significant insights into how supply chain performance can be improved, anticipating anomalies in logistics costs and performance before they occur. Machine learning is also providing insights into where automation can deliver the most significant scale advantages. Source: McKinsey & Company, Automation in logistics: Big opportunity, bigger uncertainty, April 2019. By Ashutosh Dekhne, Greg Hastings, John Murnane, and Florian Neuhaus

  • The wide variation in data sets generated from the Internet of Things (IoT) sensors, telematics, intelligent transport systems, and traffic data have the potential to deliver the most value to improving supply chains by using machine learning. Applying machine learning algorithms and techniques to improve supply chains starts with data sets that have the greatest variety and variability in them. The most challenging issues supply chains face are often found in optimizing logistics, so materials needed to complete a production run arrive on time. Source: KPMG, Supply Chain Big Data Series Part 1

  • Machine learning shows the potential to reduce logistics costs by finding patterns in track-and-trace data captured using IoT-enabled sensors, contributing to $6M in annual savings. BCG recently looked at how a decentralized supply chain using track-and-trace applications could improve performance and reduce costs. They found that in a 30-node configuration when blockchain is used to share data in real-time across a supplier network, combined with better analytics insight, cost savings of $6M a year is achievable. Source: Boston Consulting Group, Pairing Blockchain with IoT to Cut Supply Chain Costs, December 18, 2018, by Zia Yusuf, Akash Bhatia, Usama Gill, Maciej Kranz, Michelle Fleury, and Anoop Nannra

  • Reducing forecast errors up to 50% is achievable using machine learning-based techniques. Lost sales due to products not being available are being reduced up to 65% through the use of machine learning-based planning and optimization techniques. Inventory reductions of 20 to 50% are also being achieved today when machine learning-based supply chain management systems are used. Source: Digital/McKinsey, Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector? (PDF, 52 pp., no opt-in).

  • DHL Research is finding that machine learning enables logistics and supply chain operations to optimize capacity utilization, improve customer experience, reduce risk, and create new business models. DHL’s research team continually tracks and evaluates the impact of emerging technologies on logistics and supply chain performance. They’re also predicting that AI will enable back-office automation, predictive operations, intelligent logistics assets, and new customer experience models. Source: DHL Trend Research, Logistics Trend Radar, Version 2018/2019 (PDF, 55 pp., no opt-in)

  • Detecting and acting on inconsistent supplier quality levels and deliveries using machine learning-based applications is an area manufacturers are investing in today. Based on conversations with North American-based mid-tier manufacturers, the second most significant growth barrier they’re facing today is suppliers’ lack of consistent quality and delivery performance. The greatest growth barrier is the lack of skilled labor available. Using machine learning and advanced analytics manufacturers can discover quickly who their best and worst suppliers are, and which production centers are most accurate in catching errors. Manufacturers are using dashboards much like the one below for applying machine learning to supplier quality, delivery and consistency challenges. Source: Microsoft, Supplier Quality Analysis sample for Power BI: Take a tour, 2018

  • Reducing risk and the potential for fraud, while improving the product and process quality based on insights gained from machine learning is forcing inspection’s inflection point across supply chains today. When inspections are automated using mobile technologies and results are uploaded in real-time to a secure cloud-based platform, machine learning algorithms can deliver insights that immediately reduce risks and the potential for fraud. Inspectorio is a machine learning startup to watch in this area. They’re tackling the many problems that a lack of inspection and supply chain visibility creates, focusing on how they can solve them immediately for brands and retailers. The graphic below explains their platform. Source: Forbes, How Machine Learning Improves Manufacturing Inspections, Product Quality & Supply Chain Visibility, January 23, 2019

  • Machine learning is making rapid gains in end-to-end supply chain visibility possible, providing predictive and prescriptive insights that are helping companies react faster than before. Combining multi-enterprise commerce networks for global trade and supply chain management with AI and machine learning platforms are revolutionizing supply chain end-to-end visibility. One of the early leaders in this area is Infor’s Control Center. Control Center combines data from the Infor GT Nexus Commerce Network, acquired by the company in September 2015, with Infor’s Coleman Artificial Intelligence (AI) Infor chose to name their AI platform after the inspiring physicist and mathematician Katherine Coleman Johnson, whose trail-blazing work helped NASA land on the moon. Be sure to pick up a copy of the book and see the movie Hidden Figures if you haven’t already to appreciate her and many other brilliant women mathematicians’ many contributions to space exploration. ChainLink Research provides an overview of Control Center in their article, How Infor is Helping to Realize Human Potential, and two screens from Control Center are shown below.

  • Machine learning is proving to be foundational for thwarting privileged credential abuse which is the leading cause of security breaches across global supply chains. By taking a least privilege access approach, organizations can minimize attack surfaces, improve audit and compliance visibility, and reduce risk, complexity, and the costs of operating a modern, hybrid enterprise. CIOs are solving the paradox of privileged credential abuse in their supply chains by knowing that even if a privileged user has entered the right credentials but the request comes in with risky context, then stronger verification is needed to permit access.  Zero Trust Privilege is emerging as a proven framework for thwarting privileged credential abuse by verifying who is requesting access, the context of the request, and the risk of the access environment.  Centrify is a leader in this area, with globally-recognized suppliers including Cisco, Intel, Microsoft, and Salesforce being current customers.  Source: Forbes, High-Tech’s Greatest Challenge Will Be Securing Supply Chains In 2019, November 28, 2018.
  • Capitalizing on machine learning to predict preventative maintenance for freight and logistics machinery based on IoT data is improving asset utilization and reducing operating costs. McKinsey found that predictive maintenance enhanced by machine learning allows for better prediction and avoidance of machine failure by combining data from the advanced Internet of Things (IoT) sensors and maintenance logs as well as external sources. Asset productivity increases of up to 20% are possible and overall maintenance costs may be reduced by up to 10%. Source: Digital/McKinsey, Smartening up with Artificial Intelligence (AI) – What’s in it for Germany and its Industrial Sector? (PDF, 52 pp., no opt-in).

References

Accenture, Reinventing The Supply Chain With AI, 20 pp., PDF, no opt-in.

Bendoly, E. (2016). Fit, Bias, and Enacted Sensemaking in Data Visualization: Frameworks for Continuous Development in Operations and Supply Chain Management Analytics. Journal Of Business Logistics37(1), 6-17.

Boston Consulting Group, Pairing Blockchain with IoT to Cut Supply Chain Costs, December 18, 2018, by Zia Yusuf, Akash Bhatia, Usama Gill, Maciej Kranz, Michelle Fleury, and Anoop Nannra

How To Secure Mobile Devices In A Zero Trust World

  • 86% of enterprises are seeing mobile threats growing the fastest this year, outpacing other threat types.
  • 48% say they’ve sacrificed security to “get the job done” up from 32% last year.
  • 41% of those affected say the compromise is having major with lasting repercussions and 43% said that their efforts to remediate the attacks were “difficult and expensive.”

Bottom Line: The majority of enterprises, 67%, are the least confident in the security of their mobile assets than any other device or platform today according to Verizon’s Mobile Security Index 2019.

Why Mobile Devices Are the Fastest Growing Threat Surface Today     

Verizon found that 86% of enterprises see an upswing in the number, scale, and scope of mobile breach attempts in 2019. When broken out by industry, Financial Services, Professional Services, and Education are the most commonly targeted industries as the graphic below shows:

The threat surfaces every organization needs to protect is exponentially increasing today based on the combination of employee- and company-owned mobile devices. 41% of enterprises rate mobile devices as their most vulnerable threat surface this year:

Passwords and Mobile Devices Have Become A Hacker’s Paradise

“The only people who love usernames and passwords are hackers,” said Alex Simons, corporate vice president at Microsoft’s identity division in a recent Wall Street Journal article, Username and Password Hell: Why the Internet Can’t Keep You Logged In. Verizon found that mobile devices are the most vulnerable, fastest-growing threat surface there is, making it a favorite with state-sponsored and organized crime syndicates. How rapidly mobile devices are proliferating in enterprises today frequently outpace their ability to secure them, falling back on legacy Privileged Access Management (PAM) approaches that hacking syndicates know how to get around easily using compromised passwords and privileged access credentials. Here’s proof of how much of a lucrative paradise it is for hackers to target passwords and mobile devices first:

  • Hacker’s favorite way to gain access to any business is by using privileged access credentials, which are increasingly being harvested from cellphones using malware. Hacking organizations would rather walk in the front door of any organizations’ systems rather than expend the time and effort to hack in. It’s by far the most popular approach with hackers, with 74% of IT decision makers whose organizations have been breached in the past say it involved privileged access credential abuse according to a recent Centrify survey, Privileged Access Management in the Modern Threatscape. Only 48% of the organizations have a password vault, and just 21% have multi-factor authentication (MFA) implemented for privileged administrative access. The Verizon study found that malware is the most common strategy hackers use to gain access to corporate networks. MobileIron’s Global Threat Report, mid-year 2018 found that 3.5% of Android devices are harboring known malware. Of these malicious apps, over 80% had access to internal networks and were scanning nearby ports. This suggests that the malware was part of a larger attack.

Securing Mobile Devices In A Zero Trust World Needs To Happen Now

Mobile devices are an integral part of everyone’s identity today. They are also the fastest growing threat surface for every business – making identities the new security perimeter. Passwords are proving to be problematic in scaling fast enough to protect these threat surfaces, as credential abuse is skyrocketing today. They’re perennial best-sellers on the Dark Web, where buyers and sellers negotiate in bitcoin for companies’ logins and passwords – often with specific financial firms, called out by name in “credentials wanted” ads. Organizations are waking up to the value of taking a Zero Trust approach to securing their businesses, which is a great start. Passwords are still the most widely relied-on security mechanism – and continue to be the weakest link in today’s enterprise security.  That needs to change. According to the Wall Street Journal, the World Wide Web Consortium has recently ratified a standard called WebAuthN, which allows websites to authenticate users with biometric information, or physical objects like security keys, and skip passwords altogether.

MobileIron is also taking a unique approach to this challenge by introducing zero sign-on (ZSO), built on the company’s unified endpoint management (UEM) platform and powered by the MobileIron Access solution. “By making mobile devices your identity, we create a world free from the constant pains of password recovery and the threat of data breaches due to easily compromised credentials,” wrote Simon Biddiscombe, MobileIron’s President and Chief Executive Officer in his recent blog post, Single sign-on is still one sign-on too many. Simon’s latest post MobileIron: We’re making history by making passwords history, provides the company’s vision going forward with ZSO. Zero sign-on eliminates passwords as the primary method for user authentication, unlike single sign-on, which still requires at least one username and password. MobileIron paved the way for a zero sign-on enterprise with its Access product in 2017, which enabled zero sign-on to cloud services on managed devices.

Conclusion

Mobile devices are the most quickly proliferating threat surface there are today and an integral part of everyone’s identities as well. Thwarting the many breach attempts attempted daily over mobile devices and across all threat surfaces needs to start with a solid Zero Trust framework. MobileIron’s introduction of zero sign-on (ZSO) eliminates passwords as the method for user authentication, replacing single sign-on, which still requires at least one username and password. ZSO is exactly what enterprises need to secure the proliferating number of mobile devices they rely on to operate and grow in a Zero Trust world.

CIO’s Guide To Stopping Privileged Access Abuse – Part 2

Why CIOs Are Prioritizing Privileged Credential Abuse Now

Enterprise security approaches based on Zero Trust continue to gain more mindshare as organizations examine their strategic priorities. CIOs and senior management teams are most focused on securing infrastructure, DevOps, cloud, containers, and Big Data projects to stop the leading cause of breaches, which is privileged access abuse.

Based on insights gained from advisory sessions with CIOs and senior management teams, Forrester estimates that 80% of data breaches have a connection to compromised privileged credentials, such as passwords, tokens, keys, and certificates. In another survey completed by Centrify, 74% of IT decision makers surveyed whose organizations have been breached in the past, say it involved privileged access abuse. Furthermore, 65% of organizations are still sharing root or privileged access to systems and data at least somewhat often. Centrify’s survey, Privileged Access Management in the Modern Threatscape, is downloadable here.

The following are the key reasons why CIOs are prioritizing privileged access management now:

  • Identities are the new security perimeter for any business, making privileged access abuse the greatest challenge CIOs face in keeping their businesses secure and growing. Gartner also sees privileged credential abuse as the greatest threat to organizations today, and has made Privileged Account Management one of the Gartner Top 10 Security Projects for 2018, and again in 2019Forrester and Gartner’s findings and predictions reflect the growing complexity of threatscapes every CIO must protect their business against while still enabling new business growth. Banking, financial services, and insurance (BFSI) CIOs often remark in my conversations with them that the attack surfaces in their organizations are proliferating at a pace that quickly scales beyond any trust but verify legacy approach to managing access. They need to provide applications, IoT-enabled devices, machines, cloud services, and human access to a broader base of business units than ever before.
  • CIOs are grappling with the paradox of protecting the rapidly expanding variety of attack surfaces from breaches while still providing immediate access to applications, systems, and services that support their business’ growth. CIOs I’ve met with also told me access to secured resources needs to happen in milliseconds, especially to support the development of new banking, financial services, and insurance applications in beta testing today, scheduled to be launched this summer. Their organizations’ development teams expect more intuitive, secure, and easily accessible applications than ever before, which is driving CIOs to prioritize privileged access management now
  • Adapting and risk-scoring every access attempt in real-time is key to customer experiences on new services and applications, starting with response times. CIOs need a security strategy that can flex or adapt to risk contexts in real-time, assessing every access attempt across every threat surface and generating a risk score in milliseconds. The CIOs I’ve met with regularly see a “never trust, always verify, enforce least privilege” approach to security as the future of how they’ll protect every threat surface from privileged access abuse. Each of their development teams is on tight deadlines to get new services launch to drive revenue in Q3. Designing in Zero Trust with a strong focus on Zero Trust Privilege is saving valuable development time now and is enabling faster authentication times of the apps and services in testing today.

Strategies For Stopping Privileged Credential Abuse – Part 2  

Recently I wrote a CIO’s Guide To Stopping Privileged Access Abuse – Part 1 detailing five recommended strategies for CIOs on how to stop privileged credential abuse. The first five strategies focus on the following: discovering and inventorying all privileged accounts; vaulting all cloud platforms’ Root Accounts; auditing privileged sessions and analyzing patterns to find privileged credential sharing not found during audits; enforcing least privilege access now within your existing infrastructure as much as possible; and adopting multi-factor authentication (MFA) across all threat surfaces that can adapt and flex to the risk context of every request for resources.

The following are the second set of strategies CIOs need to prioritize to further protect their organizations from privileged access abuse:

  1. After completing an inventory of privileged accounts, create a taxonomy of them by assigning users to each class or category, personalizing privileged credential access to the role and entitlement level for each. CIOs tell me this is a major time saver in scaling their Privileged Access Management (PAM) strategies. Assigning every human, machine and sensor-based identity is the goal with the overarching objective being the creation of a Zero Trust-based enterprise security strategy. Recommended initial classes or categories include IT administrators who are also responsible for endpoint security; developers who require occasional access to production instances; service desk teams and service operations; the Project Management Office (PMO) and project IT; and external contractors and consultants.
  2. By each category in the taxonomy, automate the time, duration, scope, resources, and entitlements of privileged access for each focusing on the estimated time to complete each typical task. Defining a governance structure that provides real-time access to resources based on successful authentication is a must-have for protecting privileged access credentials. By starting with the attributes of time, duration, scope and properties, organizations have a head start on creating a separation of duties (SOD) model. Separation of duties is essential for ensuring that privileged user accounts don’t have the opportunity to carry out and conceal any illegal or unauthorized activities.
  3. Using the taxonomy of user accounts created and hardened using the separation of duties model, automate privileged access and approval workflows for enterprise systems. Instead of having administrators approve or semi-automate the evaluation of every human- and machine-based request for access, consider automating the process with a request and approval workflow. With time, duration, scope, and properties of privileged access already defined human- and machine-based requests for access to IT systems and services are streamlined, saving hundreds of hours a year and providing a real-time log for audit and data analysis later.
  4. Break-glass, emergency or firecall account passwords need to be vaulted, with no exceptions. When there’s a crisis of any kind, the seconds it takes to get a password could mean the difference between cloud instances and entire systems being inaccessible or not. That’s why administrators often only manually secure root passwords to all systems, cloud platforms and containers included. This is the equivalent of leaving the front door open to the data center with all systems unlocked. The recent Centrify survey found that just 48% of organizations interviewed have a password vault. 52% are leaving the keys to the kingdom available for hackers to walk through the front door of data centers and exfiltraticate data whenever they want.
  5. Continuous delivery and deployment platforms including Ansible, Chef, Puppet, and others need to be configured when first installed to eliminate the potential for privileged access abuse. The CIOs whose teams are creating new apps and services are using Chef and Puppet to design and create workloads, with real-time integration needed with customer, pricing, and services databases and the systems they run on. Given how highly regulated insurance is, CIOs are saying they need to have logs that show activity down to the API level in case of an audit. The more regulated and audited a company, the more trusted and untrusted domains are seen as the past, Zero Trust as the future based on CIO’s feedback.

Conclusion

The CIOs I regularly meet with from the banking, financial services, and insurance industries are under pressure to get new applications and services launched while protecting their business’ daily operations. With more application and services development happening in their IT teams, they’re focusing on how they can optimize the balance between security and speed. New apps, services, and the new customers they attract are creating a proliferation of new threat surfaces, making every new identity the new security perimeter.

CIO’s Guide To Stopping Privileged Access Abuse – Part I

CIOs face the paradox of having to protect their businesses while at the same time streamlining access to the information and systems their companies need to grow. The threatscape they’re facing requires an approach to security that is adaptive to the risk context of each access attempt across any threat surface, anytime. Using risk scores to differentiate between privileged users attempting to access secured systems in a riskier context than normal versus privileged credential abuse by attackers has proven to be an effective approach for thwarting credential-based breaches.

Privileged credential abuse is one of the most popular breach strategies organized crime and state-sponsored cybercrime organizations use. They’d rather walk in the front door of enterprise systems than hack in. 74% of IT decision makers surveyed whose organizations have been breached in the past say it involved privileged access credential abuse, yet just 48% have a password vault. Just 21% have multi-factor authentication (MFA) implemented for privileged administrative access. These and many other insights are from Centrify’s recent survey, Privileged Access Management in the Modern Threatscape.

How CIOs Are Solving the Paradox of Privileged Credential Abuse

The challenge to every CIO’s security strategy is to adapt to risk contexts in real-time, accurately assessing every access attempt across every threat surface, risk-scoring each in milliseconds. By taking a “never trust, always verify, enforce least privilege” approach to security, CIOs can provide an adaptive, contextually accurate Zero Trust-based approach to verifying privileged credentials. Zero Trust Privilege is emerging as a proven framework for thwarting privileged credential abuse by verifying who is requesting access, the context of the request, and the risk of the access environment.

By taking a least privilege access approach, organizations can minimize attack surfaces, improve audit and compliance visibility, and reduce risk, complexity, and the costs of operating a modern, hybrid enterprise. CIOs are solving the paradox of privileged credential abuse by knowing that even if a privileged user has entered the right credentials but the request comes in with risky context, then stronger verification is needed to permit access.

Strategies For Stopping Privileged Credential Abuse

The following are five strategies CIOs need to concentrate on to stop privileged credential abuse. Starting with an inventory of privileged accounts and progressing through finding the gaps in IT infrastructure that create opportunities for privileged credential abuse, CIOs and their teams need to take preemptive action now to avert potential breaches in the future.

In Part 1 of a CIO’s Guide to Stopping Privileged Access Abuse, below are the steps they can take to get started:

  1. Discover and inventory all privileged accounts and their credentials to define who is accountable for managing their security and use. According to a survey by Gartner, more than 65% of enterprises are allowing shared use of privileged accounts with no accountability for their use. CIOs realize that a lack of consistent governance policies creates many opportunities for privileged credential abuse. They’re also finding orphaned accounts, multiple owners for privileged credentials and the majority of system administrators having super user or root user access rights for the majority of enterprise systems.
  2. Vault your cloud platforms’ Root Accounts and federate access to AWS, Google Cloud Platform, Microsoft Azure and other public cloud consoles. Root passwords on each of the cloud platforms your business relies on are the “keys to the kingdom” and provide bad actors from inside and outside the company to exfiltrate data with ease. The recent news of how a fired employee deleted his former employer’s 23 AWS servers is a cautionary tale of what happens when a Zero Trust approach to privileged credentials isn’t adopted. Centrify’s survey found that 63% or organizations take more than a day to shut off privilege access for an employee after leaving the company. Given how AWS root user accounts have the privilege to delete all instances immediately, it’s imperative for organizations to have a password vault where AWS root account credentials are stored. Instead of local AWS IAM accounts and access keys, use centralized identities (e.g., Active Directory) and enable federated login. By doing so, you obviate the need for long-lived access keys.
  3. Audit privileged sessions and analyze patterns to find potentially privileged credential sharing or abuse not immediately obvious from audits. Audit and log authorized and unauthorized user sessions across all enterprise systems, especially focusing on root password use across all platforms. Taking this step is essential for assigning accountability for each privileged credential in use. It will also tell you if privileged credentials are being shared widely across the organization. Taking a Zero Trust approach to securing privileged credentials will quickly find areas where there could be potential lapses or gaps that invite breaches. For AWS accounts, be sure to use AWS CloudTrail and Amazon CloudWatch to monitor all API activity across all AWS instances and your AWS account.
  4. Enforce least privilege access now within your existing infrastructure as much as possible, defining a security roadmap based on the foundations of Zero Trust as your future direction. Using the inventory of all privileged accounts as the baseline, update least privilege access on each credential now and implement a process for privilege elevation that will lower the overall risk and ability for attackers to move laterally and extract data. The days of “trust but verify” are over. CIOs from insurance and financial services companies recently spoken with point out that their new business models, all of them heavily reliant on secured Internet connectivity, are making Zero Trust the cornerstone of their future services strategies. They’re all moving beyond “trust but verify” to adopt a more adaptive approach to knowing the risk context by threat surface in real-time.
  5. Adopt multi-factor authentication (MFA) across all threat surfaces that can adapt and flex to the risk context of every request for resources. The CIOs running a series of insurance and financial services firms, a few of them former MBA students of mine, say multi-factor authentication is a must-have today for preventing privileged credential abuse. Their take on it is that adding in an authentication layer that queries users with something they know (user name, password, PIN or security question) with something they have (smartphone, one-time password token or smart card), something they are (biometric identification like fingerprint) and something they’ve done (contextual pattern matching of what they normally do where) has helped thwart privileged credential abuse exponentially since they adopted it. This is low-hanging fruit: adaptive MFA has made the productivity impact of this additional validation practically moot.

Conclusion

Every CIO I know is now expected to be a business strategist first, and a technologist second. At the top of many of their list of priorities is securing the business so it can achieve uninterrupted growth. The CIOs I regularly speak with running insurance and financial services companies often speak of how security is as much a part of their new business strategies as the financial products their product design teams are developing. The bottom line is that the more adaptive and able to assess the context of risks for each privilege access attempt a company’s access management posture can become, the more responsive they can be to employees and customers alike, fueling future growth.

The State Of Cloud Business Intelligence, 2019

  • An all-time high 48% of organizations say cloud BI is either “critical” or “very important” to their operations in 2019.
  • Marketing & Sales place the greatest importance on cloud BI in 2019.
  • Small organizations of 100 employees or less are the most enthusiastic, perennial adopters and supporters of cloud BI.
  • The most preferred cloud BI providers are Amazon Web Services and Microsoft Azure.

These and other insights are from Dresner Advisory Services’ 2019 Cloud Computing and Business Intelligence Market Study. The 8th annual report focuses on end-user deployment trends and attitudes toward cloud computing and business intelligence (BI), defined as the technologies, tools, and solutions that rely on one or more cloud deployment models. What makes the study noteworthy is the depth of focus around the perceived benefits and barriers for cloud BI, the importance of cloud BI, and current and planned usage.

“We began tracking and analyzing the cloud BI market dynamic in 2012 when adoption was nascent. Since that time, deployments of public cloud BI applications are increasing, with organizations citing substantial benefits versus traditional on-premises implementations,” said Howard Dresner, founder, and chief research officer at Dresner Advisory Services. Please see page 10 of the study for specifics on the methodology.

Key insights gained from the report include the following:

  • An all-time high 48% of organizations say cloud BI is either “critical” or “very important” to their operations in 2019. Organizations have more confidence in cloud BI than ever before, according to the study’s results. 2019 is seeing a sharp upturn in cloud BI’s importance, driven by the trust and credibility organizations have for accessing, analyzing and storing sensitive company data on cloud platforms running BI applications.

  • Marketing & Sales place the greatest importance on cloud BI in 2019. Business Intelligence Competency Centers (BICC) and IT departments have an above-average interest in cloud BI as well, with their combined critical and very important scores being over 50%. Dresner’s research team found that Operations had the greatest duality of scores, with critical and not important being reported at comparable levels for this functional area. Dresner’s analysis indicates Operations departments often rely on cloud BI to benchmark and improve existing processes while re-engineering legacy process areas.

  • Small organizations of 100 employees or less are the most enthusiastic, perennial adopters and supporters of cloud BI. As has been the case in previous years’ studies, small organizations are leading all others in adopting cloud BI systems and platforms.  Perceived importance declines only slightly in mid-sized organizations (101-1,000 employees) and some large organizations (1,001-5,000 employees), where minimum scores of important offset declines in critical.

  • The retail/wholesale industry considers cloud BI the most important, followed by technology and advertising industries. Organizations competing in the retail/wholesale industry see the greatest value in adopting cloud BI to gain insights into improving their customer experiences and streamlining supply chains. Technology and advertising industries are industries that also see cloud BI as very important to their operations. Just over 30% of respondents in the education industry see cloud BI as very important.

  • R&D departments are the most prolific users of cloud BI systems today, followed by Marketing & Sales. The study highlights that R&D leading all other departments in existing cloud BI use reflects broader potential use cases being evaluated in 2019. Marketing & Sales is the next most prolific department using cloud BI systems.

  • Finance leads all others in their adoption of private cloud BI platforms, rivaling IT in their lack of adoption for public clouds. R&D departments are the next most likely to be relying on private clouds currently. Marketing and Sales are the most likely to take a balanced approach to private and public cloud adoption, equally adopting private and public cloud BI.

  • Advanced visualization, support for ad-hoc queries, personalized dashboards, and data integration/data quality tools/ETL tools are the four most popular cloud BI requirements in 2019. Dresner’s research team found the lowest-ranked cloud BI feature priorities in 2019 are social media analysis, complex event processing, big data, text analytics, and natural language analytics. This years’ analysis of most and least popular cloud BI requirements closely mirror traditional BI feature requirements.

  • Marketing and Sales have the greatest interest in several of the most-required features including personalized dashboards, data discovery, data catalog, collaborative support, and natural language analytics. Marketing & Sales also have the highest level of interest in the ability to write to transactional applications. R&D leads interest in ad-hoc query, big data, text analytics, and social media analytics.

  • The Retail/Wholesale industry leads interest in several features including ad-hoc query, dashboards, data integration, data discovery, production reporting, search interface, data catalog, and ability to write to transactional systems. Technology organizations give the highest score to advanced visualization and end-user self-service. Healthcare respondents prioritize data mining, end-user data blending, and location analytics, the latter likely for asset tracking purposes. In-memory support scores highest with Financial Services respondent organizations.

  • Marketing & Sales rely on a broader base of third party data connectors to get greater value from their cloud BI systems than their peers. The greater the scale, scope and depth of third-party connectors and integrations, the more valuable marketing and sales data becomes. Relying on connectors for greater insights into sales productivity & performance, social media, online marketing, online data storage, and simple productivity improvements are common in Marketing & Sales. Finance requiring integration to Salesforce reflects the CRM applications’ success transcending customer relationships into advanced accounting and financial reporting.

  • Subscription models are now the most preferred licensing strategy for cloud BI and have progressed over the last several years due to lower risk, lower entry costs, and lower carrying costs. Dresner’s research team found that subscription license and free trial (including trial and buy, which may also lead to subscription) are the two most preferred licensing strategies by cloud BI customers in 2019. Dresner Advisory Services predicts new engagements will be earned using subscription models, which is now seen as, at a minimum, important to approximately 90% of the base of respondents.

  • 60% of organizations adopting cloud BI rank Amazon Web Services first, and 85% rank AWS first or second. 43% choose Microsoft Azure first and 69% pick Azure first or second. Google Cloud closely trails Azure as the first choice among users but trails more widely after that. IBM Bluemix is the first choice of 12% of organizations responding in 2019.

Public Cloud Soaring To $331B By 2022 According To Gartner

Gartner is predicting the worldwide public cloud services market will grow from $182.4B in 2018 to $214.3B in 2019, a 17.5% jump in just a year. Photo credit: Getty

  • Gartner predicts the worldwide public cloud service market will grow from $182.4B in 2018 to $331.2B in 2022, attaining a compound annual growth rate (CAGR) of 12.6%.
  • Spending on Infrastructure-as-a-Service (IaaS) is predicted to increase from $30.5B in 2018 to $38.9B in 2019, growing 27.5% in a year.
  • Platform-as-a-Service (PaaS) spending is predicted to grow from $15.6B in 2018 to $19B in 2019, growing 21.8% in a year.
  • Business Intelligence, Supply Chain Management, Project and Portfolio Management and Enterprise Resource Planning (ERP) will see the fastest growth in end-user spending on SaaS applications through 2022.

Gartner’s annual forecast of worldwide public cloud service revenue was published last week, and it includes many interesting insights into how the research firm sees the current and future landscape of public cloud computing. Gartner is predicting the worldwide public cloud services market will grow from $182.4B in 2018 to $214.3B in 2019, a 17.5% jump in just a year. By the end of 2019, more than 30% of technology providers’ new software investments will shift from cloud-first to cloud-only, further reducing license-based software spending and increasing subscription-based cloud revenue.

The following graphic compares worldwide public cloud service revenue by segment from 2018 to 2022. Please click on the graphic to expand for easier reading.

Comparing Compound Annual Growth Rates (CAGRs) of worldwide public cloud service revenue segments from 2018 to 2022 reflects IaaS’ anticipated rapid growth. Please click on the graphic to expand for easier reading.

Gartner provided the following data table this week as part of their announcement:

  • Business Intelligence, Supply Chain Management, Project and Portfolio Management and Enterprise Resource Planning (ERP) will see the fastest growth in end-user spending on SaaS applications through 2022.  Gartner is predicting end-user spending on Business Intelligence SaaS applications will grow by 23.3% between 2017 and 2022.  Spending on SaaS-based Supply Chain Management applications will grow by 21.2% between 2017 and 2022. Project and Portfolio Management SaaS-based applications will grow by 20.9% between 2017 and 2022. End-user spending on SaaS ERP systems will grow by 19.2% between 2017 and 2022.

Sources: Gartner Forecasts Worldwide Public Cloud Revenue to Grow 17.5 Percent in 2019 and Forecast: Public Cloud Services, Worldwide, 2016-2022, 4Q18 Update (Gartner client access)

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