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Posts from the ‘Enterprise software’ Category

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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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.

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

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.

High-Tech’s Greatest Challenge Will Be Securing Supply Chains In 2019

Bottom Line: High-tech manufacturers need to urgently solve the paradox of improving supply chain security while attaining greater visibility across supplier networks if they’re going make the most of smart, connected products’ many growth opportunities in 2019.

The era of smart, connected products is revolutionizing every aspect of manufacturing today, from suppliers to distribution networks. Capgemini estimates that the size of the connected products market will be $519B to $685B by 2020. Manufacturers expect close to 50 percent 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).

Smart, connected products free manufacturers and their supply chains from having to rely on transactions and the price wars they create. The smarter the product, the greater the services revenue opportunities. And the more connected a smart product is using IoT and Wi-Fi sensors the more security has to be designed into every potential supplier evaluation, onboarding, quality plan, and ongoing suppliers’ audits. High-tech manufacturers are undertaking all of these strategies today, fueling them with real-time monitoring using barcoding, RFID and IoT sensors to improve visibility across their supply chains.

Gaining even greater visibility into their supply chains using cloud-based track-and-trace systems capable of reporting back the condition of components in transit to the lot and serialized pack level, high-tech suppliers are setting the gold standard for supply chain transparency and visibility. High-tech supply chains dominate many other industries’ supplier networks on accuracy, speed, and scale metrics on a consistent basis, yet the industry is behind on securing its vast supplier network. Every supplier identity and endpoint is a new security perimeter and taking a Zero Trust approach to securing them is the future of complex supply chains. With Zero Trust Privilege, high-tech manufacturers can secure privileged access to infrastructure, DevOps, cloud, containers, Big Data, production, logistics and shipping facilities, systems and teams.

High-Tech Needs to Confront Its Supply Chain Security Problem, Not Dismiss It

It’s ironic that high-tech supply chains are making rapid advances in accuracy and visibility yet still aren’t vetting suppliers thoroughly enough to stop counterfeiting, or worse. Bloomberg’s controversial recent article,The Big Hack: How China Used a Tiny Chip to Infiltrate U.S. Companies, explains how Amazon Web Services (AWS) was considering buying Portland, Oregon-based Elemental Technologies for its video streaming technology, known today as Amazon Prime Video. As part of the due diligence, AWS hired a third-party company to scrutinize Elemental’s security all the way up to the board level. The Elemental servers that handle the video compression were assembled by Super Micro Computer Inc., a San Jose-based company in China. Nested on the servers’ motherboards, the testers found a tiny microchip, not much bigger than a grain of rice, that wasn’t part of the boards’ original design that could create a stealth doorway into any network the machines were attached to. Apple (who is also an important Super Micro customer) and AWS deny this ever happened, yet 17 people have confirmed Supermicro had altered hardware, corroborating Bloomberg’s findings.

The hard reality is that the scenario Bloomberg writes about could happen to any high-tech manufacturer today. When it comes to security and 3rd party vendor risk management, many high-tech supply chains are stuck in the 90s while foreign governments, their militaries and the terrorist organizations they support are attempting to design in the ability to breach any network at will. How bad is it?  81% of senior executives involved in overseeing their companies’ global supply chains say 3rd party vendor management including recruiting suppliers is riskiest in China, India, Africa, Russia, and South America according to a recent survey by Baker & McKenzie.

PriceWaterhouseCoopers (PwC) and the MIT Forum for Supply Chain Innovation collaborated on a study of 209 companies’ supply chain operations and approaches to 3rd party vendor risk management. The study, PwC and the MIT Forum for Supply Chain Innovation: Making the right risk decisions to strengthen operations performance, quantifies the quick-changing nature of supply chains. 94% say there are changes in the extended supply chain network configuration happening frequently. Relying on trusted and untrusted domain controllers from server operating systems that are decades old can’t keep up with the mercurial pace of supply chains today.

Getting in Control of Security Risks in High-Tech Supply Chains

It’s time for high-tech supply chains to go with a least privilege-based approach to verifying who or what is requesting access to any confidential data across the supply chains. Further, high-tech manufacturers need to extend access request verification to include the context of the request and the risk of the access environment. Today it’s rare to find any high-tech manufacturer going to this level of least-privilege access approach, yet it’s the most viable approach to securing the most critical parts of their supply chains.

By taking a least-privilege access approach, high-tech manufacturers and their suppliers can minimize attack surfaces, improve audit and compliance visibility, and reduce risk, complexity, and operating costs across their hybrid manufacturing ecosystem.

Key actions that high-tech manufacturers can take to secure their supply chain and ensure they don’t end up in an investigative story of hacked supply chains include the following:

  • Taking a Zero Trust approach to securing every endpoint provides high-tech manufacturers with the scale they need to grow. High-tech supply chains are mercurial and fast-moving by nature, guaranteeing they will quickly scale faster than any legacy approaches enterprise security management. Vetting and then onboarding new suppliers needs to start by protecting every endpoint to the production and sourcing level, especially for next-generation smart, connected products.
  • Smart, connected products and the product-as-a-service business models they create are all based on real-time, rich, secured data streams that aren’t being eavesdropped on with components no one knows about. Taking a Zero Trust Privilege-based approach to securing access to diverse supply chains is needed if high-tech manufacturers are going to extend beyond legacy Privileged Access Management (PAM) to secure data being generated from real-time monitoring and data feeds from their smart, connected products today and in the future.
  • Quality management, compliance, and quality audits are all areas high-tech manufacturers excel in today and provide a great foundation to scale to Zero Trust Privilege. High-tech manufacturers have the most advanced quality management, inbound inspection and supplier quality audit techniques in the world. It’s time for the industry to step up on the security side too. By only granting least-privilege access based on verifying who is requesting access, the context of the request, and the risk of the access environment, high-tech manufacturers can make rapid strides to improve supply chain security.
  • Rethink the new product development cycles for smart, connected products and the sensors they rely on, so they’re protected as threat surfaces when built. Designing in security to the new product development process level and further advancing security scrutiny to the schematic and board design level is a must-do. In an era of where we have to assume bad actors are everywhere, every producer of high-tech products needs to realize their designs, product plans, and roadmaps are at risk. Ensuring the IOT and Wi-Fi sensors in smart, connected products aren’t designed to be hackable starts with a Zero Trust approach to defining security for supplier, design, and development networks.

Conclusion

The era of smart, connected products is here, and supply chains are already reverberating with the increased emphasis on components that are easily integrated and have high-speed connectivity. Manufacturing CEOs say it’s exactly what their companies need to grow beyond transaction revenue and the price wars they create. While high-tech manufacturers excel at accuracy, speed, and scale, they are falling short on security. It’s time for the industry to re-evaluate how Zero Trust can stabilize and secure every identity and threat surface across their supply chains with the same precision and intensity quality is today.

Which CRM Applications Matter Most In 2018

 

According to recent research by Gartner,

  • Marketing analytics continues to be hot for marketing leaders, who now see it as a key business requirement and a source of competitive differentiation
  • Artificial intelligence (AI) and predictive technologies are of high interest across all four CRM functional areas, and mobile remains in the top 10 in marketing, sales and customer service.
  • It’s in customer service where AI is receiving the highest investments in real use cases rather than proofs of concept (POCs) and experimentation.
  • Sales and customer service are the functional areas where machine learning and deep neural network (DNN) technology is advancing rapidly.

These and many other fascinating insights are from Gartner’s What’s Hot in CRM Applications in 2018 by Ed Thompson, Adam Sarner, Tad Travis, Guneet Bharaj, Sandy Shen and Olive Huang, published on August 14, 2018. Gartner clients can access the study here  (10 pp., PDF, client access reqd.).

Gartner continually tracks and analyzes the areas their clients have the most interest in and relies on that data to complete their yearly analysis of CRM’s hottest areas. Inquiry topics initiated by clients are an excellent leading indicator of relative interest and potential demand for specific technology solutions. Gartner organizes CRM technologies into the four category areas of Marketing, Sales, Customer Service, and Digital Commerce.

The following graphic from the report illustrates the top CRM applications priorities in Marketing, Sales, Customer Service, and Digital Commerce.

Key insights from the study include the following:

  • Marketing analytics continues to be hot for marketing leaders, who now see it as a key business requirement and a source of competitive differentiation. In my opinion and based on discussions with CMOs, interest in marketing analytics is soaring as they are all looking to quantify their team’s contribution to lead generation, pipeline growth, and revenue. I see analytics- and data-driven clarity as the new normal. I believe that knowing how to quantify marketing contributions and performance requires CMOs and their teams to stay on top of the latest marketing, mobile marketing, and predictive customer analytics apps and technologies constantly. The metrics marketers choose today define who they will be tomorrow and in the future.
  • Artificial intelligence (AI) and predictive technologies are of high interest across all four CRM functional areas, and mobile remains in the top 10 in marketing, sales and customer service. It’s been my experience that AI and machine learning are revolutionizing selling by guiding sales cycles, optimizing pricing and enabling CPQ to define and deliver smart, connected products. I’m also seeing CMOs and their teams gain value from Salesforce Einstein and comparable intelligent agents that exemplify the future of AI-enabled selling. CMOs are saying that Einstein can scale across every phase of customer relationships. Based on my previous consulting in CPQ and pricing, it’s good to see decades-old core technologies underlying Price Optimization and Management are getting a much-needed refresh with state-of-the-art AI and machine learning algorithms, which is one of the factors driving their popularity today. Using Salesforce Einstein and comparable AI-powered apps I see sales teams get real-time guidance on the most profitable products to sell, the optimal price to charge, and which deal terms have the highest probability of closing deals. And across manufacturers on a global scale sales teams are now taking a strategic view of Configure, Price, Quote (CPQ) as encompassing integration to ERP, CRM, PLM, CAD and price optimization systems. I’ve seen global manufacturers take a strategic view of integration and grow far faster than competitors. In my opinion, CPQ is one of the core technologies forward-thinking manufacturers are relying on to launch their next generation of smart, connected products.
  • It’s in customer service where AI is receiving the highest investments in real use cases rather than proofs of concept (POCs) and experimentation. It’s fascinating to visit with CMOs and see the pilots and full production implementations of AI being used to streamline customer service. One CMO remarked how effective AI is at providing greater contextual intelligence and suggested recommendations to customers based on their previous buying and services histories. It’s interesting to watch how CMOs are attempting to integrate AI and its associated technologies including ChatBots to their contribution to Net Promoter Scores (NPS). Every senior management team running a marketing organization today has strong opinions on NPS. They all agree that greater insights gained from predictive analytics and AI will help to clarify the true value of NPS as it relates to Customer Lifetime Value (CLV) and other key metrics of customer profitability.
  • Sales and customer service are the functional areas where machine learning and deep neural network (DNN) technology is advancing rapidly.  It’s my observation that machine learning’s potential to revolutionize sales is still nascent with many high-growth use cases completely unexplored. In speaking with the Vice President of Sales for a medical products manufacturer recently, she said her biggest challenge is hiring sales representatives who will have longer than a 19-month tenure with the company, which is their average today.  Imagine, she said, knowing the ideal attributes and strengths of their top performers and using machine learning and AI to find the best possible new sales hires. She and I discussed the spectrum of companies taking on this challenge, with Eightfold being one of the leaders in applying AI and machine learning to talent management challenges.

Source: Gartner by Ed Thompson, Adam Sarner, Tad Travis, Guneet Bharaj,  Sandy Shen and Olive Huang, published on August 14, 2018.

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