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Posts from the ‘Enterprise software’ Category

Your Mobile Phone Is Your Identity. How Do You Protect It?

 The average cost of a data breach has risen 12% over the past 5 years and is now $3.92M. U.S.-based breaches average $8.19M in losses, leading all nations. Not integrating mobile phone platforms and protecting them with a Zero Trust Security framework can add up to $240K to the cost of a breach. Companies that fully deploy security automation technologies experience around half the cost of a breach ($2.65M on average) compared to those that do not deploy these technologies ($5.16M on average). These and many other fascinating insights are from the 14th annual IBM Security Cost of a Data Breach Report, 2019. IBM is making a copy of the report available here for download (76 pp., PDF, opt-in). IBM and Ponemon Institute collaborated on the report, recruiting 507 organizations that have experienced a breach in the last year and interviewing more than 3,211 individuals who are knowledgeable about the data breach incident in their organizations. A total of 16 countries and 17 industries were included in the scope of the study. For additional details regarding the methodology, please see pages 71 - 75 of the report. Key insights from the report include the following: Lost business costs are 36.2% of the total cost of an average breach, making it the single largest loss component of all. Detection and escalation costs are second at 31.1%, as it can take up to 206 days to first identify a breach after it occurs and an additional 73 days to contain the breach. IBM found the average breach lasts 279 days. Breaches take a heavy toll on the time resources of any organization as well, eating up 76% of an entire year before being discovered and contained. U.S.-based breaches average $8.19M in losses, leading all nations with the highest country average. The cost of U.S.-based breaches far outdistance all other countries and regions of the world due to the value and volume of data exfiltrated from enterprise IT systems based in North America. North American enterprises are also often the most likely to rely on mobile devices to enable greater communication and collaboration, further exposing that threat surface. The Middle East has the second-highest average breach loss of $5.97M. In contrast, Indian and Brazilian organizations had the lowest total average cost at $1.83M and $1.35M, respectively. Data breach costs increase quickly in integration-intensive corporate IT environments, especially where there is a proliferation of disconnected mobile platforms. The study found the highest contributing costs associated with a data breach are caused by third parties, compliance failures, extensive cloud migration, system complexity, and extensive IoT, mobile and OT environments. This reinforces that organizations need to adopt a Zero Trust Security (ZTS) framework to secure the multiple endpoints, apps, networks, clouds, and operating systems across perimeter-less enterprises. Mobile devices are enterprises’ fasting growing threat surfaces, making them one of the highest priorities for implementing ZTS frameworks. Companies to watch in this area include MobileIron, which has created a mobile-centric, zero-trust enterprise security framework. The framework is built on the foundation of unified endpoint management (UEM) and additional zero trust-enabling technologies, including zero sign-on (ZSO), multi-factor authentication (MFA), and mobile threat detection (MTD). This approach to securing access and protect data across the perimeter-less enterprise is helping to alleviate the high cost of data breaches, as shown in the graphic below. Accidental, inadvertent breaches from human error and system glitches are still the root cause for nearly half (49%) of the data breaches. And phishing attacks on mobile devices that are lost, stolen or comprised in workplaces are a leading cause of breaches due to human error. While less expensive than malicious attacks, which cost an average of $4.45M, system glitches and human error still result in costly breaches, with an average loss of $3.24M and $3.5M respectively. To establish complete control over data, wherever it lives, organizations need to adopt Zero Trust Security (ZTS) frameworks that are determined by “never trust, always verify.”. For example, MobileIron’s mobile-centric zero-trust approach validates the device, establishes user context, checks app authorization, verifies the network, and detects and remediates threats before granting secure access to a device or user. This zero-trust security framework is designed to stop accidental, inadvertent and maliciously-driven, intentional breaches. The following graphic compares the total cost for three data breach root causes: Conclusion Lost business is the single largest cost component of any breach, and it takes years to fully recover from one. IBM found that 67% of the costs of a breach accrue in the first year, 22% accrue in the second year and 11% in the third. The more regulated a company’s business, the longer a breach will accrue costs and impact operations. Compounding this is the need for a more Zero Trust-based approach to securing every endpoint across an organization. Not integrating mobile phone platforms and protecting them with a Zero Trust Security (ZTS) framework can add up to $240K to the cost of a breach. Companies working to bridge the gap between the need for securing mobile devices with ZTS frameworks include MobileIron, which has created a mobile-centric, zero-trust enterprise security framework. There’s a significant amount of innovation happening with Identity Access Management that thwarts privileged account abuse, which is the leading cause of breaches today. Centrify’s most recent survey, Privileged Access Management in the Modern Threatscape, found that 74% of all breaches involved access to a privileged account. Privileged access credentials are hackers’ most popular technique for initiating a breach to exfiltrate valuable data from enterprise systems and sell it on the Dark Web.

  • The average cost of a data breach has risen 12% over the past 5 years and is now $3.92M.
  • U.S.-based breaches average $8.19M in losses, leading all nations.
  • Not integrating mobile phone platforms and protecting them with a Zero Trust Security framework can add up to $240K to the cost of a breach.
  • Companies that fully deploy security automation technologies experience around half the cost of a breach ($2.65M on average) compared to those that do not deploy these technologies ($5.16M on average).

These and many other fascinating insights are from the 14th annual IBM Security Cost of a Data Breach Report, 2019. IBM is making a copy of the report available here for download (76 pp., PDF, opt-in). IBM and Ponemon Institute collaborated on the report, recruiting 507 organizations that have experienced a breach in the last year and interviewing more than 3,211 individuals who are knowledgeable about the data breach incident in their organizations. A total of 16 countries and 17 industries were included in the scope of the study. For additional details regarding the methodology, please see pages 71 – 75 of the report.

Key insights from the report include the following:

  • Lost business costs are 36.2% of the total cost of an average breach, making it the single largest loss component of all. Detection and escalation costs are second at 31.1%, as it can take up to 206 days to first identify a breach after it occurs and an additional 73 days to contain the breach. IBM found the average breach lasts 279 days. Breaches take a heavy toll on the time resources of any organization as well, eating up 76% of an entire year before being discovered and contained.

  • U.S.-based breaches average $8.19M in losses, leading all nations with the highest country average. The cost of U.S.-based breaches far outdistance all other countries and regions of the world due to the value and volume of data exfiltrated from enterprise IT systems based in North America. North American enterprises are also often the most likely to rely on mobile devices to enable greater communication and collaboration, further exposing that threat surface. The Middle East has the second-highest average breach loss of $5.97M. In contrast, Indian and Brazilian organizations had the lowest total average cost at $1.83M and $1.35M, respectively.

  • Data breach costs increase quickly in integration-intensive corporate IT environments, especially where there is a proliferation of disconnected mobile platforms. The study found the highest contributing costs associated with a data breach are caused by third parties, compliance failures, extensive cloud migration, system complexity, and extensive IoT, mobile and OT environments. This reinforces that organizations need to adopt a Zero Trust Security (ZTS) framework to secure the multiple endpoints, apps, networks, clouds, and operating systems across perimeter-less enterprises. Mobile devices are enterprises’ fasting growing threat surfaces, making them one of the highest priorities for implementing ZTS frameworks. Companies to watch in this area include MobileIron, which has created a mobile-centric, zero-trust enterprise security framework. The framework is built on the foundation of unified endpoint management (UEM) and additional zero trust-enabling technologies, including zero sign-on (ZSO), multi-factor authentication (MFA), and mobile threat detection (MTD). This approach to securing access and protect data across the perimeter-less enterprise is helping to alleviate the high cost of data breaches, as shown in the graphic below.

  • Accidental, inadvertent breaches from human error and system glitches are still the root cause for nearly half (49%) of the data breaches. And phishing attacks on mobile devices that are lost, stolen or comprised in workplaces are a leading cause of breaches due to human error. While less expensive than malicious attacks, which cost an average of $4.45M, system glitches and the human error still result in costly breaches, with an average loss of $3.24M and $3.5M respectively. To establish complete control over data, wherever it lives, organizations need to adopt Zero Trust Security (ZTS) frameworks that are determined by “never trust, always verify.”. For example, MobileIron’s mobile-centric zero-trust approach validates the device, establishes user context, checks app authorization, verifies the network, and detects and remediates threats before granting secure access to a device or user. This zero-trust security framework is designed to stop accidental, inadvertent and maliciously-driven, intentional breaches. The following graphic compares the total cost for three data breach root causes:

Conclusion

Lost business is the single largest cost component of any breach, and it takes years to fully recover from one. IBM found that 67% of the costs of a breach accrue in the first year, 22% accrue in the second year and 11% in the third.  The more regulated a company’s business, the longer a breach will accrue costs and impact operations. Compounding this is the need for a more Zero Trust-based approach to securing every endpoint across an organization.

Not integrating mobile phone platforms and protecting them with a Zero Trust Security (ZTS) framework can add up to $240K to the cost of a breach. Companies working to bridge the gap between the need for securing mobile devices with ZTS frameworks include MobileIron, which has created a mobile-centric, zero-trust enterprise security framework. There’s a significant amount of innovation happening with Identity Access Management that thwarts privileged account abuse, which is the leading cause of breaches today. Centrify’s most recent survey, Privileged Access Management in the Modern Threatscape, found that 74% of all breaches involved access to a privileged account. Privileged access credentials are hackers’ most popular technique for initiating a breach to exfiltrate valuable data from enterprise systems and sell it on the Dark Web.

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AI Is Predicting The Future Of Online Fraud Detection

Bottom Line: Combining supervised and unsupervised machine learning as part of a broader Artificial Intelligence (AI) fraud detection strategy enables digital businesses to quickly and accurately detect automated and increasingly complex fraud attempts.

Recent research from the Association of Certified Fraud Examiners (ACFE)KPMGPwC, and others reflects how organized crime and state-sponsored fraudsters are increasing the sophistication, scale, and speed of their fraud attacks. One of the most common types of emerging attacks is based on using machine learning and other automation techniques to commit fraud that legacy approaches to fraud prevention can’t catch. The most common legacy approaches to fighting online fraud include relying on rules and predictive models that are no longer effective at confronting more advanced, nuanced levels of current fraud attempts. Online fraud detection needs AI to stay at parity with the quickly escalating complexity and sophistication of today’s fraud attempts.

Why AI is Ideal for Online Fraud Detection

It’s been my experience that digitally-based businesses that have the best track record of thwarting online fraud rely on AI and machine learning to do the following:

  • Actively use supervised machine learning to train models so they can spot fraud attempts quicker than manually-based approaches. Digitally-based businesses I’ve talked with say having supervised machine learning categorize and then predict fraudulent attempts is invaluable from a time-saving standpoint alone. Adopting supervised machine learning first is easier for many businesses as they have analytics teams on staff who are familiar with the foundational concepts and techniques. Digital businesses with high-risk exposure given their business models are adopting AI-based online fraud detection platforms to equip their fraud analysts with the insights they need to identify and stop threats early.
  • Combine supervised and unsupervised machine learning into a single fraud prevention payment score to excel at finding anomalies in emerging data. Integrating the results of fraud analysis based on supervised and unsupervised machine learning into one risk score is one way AI enables online fraud prevention to scale today. Leaders in this area of online fraud prevention can deliver payment scores in 250 milliseconds, using AI to interpret the data and provide a response. A more integrated approach to online fraud prevention that combines supervised and unsupervised machine learning can deliver scores that are twice as predictive as previous approaches.
  • Capitalizes on large-scale, universal data networks of transactions to fine-tune and scale supervised machine learning algorithms, improving fraud prevention scores in the process. The most advanced digital businesses are looking for ways to fine-tune their machine learning models using large-scale universal data sets. Many businesses have years of transaction data they rely on initially for this purpose. Online fraud prevention platforms also have large-scale universal data networks that often include billions of transactions captured over decades, from thousands of customers globally.

The integration of these three factors forms the foundation of online fraud detection and defines its future growth trajectory. One of the most rapid areas of innovation in these three areas is the fine-tuning of fraud prevention scores. Kount’s unique approach to creating and scaling its Omniscore indicates how AI is immediately redefining the future of online fraud detection.

Kount is distinct from other online fraud detection platforms due to the company’s ability to factor in all available historical data in their universal data network that includes billions of transactions accumulated over 12 years, 6,500 customers, across over 180 countries and territories, and multiple payment networks.

Insights into Why AI is the Future of Online Fraud Detection

Recent research studies provide insights into why AI is the future of online fraud detection. According to the Association of Certified Fraud Examiners (ACFE) inaugural Anti-Fraud Technology Benchmarking Report, the amount organizations are expected to spend on AI and machine learning to thwart online fraud is expected to triple by 2021. The ACFE study also found that only 13% of organizations currently use AI and machine learning to detect and deter fraud today. The report predicts another 25% plan to adopt these technologies in the next year or two – an increase of nearly 200%. The ACFE study found that AI and machine learning technology will most likely be adopted in the next two years to fight fraud, followed by predictive analytics and modeling.

PwC’s 2018 Global Economic Crime and Fraud Survey is based on interviews with 7,200 C-level and senior management respondents across 123 different nations and territories and was conducted to determine the true state of digital fraud prevention across the world. The study found that 42% of companies said they had increased funds used to combat fraud or economic crime. In addition, 34% of the C-level and senior management executives also said that existing approaches to combatting online fraud was generating too many false positives. The solution is to rely more on machine learning and AI in combination with predictive analytics as the graphic below illustrates. Kount’s unique approach to combining these technologies to define their Omniscore reflects the future of online fraud detection.

AI is a necessary foundation of online fraud detection, and for platforms built on these technologies to succeed, they must do three things extremely well. First, supervised machine learning algorithms need to be fine-tuned with decades worth of transaction data to minimize false positives and provide extremely fast responses to inquiries. Second, unsupervised machine learning is needed to find emerging anomalies that may signal entirely new, more sophisticated forms of online fraud. Finally, for an online fraud platform to scale, it needs to have a large-scale, universal data network of transactions to fine-tune and scale supervised machine learning algorithms that improve the accuracy of fraud prevention scores in the process.

AWS Certifications Increase Tech Pay Up To $12K A Year

AWS Certifications Increase Tech Pay Up To $12K A Year

  • AWS and Google certifications are among the most lucrative in North America, paying average salaries of $129,868 and $147,357 respectively.
  • Cross-certifying on AWS is providing a $12K salary bump to IT professionals who already have Citrix and Red Hat/Linux certifications today
  • Globally, four of the five top-paying certifications are in cloud computing.

These and many other insights of which certifications provide the highest salaries by region of the world are from the recently published Global Knowledge 2019 IT Skills and Salary ReportThe report is downloadable here (27 pp., PDF, free, opt-in). The methodology is based on 12,271 interviews across non-management IT staffs (29% of interviews), mid-level professionals including managers and team leads (43%), and senior-level and executive roles (28%) across four global regions. For additional details regarding the study’s methodology, please see page 24 of the report.

Key insights from the report include the following:

  • Cross-certifying on AWS is providing a $12K salary bump to IT professionals who already have Citrix and Red Hat/Linux certifications. Citrix certifications pay an average salary of $109,546 and those earning an AWS certification see a $12,339 salary bump on average. Red Hat/Linux certification-based jobs pay an average of $113,165 and are seeing an average salary bump of $12,553.  Cisco-certified IT professionals who gain AWS certification increase their salaries on average from $101,533 to $111,869, gaining a 10.2% increase. The following chart compares the salary bump AWS certifications are providing to IT professionals with seven of the more popular certifications (please click on the graphic to expand for easier reading).

  • AWS and Google certifications are among the most lucrative in North America, paying average salaries of $129,868 and $147,357 while the most popular are cybersecurity, governance, compliance, and policy. 27% of all respondents to Global Knowledge’s survey have at least one certification in this category. Nearly 18% are ITIL certified. In North American, the most popular certification categories beyond cybersecurity are CompTIA, Microsoft, and Cisco. The following table from the report provides an overview of salary by certification category (please click on the graphic to expand for easier reading).

  • AWS Certified Solutions Architect – Associate is the most popular AWS certification today, with 72% of respondents having achieved its requirements. Certified Solutions Architect – Associate leads the top five most commonly held AWS certifications today according to the survey. AWS Certified Developer – Associate (33%), AWS Certified SysOps Administrator – Associate (24%), AWS Certified Solutions Architect – Professional (16%) and AWS Certified Cloud Practitioner round out the top five most common AWS certifications across the 12,271 global respondents to the Global Knowledge survey.

Roadmap To Zero Trust For Small Businesses

Bottom Line:  Small businesses don’t need to sacrifice security due to budget constraints or productivity requirements – a Zero Trust roadmap can help them keep growing and stop breaches.

Having worked my way through college in a series of small businesses and having neighbors and friends who operate several today, I see how cloud, databases, and network devices save thousands of dollars, hours of tedious work, and streamline operations. Good friends running an AI startup, whose remarkable ability to turn whiteboard discussions into prototypes in a day, are a case in point. Keeping breach attempts from interrupting their growth needs to start with a roadmap to Zero Trust so these businesses can keep flourishing.

Defining A Zero Trust Roadmap

Most successful small businesses and my friends’ growing startup share the common trait of moving at a quick pace. They’re hiring new employees, contractors and adding new locations in days, not months. The startups and small businesses I work with are adding experts in AI, development, machine learning, sales, and marketing from around the world quickly. Each new employee, contractor, and occasional supplier receives their account login to cloud systems used for running the business, and then they’re given their first assignments.

Small Businesses Don’t Need To Sacrifice Speed For Security

Small businesses and startups run so fast there’s often a perception that achieving greater security will slow them down. In a Zero Trust world, they don’t need to spend a lot of sacrifice speed for security. Following a Zero Trust roadmap can protect their systems, valuable intellectual property, and valuable time by minimizing the risk of falling victim to costly breaches.

Here’s what small businesses and startups need to include on their Zero Trust roadmaps to reduce the potential for time-consuming, costly breaches that could steal not just data but market momentum too:

  • Put Multi-Factor Authentication (MFA) into place for every contractor, admin user, and partner account immediately. Implementing MFA is highly recommended as it can reduce the risk of privileged access credential abuse. A recent survey by Centrify found that 74% of all breaches involved privileged access abuse. Centrify also found that 58% of organizations do not use Multi-Factor Authentication (MFA) for privileged administrative access to servers, leaving their IT systems and infrastructure exposed to hacking attempts, including unchallenged privileged access abuse.
  • Get a shared account and password vault to reduce the risk of being breached by privileged access abuse. Password vaults are a must-have for any business that relies on intellectual property (IP), patents, source code under development, and proprietary data that is pivotal to the company’s growth. Vaults make sure only trusted applications can request privileged account credentials by first identifying, then validating system accounts before passwords are retrieved. Another major advantage of vaults is that they minimize attack surfaces for small businesses and startups.
  • Secure Remote Access needs to be in place to ensure employee, contractor, and IT systems contractors are given least privilege access to only the resources they need. Small businesses and startups growing fast often don’t have the expertise on staff to manage their IT systems. It’s cheaper for many to have an IT service manage server maintenance, upgrades, and security. Secure Remote Access is predicated on the “never trust, always verify, enforce least privilege” Zero Trust approach to grant access to specific resources.
  • Implement real-time audit and monitoring to track all privileged sessions and metadata auditing everything across all systems to deliver a comprehensive picture of intentions and outcomes. Creating and adding to an ongoing chronology of login and resource attempts is invaluable for discovering how a security incident first gets started, and for meeting compliance requirements. It’s much easier to identify and thwart privileged credential abuse based on the insights gained from the single system of record a real-time audit and monitoring service creates. As small businesses and startups grow, the data that real-time audits and monitoring generate are invaluable in proving privileged access is controlled and audited to meet the regulatory compliance requirements of SOX, HIPAA, FISMA, NIST, PCI, MAS, and other regulatory standards.
  • Privileged access credentials to network devices need to be part of the Zero Trust Roadmap. Small businesses and startups face a continual time shortage and sometimes forget to change the manufacturer default passwords which are often weak and well known in the hacker community. That’s why it needs to be a priority to include the network device portfolio in A Zero Trust Privilege-based security roadmap and strategy. Security admins need to have these included in the shared account and passwords vault.

Conclusion

The five factors mentioned here are the start of building a scalable, secure Zero Trust roadmap that will help alleviate the leading cause of breaches today, which is privileged access credential abuse. For small businesses who are outsourcing IT and security administration, the core elements of the Zero Trust roadmap provide them the secure login and a “never trust, always verify, enforce least privilege” strategy that can scale with their business. With Zero Trust Privilege, small businesses and startups will be able to grant least privilege access based on verifying who is requesting access, the context of the request, and the risk of the access environment

10 Charts That Will Change Your Perspective Of AI In Marketing

  • Top-performing companies are more than twice as likely to be using AI for marketing (28% vs. 12%) according to Adobe’s latest Digital Intelligence Briefing.
  • Retailers are investing $5.9B this year in AI-based marketing and customer service solutions to improve shoppers’ buying experiences according to IDC.
  • Financial Services marketers lead all other industries in AI application adoption, with 37% currently using them today.
  • Sales and Marketing teams most often collaborate using Configure-Price-Quote (CPQ) and Marketing Automation AI-based applications, with sales leaders predicting AI adoption will increase 155% across sales teams in two years.

Artificial Intelligence enables marketers to understand sales cycles better, correlating their strategies and spending to sales results. AI-driven insights are also helping to break down data silos so marketing and sales can collaborate more on deals. Marketing is more analytics and quant-driven than ever before with the best CMOs knowing which metrics and KPIs to track and why they fluctuate.

The bottom line is that machine learning and AI are the technologies CMOs and their teams need to excel today. The best CMOs balance the quant-intensive nature of running marketing with qualitative factors that make a company’s brand and customer experience unique. With greater insight into how prospects make decisions when, where, and how to buy, CMOs are bringing a new level of intensity into driving outcomes. An example of this can be seen from the recent Forbes Insights and Quantcast research, Lessons of 21st-Century Brands Modern Brands & AI Report (17 pp., PDF, free, opt-in). The study found that AI enables marketers to increase sales (52%), increase in customer retention (51%), and succeed at new product launches (49%). AI is making solid contributions to improving lead quality, persona development, segmentation, pricing, and service.

The following ten charts provide insights into how AI is transforming marketing:

  • 21% of sales leaders rely on AI-based applications today, with the majority collaborating with marketing teams sharing these applications. Sales leaders predict that their use of AI will increase 155% in the next two years. Sales leaders predict AI will reach critical mass by 2020 when 54% expect to be using these technologies. Marketing and sales are relying on AI-based marketing automation, configure-price-quote (CPQ), and intelligent selling systems to increase revenue and profit growth significantly in the next two years. Source: Salesforce Research, State of Sales, 3rd edition. (58 pp., PDF, free, opt-in).

  • AI sees the most significant adoption by marketers working in $500M to $1B companies, with conversational AI for customer service is the most dominant. Businesses with between $500M to $1B lead all other revenue categories in the number and depth of AI adoption use cases. Just over 52% of small businesses with sales of $25M or less are using AI for predictive analytics for customer insights. It’s interesting to note that small companies are the leaders in AI spending, at 38.1%, to improve marketing ROI by optimizing marketing content and timing. Source: The CMO Survey: Highlights and Insights Report, February 2019. Duke University, Deloitte and American Marketing Association. (71 pp., PDF, free, no opt-in).

  • 22% of marketers currently are using AI-based applications with an additional 57% planning to use in the next two years. There are nine dominant use cases marketers are concentrating on today, ranging from personalized channel experiences to programmatic advertising and media buying to predictive customer journeys and real-time next best offers. Source: Salesforce’s State of Marketing Study, 5th edition

  • Content personalization and predictive analytics from customer insights are the two areas CMOs most prioritize AI spending today. The CMO study found that B2B service companies are the top user of AI for content personalization (62.2%) and B2B product companies use AI for augmented and virtual reality, facial recognition and visual search more than any other business types. Source: CMOs’ Top Uses For AI: Personalization and Predictive Analytics. Marketing Charts. March 14, 2019

  • Personalizing the overall customer journey and driving next-best offers in real-time are the two most common ways marketing leaders are using AI today, according to Salesforce. Improving customer segmentation, improving advertising and media buying, and personalizing channel experiences are the next fastest-growing areas of AI adoption in marketing today. Source: Salesforce’s State of Marketing Study, 5th edition

  • 81% of marketers are either planning to or are using AI in audience targeting this year. 80% are currently using or planning to use AI for audience segmentation. EConsultancy’s study found marketers are enthusiastic about AI’s potential to increase marketing effectiveness and track progress. 88% of marketers interviewed say AI will enable them t be more effective in getting to their goals. Source: Dream vs. Reality: The State of Consumer First and Omnichannel Marketing. EConsultancy (36 pp., PDF, free, no opt-in).

  • Over 41% of marketers say AI is enabling them to generate higher revenues from e-mail marketing. They also see an over 13% improvement in click-thru rates and 7.64% improvement in open rates. Source: 4 Positive Effects of AI Use in Email Marketing, Statista (infographic), March 1, 2019.

Additional data sources on AI’s use in Marketing:

15 examples of artificial intelligence in marketing, eConsultancy, February 28, 2019

4 Positive Effects of AI Use in Email Marketing, Statista, March 1, 2019

4 Ways Artificial Intelligence Can Improve Your Marketing (Plus 10 Provider Suggestions), Forbes, Kate Harrison, January 20, 2019

AI: The Next Generation Of Marketing Driving Competitive Advantage Throughout The Customer Life Cycle, Forrester Consulting. February 2017 (10 pp., PDF, free, no opt-in).

Artificial Intelligence for Marketing (complete book) (361 pp., PDF, free, no opt-in)

Artificial Intelligence Roundup, eMarketer, May 2018 (15 pp., PDF, free, no opt-in)

Digital Intelligence Briefing, Adobe, 2018 (43 pp., PDF, free, no opt-in).

How 28 Brands Are Using AI to Enhance Their Marketing [Infographic], Impact Blog

How AI Is Changing Sales, Harvard Business Review, July 30, 2018

How Top Marketers Use Artificial Intelligence On-Demand Webinar with Vala Afshar, Chief Digital Evangelist, Salesforce and Meghann York, Director, Product Marketing, Salesforce

How To Win Tomorrow’s Car Buyers – Artificial Intelligence in Marketing & Sales, McKinsey Center for Future Mobility, McKinsey & Company. February 2019. (44 pp., PDF, free, no opt-in)

IDC MarketScape: Worldwide Artificial Intelligence in Enterprise Marketing Clouds 2017 Vendor Assessment, (11 pp., PDF, free, no opt-in.)

In-depth: Artificial Intelligence 2019, Statista Digital Market Outlook, February 2019 (client access reqd).

Leading reasons to use artificial intelligence (AI) for marketing personalization according to industry professionals worldwide in 2018, Statista.

Lessons of 21st-Century Brands Modern Brands & AI Report, Forbes Insights and Quantcast Study (17 pp., PDF, free, opt-in),

Powerful pricing: The next frontier in apparel and fashion advanced analytics, McKinsey & Company, December 2018

Share of marketing and agency professionals who are comfortable with AI-enabled technology automated handling of their campaigns in the United States as of June 2018, Statista.  

The CMO Survey: Highlights and Insights Report, February 2019. Duke University, Deloitte and American Marketing Association. (71 pp., PDF, free, no opt-in).

Visualizing the uses and potential impact of AI and other analytics, McKinsey Global Institute, April 2018.  Interactive page based on Tableau data set can be found here.

What really matters in B2B dynamic pricing, McKinsey & Company, October 2018

Winning tomorrow’s car buyers using artificial intelligence in marketing and sales, McKinsey & Company, February 2019

Worldwide Spending on Artificial Intelligence Systems Will Grow to Nearly $35.8 Billion in 2019, According to New IDC Spending Guide, IDC; March 11, 2019

Customer Experiences Define Success In A Digital-First World

Customer Experiences Define Success In A Digital-First World

  • 91% of enterprises have adopted or have plans to adopt a digital-first strategy. Of these enterprises, 48% already have a digital-first approach in place.
  • Creating better customer experiences (67%), improving process efficiency through automation (53%), and driving new revenue (48%) are the top three digital business strategies enterprises are investing in today.
  • 35% of enterprises have experienced revenue growth due to digital business initiatives over the past 12 months.
  • 5G, Artificial Intelligence, and Machine Learning are the top technologies being researched by enterprises who are defining digital business strategies.
  • Enterprises are planning to spend $15.3M on digital initiatives over the next 12 months. 59% will be allocated to technology, and 41% will be dedicated to people and skills.

These and many other fascinating insights are from the second annual IDG Digital Business study, The State of Digital Business Transformation 2019. You can download a summary of the slides here (7 pp., PDF, opt-in). The survey’s methodology is based on 702 interviews across nine industries with technology, financial services, and business services (consulting, legal and real estate) comprising 43% of all respondents. IDG relied on CIO, Computerworld, CSO, InfoWorld, and Network World visitors as their primary respondent base. For additional details regarding the methodology, please see page 2 of the study.

The study’s primary goal was to gain a better understanding of where organizations are in their approaches to becoming digital-first businesses. The study captures the strategies and technologies businesses are adopting to ensure digitally-driven growth with customer experience improvements being proven as a growth catalyst. Key insights from the survey include the following:

  • 52% of enterprises define digital business as meeting customer experience expectations, jumping to 65% for financial services enterprises. Customer expectations rule all other categories of how an enterprise defines a digital business. 49% define digital business as enabling worker productivity with mobile apps, data access, and AI-assisted automation. The following graphic compares how enterprises define their digital business. Please click on the graphic to expand for easier reading.

Customer Experiences Define Success In A Digital-First World

  • Mobile devices and apps are enterprises’ platform of choice for launching digital-first strategies in 2019. Mobile apps and the platforms supporting them provide the needed scale, speed-to-market, and performance gains through application-level improvements that all businesses need to gain initial adoption and growth with their digital-first strategies. IDG found that private cloud and business process management are the second- and third-most used technologies to drive digital-first initiatives. Enterprises also have a considerable lead when it comes to mobile app availability: 74% have mobile apps today compared to 51% of SMBs.

  • Internet of Things (IoT), Artificial Intelligence (AI) and machine learning are the leading three initiatives enterprises have in pilot today as part of their digital-first initiatives. 21% of all organizations surveyed are in one or more IoT pilots, and 20% of organizations are piloting AI and machine learning projects today. Nearly a third of all organizations (29%) have multi-cloud configurations in production today, and 25% have software-defined Wide Area Networks (WANs).

  • 57% of enterprises (companies with over 1K employees) say improving new product and service offerings by digitally enabling operations is the single greatest source of revenue growth. Digitally enabling or streamlining new product and development processes and the systems supporting them also improve the ability to innovate and size new opportunities (49%). It makes sense that once the new product development process is more digitally enabled, an organization will be able to more efficiently launch new capabilities (47% in enterprises) and improve sales capacity including upsell and cross-sell (41% overall).

  • Creating better customer experiences (67%), improving process efficiency through automation (53%), and driving new revenue (48%) are the top three digital business strategies enterprises are investing in today. Business Management, including General Managers with P&L responsibility, are placing a high priority on creating a better customer experience, far above all else. They’re the revenue drivers of businesses adopting a digital-first strategy today as well, over 10% higher than IT Management and 12% higher than IT executives.

  • In the most successful digital-first businesses, the CIO the most visible, vocal, and successful in leading change management initiatives. Six of the nine core dimensions of a successful digital enablement strategy are dominated by CIOs. Technology Needs Assessment (48%), IT Skills Assessment (48%) and Change Management (33%) are the three areas CIOs are making the greatest contribution to digital-first strategies on the part of their businesses. It’s important to note that CIOs are far and away, the champion and leader of data management strategies as well.

  • Enterprises are placing a high priority on data security and protection as part of the digital-first initiatives, with 27% having cybersecurity systems in place. It’s encouraging to see business and IT leaders making data and system security their highest priority, getting results quickly in this area. Technology needs assessment, and IT skills assessment (both 24%) are also areas where enterprises are making strong progress. As the CIO owns these areas and is also the person most likely to be owning change management, it’s understandable how advanced digital-first businesses are on these two dimensions. The following graphic compares the progress enterprises are making in becoming a digitally-driven business.

Machine Learning Is Helping To Stop Security Breaches With Threat Analytics

Bottom Line: Machine learning is enabling threat analytics to deliver greater precision regarding the risk context of privileged users’ behavior, creating notifications of risky activity in real time, while also being able to actively respond to incidents by cutting off sessions, adding additional monitoring, or flagging for forensic follow-up.

Separating Security Hacks Fact from Fiction

It’s time to demystify the scale and severity of breaches happening globally today. A commonly-held misconception or fiction is that millions of hackers have gone to the dark side and are orchestrating massive attacks on any and every business that is vulnerable. The facts are far different and reflect a much more brutal truth, which is that businesses make themselves easy to hack into by not protecting their privileged access credentials. Cybercriminals aren’t expending the time and effort to hack into systems; they’re looking for ingenious ways to steal privileged access credentials and walk in the front door. According to Verizon’s 2019 Data Breach Investigations Report, ‘Phishing’ (as a pre-cursor to credential misuse), ‘Stolen Credentials’, and ‘Privilege Abuse’ account for the majority of threat actions in breaches (see page 9 of the report).

It only really takes one compromised credential to potentially impact millions — whether it’s millions of individuals or millions of dollars. Undeniably, identities and the trust we place in them are being used against us. They have become the Achilles heel of our cybersecurity practices. According to a recent study by Centrify among 1,000 IT decision makers, 74% of respondents whose organizations have been breached acknowledged that it involved access to a privileged account. This number closely aligns with Forrester Research’s estimate “that at least 80% of data breaches . . . [involved] compromised privileged credentials, such as passwords, tokens, keys, and certificates.”

While the threat actors might vary according to Verizon’s 2019 Data Breach Investigations Report, the cyber adversaries’ tactics, techniques, and procedures are the same across the board. Verizon found that the fastest growing source of threats are from internal actors, as the graphic from the study illustrates below:


Internal actors are the fastest growing source of breaches because they’re able to obtain privileged access credentials with minimal effort, often obtaining them through legitimate access requests to internal systems or harvesting their co-workers’ credentials by going through the sticky notes in their cubicles. Privileged credential abuse is a challenge to detect as legacy approaches to cybersecurity trust the identity of the person using the privileged credentials. In effect, the hacker is camouflaged by the trust assigned to the privileged credentials they have and can roam internal systems undetected, exfiltrating sensitive data in the process.

The reality is that many breaches can be prevented by some of the most basic Privileged Access Management (PAM) tactics and solutions, coupled with a Zero Trust approach. Most organizations are investing the largest chunk of their security budget on protecting their network perimeter rather than focusing on security controls, which can affect positive change to protect against the leading attack vector: privileged access abuse.

The bottom line is that investing in securing perimeters leaves the most popular attack vector of all unprotected, which are privileged credentials. Making PAM a top priority is crucial to protect any business’ most valuable asset; it’s systems, data, and the intelligence they provide. Gartner has listed PAM on its Top 10 Security Projects for the past two years for a good reason.

Part of a cohesive PAM strategy should include machine learning-based threat analytics to provide an extra layer of security that goes beyond a password vault, multi-factor authentication (MFA), or privilege elevation.

How Machine Learning and Threat Analytics Stop Privileged Credential Abuse 

Machine learning algorithms enable threat analytics to immediately detect anomalies and non-normal behavior by tracking login behavioral patterns, geolocation, and time of login, and many more variables to calculate a risk score. Risk scores are calculated in real-time and define if access is approved, if additional authentication is needed, or if the request is blocked entirely.

Machine learning-based threat analytics also provide the following benefits:

  • New insights into privileged user access activity based on real-time data related to unusual recent privilege change, the command runs, target accessed, and privilege elevation.
  • Gain greater understanding and insights into the specific risk nature of specific events, computing a risk score in real time for every event expressed as high, medium, or low level for any anomalous activity.
  •  Isolate, identify, and track which security factors triggered an anomaly alert.
  • Capture, play, and analyze video sessions of anomalous events within the same dashboard used for tracking overall security activity.
  • Create customizable alerts that provide context-relevant visibility and session recording and can also deliver notifications of anomalies, all leading to quicker, more informed investigative action.

What to Look for In Threat Analytics 
Threat analytics providers are capitalizing on machine learning to improve the predictive accuracy and usability of their applications continually. What’s most important is for any threat analytics application or solution you’re considering to provide context-aware access decisions in real time. The best threat analytics applications on the market today are using machine learning as the foundation of their threat analytics engine. These machine learning-based engines are very effective at profiling the normal behavior pattern for any user on any login attempt, or any privileged activity including commands, identifying anomalies in real time to enable risk-based access control. High-risk events are immediately flagged, alerted, notified, and elevated to IT’s attention, speeding analysis, and greatly minimizing the effort required to assess risk across today’s hybrid IT environments.

The following is the minimum set of features to look for in any privilege threat analytics solution:

  • Immediate visibility with a flexible, holistic view of access activity across an enterprise-wide IT network and extended partner ecosystem. Look for threat analytics applications that provide dashboards and interactive widgets to better understand the context of IT risk and access patterns across your IT infrastructure. Threat analytics applications that give you the flexibility of tailoring security policies to every user’s behavior and automatically flagging risky actions or access attempts, so that you’ll gain immediate visibility into account risk, eliminating the overhead of sifting through millions of log files and massive amounts of historical data.
  • They have intuitively designed and customizable threat monitoring and investigation screens, workflows, and modules. Machine learning is enabling threat analytics applications to deliver more contextually-relevant and data-rich insights than has ever been possible in the past. Look for threat analytics vendors who offer intuitively designed and customizable threat monitoring features that provide insights into anomalous activity with a detailed timeline view. The best threat analytics vendors can identify the specific factors contributing to an anomaly for a comprehensive understanding of a potential threat, all from a single console. Security teams can then view system access, anomaly detection in high resolutions with analytics tools such as dashboards, explorer views, and investigation tools.
  • Must provide support for easy integration to Security Information and Event Management (SIEM) tools. Privileged access data is captured and stored to enable querying by log management and SIEM reporting tools. Make sure any threat analytics application you’re considering has installed, and working integrations with SIEM tools and platforms such as Micro Focus® ArcSight™, IBM® QRadar™, and Splunk® to identify risks or suspicious activity quickly.
  • Must Support Alert Notification by Integration with Webhook-Enabled Endpoints. Businesses getting the most value out of their threat analytics applications are integrating with Slack or existing onboard incident response systems such as PagerDuty to enable real-time alert delivery, eliminating the need for multiple alert touch points and improving time to respond. When an alert event occurs, the threat analytics engine allows the user to send alerts into third-party applications via Webhook. This capability enables the user to respond to a threat alert and contain the impact of a breach attempt.

Conclusion 
CentrifyForresterGartner, and Verizon each have used different methodologies and reached the same conclusion from their research: privileged access abuse is the most commonly used tactic for hackers to exfiltrate sensitive data. Breaches based on privileged credential abuse are extremely difficult to stop, as these credentials often have the greatest levels of trust and access rights associated with them. Leveraging threat analytics applications using machine learning that is adept at finding anomalies in behavioral data and thwarting a breach by denying access is proving very effective against privileged credential abuse.

Companies, including Centrify, use risk scoring combined with adaptive MFA to empower a least-privilege access approach based on Zero Trust. This Zero Trust Privilege approach verifies who or what is requesting privileged access, the context behind the request, and the risk of the access environment to enforce least privilege. These are the foundations of Zero Trust Privilege and are reflected in how threat analytics apps are being created and improved today.

Smart Machines Are The Future Of Manufacturing

Smart Machines Are The Future Of Manufacturing

  • Industrial Internet of Things (IIoT) presents integration architecture challenges that once solved can enable use cases that deliver fast-growing revenue opportunities.
  • ISA-95 addressed the rise of global production and distributed supply chains yet are still deficient on the issue of data and security, specifically the proliferation of IIoT sensors, which are the real security perimeter of any manufacturing business.
  • Finding new ways to excel at predictive maintenance, and cross-vendor shop floor integration are the most promising applications.
  • IIoT manufacturing systems are quickly becoming digital manufacturing platforms that integrate ERP, MES, PLM and CRM systems to provide a single unified view of product configurations.

These and many other fascinating insights are from an article McKinsey published titled IIoT platforms: The technology stack as value driver in industrial equipment and machinery which explores how the Industrial Internet of things (IIoT) is redefining industrial equipment and machinery manufacturing. It’s based on a thorough study also published this month, Leveraging Industrial Software Stack Advancement For Digital TransformationA copy of the study is downloadable here (PDF, 50 pp., no opt-in). The study shows how smart machines are the future of manufacturing, exploring how IIoT platforms are enabling greater machine-level autonomy and intelligence.

The following are the key takeaways from the study:

  • Capturing IIoT’s full value potential will require more sophisticated integrated approaches than current automation protocols provide. IIoT manufacturing systems are quickly becoming digital manufacturing platforms that integrate ERP, MES, PLM and CRM systems to provide a single unified view of product configurations and support the design-to-manufacturing process. Digital manufacturing platforms are already enabling real-time monitoring to the machine and shop floor level. The data streams real-time monitoring is delivering today is the catalyst leading to greater real-time analytics accuracy, machine learning adoption and precision and a broader integration strategy to the PLC level on legacy machinery. Please click on the graphic to expand for easier reading.

  • Inconsistent data structures at the machine, line, factory and company levels are slowing down data flows and making full transparency difficult to attain today in many manufacturers. Smart machines with their own operating systems that orchestrate IIoT data and ensure data structure accuracy are being developed and sold now, making this growth constraint less of an issue. The millions of legacy industrial manufacturing systems will continue to impede IIoT realizing its full potential, however. The following graphic reflects the complexities of making an IIoT platform consistent across a manufacturing operation. Please click on the graphic to expand for easier reading.

  • Driven by price wars and commoditized products, manufacturers have no choice but to pursue smart, connected machinery that enables IIoT technology stacks across shop floors. The era of the smart, connected machines is here, bringing with it the need to grow services and software revenue faster than transaction-based machinery sales. Machinery manufacturers are having to rethink their business models and redefine product strategies to concentrate on operating system-like functionality at the machine level that can scale and provide a greater level of autonomy, real-time data streams that power more accurate predictive maintenance, and cross-vendor shop floor integration. Please click on the graphic for easier reading.

  • Machines are being re-engineered starting with software and services as the primary design goals to support new business models. Machinery manufacturers are redefining existing product lines to be more software- and services-centric. A few are attempting to launch subscription-based business models that enable them to sell advanced analytics of machinery performance to customers. The resulting IIoT revenue growth will be driven by platforms as well as software and application development and is expected to be in the range of 20 to 35%. Please click on the graphic to expand for easier reading.

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

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

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

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

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

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

Key Takeaways from BDO’s Industry 4.0 Study

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

References

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

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

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

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