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Posts from the ‘Cloud Computing’ Category

74% Of Data Breaches Start With Privileged Credential Abuse

Centrify’s survey shows organizations are granting too much trust and privilege, opening themselves up to potential internal and externally-driven breaches initiated with compromised privileged access credentials. Photo credit: iStock

Enterprises who are prioritizing privileged credential security are creating a formidable competitive advantage over their peers, ensuring operations won’t be interrupted by a breach. However, there’s a widening gap between those businesses protected from a breach and the many who aren’t. In quantifying this gap consider the typical U.S.-based enterprise will lose on average $7.91M from a breach, nearly double the global average of $3.68M according to IBM’s 2018 Data Breach Study.

Further insights into how wide this gap is are revealed in Centrify’s Privileged Access Management in the Modern Threatscape survey results published today. The study is noteworthy as it illustrates how wide the gap is between enterprises’ ability to avert and thwart breaches versus their current levels of Privileged Access Management (PAM) and privileged credential security. 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, and 65% are sharing root or privileged access to systems and data at least somewhat often.

Addressing these three areas with a Zero Trust approach to PAM would make an immediate difference in security.

“What’s alarming is that the survey reveals many organizations, armed with the knowledge that they have been breached before, are doing too little to secure privileged access. IT teams need to be taking their Privileged Access Management much more seriously, and prioritizing basic PAM strategies like vaults and MFA while reducing shared passwords,” remarked Tim Steinkopf, Centrify CEO. FINN Partners, on behalf of Centrify, surveyed 1,000 IT decision makers (500 in the U.S. and 500 in the U.K.) online in October 2018. Please see the study here for more on the methodology.

How You Choose To Secure Privileged Credentials Determines Your Future 

Identities are the new security perimeter. Threats can emerge within and outside any organization, at any time. Bad actors, or those who want to breach a system for financial gain or to harm a business, aren’t just outside. 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.

Attackers are increasingly logging in using weak, stolen, or otherwise compromised credentials. Centrify’s survey underscores how the majority of organizations’ IT departments have room for improvement when it comes to protecting privileged access credentials, which are the ‘keys to the kingdom.’ Reading the survey makes one realize that forward-thinking enterprises who are prioritizing privileged credential security gain major cost and time advantages over their competitors. They’re able to keep their momentum going across every area of their business by not having to recover from breaches or incur millions of dollars on losses or fines as the result of a breach.

One of the most promising approaches to securing every privileged identity and threat space within and outside an organization is Zero Trust Privilege (ZTP). ZTP enables an organizations’ IT team to grant least privilege access based on verifying who is requesting access, the context of the request, and the risk of the access environment.

Key Lessons Learned from the Centrify Survey

How wide the gap is between organizations who see identities as the new security perimeter and are adopting a Zero Trust approach to securing them and those that aren’t is reflected in the results of Centrify’s Privileged Access Management in the Modern Threatscape surveyThe following are the key lessons learned of where and how organizations can begin to close the security gaps they have that leave them vulnerable to privileged credential abuse and many other potential threats:

  • Organizations’ most technologically advanced areas that are essential for future growth and attainment of strategic goals are often the most unprotected. Big Data, cloud, containers and network devices are the most important areas of any IT infrastructure. According to Centrify’s survey, they are the most unprotected as well. 72% of organizations aren’t securing containers with privileged access controls. 68% are not securing network devices like hubs, switches, and routers with privileged access controls. 58% are not securing Big Data projects with privileged access controls. 45% are not securing public and private cloud workloads with privileged access controls. The study finds that UK-based businesses lag U.S.-based ones in each of these areas as the graphic below shows:

  • Only 36% of U.K. organizations are very confident in their company’s current IT security software strategies, compared to 65% in the U.S. The gap between organizations with hardened security strategies that have a higher probability of withstanding breach attempts is wide between U.K. and U.S.-based businesses. 44% of U.K. respondents weren’t positive about what Privileged Access Management is, versus 26% of U.S. respondents. 60% of U.K. respondents don’t have a password vault.

  • Just 35% of U.S. organizations and 30% of those in the UK are relying on Privileged Access Management to manage partners’ access to privileged credentials and infrastructure. Partners are indispensable for scaling any new business strategy and expanding an existing one across new markets and countries. Forward-thinking organizations look at every partner associates’ identity as a new security perimeter. The 35% of U.S.-based organizations doing this have an immediate competitive advantage over the 65% who aren’t. By enforcing PAM across their alliances and partnerships, organizations can achieve uninterrupted growth by eliminating expensive and time-consuming breaches that many businesses never fully recover from.
  • Organizations’ top five security projects for 2019 include protecting cloud data, preventing data leakage, analyzing security incidents, improving security education/awareness and encrypting data. These top five security projects could be achieved at scale by having IT teams implement a Zero Trust-based approach to Privileged Access Management (PAM). The time, cost and scale advantages of getting the top five security projects done using Zero Trust would free up IT teams to focus on projects that deliver direct revenue gains for example.

Conclusion

Centrify’s survey shows organizations are granting too much trust and privilege, opening themselves up to potential internal and externally-driven breaches initiated with compromised privileged access credentials. It also reveals that there is a strong desire to adhere to best practices when it comes to PAM (51% of respondents) and that the reason it is not being adequately implemented rarely has to do with prioritization or difficulty but rather budget constraints and executive buy-in.

The survey also shows U.K. – and U.S.-based organizations need to realize identity is the new security perimeter. For example, only 37% of respondents’ organizations are able to turn off privileged access for an employee who leaves the company within one day, leaving a wide-open exposure point that can continue to be exploited.

There are forward-thinking organizations who are relying on Zero Trust Privilege as a core part of their digital transformation efforts as well. The survey found that given a choice, respondents are most likely to say digital transformation (40%) is one of the top 3 projects they’d prefer to work on, followed by Endpoint Security (37%) and Privileged Access Management (28%). Many enterprises see digital transformation’s missing link being Zero Trust and the foundation for redefining their businesses by defining every identity as a new security perimeter, so they can securely scale and grow faster than before.

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Vodafone’s 2019 IoT Barometer Reflects Robust Growth In The Enterprise

  • 85% of enterprises who develop deep expertise with IoT succeed at driving revenue faster than competitors.
  • 81% of enterprises say Artificial Intelligence streamlines interpreting and taking action on data insights gained from IoT systems and sensors.
  • 68% of enterprises are using IoT to track the security of physical assets, making this use case the most common across enterprises today.
  • Transport & Logistics and Manufacturing & Industrials saw the most significant increase in adoption between 2018 and 2019.

These and many other fascinating insights are from the 6th annual Vodafone IoT Barometer, 2019.  The entire report can be downloaded here (PDF, 32 pp., e-mail opt-in). The methodology is based on 1,758 interviews distributed across the Americas (22%), EMEA (49%) and Asia-Pacific (29%). Eight vertical markets were included with manufacturing (22%), healthcare and wellness (14%) and retail, leisure, and hospitality (14%) being the three most represented markets.  Vodaphone is making an interactive tool available here for exploring the results.

Key insights from Vodafone’s 2019 IoT Barometer include the following:

  • 34% of global businesses are now using IoT in daily operations, up from 29% in 2018, with 95% of IoT adopters are already seeing measurable benefits. 81% of IoT adopters say their reliance on IoT has grown, and 76% of adopters say IoT is mission-critical to them. 58% are using analytics platforms to get more insights from their IoT data to improve decision making. 71% of enterprises who have adopted IoT expect their company and others like them will start listing data resources on their balance sheets as assets within five years.

  • 95% of enterprises adopting IoT are achieving tangible benefits and positive ROI. 52% of enterprises report significant returns on their IoT investments. 79% say IoT is enabling positive outcomes that would have been impossible without it, further reflecting robust growth in the enterprise. Across all eight vertical markets reducing operating costs (53%) and gaining more accurate data and insights (48%) are the most common benefits. Transitioning an IoT pilot to production based on cost reduction and improved visibility creates a compelling ROI for many enterprises. The following graphic compares IoT’s benefits to enterprises. Please click on the graphic to expand for easier reading.

  • Transport & Logistics and Manufacturing & Industrials saw the greatest increase in adoption between 2018 and 2019. Transport and Logistics had the highest IoT adoption rate at 42% followed by Manufacturing and Industrials at 39%. Manufacturers are facing the challenges of improving production efficiency and product quality while accelerating time-to-market for next-generation smart, connected products. IoT contributes to productivity improvements and creates opportunities for services-based business models, two high priorities for manufacturers in 2019 and beyond.  The following graphic from the interactive tool compares IoT adoption by industry based on Vodaphone’s IoT barometer data over the last six years:

  • 89% of most sophisticated enterprises have multiple full-scale projects in production, orchestrating IoT with analytics, AI and cloud, creating a technology stack that delivers real-time insights. Enterprises who lead IoT adoption in their industries rely on integration to gain scale and speed advantages quickly over competitors. The greater the real-time integration, the greater the potential to digitally transform an enterprise and remove roadblocks that get in the way of growing. 95% of adopters where IoT is fully integrated say it’s enabling their digital transformation, compared with 55% that haven’t started integration. The following graphics reflect how integrated enterprises’ IoT projects are with existing business systems and processes and the extent to which enterprises agree that IoT is enabling digital transformation.

  • 68% of enterprises are using IoT to track the security of physical assets, making this use case the most common across enterprises today. 57% of all enterprises are using IoT to manage risk and compliance. 53% are using it to increase revenue and cut costs, with 82% of high performing enterprises rely on IoT to manage risk and compliance. The following graphic compares the types of variables enterprises are using IoT to track today and plan to in the future.

  • IoT adoption is soaring in Americas-based enterprises, jumping from 27% in 2018 to 40% in 2019. The Americas region leads the world in terms of IoT usage assessed by strategy, integration, and implementation of IoT deployments. 73% of Americas-based enterprises are the most likely to report significant returns from their IoT investments compared to 47% for Asia-Pacific (APAC) and 45% for Europe, Middle East and Africa (EMEA).
  • 52% of IoT-enabled enterprises plan to use 5G when it becomes available. Enterprises are looking forward to 5G’s many advantages including improved security via stronger encryption, more credentialing options, greater quality of service management, more specialized services and near-zero latency. Vodafone predicts 5G will be a strong catalyst of growth for emerging IoT applications including connected cars, smart cities, eHealth and industrial automation.

 

10 Ways AI & Machine Learning Are Revolutionizing Omnichannel

Disney, Oasis, REI, Starbucks, Virgin Atlantic, and others excel at delivering omnichannel experiences using AI and machine learning to fine-tune their selling and service strategies. Source: iStock

Bottom Line: AI and machine learning are enabling omnichannel strategies to scale by providing insights into the changing needs and preferences of customers, creating customer journeys that scale, delivering consistent experiences.

For any omnichannel strategy to succeed, each customer touchpoint needs to be orchestrated as part of an overarching customer journey. That’s the only way to reduce and eventually eliminate customers’ perceptions of using one channel versus another. What makes omnichannel so challenging to excel at is the need to scale a variety of customer journeys in real-time as customers are also changing.

89% of customers used at least one digital channel to interact with their favorite brands and just 13% found the digital-physical experiences well aligned according to Accenture’s omnichannel study. AI and machine learning are being used to close these gaps with greater intelligence and knowledge. Omnichannel strategists are fine-tuning customer personas, measuring how customer journeys change over time, and more precisely define service strategies using AI and machine learning. Disney, Oasis, REI, Starbucks, Virgin Atlantic, and others excel at delivering omnichannel experiences using AI and machine learning for example.

Omnichannel leaders including Amazon use AI and machine learning to anticipate which customer personas prefer to speak with a live agent versus using self-service for example. McKinsey also found omnichannel customer care expectations fall into the three categories of speed and flexibility, reliability and transparency, and interaction and care. Omnichannel customer journeys designed deliver on each of these three categories excel and scale between automated systems and live agents as the following example from the McKinsey article, How to capture what the customer wants illustrate:

The foundation all great omnichannel strategies are based on precise customer personas, insight into how they are changing, and how supply chains and IT need to flex and change too. AI and machine learning are revolutionizing omnichannel on these three core dimensions with greater insight and contextual intelligence than ever before.

10 Ways AI & Machine Learning Are Revolutionizing Omnichannel

The following are 10 ways AI & machine learning are revolutionizing omnichannel strategies starting with customer personas, their expectations, and how customer care, IT infrastructure and supply chains need to stay responsive to grow.

  1. AI and machine learning are enabling brands, retailers and manufacturers to more precisely define customer personas, their buying preferences, and journeys. Leading omnichannel retailers are successfully using AI and machine learning today to personalize customer experiences to the persona level. They’re combining brand, event and product preferences, location data, content viewed, transaction histories and most of all, channel and communication preferences to create precise personas of each of their key customer segments.
  2. Achieving price optimization by persona is now possible using AI and machine learning, factoring in brand and channel preferences, previous purchase history, and price sensitivity. Brands, retailers, and manufacturers are saying that cloud-based price optimization and management apps are easier to use and more powerful based on rapid advances in AI and machine learning algorithms than ever before. The combination of easier to use, more powerful apps and the need to better manage and optimize omnichannel pricing is fueling rapid innovation in this area. The following example is from Microsoft Azure’s Interactive Pricing Analytics Pre-Configured Solution (PCS). Source: Azure Cortana Interactive Pricing Analytics Pre-Configured Solution.

  1. Capitalizing on insights gained from AI and machine learning, omnichannel leaders are redesigning IT infrastructure and integration so they can scale customer experiences. Succeeding with omnichannel takes an IT infrastructure capable of flexing quickly in response to change in customers’ preferences while providing scale to grow. Every area of a brand, retailer or manufacturer’s supply chain from their supplier onboarding, quality management and strategic sourcing to yard management, dock scheduling, manufacturing, and fulfillment need to be orchestrated around customers. Leaders include C3 Solutions who offers a web-based Yard Management System (YMS) and Dock Scheduling System that can integrate with ERP, Supply Chain Management (SCM), Warehouse Management Systems (WMS) and many others via APIs. The following graphic illustrates how omnichannel leaders orchestrate IT infrastructure to achieve greater growth. Source: Cognizant, The 2020 Customer Experience.

  1. Omnichannel leaders are relying on AI and machine learning to digitize their supply chains, enabling on-time performance, fueling faster revenue growth. For any omnichannel strategy to succeed, supply chains need to be designed to excel at time-to-market and time-to-customer performance at scale. 54% of retailers pursuing omnichannel strategies say that their main goal in digitizing their supply chains was to deliver greater customer experiences. 45% say faster speed to market is their primary goal in digitizing their supply chain by adding in AI and machine learning-driven intelligence. Source: Digitize Today To Future-Proof Tomorrow (PDF, 16 pp., opt-in).

  1. AI and machine learning algorithms are making it possible to create propensity models by persona, and they are invaluable for predicting which customers will act on a bundling or pricing offer. By definition propensity models rely on predictive analytics including machine learning to predict the probability a given customer will act on a bundling or pricing offer, e-mail campaign or other call-to-action leading to a purchase, upsell or cross-sell. Propensity models have proven to be very effective at increasing customer retention and reducing churn. Every business excelling at omnichannel today rely on propensity models to better predict how customers’ preferences and past behavior will lead to future purchases. The following is a dashboard that shows how propensity models work. Source: customer propensities dashboard is from TIBCO.

  1. Combining machine learning-based pattern matching with a product-based recommendation engine is leading to the development of mobile-based apps where shoppers can virtually try on garments they’re interested in buying. Machine learning excels at pattern recognition, and AI is well-suited for creating recommendation engines, which are together leading to a new generation of shopping apps where customers can virtually try on any garment. The app learns what shoppers most prefer and also evaluates image quality in real-time, and then recommends either purchase online or in a store. Source: Capgemini, Building The Retail Superstar: How unleashing AI across functions offers a multi-billion dollar opportunity.

  1. 56% of brands and retailers say that order track-and-traceability strengthened with AI and machine learning is essential to delivering excellent customer experiences. Order tracking across each channel combined with predictions of allocation and out-of-stock conditions using AI and machine learning is reducing operating risks today. AI-driven track-and-trace is invaluable in finding where there are process inefficiencies that slow down time-to-market and time-to-customer. Source: Digitize Today To Future-Proof Tomorrow (PDF, 16 pp., opt-in).
  2. Gartner predicts that by 2025, customer service organizations who embed AI in their customer engagement center platforms will increase operational efficiencies by 25%, revolutionizing customer care in the process. Customer service is often where omnichannel strategies fail due to lack of real-time contextual data and insight. There’s an abundance of use cases in customer service where AI and machine learning can improve overall omnichannel performance. Amazon has taken the lead on using AI and machine learning to decide when a given customer persona needs to speak with a live agent. Comparable strategies can also be created for improving Intelligent Agents, Virtual Personal Assistants, Chatbot and Natural Language (NLP) performance.  There’s also the opportunity to improve knowledge management, content discovery and improve field service routing and support.
  3. AI and machine learning are improving marketing and selling effectiveness by being able to track purchase decisions back to campaigns by channel and understand why specific personas purchased while others didn’t. Marketing is already analytically driven, and with the rapid advances in AI and machine learning, markets will for the first time be able to isolate why and where their omnichannel strategies are succeeding or failing. By using machine learning to qualify the further customer and prospect lists using relevant data from the web, predictive models including machine learning can better predict ideal customer profiles. Each omnichannel sales lead’s predictive score becomes a better predictor of potential new sales, helping sales prioritize time, sales efforts and selling strategies.
  4. Predictive content analytics powered by AI and machine learning are improving sales close rates by predicting which content will lead a customer to buy. Analyzing previous prospect and buyer behavior by persona using machine learning provides insights into which content needs to be personalized and presented when to get a sale. Predictive content analytics is proving to be very effective in B2B selling scenarios, and are scaling into consumer products as well

What IoT Leaders Do To Drive Greater Results

  • IoT Leaders are achieving cost and revenue gains of at least 15% or more, while laggards see less than 5%.
  • Pursuing 80% more IoT use cases compared to their peers, IoT Leaders are progressing faster down the learning curve of monetizing their application areas.
  • IoT Leaders anticipate that their IoT use cases will boost their gross profits by 13% over the next three years, three times as much as IoT laggards.

What IoT leaders do to excel and drive greater results compared to their peers is explored in the recent McKinsey report, What separates leaders from laggards in the Internet of Things. The study is based on interviews with 300 IoT executive-level practitioners from companies with more than $500M revenues which are implementing large-scale IoT strategies with projects that have progressed from pilot to production. Enterprises from 11 major industry segments from Canada, China, Germany, and the United States were included in the survey.

McKinsey found 16% of enterprises have IoT programs in production, delivering aggregate cost and revenue impacts of at least 15%. The study also found 16% of enterprises are lagging, attaining aggregate revenue and cost improvements of less than 5%. The following graphic compares companies by the level of financial impact from IoT initiatives:

Nine practices differentiate IoT Leaders from laggards, and the study provides a fascinating look into each based on the survey data. Key insights into IoT Leader’s practice areas is provided here:

  • Leaders are more aggressive about pursuing a greater number, scope, and variety of IoT applications and use cases than their less successful peers. What IoT Leaders learn quickly is how steep the IoT learning curve is, and how it’s essential to run as many IoT pilots as possible to learn more. Leaders discover the first 15 or so IoT use cases typically have a modest payback, with the average payback rising until approximately 30 use cases have been achieved. IoT Leaders anticipate that their IoT use cases will boost their gross profits by 13% over the next three years, three times as much as IoT laggards. The following graphic illustrates the financial impact per IoT use case by the cumulative number of IoT use cases enterprises initiate.

  • Leaders are more willing than their peers to change business processes to unlock IoT’s value. McKinsey found IoT Leaders are three times more likely than their peers to say that managing changes to business processes is one of the three most important capabilities for implementing IoT. CEOs who champion their company’s IoT initiatives make strong contributions in this area, removing barriers and roadblocks quickly to keep IoT programs moving forward.
  • Leaders design, pilot and move to production IoT use cases that rely on advanced endpoints far more than their peers. McKinsey finds that IoT Leaders are more visionary and aggressive than peers in developing applications with advanced endpoints.  Leaders are gaining expertise and mastery of how to creatively use advanced endpoints today, reporting higher levels of satisfaction and positive results.

  • Leaders clearly define how IoT will create value and excel in building effective business cases. McKinsey found that IoT Leaders are 75% more likely than their peers to cite the preparation of a strong business case as a critical success factor for their IoT programs. The study’s respondents who have an IoT vision that includes a strong value proposition, a proven delivery model, and a business model that drives revenue are getting results faster than their peers. 35% of Leaders rate the importance of “strong business case and vision for value creation” as one of the top three success factors versus 20% of laggards. Leaders leave nothing to chance when it comes to defining how IoT will deliver business value either in the form of greater revenue or reduced costs.

  • A CEO’s involvement and support are essential for any enterprise to succeed with  IoT. Based on personal experience with IoT pilots, C-level executives are indispensable in removing barriers and making process-level changes necessary for success. 72% of the surveyed executives agree. A vital catalyst of any enterprise succeeding with IoT is a clear, unequivocal time commitment on the part of the CEO. Enterprises in the Leaders quintile were 2.4 more likely than laggards to report that their CEO serves as the champion of IoT efforts as the following graphic illustrates:

  • Leaders credit strong alignment with IoT strategies and priorities enterprise-wide as a critical factor in their success. IoT initiatives and pilots on their way to production require executives, managers, and frontline workers to learn fresh skills and collaborate across business and functional boundaries in new ways. Enterprises need to have a strong unifying vision of where they’re going with IoT, with the CEO championing the change management required to make sure they succeed.
  • Leaders begin by adding IoT capability to existing products and services first. McKinsey found that Leaders are three times more likely than their peers to make their top priority adding IoT capabilities to existing products. They focus on how to turn the current scale they’ve achieved with suppliers, selling and service networks into a formidable competitive advantage. They’re also more adept at cross-selling and up-selling IoT-enabled products by capitalizing on current customer relationships. The following graphic compares enterprises’ single highest-priority IoT effort:

  • Leaders excel at tapping into, scaling and relying on an ecosystem of partners for innovation versus doing it all themselves. McKinsey finds that IoT Leaders excel at scaling their partner ecosystems faster and more strategically than their peers. IoT Leaders also rely more on partners for the latest technology innovations instead of attempting to create them entirely on their own. They’re also deliberately choosing IoT platforms that support third-party developers and the advanced endpoints as the graphic below shows:

  • Leaders prepare for cyber attacks, so they don’t slow things down. McKinsey found that 30% of enterprises from both IoT Leaders and their peers say that they’ve experienced cyber attacks that have resulted in high to severe damage. 57% of Leaders had been the target of cyber attacks compared to 44% of their peers. The higher number of cyber attacks happening for Leaders is due to the broader threat surface their many pilots, and production-level use cases create. The more distributed and varied IoT use cases are the greater the risk of privileged credential abuse as well. Thwarting privileged credential abuse needs to start with a least privilege access approach, minimizing each attack surface, improving audit and compliance visibility while reducing risk, complexity, and costs. Leaders in Zero Trust include CentrifyMobileIronPalo Alto Networks, and others.

Top 25 IoT Startups To Watch In 2019

 

  • 26,792 startups are relying on IoT as one of their main technologies to launch new products and services and support platform-based business models according to Crunchbase.
  • 78.4% of IoT startups Crunchbase tracks have had two funding rounds or less with seed, angel and early-stage rounds being the most common.
  • IoT startup funding reached $16.7B in Q4, 2018, with last years’ funding levels 94% over 2017 according to Venture Scanner.
  • By 2020, 50% of IoT spending will be driven by discrete manufacturing, transportation and logistics, and utilities according to the Boston Consulting Group.

The most successful IoT startups selling into enterprises excel at orchestrating analytics, Artificial Intelligence (AI), and real-time monitoring to deliver exceptional customer experiences. As a group, these top 25 IoT startups are showing early potential at enabling profitable new business models, revitalizing industries that have experienced single single-digit growth recently. Each of these startups is taking a unique approach to solving some of the enterprises’ most challenging problems, and in so doing creating valuable new patents that further fuel IoT adoption and growth.

The top 25 startups are concentrating on how to make IoT a growth catalyst for enterprises by designing in AI integration at the platform level. McKinsey found that 27% of AI early adopters are more likely to report using AI to grow their market than companies only experimenting with or partially adopting AI. 52% are more likely to report using it to increase their market share. These and many other survey results are from McKinsey Global Institute’s Artificial Intelligence: The Next Digital Frontier? (PDF, 80 pp., no opt-in).

Top 25 IoT Startups To Watch In 2019

The following list of 25 IoT startups are based on an analysis of their ability to attract new customers, current and projected revenue growth, patents’ current value and potential, and position in their chosen markets. Presented below are the top 25 IoT startups to watch this year:

  1. Armis Security – Armis takes a unique approach to provide visibility into IoT-enabled devices that are unmanaged across an IT network. The company’s solutions treat every IoT device as a threat surface, enabling enterprises to prohibit access to IoT devices and networks based on security guidelines. Another unique aspect of this company’s approach to deployment is the ability to use an enterprises’ existing infrastructure for rapid deployments. Founded in 2015 the company has active customers in finance, healthcare, manufacturing, and high technology industries. Armis Security has raised a total of $47M in funding over 3 Their latest funding was raised on Apr 9, 2018, from a Series B round of $30M from Bain Capital Ventures and Red Dot Capital Partners. Crunchbase reports Armis Security has $2.1M in revenue annually and competes with DigiCert, Skybox Security, and Aruba Networks most often in sales cycles.
  2. Crate.io – Crate.io’s open source SQL database features integrated search for storing and analyzing machine data in real time. The company was founded in 2013 with the purpose of providing SQL developers with an open source SQL database to capture, analyze and manage their machine learning and AI-based data. CrateDB is an open source distributed database offering the scalability and performance of NoSQL with the power and ease of standard SQL. The CrateDB Cloud for Azure IoT is a turnkey data layer, offered as a hosted cloud service on Azure, enabling faster development of IoT platforms and data-driven smart factories. Most CrateDB customers use it for operational analytics workloads, performing fast time series, geospatial, text search, machine learning queries against streams of data and data at rest in Industrial IoT, enterprise cybersecurity & systems monitoring in all industries, smart city and building infrastructure, Vehicle fleet tracking & management and marketing analytics. The company has raised $17.9M in funding over 4 rounds.
  3. Dragos – Dragos specializes in industrial (ICS/IIoT) cybersecurity. Their cloud-based Dragos Platform collects, detects, and automates asset inventorying and visualization, threat detection through threat behavior analytics, and security operations and incident response workflows. Dragos also has a Threat Operations Center that provides customers access to dedicated ICS incident response and threat hunting services as well as industrial specific intelligence reporting on vulnerabilities, threats, and community events. Dragos has raised a total of $48.2M in funding over 3 Their latest funding was raised on Nov 14, 2018, from a $37M Series B round with Canaan Partners.
  4. Drayson Technologies – Drayson Technologies provides an IoT platform startup that is combining wireless charging technology and machine learning software to create smart sensor networks that deliver greater energy and cost efficiencies to its customers. Drayson is known for its expertise in energy-efficient and cost-effective IoT data collection and analysis, which also contributes to their customers’ ability to reduce the cost of deploying, owning and running IoT networks.
  5. Element Analytics –Element Analytics is rapidly establishing itself as a startup to watch in the fields of chemicals & refining, manufacturing, metals & mining, pulp & paper, and upstream oil & gas. Their Element Platform helps industrial organizations easily and rapidly use industrial time-series data to improve production efficiency and product quality. Their platform prepares time-series data, enriches it with analytically relevant context, creating greater contextual insights. The Element Analytics platform also enables machine-learning modeling to surface reliability, productivity, and sustainability insights for operations. Element Analytics has raised a total of $22M in funding over 3 Their latest funding was raised on Jan 8, 2018, from a Series A round. Kleiner Perkins participated in the first two rounds, funding a total of $7M.
  6. FogHorn – FogHorn is a fascinating startup to watch because they excel at embedding real-time analytics and machine-learning support into size- and space- constrained commercial and industry IoT application areas. Realizing that industrial manufacturing and distribution sites often have unreliable Internet connections if they have any at all, Foghorn has designed a miniaturized, scalable complex-event processing (CEP) software engine that is capable of producing analytics in real-time. The FogHorn Lightning™ platform includes the CEP software engine, enabling high-performance edge computing, advanced analytics, Machine Learning, and AI to be implemented highly constrained environments of IIoT. The company has also created a new class of high-performance programming language called Vel ™ which transforms any gateway, programmable logic controller (PLC), industrial PC, or another edge device into an advanced edge computing system. FogHorn has raised a total of $47.5M in funding over 4 Their latest funding was raised on Oct 4, 2017, from a Series B round. The FogHorn Technology Platform is shown below:

  1. GEM – GEM specializes in providing IoT, analytics, and machine learning platforms and solutions for the manufacturing industry, with a specific focus on Overall Equipment Effectiveness (OEE) and predictive maintenance. The company has been able to gain customers in energy, retail, and GEM’s value proposition is based on their ability to increase manufacturers’ OEE levels through greater real-time insights. The GEM Precare platform captures operational data and KPIs in real-time including availability, OEE, performance, quality, MTBF, MTBA, machine statuses, status reasons, and alarms. The following is an example of the GENM technology platform:

  1. IoTium – This is a fascinating company to track due to their patented technology that enables secure connections between Network as a Service (NaaS), legacy onsite systems and cloud-based applications. Customers include CBRE, Emerson, Intelligent Buildings, Obernel, Rexnord, and Sunbelt Controls. IoTium is well positioned to gain new customers in building and industrial automation, oil & gas, manufacturing, transportation, and smart city industries. IoTium has raised a total of $22M in funding over 2 rounds with investors GE Ventures, March Capital, and Juniper Networks. Their latest funding was raised on Sep 19, 2018, from a Series B round.
  2. InfluxData – InfluxData created InfluxDB, their Open Source Platform specifically designed to analyze metrics and events (time series data) for DevOps and IoT applications. Whether the data comes from humans, sensors, or machines, InfluxData enables developers to build monitoring, analytics, and IoT applications at scale, delivering measurable business value quickly. The company reports having 400 customers including Cisco, eBay, IBM, and InfluxData has raised a total of $59.9M in funding over 4 rounds. Their latest funding was raised on Feb 13, 2018, from a Series C round.
  3. Karamba Security – Karamba Security is focused on solving the security challenges of connected vehicles. The company offers Electronic Control Unit (ECU) endpoint security to protect any vehicle with an IoT connection or IP address. What makes this startup so interesting is how they are using patented technologies to reduce IoT-based attacks on vehicles by blocking them autonomously. Internet connectivity or extensive developer work is not needed to implement Karamba across a vehicle fleet. Each device can be reset to its factory settings, eliminating the threat of a vehicle being hacked. Karamba Security has raised a total of $27M in funding over 4 Their latest funding was raised on Apr 10, 2018, from a Series B round.
  4. MachineMetrics – What makes MachineMetrics an interesting company to watch is their innovative approach to using Artificial Intelligence (AI) to discover new insights into manufacturer’s data that improve product quality and performance. It’s one of the first startups to combine Industrial Internet of Things (IIoT) and AI and provide a scalable platform for discrete manufacturers and heavy equipment builders. They’ve also developed an expertise at edge connectivity in manufacturing environments that have enabled greater real-time visibility and more meaningful manufacturing analytics than has been possible in the past. They’re using AI to drive their prescriptive and predictive alerts. MachineMetrics has raised a total of $13.4M in funding over 3 Their latest funding was raised on Dec 11, 2018, from a Series A round. The following is a Workstation View from the MachineMetrics Production platform:

  1. MagicCube – MagicCube is a device independent IoT security platform that protects against on-device, cloud, and network attacks. The MagicCube solution secures digital transactions on any device, in transit, and in the cloud with the same level of security as device hardware solutions without the complexity and cost associated with hardware deployments. MagicCube, Inc. has raised a total of $10.7M in funding over 2 Their latest funding was raised on Aug 8, 2017, from a Series A round.
  2. Myriota – What makes Myriota a fascinating company to watch is their innovative advances in ultra-low-cost satellite Internet of Things (IoT) connectivity and the alliances they are creating, including on with SpaceX. Myriota’s nano-satellite was launched into space aboard the SpaceX Falcon 9 rocket in December 2018. Myriota uses exactEarth’s Low Earth Orbit (LEO) satellite constellation for its connectivity solutions. Myriota is a global leader in low-cost satellite IoT connectivity, providing aggregated sensor reading, environmental sensing, and online tracking and condition monitoring of remote assets. The company has raised a total of $15M in funding over 1 round. This was a Series A round raised on Mar 26, 2018.
  3. Particle – Particle is an Internet of Things (IoT) device platform that enables organizations to develop and fine-tune connectivity across operations using scalable APIs and software development resources. Particle’s development platform is designed to provide organizations with the tools they need to prototype IoT solutions to scale quickly and securely. Over 150,000 product builders in more than 170 countries and half of the Fortune 500 have deployed connected IoT devices powered by Particle. Particle’s customers include NASA, SpaceX, consumer hot tub manufacturer Jacuzzi, and Venture-backed by Root Ventures, Spark Capital, Qualcomm Ventures, and Particle is based in San Francisco, CA and Shenzhen, China. Particle has raised a total of $35.8M in funding over 7 rounds. Their latest funding was raised on Jul 19, 2017, from a Series B round.
  4. Samsara – What makes Samsara noteworthy is their prioritizing how sensor data can increase the safety and efficiency of physical operations, contributing to productivity gains while reducing costs. Samsara is attracting customers from the transportation, logistics, construction, food production, energy, and manufacturing industries with their ability to improve the safety, efficiency, and quality of operations. Samsara builds sensor systems that combine wireless sensors with remote networking and cloud-based analytics. As of February 2019, the company has over 5,000 customers and has a run rate of 200,000 new devices being added every year. Samsara has raised a total of $230M in funding over 5 Their latest funding was raised on Dec 28, 2018, from a Series E round. An example of the company’s Fleet Summary is shown below:

  1. SCADAfence – SCADAfence provides cybersecurity solutions designed to ensure the operational continuity of industrial (ICS/SCADA) networks. The startup excels at integrating Industrial IoT, analytics, realtime monitoring and machine-to-machine connectivity to provide scalable cybersecurity solutions for production networks. As of February 2019 the company has customers in the pharmaceutical, chemical, food & beverage and automotive industries. SCADAFence offers a solution suite that includes continuous real-time monitoring of the industrial environment as well as lightweight tools designed to automate the process of security assessment. The suite provides visibility of day-to-day operations, detection of cyber-attacks and forensics tools designed to improve responsiveness. SCADAfence has raised a total of $10M in funding over 3 Their latest funding was raised on Nov 21, 2017, from a Series A round.
  2. SequoiaDB – SequoiaDB develops and provides commercial support for the open source database SequoiaDB, a document-oriented NewSQL database that supports JSON transaction processing and SQL query. Their database can either be a standalone product to interface with applications providing high performance and horizontally scalable data storage and processing functions or serve as the frontend of Hadoop and Spark for both real-time query and data analysis. It is designed to integrate with Spark, Hadoop/Cloudera. SequoiaDB has raised a total of $40M in funding over 3 Their latest funding was raised on Sep 19, 2018, from a Series C round.
  3. Sight Machine – This is a fascinating startup to watch, I’ve been tracking Sight Machine for several years. The company is succeeding at attracting Fortune 500-level manufacturers as clients by providing them with AI-driven insights into how they can improve operations. Sight Machine’s AI and analytics platform, purpose-built for discrete and process manufacturing, uses artificial intelligence, machine learning, and advanced analytics to help address critical challenges in quality and productivity throughout the enterprise. The platform is powered by the industry’s only Plant Digital Twin, which enables real-time visibility and actionable insights for every machine, line, and plant throughout an enterprise. Sight Machine is optimized to run on the major cloud platforms including AWS, Google Cloud Platform, and Microsoft Azure. The company has raised a total of $30.5M in funding over 5 Their latest funding was raised on Dec 23, 2017, from a Series B round. An example of a Sight Machine dashboard is shown below:

  1. Splice Machine –. Splice Machine provides an open-source dual-engine RDBMS for mixed operational and analytical workloads, powered by Apache Hadoop® and Apache Spark™. The Splice Machine RDBMS executes operational workloads on Apache HBase® and analytical workloads on Apache Spark. Splice Machine is known for its ease of development and use for IoT-based applications and is successfully offload operational and analytical workloads from Oracle, Teradata, and Netezza legacy systems. The company excels at ETL, operational reporting or real-time applications and use cases. Splice Machine has raised a total of $40M in funding over 4 Their latest funding was raised on Dec 20, 2017, from Salesforce Ventures.
  2. SWIM.AI – Swim provides edge-based software that executes real-time analytics and machine learning for enterprises, equipment manufacturers, smart-cities, and IoT and IIoT businesses. Its software locally processes and analyzes massive volumes of streaming data from devices/sensors/equipment where it is created, reducing network volumes, and generating real-time machine-learning business insights. Swim deploys its software at the edge to transform data into insights in real-time and delivers them to businesses, staff, operators, and customers. Swim has successfully been deployed and is in use in existing equipment and brownfield environments. In manufacturing customers’ operations Swim is improving real-time synchronization across multiple systems, reduce project implementation costs, optimizing efficiency using machine learning insights from full resolution edge data and making insights available via real-time APIs. Swim.ai has raised a total of $10M in funding over 2 rounds. Their latest funding was raised on Jul 17, 2018, from a Series B round. Swim’s model is shown below:

  1. Tulip – Tulip was started by a team of engineers out of the MIT Media Lab, and the company’s platform is based on over ten years of research in digital manufacturing. Their self-service technology fills the gap between rigid back-end manufacturing IT systems and the dynamic operations taking place on the shop floor. Tulip’s Manufacturing App Platform combines research in intelligent hardware sensors, computer vision, assistive user interfaces, and applied machine learning. Tulip was launched to bring these latest technological developments from the lab to the factory floor. Today, Tulip’s Manufacturing App Platform is deployed at dozens of global customers in six countries across multiple industries including Electronics, Aerospace & Defense, Medical Devices, Footwear, Pharmaceuticals, and Contract Manufacturing. Tulip Interfaces has raised a total of $13M in funding over 3
  2. Tuya Smart – Tuya Smart is an IoT solution provider for device manufacturers. Their platform enables fast, agile app development, allowing smart device manufacturers to bring their product to market quickly and at competitive prices. Tuya Smart is founded by Jerry Wang, a founding executive of AliYun, Alibaba’s cloud division, along with a group of veterans from Alibaba, Baidu and Haier Electronics. With extensive knowledge in cloud computing, software development, and hardware and supply chain management, Tuya Smart’s team is enabling manufacturers to produce next-generation smart, connected products. Tuya has raised a total of $200M in funding over 3 Their latest funding was raised on Jul 24, 2018, from a Series C round.
  3. Uptake – Uptake Technologies provides a predictive analytics and asset performance management (APM) platform gaining traction in key industrial IoT market segments today. The Uptake platform analyzes data from inside a company and from third party sources to predict and prevent failures, uncover hidden profits, and discover new opportunities to healthcare, insurance, locomotives, construction, manufacturing, and other industries. Uptake Technologies offers a platform for equipment monitoring, diagnostic troubleshooting, event, and condition prediction, and task management to improve uptime, streamline operations, and spot growth opportunities. Key customers include Caterpillar, Progress Rail, Berkshire Hathaway Energy, and the U.S. Army.
  4. VDOO– VDOO has developed a platform of automated solutions to help IoT makers put the right security in their devices before release and enable post-deployment security. The end-to-end platform takes the maker from security analysis to implementation guidance to certification and enables IoT makers to quickly add the right security to their devices with minimal resources. VDOO’s solution is built upon a comprehensive taxonomy of IoT devices and consists of five interrelated and integrated products including the Security Requirements Generator, Security Gap Analysis, Actionable Security Plan, Certification, and Post-Deployment Security Enablement. VDOO has raised a total of $13M in funding over 1 round. This was a Series A round raised on Jan 17, 2018.
  5. Xage Security – Xage provides decentralized security services for industrial manufacturing and distribution businesses including oil and gas, transportation, and utilities. The Xage architecture relies on blockchain to provide a distributed, scalable and highly reliable data store that prevents hackers from attacking and gaining access through any threat surface in an organization. Xage takes a unique approach to using blockchain to thwart hacking attempts at scale, by simultaneously protecting every active ledger in an organization. Xage Security has raised a total of $16M in funding over 2 Their latest funding was raised on Dec 28, 2018, from a Series A round.

Digital Transformation’s Missing Link Is Zero Trust

    • Enterprises will invest $2.4T by 2020 in digital transformation technologies including cloud platforms, cognitive systems, IoT, mobile, robotics, and integration services according to the World Economic Forum.
    • Digital transformation software and services revenue in the U.S. is predicted to reach $490B in 2025, soaring from $190B in 2019, attaining a Compound Annual Growth Rate (CAGR) of 14.49% according to Grand View Research published by Statista.
    • IDC predicts worldwide spending on the technologies and services that enable the digital transformation of business practices, products, and organizations will reach $1.97T in 2022.
    • Legacy approaches to Privileged Access Management (PAM) don’t protect the new threatscapes digital transformation initiatives create, making Zero Trust Privilege essential for enterprises.

B2B customers, including manufacturers looking to replace legacy production equipment with smart, connected machines, have high expectations when it comes to product quality, ease of integration, and intuitive user experiences. Replacing factories full of legacy assets with smart, connected machinery is one of the most powerful catalysts driving digital transformation today. Innovative smart, connected machinery and the performance gains they provide are the oxygen that keeps customer relationships alive. That’s why digital transformation forecasts from the World Economic Forum, Grand View ResearchIDC, and many others predict perennial growth. The many forecasts reflect a fundamental truth: digital transformation done with intensity creates a customer-driven renaissance for any business.

Businesses digitally transforming themselves are succeeding because they’ve made themselves accountable and transparent to customers. Earning and protecting that trust is the heartbeat of any business’ growth. 51% of enterprises invest in digital transformation to capture growth opportunities in new markets, with 46% investing to stay in front of evolving customer behaviors and preferences. Brian Solis’ excellent report, The State of Digital Transformation, 2018 – 2019 Edition (31 pp., PDF, opt-in) shows how digitally transforming any business with the customer first leads to greater growth. The graphic from his study illustrates this point:

 

Closing The Digital Transformation Gap With Zero Trust

Gaps exist between the results digital transformation initiatives are delivering today, and the customer-driven value they’re capable of. According to Gartner, 75% of digital transformation projects are not aligned internally today, leading to delayed new product launches, mediocre experiences, and greater security risks than ever before. Interactive, IoT-enabled experiences and products are expanding the threatscape of enterprises to include Big Data, cloud, containers, DevOps, IoT systems, and more. With that comes a host of new exposure points, many of which allow access to sensitive data that must be protected with modern Privileged Access Management solutions that reduce risk in these modern enterprise use cases.

The new security perimeter is identity. Forrester estimates that 80% of data breaches are caused by privileged access abuse. Every smart, connected machine that replaces legacy production equipment is another identity that defines a manufacturer’s security perimeter.

As the use cases and adoption of smart, connected machines proliferate, so too does the urgency that manufacturers need to replace their legacy approaches to Privileged Access Management (PAM). Relying on outdated strategies for protecting administrative access to all machines needs to be replaced with a “never trust, always verify, enforce least privilege” approach.

IT needs to improve how they’re protecting the most privileged access credentials, the ‘keys to the kingdom,’ by granting just-enough, just-in-time privilege. Of the many cybersecurity approaches available today, Zero Trust Privilege (ZTP) enables IT to grant least privilege access based on verifying who is requesting access, the context of the request, and the risk of the access environment.

The more diverse any digital transformation strategy, the greater the risk of privileged credential abuse. Thwarting privileged credential abuse needs to start with a least privilege access approach, minimizing each attack surface, improving audit and compliance visibility while reducing risk, complexity, and costs. Leaders in Zero Trust include CentrifyMobileIronPalo Alto Networks, and others. Of these companies, Centrify’s approach to Zero Trust to prevent privileged access abuse shows the greatest potential for securing digital transformation initiatives and strategies.

How To Secure Digital Transformation Strategies

IDG Research found in their Security Priorities for 2018 study that 71% of security-focused IT decision-makers are aware of the Zero Trust model and 18% of enterprises are either running pilots or have implemented Zero Trust.

Zero Trust Privilege (ZTP) is the force multiplier digital transformation initiatives need to reach their true potential by securing administrative access to the complex mix of machinery and infrastructure – and the sensitive data they hold and use – that manufacturers rely on daily.

Starting with a strategic perspective, ZTP’s contribution to securing digital transformation deployments apply to every area of planning, pilots, platforms, product, and service data being designed to stop the leading cause of breaches, which is privileged credential abuse. The following graphic illustrates how ZTP needs to span every aspect of an enterprise’s digital transformation capabilities.

Source: World Economic Forum, Digital Transformation Initiative, May 2018

Conclusion

By 2020, 30% of Global 2000 companies will have allocated capital budget equal to at least 10% of revenue to fuel their digital transformation strategies according to IDC.  European spending on technologies and services that enable the digital transformation of business practices, products, and organizations is forecasted to reach $378.2B in 2022. The perennial growth these forecasts promise is predicated on enterprises delivering new experiences and innovative products, which create the oxygen that keeps their customer relationships alive.

Amidst all the potential for growth, enterprises need to realize every new infrastructure element, machine, or connected production asset is a new identity that collectively comprises the fabric of their security perimeter. Legacy cybersecurity approaches won’t scale to protect the proliferating number of smart machines being put into use today. Relying entirely on legacy approaches to PAM, where privileged access to systems and resources only inside the network are secure, is failing today. Smart, connected machinery and the products and experiences they deliver require an entirely new cybersecurity strategy, one based on a “never trust, always verify, enforce least privilege” approach. Centrify Zero Trust Privilege shows potential to meet this challenge by granting least privilege access based on verifying who is requesting access, the context of the request, and the risk of the access environment.

Top 10 Ways Internet Of Things And Blockchain Strengthen Supply Chains

  • The majority of enterprises are prioritizing their blockchain pilots that concentrate on supply chains improvements (53%) and the Internet of Things (51%) according to Deloitte’s latest blockchain survey.
  • By 2023, blockchain will support the global movement and tracking of $2T of goods and services annually based on a recent Gartner
  • By 2020, Discrete Manufacturing, Transportation & Logistics and Utilities industries are projected to spend $40B each on IoT platforms, systems, and services.
  • The Supply Chain Management enterprise software market is growing from $12.2B in 2017 to $20.4B in 2022, achieving a 10.7% Compound Annual Growth Rate (CAGR) according to Gartner’s latest market forecast.
  • Of the many blockchain and IoT Proof of Concept (POC) pilots running today, track-and-trace shows the most significant potential of moving into production.

Combining blockchain’s distributed ledger framework with the Internet of Things’ (IoT) proven real-time monitoring and tracking capability is redefining supply chains. Blockchain shows potential for increasing the speed, scale, and visibility of supply chains, eliminating counterfeit-goods transactions while also improving batching, routing and inventory control. Blockchain’s shared, distributed ledger architecture is becoming a growth catalyst for IoT’s adoption and commercial use in organizations.

Blockchain and IoT are defining the future of supply chains based on the initial success of Proof of Concept (POC) pilots focused on the logistics, storage and track-and-trace areas of supply chains across manufacturing. Supply-chain centric pilots are the most popular today, with enterprises looking at how they can get more value out of IoT using blockchain. One CIO told me recently his company deliberately spins up several POCs at once, adding “they’re our proving grounds, we’re pushing blockchain and IoT’s limits to see if they can solve our most challenging supply chain problems and we’re learning a tremendous amount.” The senior management team at the manufacturer says the pilots are worth it if they can find a way to increase inventory turns just 10% using blockchain and IoT. They’re also running Proof of Concept pilots to optimize batching, routing and delivery of goods, reduce fraud costs, and increase track-and-trace accuracy and speed. Of the many pilots in progress, track-and-trace shows the greatest potential to move into production today.

The following are the top 10 ways IoT and blockchain are defining the future of supply chains:

  • Combining IoT’s real-time monitoring support with blockchain’s shared distributed ledger strengthens track-and-trace accuracy and scale, leading to improvements across supply chains. Improving track-and-trace reduces the need for buffer stock by providing real-time visibility of inventory levels and shipments. Urgent orders can also be expedited and rerouted, minimizing disruptions to production schedules and customer shipments.  The combination of blockchain and IoT sensors is showing potential to revolutionize food supply chains, where sensors are used to track freshness, quality, and safety of perishable foods.  The multiplicative effects of combining IoT and blockchain to improve track-and-traceability are shown in the context of the following table from the Boston Consulting Group. Please click on the graphic to expand for easier reading.

  • Improving inventory management and reducing bank fees for letters of credit by combining blockchain and IoT show potential to deliver cost savings. A recent study by Boston Consulting Group, Pairing Blockchain with IoT to Cut Supply Chain Costs, completed a hypothetical analysis of how much a $1B electronics equipment company implementing blockchain-as-a-service, a decentralized track-and-trace application, and 30 nodes that share among key supply chain stakeholders could save. The study found that the electronics equipment company could save up to $6M a year or .6% of annual sales. A summary of the business case is shown here:

  • Combining blockchain and IoT is providing the pharmaceutical and healthcare industry with stronger serialization techniques, reducing counterfeit drugs and medical products. Pharmaceutical serialization is the process of assigning a unique identity (e.g., a serial number) to each sealable unit, which is then linked to critical information about the product’s origin, batch number, and expiration date. According to the World Health Organization (WHO) approximately 1 million people each year die from counterfeit drugs, 50% of pharmaceutical products sold through rogue websites are considered fake, and up to 30% of pharmaceutical products sold in emerging markets are counterfeit according to a recent study by DHL Research. DHL and Accenture are finalizing a blockchain-based track-and-trace serialization prototype comprising a global network of nodes across six geographies. The system comprehensively documents each step that a pharmaceutical product takes on its way to the store shelf and eventually the consumer. The following graphic illustrates the workflow.

  • Improving distribution and logistics, tracking asset maintenance, improving product quality, preventing counterfeit products and enabling digital marketplaces are the use cases Capgemini predicts blockchain will have the greatest impact. IoT’s potential contribution in each of these five use case areas continues to accelerate as real-time monitoring dominates manufacturing. Tracking provenace, contracts management, digital threads, and trade financing also show potential for high adoption. The following graphic illustrates blockchain use cases in the supply chain.

  • Combining blockchain and IoT is enabling manufacturers to pursue and excel at digital twin initiatives across their value chains. A digital twin is a dynamic, digital representation of a physical asset which enables companies to track its past, current and future performance throughout the asset’s lifecycle. The asset, for example, a vehicle or spare part, sends performance data and events directly to its digital twin, even as it moves from the hands of the manufacturer to the dealer and ultimately the new owner. Blockchain can be used to securely document everything related to the asset and IoT provides the real-time monitoring and updates. Microsoft and VISEO are partnering to use blockchain to connect each new vehicle’s maintenance events to the vehicle’s digital twin. The graphic below illustrates how digital twins streamline additive manufacturing.

  • 54% of suppliers and 51% of customers are expecting the organizations they do business with to take a leadership position on blockchain and IoT. The majority of suppliers and customers expect the manufacturers, suppliers, and vendors they do business with to take a leadership position on these two emerging technologies and define a vision with them in it. Deloitte’s excellent study, Breaking Blockchain Open, Deloitte’s 2018 Global Blockchain Survey, provides insights into how supplier and customer expectations are a factor in driving blockchain and IoT adoption, further helping to shape the future of supply chains.

  • Consumer products and manufacturing lead adoption of blockchain today, followed by life sciences according to the latest Deloitte estimates. IoT adoption is flourishing in manufacturing, transportation & logistics and utilities. By 2020, each of these industries is projected to spend $40B each on IoT platforms, systems, and services. The following graphic compares blockchain adoption levels by industry. Given how dependent manufacturers are on supply chains, the high adoption rates for blockchain and IoT make sense. Please click on the graphic to expand for easier reading.

  • 32% of enterprises are adopting blockchain to gain greater speed compared to existing systems, and 28% believe blockchain will open up new business models and revenue sources. The majority of manufacturers, transportation & logistics and utilities companies have real-time monitoring running on their shop floors and across their production facilities today. Many are transitioning from Wi-Fi enabled monitoring to IoT, which creates a real-time data stream that blockchain ledgers categorize and track to provide greater track-and-trace speed and accuracy. A recent Capgemini survey found that 76% of manufacturers also plan to have a product-as-a-service strategy to drive revenue in less than two years.

  • Blockchain has the potential to deliver between $80B and $110B in value across seven strategic financial sectors when supported by IoT, redefining their supply chains in the process. McKinsey completed an extensive analysis of over 60 viable use case for blockchain in financial services where IoT would provide greater visibility across transactions. The combination of technologies has the potential to deliver over $100B in value.

  • Reducing product waste and perishable foods’ product margins while increasing traceability is attainable by combining blockchain and IoT. IBM’s Food Trust uses blockchain technology to create greater accountability, traceability, and visibility in supply chains. It’s the only consortium of its kind that connects growers, processors, distributors, and retailers through a permissioned, permanent and shared record of food system data. Partners include Carrefour, Dole, Driscoll’s, Golden State Foods, McCormick and Co., McLane Co., Nestlé, ShopRite parent Wakefern Food Corp.,  grocery group purchasing organization Topco Associates  The Kroger Co., Tyson Foods, Unilever and Walmart. An example of the Food Trust’s traceability application is shown below:

Additional Research:

Abdel-Basset, M., Manogaran, G., & Mohamed, M. (2018). Internet of Things (IoT) and its impact on supply chain: A framework for building smart, secure and efficient systems. Future Generation Computer Systems86, 614–628.

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

Capgemini Research Institute, Does blockchain hold the key to a new age of supply chain transparency and trust?, 2018 (PDF, 32 pp., no opt-in)

DHL Trend Research, Blockchain In Research,  Perspectives on the upcoming impact of blockchain technology and use cases for the logistics industry (PDF, 28 pp., no opt-in)

Deloitte, Breaking Blockchain Open, Deloitte’s 2018 Global Blockchain Survey,48 pp., PDF, no opt-in. Summary available here.

Deloitte, Continuous Interconnected Supply Chain, Using Blockchain & Internet-of-Things in supply chain traceability (PDF, 24 pp., no opt-in)

Deloitte University Press,  3D opportunity for blockchain Additive manufacturing links the digital thread, 2018 (PDF, 20 pp, no opt-in)

EBN, How IoT, AI, & Blockchain Empower Tomorrow’s Autonomous Supply Chain, June 18, 2018

Forbes, How Blockchain Can Improve Manufacturing In 2019, October 28, 2018.

Forbes, 10 Charts That Will Challenge Your Perspective Of IoT’s Growth, June 6, 2018

Gettens, D., Jauffred, F., & Steeneck, D. W. (2016). IoT Can Drive Big Savings in the Post-Sales Supply Chain. MIT Sloan Management Review, 60(2), 19–21. Accessible on the MIT Sloan Management Review site here.

Jagtap, S., & Rahimifard, S. (2019). Unlocking the potential of the internet of things to improve resource efficiency in food supply chains. Springer International Publishing© Springer Nature Switzerland AG.

McKinsey & Company, Blockchain beyond the hype: What is the strategic business value?, June, 2018

McKinsey & Company, Blockchain Technology in the Insurance Sector, Quarterly meeting of the Federal Advisory Committee on Insurance (FACI) Jan 5, 2017

McKinsey & Company, The IoT as a growth driver, By Markus Berger-De Leon, Thomas Reinbacher, and Dominik Wee. March 2018

McKinsey & Company, How digital manufacturing can escape ‘pilot purgatory’,  by Andreas Behrendt, Richard Kelly, Raphael Rettig, and Sebastian Stoffregen. July 2018

Miller, D. (2018). Blockchain and the Internet of Things in the Industrial Sector. IT Professional20(3), 15-18.

PwC, Global Blockchain Survey, 2018.

Queiroz, M. M., & Wamba, S. F. (2019). Blockchain adoption challenges in supply chain: An empirical investigation of the main drivers in India and the USA. International Journal of Information Management46, 70-82.

Reyna, A., Martín, C., Chen, J., Soler, E., & Díaz, M. (2018). On blockchain and its integration with IoT. Challenges and opportunities. Future Generation Computer Systems88, 173–190

Smith, K. J., & Dhillon, G. (2019). Supply Chain Virtualization: Facilitating Agent Trust Utilizing Blockchain Technology. In Revisiting Supply Chain Risk (pp. 299-311). Springer, Cham.

Tu, M., Lim, M. K., & Yang, M.-F. (2018). IoT-based production logistics and supply chain system – Part 1. Industrial Management & Data Systems118(1), 65–95.

Tu, M., K. Lim, M., & Yang, M.-F. (2018). IoT-based production logistics and supply chain system – Part 2. Industrial Management & Data Systems118(1), 96–125.

Wall Street Journal, 5 Supply Chain Use Cases for IoT, Blockchain, November 8, 2018

How Machine Learning Improves Manufacturing Inspections, Product Quality & Supply Chain Visibility

Bottom Line: Manufacturers’ most valuable data is generated on shop floors daily, bringing with it the challenge of analyzing it to find prescriptive insights fast – and an ideal problem for machine learning to solve.

Manufacturing is the most data-prolific industry there is, generating on average 1.9 petabytes of data every year according to the McKinsey Global Insititute. Supply chains, sourcing, factory operations, and the phases of compliance and quality management generate the majority of data.

The most valuable data of all comes from product inspections that can immediately find exceptionally strong or weak suppliers, quality management and compliance practices in a factory. Manufacturing’s massive problem is in getting quality inspection results out fast enough across brands & retailers, other factories, suppliers and vendors to make a difference in future product quality.

How A Machine Learning Startup Is Revolutionizing Product Inspections

Imagine you’re a major brand or retailer and you’re relying on a network of factories across Bangladesh, China, India, and Southeast Asia to produce your new non-food consumer goods product lines including apparel. Factories, inspection agencies, suppliers and vendors that brands and retailers like you rely on vary widely on ethics, responsible sourcing, product quality, and transparency. With your entire consumer goods product lines (and future sales) at risk based on which suppliers, factories and product inspection agencies you choose, you and your companies’ future are riding on the decisions you make.

These career- and company-betting challenges and the frustration of gaining greater visibility into what’s going on in supply chains to factory floors led Carlos Moncayo Castillo and his brothers Fernando Moncayo Castillo and Luis Moncayo Castillo to launch Inspectorio. They were invited to the Target + Techstars Retail Accelerator in the summer of 2017, a competition they participated in with their cloud-based inspection platform that includes AI and machine learning and pervasive support for mobile technologies. Target relies on them today to bring greater transparency to their supply chains. “I’ve spent years working in non-food consumer goods product manufacturing seeing the many disconnects between inspections and suppliers, the lack of collaboration and how gaps in information create too many opportunities for corruption – I had to do something to solve these problems,” Carlos said. The many problems that a lack of inspection and supply chain visibility creates became the pain Inspectorio focused on solving immediately for brands and retailers. The following is a graphic of their platform:

Presented below are a few of the many ways the combining of a scalable inspection cloud platform combined with AI, machine learning and mobile technologies are improving inspections, product quality, and supply chain visibility:

  • Enabling the creation of customized inspector workflows that learn over time and are tailored to specific products including furniture, toys, homeware and garments, the factories they’re produced in, quality of the materials used. Inspectorio’s internal research has found 74% of all inspections today are done manually using a pen and paper, with results reported in Microsoft Word, Excel or PDFs, making collaboration slow and challenging. Improving the accuracy, speed and scale of inspection workflows including real-time updates across production networks drive major gains in quality and supply chain performance.
  • Applying constraint-based algorithms and logic to understand why there are large differences in inspection results between factories is enabling brands & retailers to manage quality faster and more completely. Uploading inspections in real-time from mobile devices to an inspection platform that contains AI and machine learning applications that quickly parse the data for prescriptive insights is the future of manufacturing quality. Variations in all dimensions of quality including factory competency, supplier and production assembly quality are taken into account. In a matter of hours, inspection-based data delivers the insights needed to avert major quality problems to every member of a production network.
  • Reducing risk, the potential for fraud, while improving the product and process quality based on insights gained from machine learning is forcing inspection’s inflection point. 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. One of the most powerful catalysts driving inspections’ inflection point is the combination of automated workflows that deliver high-quality data that machine learning produces prescriptive insights from. And those insights are shared on performance dashboards across every brand, retailer, supplier, vendor and factory involved in shared production strategies today.
  • Matching the most experienced inspector for a given factory and product inspection drastically increases accuracy and quality. When machine learning is applied to the inspector selection and assignment process, the quality, and thoroughness of inspections increase. For the first time, brands, retailers, and factories have a clear, quantified view of Inspector Productivity Analysis across the entire team of inspectors available in a given region or country. Inspections are uploaded in real-time to the Inspectorio platform where advanced analytics and additional machine learning algorithms are applied to the data, providing greater prescriptive insights that would have ever been possible using legacy manual methods. Machine learning is also making recommendations to inspectors on which defects to look for first based on the data patterns obtained from previous inspections.
  • Knowing why specific factories and products generated more Corrective Action/Preventative Action (CAPA) than others and how fast they have been closed in the past and why is now possible. Machine learning is making it possible for entire production networks to know why specific factory and product combinations generate the most CAPAs. Using constraint-based logic, machine learning can also provide prescriptive insights into what needs to be improved to reduce CAPAs, including their root cause.

Predicting The Future Of Next-Gen Access And Zero Trust Security In 2019

Bottom Line:  The most valuable catalyst all digital businesses need to continue growing in 2019 is a Zero Trust Security (ZTS) strategy based on Next-Gen Access (NGA) that scales to protect every access point to corporate data, recognizing that identities are the new security perimeter.

The faster any digital business is growing, the more identities, devices and network endpoints proliferate. The most successful businesses of 2019 and beyond are actively creating entirely new digital business models today. They’re actively recruiting, and onboarding needed experts independent of their geographic locations and exploring new sourcing and patent ideas with R&D partners globally. Businesses are digitally transforming themselves at a faster rate than ever before. Statista projects businesses will spend $190B on digital transformation in 2019, soaring to $490B by 2025, attaining a 14.4% Compound Annual Growth Rate (CAGR) in six years.

Security Perimeters Make Or Break A Growing Business

80% of IT security breaches involve privileged credential access according to a recent Forrester study. The Verizon Mobile Security Index 2018 Report found that 89% of organizations are relying on just a single security strategy to keep their mobile networks safe. A typical data breach cost the average company $3.86M in 2018, up 6.4% from $3.62M in 2017 according to IBM Security’s latest  2018 Cost of a Data Breach Study.

The hard reality for any digital business is realizing that their greatest growth asset is how well they protect the constantly expanding perimeter of their business. Legacy approaches to securing infrastructure that relies on trusted and untrusted domains can’t scale to protect every identity and device that comprises a company’s rapidly changing new security perimeter. All these factors and more are why Zero Trust Security (ZTS) enabled by Next-Gen Access (NGA) is as essential to digital businesses’ growth as their product roadmaps, pricing strategies, and services with Idaptive being an early leader in the market. To learn more about Identity-as-a-Service please see the Forrester report, The Forrester Wave™: Identity-As-A-Service, Q4 2017 (client access required)

Predicting The Future Of Next-Gen Access And Zero Trust Security

The following are predictions of how Next-Gen Access (NGA) powered by Zero Trust Security (ZTS) will evolve in 2019:

  • Behavior-based scoring algorithms will improve markedly in 2019, improving the user experience by calculating risk scores with greater precision than before. Thwarting attacks start with a series of behavior-based algorithms that calculate a risk score based on a wide variety of variables including past access attempts, device security posture, operating system, location, time of day, and many other measurable factors. Expect to see these algorithms and the risk scores they generate using machine learning techniques improve from accuracy and contextual intelligence standpoint in 2019. Leading companies in the field including Idaptive are actively investing in machine learning technologies to accomplish this today.
  • Multifactor Authentication (MFA) adoption soars as digital businesses seek to protect new R&D projects, patents in progress, roadmaps, and product plans. State-sponsored hacking organizations and organized crime see the intellectual property in fast-growing digital businesses as among the most valuable assets they can exfiltrate and sell on the Dark Web. MFA, one of the most effective single defenses against compromised passwords, will be adopted by the most successful businesses in AI, aerospace & defense, chip design for cellular and IoT devices, e-commerce, enterprise software and more.
  • Smart, connected products without adequate security designed in will proliferate in 2019, further challenging the security perimeters of the digital businesses. The era of smart, connected products is here, with Capgemini estimating the size of the connected products market will be $519B to $685B by 2020. Manufacturers expect close to 50% of their products to be smart, connected products by 2020, according to Capgemini’s Digital Engineering: The new growth engine for discrete manufacturers. The study is downloadable here (PDF, 40 pp., no opt-in). With every smart, connected device creating a new threat surface for a company, expect to see at least one device manufacturer design Zero Trust Security (ZTS) support to the board level to increase their sales into enterprises by reducing the threat of a breach starting from their device.
  • Looking for greater track and traceability, healthcare and medical products supply chains will adopt Zero Trust Security (ZTS). What’s going to make this an urgent issue in healthcare and medical products are the combined effects of greater regulatory reporting and compliance, combined with the pressure to improve time-to-market for new products and delivery accuracy for current customers. The pillars of ZTS are a perfect fit for healthcare and medical supply chains’ need for track and traceability. These pillars are real-time user verification, device validation, and intelligently limiting access, while also learning and adapting to verified user behaviors.
  • Real-time Security Analytics Services is going to thrive in 2019 as digital businesses seek insights into how they can fine-tune their ZTS strategies across every threat surface and machine learning algorithms improve. Many enterprises are in for an epiphany in 2019 when they see just how many potential breaches they’ve stopped using a combination of security strategies including Single Sign-On (SSO) and Multi-factor Authentication (MFA). Machine learning algorithms will continue to improve using behavior-based scoring, further improving the user experience. Leaders in the field include Idaptive who is setting a rapid pace of innovation in Real-Time Security Analytics Services.   

Conclusion

Security is at an inflection point today. Long-standing methods of protecting IT systems and a businesses’ assets can’t scale to protect every new identity, device or threat surface. When every identity is a new security perimeter, a new approach is needed to securing any digital business. The pillars of ZTS including real-time user verification, device validation, and intelligently limiting access, while also learning and adapting to verified user behaviors are proving to be effective at thwarting breaches and securing company’ digital assets of all kinds. It’s time for more digital businesses to see security as the growth catalyst it is and take action now to ensure their operations continue to flourish.

Microsoft Leads The AI Patent Race Going Into 2019

  • There have been over 154,000 AI patents filed worldwide since 2010 with the majority being in health fields (29.5%), Industry-specific solutions (25.3%) and AI-based digital security (15.7%).
  • AI-based marketing patents are the fasting growing global category, reaching a Compound Annual Growth Rate (CAGR) of 29.3% between 2010 and 2018.
  • The second- and third-fastest growing global AI patent categories between 2010 and 2018 are AI-based digital security (23.4% CAGR) and AI-based mobility (23% CAGR).
  • 79,936 patents were filed in the United States between 2010 and 2018, with the majority being in the health field (32.6%) followed by Industry-specific solutions (20.5%) and AI-based digital security (18%).
  • Machine learning dominates the AI patent landscape today, leading all categories of AI patents including deep learning and neural networks.

These and many other insights are from an excellent presentation recently given by Kai Gramke, Managing Director of EconSight titled Artificial Intelligence As A Key Technology and Driver of Technological Progress. EconSight clients include the Swiss Federal Council, German Federal Chancellery, leading European think tanks, research institutes and half of the German DAX-30 companies.  The presentation and information shared in this post were generated using the PatentSight analytics platform. PatentSight is a LexisNexis company and you can learn more about them here.  The following are the key takeaways from Kai’s recent research and presentation using PatentSight:

  • EconSight finds that Microsoft leads the AI patent race going into 2019 with 697 world class patents that the firm classifies as having a significant competitive impact as of November 2018. Out of the top 30 companies and research institutions as defined by EconSight in their recent analysis, Microsoft has created 20% of all patents in the global group of patent-producing companies and institutions. The following graphic provides a comparison of the top 3o in the group. Please click on the graphic to expand it for easier reading.

  • Machine learning dominates the AI patent landscape today, leading all categories of AI patents including deep learning and neural networks.  Machine learning is based on the foundational concepts of Bayesian analysis, data mining, and predictive analytics. Machine learning algorithms and the applications they rely on are designed to find patterns in large-scale data sets, while also being able to solve complex, constraint-based problems by learning from the data.  Enterprise software companies including Microsoft, SAP, and others are actively developing AI technologies that integrate into their existing platforms, streamlining adoption across their many customers. Please click on the graphic to expand for easier reading.

  • There have been 225,833 AI-based patents filed globally since 2000, with 30.7% being Industry specific (Industry 4.0 on the graphic below) followed by health-related patents (28.1%) 13.8% of all AI-based patents are for digital security and 11.9% for energy. It’s interesting to note that the fastest growing patents between 2000 and 2018 are for applying AI to marketing (22% CAGR) and AI-based digital security (18.8% CAGR). Please click on the graphic to expand for easier reading.

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