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Posts from the ‘SaaS’ Category

How Blockchain Can Improve Manufacturing In 2019

  • The business value-add of blockchain will grow to slightly more than $176B by 2025, then exceed $3.1T by 2030 according to Gartner.
  • Typical product recalls cost $8M, and many could be averted with improved track-and-traceability enabled by blockchain.
  • Combining blockchain and IoT will revolutionize product safety, track-and-traceability, warranty management, Maintenance, Repair & Overhaul (MRO), and lead to new usage-based business models for smart, connected products.
  • By 2023, 30% of manufacturing companies with more than $5B in revenue will have implemented Industry 4.0 pilot projects using blockchain, up from less than 5% today according to Gartner.

Blockchain’s greatest potential to deliver business value is in manufacturing. Increasing visibility across every area of manufacturing starting with suppliers, strategic sourcing, procurement, and supplier quality to shop floor operations including machine-level monitoring and service, blockchain can enable entirely new manufacturing business models. Supply chains are the foundation of every manufacturing business, capable of making use of blockchain’s distributed ledger structure and block-based approach to aggregating value-exchange transactions to improve supply chain efficiency first. By improving supplier order accuracy, product quality, and track-and-traceability, manufacturers will be able to meet delivery dates, improve product quality and sell more.

Capgemini Research Institute’s recent study, Does blockchain hold the key to a new age of supply chain transparency and trust? provide valuable insights into how blockchain can improve supply chains and manufacturing. A copy of the study is available here (PDF, 32 pp., no opt-in). Capgemini surveyed 731 organizations globally regarding their existing and planned blockchain initiatives. Initial interviews yielded 447 organizations who are currently experimenting with or implementing blockchain. Please see pages 25 & 26 of the study for additional details regarding the methodology.

Key takeaways of the study include the following:

  • Typical product recalls cost $8M, and many could be averted with improved track-and-traceability enabled by blockchain. Capgemini found that there was 456 food recalls alone in the U.S. last year, costing nearly $3.5B. Blockchain’s general ledger structure provides a real-time audit trail for all transactions secured against modifications making it ideal for audit and compliance-intensive industries.

  • Gaining greater cost savings (89%), enhancing traceability (81%) and enhancing transparency (79%) are the top three drivers behind manufacturer’s blockchain investments today. Additional drivers include increasing revenues (57%), reducing risks (50%), creating new business opportunities (44%) and being more customer-centric (38%). The following graphic from the study illustrates the manufacturer’s priorities for blockchain. Capgemini finds that improving track-and-traceability is a primary driver across all manufacturers, consistent with the broader trend of manufacturers adopting software applications that improve this function today. That’s also understandable given how additional regulatory compliance requirements are coming in 2019 and those manufacturers competing in highly regulated industries including aerospace & defense, medical devices, and pharma are exploring how blockchain can give them a competitive edge now

  • Digital marketplaces, tracking critical supply chain parameters, tracking components quality, preventing counterfeit products, and tracking asset maintenance are the five areas Capgemini predicts blockchain will see the greatest adoption. Based on interviews with industry experts and startups, Capgemini found 24 blockchain use cases which are compared by level of adoption and complexity in the graphic below. The use cases reflect how managing supplier contracts is already emerging as one of the most popular blockchain use cases for manufacturing organizations today and will accelerate as compliance becomes even more important in 2019.

  • Manufacturers have the most at-scale deployments of blockchain today, leading all industries included in the study. Blockchain adoption is still nascent across all industries included in the study, with 6% of manufacturers having at-scale implementations today. Customer products manufacturers lead in pilots, with 15% actively [purusing blockchain in limited scope today. And retailers trail all industries with 91% having only proofs of concept.

  • Combining IoT and blockchain at the shipping container level in supply chains increases authenticity, transparency, compliance to product and contractual requirements while reducing counterfeiting. In highly regulated industries including Aerospace & Defense (A&D), Consumer Packaged Goods (CPG), medical devices, and pharma, combining IoT and blockchain provides real-time data on the shipping container conditions, tamper-proof storage, each shipment’s locational history and if there have been changes in temperature and product condition. Capgemini sees use cases where a change in a shipment’s temperature as measured by a sensor change sends alerts regarding contractual compliance of perishable meats and produce, averting the potential of bad product quality and rejected shipments once they reach their destination.

  • Capgemini found that 13% of manufacturers are Pacesetters and are either implementing blockchain at scale or have pilots in at least one site. Over 60% of Pacesetters believe that blockchain is already transforming the way they collaborate with their partners. Encouraged by these results, Pacesetters are set to increase their blockchain investment by 30% in the next three years. They lead early stage experimenters and all implementers on three core dimensions of organizational readiness. These include end-to-end visibility across functions, detailed and defined supportive processes, and availability of the right talent to succeed.

  • Lack of a clear ROI, immature technology and regulatory challenges are the top three hurdles Pacesetter-class manufacturers face in getting blockchain initiatives accepted and into production. All implementations face these three challenges in addition to having to overcome the lack of complementary IT systems at the partner organizations. The following graphic compares the hurdles all manufacturers face in getting blockchain projects implemented by the level of manufacturers adoption success (Pacesetter, early-stage experimenters, all implementers).

Source: Capgemini Research Institute, Does blockchain hold the key to a new age of supply chain transparency and trust? October, 2018

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2018 Roundup Of Cloud Computing Forecasts And Market Estimates

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

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

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

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

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

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

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

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

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

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

Additional Resources:

Google Needs To Make Machine Learning Their Growth Fuel

  • In 2017 Google outspent Microsoft, Apple, and Facebook on R&D spending with the majority being on AI and machine learning.
  • Google needs new AI- and machine learning-driven businesses that have lower Total Acquisition Costs (TAC) to offset the rising acquisition costs of their ad and search businesses.
  • One of the company’s initial forays into AI and machine learning was its $600M acquisition of AI startup DeepMind in January 2014.
  • Google has launched two funds dedicated solely to AI: Gradient Ventures and the Google Assistant Investment Program, both of which are accepting pitches from AI and machine learning startups today.
  • On its Q4’17 earnings call, the company announced that its cloud business is now bringing in $1B per quarter. The number of cloud deals worth $1M+ that Google has sold more than tripled between 2016 and 2017.
  • Google’s M&A strategy is concentrating on strengthening their cloud business to better compete against Amazon AWS and Microsoft Azure.

These and many other fascinating insights are from CB Insight’s report, Google Strategy Teardown (PDF, 49 pp., opt-in). The report explores how Alphabet, Google’s parent company is relying on Artificial Intelligence (AI) and machine learning to capture new streams of revenue in enterprise cloud computing and services. Also, the report looks at how Alphabet can combine search, AI, and machine learning to revolutionize logistics, healthcare, and transportation. It’s a thorough teardown of Google’s potential acquisitions, strategic investments, and partnerships needed to maintain search dominance while driving revenue from new markets.

Key takeaways from the report include the following:

  • Google needs new AI- and machine learning-driven businesses that have lower Total Acquisition Costs (TAC) to offset the rising acquisition costs of their ad and search businesses. CB Insights found Google is experiencing rising TAC in their core ad and search businesses. With the strategic shift to mobile, Google will see TAC escalate even further. Their greatest potential for growth is infusing greater contextual intelligence and knowledge across the entire series of companies that comprise Alphabet, shown in the graphic below.

  • Google has launched two funds dedicated solely to AI: Gradient Ventures and the Google Assistant Investment Program, both of which are accepting pitches from AI and machine learning startups today. Gradient Ventures is an ROI fund focused on supporting the most talented founders building AI-powered companies. Former tech founders are leading Gradient Ventures, assisting in turning ideas into companies. Gradient Venture’s portfolio is shown below:

  • In 2017 Google outspent Microsoft, Apple, and Facebook on R&D spending with the majority being on AI and machine learning. Amazon dominates R&D spending across the top five tech companies investments in R&D in 2017 with $22.6B. Facebook leads in percent of total sales invested in R&D with 19.1%.

  • Google AI led the development of Google’s highly popular open source machine software library and framework Tensor Flow and is home to the Google Brain team. Google’s approach to primary research in the fields of AI, machine learning, and deep learning is leading to a prolific amount of research being produced and published. Here’s the search engine for their publication database, which includes many fascinating studies for review. Part of Google Brain’s role is to work with other Alphabet subsidiaries to support and lead their AI and machine learning product initiatives. An example of this CB Insights mentions in the report is how Google Brain collaborated with autonomous driving division Waymo, where it has helped apply deep neural nets to vehicles’ pedestrian detection The team has also been successful in increasing the number of AI and machine learning patents, as CB Insight’s analysis below shows:

  • Mentions of AI and machine learning are soaring on Google quarterly earnings calls, signaling senior management’s prioritizing these areas as growth fuel. CB Insights has an Insights Trends tool that is designed to analyze unstructured text and find linguistics-based associations, models and statistical insights from them. Analyzing Google earnings calls transcripts found AI and machine learning mentions are soaring during the last call.

  • Google’s M&A strategy is concentrating on strengthening their cloud business to better compete against Amazon AWS and Microsoft Azure. Google acquired Xively in Q1 of this year followed by Cask Data and Velostrata in Q2. Google needs to continue acquiring cloud-based companies who can accelerate more customer wins in the enterprise and mid-tier, two areas Amazon AWS and Microsoft Azure have strong momentum today.

Identities Are The New Security Perimeter

  • Privileged credentials for accessing an airport’s security system were recently for sale on the Dark Web for just $10, according to McAfee.
  • 18% of healthcare employees are willing to sell confidential data to unauthorized parties for as little as $500 to $1,000, and 24% of employees know of someone who has sold privileged credentials to outsiders, according to a recent Accenture survey.
  • Apple employees in Ireland have been offered as much as €20,000 ($22,878) in exchange for their privilege access credentials in 2016, according to Business Insider.
  • Privileged access credentials belonging to more than 1 million staff at a top UK law firm have been found for sale on the Dark Web.

There’s been a 135% year-over-year increase in financial data for sale on the Dark Web between the first half of 2017 and the first half of 2018. The Dark Web is now solidly established as a globally-based trading marketplace for a myriad of privileged credentials including access procedures with keywords, and corporate logins and passwords where transactions happen between anonymous buyers and sellers. It’s also the online marketplace of choice where disgruntled, angry employees turn to for revenge against employers. An employee at Honeywell, angry over not getting a raise, used the Dark Web as an intermediary to sell DEA satellite tracking system data he accessed from unauthorized accounts he created to Mexican drug cartels for $2M. He was caught in a sting operation, the breach was thwarted, and he was arrested.

Your Most Vulnerable Threat Surface Is A Best Seller

Sites on the Dark Web offer lucrative payment in bitcoin and other anonymous currencies for administrators’ accounts at leading European, UK and North American banking institutions and corporations. Employees are offering their privileged credentials for sale to the highest bidder out of anger, revenge or for financial gain anonymously from online auction sites.

Privileged access credentials are a best-seller because they provide the intruder with “the keys to the kingdom.” By leveraging a “trusted” identity, a hacker can operate undetected and exfiltrate sensitive data sets without raising any red flags. This holds especially true when the organizations are not applying multi-factor authentication (MFA) or risk-based access controls to limit any type of lateral movement after unauthorized access. Without these security measures in place, hackers can quickly access any digital businesses’ most valuable systems to exfiltrate valuable data or sabotage systems and applications.

81% of all hacking-related breaches leverage either stolen and weak passwords, according to Verizon’s 2017 Data Breach Investigations Report. A recent study by Centrify and Dow Jones Customer Intelligence titled, CEO Disconnect is Weakening Cybersecurity (31 pp, PDF, opt-in), found that CEOs can reduce the risk of a security breach by rethinking their Identity and Access Management (IAM) strategies. 68% of executives whose companies experienced significant breaches in hindsight believe that the breach could have been prevented by implementing more mature identity and access management strategies.

In A Zero Trust World, Identities Are The New Security Perimeter

The buying and selling of privileged credentials are proliferating on the Dark Web today and will exponentially increase in the years to come. Digital businesses need to realize that dated concepts of trusted and untrusted domains have been rendered ineffective. Teams of hackers aren’t breaking into secured systems; they’re logging in.

Digital businesses who are effective in thwarting privileged credential access have standardized on Zero Trust Security (ZTS) to ensure every potentially compromised endpoint, and threat surface within and outside a company is protected. Not a single device, login attempt, resource requested or other user-based actions are trusted, they are verified through Next-Gen Access (NGA).

Zero Trust Security relies upon four pillars: real-time user verification, device validation, access and privilege limitation, while also learning and adapting to verified user behaviors. Leaders in this area such as Centrify are relying on machine learning technology to calculate risk scores based on a wide spectrum of variables that quantitatively define every access attempt, including device, operating system, location, time of day, and several other key factors.

Depending on their risk scores, users are asked to validate their true identity through MFA further. If there are too many login attempts, risk scores increase quickly, and the NGA platform will automatically block and disable an account. All this happens in seconds and is running on a 24/7 basis ― monitoring every attempted login from anywhere in the world.

A recent Forrester Research thought leadership paper titled, Adopt Next-Gen Access to Power Your Zero Trust Strategy (14 pp., PDF, opt-in), provides insights into how NGA enables ZTS to scale across enterprises, protecting every endpoint and threat surface. The study found 32% of enterprises are excelling at the four ZTS pillars of verifying the identity of every user, validating every device using Mobile Data Management (MDM) and Mobile App Management (MAM), limiting access and privileges and learning and adapting using machine learning to analyze user behavior and gain greater insights from analytics.

NGA is a proven strategy for thwarting stolen and sold privileged access credentials from gaining access to a digital business’ network and systems, combining Identity-as-a-Service, Enterprise Mobility Management (EMM) and Privileged Access Management (PAM). Forrester found that scalable Zero Trust Security strategies empowered by NGA lead to increased organization-wide productivity (71%), reduced overall risk (70%) and reduced cost on compliance initiatives (70%).

Additionally, insights gained from user behavior through machine learning allow for greater efficiency — both on reduced compliance (31% more confident) and overall security costs (40% more likely to be confident), as well through increased productivity for the organization (8% more likely to be confident). The following graphic from the study ranks respondents’ answers.

Conclusion

Making sure your company’s privileged access credentials don’t make the best seller list on the Dark Web starts with a strong, scalable ZTS strategy driven by NGA. Next-Gen Access continually learns the behaviors of verified users, solving a long-standing paradox of user experience in security and access management. However, every digital business needs to focus on how the four pillars of Zero Trust Security apply to them and how they can take a pragmatic, thorough approach to secure every threat surface they have.

Glassdoor’s 10 Highest Paying Tech Jobs Of 2018

  • Software Engineering Manager is the highest paying position with an average salary of $163,500 with 31,621 open positions on Glassdoor today.
  • Over 368,000 open positions are available across the 10 highest paying jobs on Glassdoor today.
  • $147,000 is the average salary of the top 10 tech jobs on Glassdoor today.
  • 12.7% of all open positions are for software engineers, making this job the most in-demand in tech today.

Glassdoor is best known for its candid, honest reviews of employers written anonymously by employees. It is now common practice and a good idea for anyone considering a position with a new employer to check them out on Glassdoor first. With nearly 40 million reviews on more than 770,000 companies. Glassdoor is now the 2nd most popular job site professionals rely on in the U.S., attracting approximately 59 million job seekers a month. The Chief Human Resources Officer of one of the largest and best-known cloud-based enterprise software companies told me recently she gets 2X more applications from Glassdoor for any given position than any other recruiting site or channel.

Earlier this month Glassdoor Economic Research published the results of research completed on how base pay compares between tech and non-tech jobs.  The research team gathered a sample of tech companies with at least 100 job postings on Glassdoor as of June 26, 2018. Glassdoor defined tech roles as those positions requiring knowledge of code, software or data. The study found the following to be the 10 highest paying tech jobs today:

Walmart eCommerce, Microsoft, Intel, Amazon, and Google have the highest concentration of tech jobs as a percentage of all positions open. Workday, Salesforce, Verizon, and IBM have the highest concentration of non-tech positions available today.

Source: Glassdoor Economic Research Blog, Landing a Non-Tech Job in Tech: Who’s Hiring Today? July 19, 2018

Global State Of Enterprise Analytics, 2018

  • 71% of enterprises globally predict their investments in data and analytics will accelerate in the next three years and beyond.
  • 57% of enterprises globally have a Chief Data Officer, a leadership role that is pivotal in helping to democratize data and analytics across any organization.
  • 52% of enterprises are leveraging advanced and predictive analytics today to provide greater insights and contextual intelligence into operations.
  • 41% of all enterprises are considering a move to cloud-based analytics in the next year.
  • Cloud Computing (24%), Big Data (20%), and AI/Machine Learning (18%) are the three technologies predicted to have the greatest impact on analytics over the next five years.
  • Just 16% of enterprises have enabled at least 75% of their employees to have access to company data and analytics.

These and many other fascinating insights are from MicroStrategy’s latest research study, 2018 Global State of Enterprise Analytics Report.  You can download a copy here (PDF, 44 pp., opt-in). The study is based on surveys completed in April 2018 with 500 globally-based enterprise analytics and business intelligence professionals on the state of their organizations’ analytics initiatives across 20 industries. Participants represented organizations with 250 to 20,000 employees worldwide from five nations including Brazil, Germany, Japan, the United Kingdom and the United States. For additional details on the methodology, please see the study here. The study’s results underscore how enterprises need to have a unified data strategy that reflects their growth strategies and new business models’ information needs.

Key takeaways from the study include the following:

  • Driving greater process and cost efficiencies (60%), strategy and change (57%) and monitoring and improving financial performance (52%) are the top three ways enterprises globally are using data and analytics today. The study found that enterprises are also relying on data and analytics to gain greater insights into how current products and services are used (51%), managing risk (50%) and attain customer growth and retention (49%). Across the five nations surveyed, Japan leads the world in the use of data and analytics to drive process and cost efficiencies (65%). UK-based enterprises lead all nations in their use of data and analytics to analyze how current products and services are being used.  The report provides graphical comparisons of the five nations’ results.

  • Cloud Computing, Big Data, and AI/Machine Learning are the three technologies predicted to have the greatest global impact on analytics over the next five years. Japanese enterprises predict cloud computing will have the greatest impact on the future of analytics (28%) across the five nations’ enterprises interviewed. AI/Machine Learning is predicted to have the greatest impact on analytics in the U.K. (26%) globally as is Big Data in Germany (29%). Please see the study for country-specific prioritization of technologies.

  • 52% of enterprises are leveraging advanced and predictive analytics today to provide greater insights and contextual intelligence into operations. Additional leverage areas include distribution of analytics via e-mail and collaboration tools (49%), analytics embedded in other apps including Salesforce (44%) and mobile productivity apps (39%). Japanese enterprises lead the world in their adoption of advanced and predictive analytics (60%). German enterprises lead the world in the adoption of analytics for collaboration via e-mail and more real-time data and knowledge-sharing methods (50%).

  • 59% of enterprises are using Big Data Analytics, leading all categories of intelligence applications. Enterprise reporting (47%), data discovery (47%), mobile productivity apps (44%) and embedded apps (42%) are the top five intelligence applications in use globally by enterprises today. Big Data’s dominance in the survey results can be attributed to the top five industries in the sampling frame is among the most prolific in data generation and use. Manufacturing (15%) is the most data-prolific industry on the planet. Additional industries that generate massive amounts of data dominate the survey’s demographics including software technology-based businesses (14%), banking (13%), retail (11%), and financial services/business services (6%).

  • 27% of global enterprises prioritize security over any other factor when evaluating a new analytics vendor. The three core attributes of a scalable, comprehensive platform, ease of use, and a vendor’s products having an excellent reputation are all essential. Enterprises based in four of the five nations also prioritize security as the most critical success factor they evaluate potential analytics vendors to do business with. Enterprise scalability is most important in the U.S., with 26% of enterprises interviewed saying this is the most important priority in evaluating a new analytics vendor.

  • Data privacy and security concerns (49%) is the most formidable barrier enterprises face in gaining more effective use of their data and analytics. Enterprises from four of the five nations say data privacy and security are the most significant barrier they face in getting more value from analytics. In Japan, the greatest barrier is access limited to data across the organization (40%).

  • Globally 41% of all enterprises are considering a move to the cloud in the next year. 64% of U.S.-based enterprises are considering moving to a cloud-based analytics platform or solution in the next year. The U.S. leads enterprises from all five nations in planned cloud-based analytics cloud adoption as the graphic below illustrates.

10 Ways To Improve Cloud ERP With AI And Machine Learning

Capitalizing on new digital business models and the growth opportunities they provide are forcing companies to re-evaluate ERP’s role. Made inflexible by years of customization, legacy ERP systems aren’t delivering what digital business models need today to scale and grow.

Legacy ERP systems were purpose-built to excel at production consistency first at the expense of flexibility and responsiveness to customers’ changing requirements. By taking a business case-based approach to integrating Artificial Intelligence (AI) and machine learning into their platforms, Cloud ERP providers can fill the gap legacy ERP systems can’t.

Closing Legacy ERP Gaps With Greater Intelligence And Insight

Companies need to be able to respond quickly to unexpected, unfamiliar and unforeseen dilemmas with smart decisions fast for new digital business models to succeed. That’s not possible today with legacy ERP systems. Legacy IT technology stacks and the ERP systems they are built on aren’t designed to deliver the data needed most.

That’s all changing fast. A clear, compelling business model and successful execution of its related strategies are what all successful Cloud ERP implementations share. Cloud ERP platforms and apps provide organizations the flexibility they need to prioritize growth plans over IT constraints. And many have taken an Application Programming Interface (API) approach to integrate with legacy ERP systems to gain the incremental data these systems provide. In today’s era of Cloud ERP, rip-and-replace isn’t as commonplace as reorganizing entire IT architectures for greater speed, scale, and customer transparency using cloud-first platforms.

New business models thrive when an ERP system is constantly learning. That’s one of the greatest gaps between what Cloud ERP platforms’ potential and where their legacy counterparts are today. Cloud platforms provide greater integration options and more flexibility to customize applications and improve usability which is one of the biggest drawbacks of legacy ERP systems. Designed to deliver results by providing AI- and machine learning insights, Cloud ERP platforms, and apps can rejuvenate ERP systems and their contributions to business growth.

The following are the 10 ways to improve Cloud ERP with AI and machine learning, bridging the information gap with legacy ERP systems:

  1. Cloud ERP platforms need to create and strengthen a self-learning knowledge system that orchestrates AI and machine learning from the shop floor to the top floor and across supplier networks. Having a cloud-based infrastructure that integrates core ERP Web Services, apps, and real-time monitoring to deliver a steady stream of data to AI and machine learning algorithms accelerates how quickly the entire system learns. The Cloud ERP platform integration roadmap needs to include APIs and Web Services to connect with the many suppliers and buyer systems outside the walls of a manufacturer while integrating with legacy ERP systems to aggregate and analyze the decades of data they have generated.

  1. Virtual agents have the potential to redefine many areas of manufacturing operations, from pick-by-voice systems to advanced diagnostics. Apple’s Siri, Amazon’s Alexa, Google Voice, and Microsoft Cortana have the potential to be modified to streamline operations tasks and processes, bringing contextual guidance and direction to complex tasks. An example of one task virtual agents are being used for today is guiding production workers to select from the correct product bin as required by the Bill of Materials. Machinery manufacturers are piloting voice agents that can provide detailed work instructions that streamline configure-to-order and engineer-to-order production. Amazon has successfully partnered with automotive manufacturers and has the most design wins as of today. They could easily replicate this success with machinery manufacturers.

  1. Design in the Internet of Things (IoT) support at the data structure level to realize quick wins as data collection pilots go live and scale. Cloud ERP platforms have the potential to capitalize on the massive data stream IoT devices are generating today by designing in support at the data structure level first. Providing IoT-based data to AI and machine learning apps continually will bridge the intelligence gap many companies face today as they pursue new business models. Capgemini has provided an analysis of IoT use cases shown below, highlighting how production asset maintenance and asset tracking are quick wins waiting to happen. Cloud ERP platforms can accelerate them by designing in IoT support.

  1. AI and machine learning can provide insights into how Overall Equipment Effectiveness (OEE) can be improved that aren’t apparent today. Manufacturers will welcome the opportunity to have greater insights into how they can stabilize then normalize OEE performance across their shop floors. When a Cloud ERP platform serves as an always-learning knowledge system, real-time monitoring data from machinery and production assets provide much-needed insights into areas for improvement and what’s going well on the shop floor.

  1. Designing machine learning algorithms into track-and-traceability to predict which lots from which suppliers are most likely to be of the highest or lowest quality. Machine learning algorithms excel at finding patterns in diverse data sets by continually applying constraint-based algorithms. Suppliers vary widely in their quality and delivery schedule performance levels. Using machine learning, it’s possible to create a track-and-trace application that could indicate which lot from which supplier is the riskiest and those that are of exceptional quality as well.
  2. Cloud ERP providers need to pay attention to how they can help close the configuration gap that exists between PLM, CAD, ERP and CRM systems by using AI and machine learning. The most successful product configuration strategies rely on a single, lifecycle-based view of product configurations. They’re able to alleviate the conflicts between how engineering designs a product with CAD and PLM, how sales & marketing sell it with CRM, and how manufacturing builds it with an ERP system. AI and machine learning can enable configuration lifecycle management and avert lost time and sales, streamlining CPQ and product configuration strategies in the process.
  3. Improving demand forecasting accuracy and enabling better collaboration with suppliers based on insights from machine learning-based predictive models is attainable with higher quality data. By creating a self-learning knowledge system, Cloud ERP providers can vastly improve data latency rates that lead to higher forecast accuracy. Factoring in sales, marketing, and promotional programs further fine-tunes forecast accuracy.
  4. Reducing equipment breakdowns and increasing asset utilization by analyzing machine-level data to determine when a given part needs to be replaced. It’s possible to capture a steady stream of data on each machine’s health level using sensors equipped with an IP address. Cloud ERP providers have a great opportunity to capture machine-level data and use machine learning techniques to find patterns in production performance by using a production floor’s entire data set. This is especially important in process industries where machinery breakdowns lead to lost sales. Oil refineries are using machine learning models comprise more than 1,000 variables related to material input, output and process perimeters including weather conditions to estimate equipment failures.
  5. Implementing self-learning algorithms that use production incident reports to predict production problems on assembly lines needs to happen in Cloud ERP platforms. A local aircraft manufacturer is doing this today by using predictive modeling and machine learning to compare past incident reports. With legacy ERP systems these problems would have gone undetected and turned into production slowdowns or worse, the line having to stop.
  6. Improving product quality by having machine learning algorithms aggregate, analyze and continually learn from supplier inspection, quality control, Return Material Authorization (RMA) and product failure data. Cloud ERP platforms are in a unique position of being able to scale across the entire lifecycle of a product and capture quality data from the supplier to the customer. With legacy ERP systems manufacturers most often rely on an analysis of scrap materials by type or caused followed by RMAs. It’s time to get to the truth about why products fail, and machine learning can deliver the insights to get there.

10 Charts That Will Change Your Perspective Of Big Data’s Growth

  • 10 Charts That Will Change Your Perspective Of Big Data's GrowthWorldwide Big Data market revenues for software and services are projected to increase from $42B in 2018 to $103B in 2027, attaining a Compound Annual Growth Rate (CAGR) of 10.48% according to Wikibon.
  • Forrester predicts the global Big Data software market will be worth $31B this year, growing 14% from the previous year. The entire global software market is forecast to be worth $628B in revenue, with $302B from applications.
  • According to an Accenture study, 79% of enterprise executives agree that companies that do not embrace Big Data will lose their competitive position and could face extinction. Even more, 83%, have pursued Big Data projects to seize a competitive edge.
  • 59% of executives say Big Data at their company would be improved through the use of AI according to PwC.

Sales and Marketing, Research & Development (R&D), Supply Chain Management (SCM) including distribution, Workplace Management and Operations are where advanced analytics including Big Data are making the greatest contributions to revenue growth today. McKinsey Analytics’ study Analytics Comes of Age, published in January 2018 (PDF, 100 pp., no opt-in) is a comprehensive overview of how analytics technologies and Big Data are enabling entirely new ecosystems, serving as a foundational technology for Artificial Intelligence (AI). McKinsey finds that analytics and Big Data are making the most valuable contributions in the Basic Materials and High Tech industries. The first chart in the following series of ten is from the McKinsey Analytics study, highlighting how analytics and Big Data are revolutionizing many of the foundational business processes of Sales and Marketing.

The following ten charts provide insights into Big Data’s growth:

  • Nearly 50% of respondents to a recent McKinsey Analytics survey say analytics and Big Data have fundamentally changed business practices in their sales and marketing functions. Also, more than 30% say the same about R&D across industries, with respondents in High Tech and Basic Materials & Energy report the greatest number of functions being transformed by analytics and Big Data. Source: Analytics Comes of Age, published in January 2018 (PDF, 100 pp., no opt-in).

  • Worldwide Big Data market revenues for software and services are projected to increase from $42B in 2018 to $103B in 2027, attaining a Compound Annual Growth Rate (CAGR) of 10.48%. As part of this forecast, Wikibon estimates the worldwide Big Data market is growing at an 11.4% CAGR between 2017 and 2027, growing from $35B to $103B. Source: Wikibon and reported by Statista.

  • According to NewVantage Venture Partners, Big Data is delivering the most value to enterprises by decreasing expenses (49.2%) and creating new avenues for innovation and disruption (44.3%). Discovering new opportunities to reduce costs by combining advanced analytics and Big Data delivers the most measurable results, further leading to this category being the most prevalent in the study. 69.4% have started using Big Data to create a data-driven culture, with 27.9% reporting results. Source: NewVantage Venture Partners, Big Data Executive Survey 2017 (PDF, 16 pp.)

  • The Hadoop and Big Data Market are projected to grow from $17.1B in 2017 to $99.31B in 2022 attaining a 28.5% CAGR. The greatest period of projected growth is in 2021 and 2022 when the market is projected to jump $30B in value in one year. Source: StrategyMRC and reported by Statista.

  • Big Data applications and analytics is projected to grow from $5.3B in 2018 to $19.4B in 2026, attaining a CAGR of 15.49%. Big Data market worldwide includes Professional Services is projected to grow from $16.5B in 2018 to $21.3B in 2026. Source: Wikibon and reported by Statista.

  • Comparing the worldwide demand for advanced analytics and Big Data-related hardware, services and software, the latter category’s dominance becomes clear. The software segment is projected to increase the fastest of all categories, increasing from $14B in 2018 to $46B in 2027 attaining a CAGR of 12.6%. Sources: WikibonSiliconANGLE; Statista estimates and reported by Statista.

  • Advanced analytics and Big Data revenue in China are projected to be worth ¥57.8B ($9B) by 2020. The Chinese market is predicted to be one of the fastest growing globally, growing at a CAGR of 31.72% in the forecast period. Sources: Social Sciences Academic Press (China) and Statista.

  • Non-relational analytic data stores are projected to be the fastest growing technology category in Big Datagrowing at a CAGR of 38.6% between 2015 and 2020. Cognitive software platforms (23.3% CAGR) and Content Analytics (17.3%) round out the top three fastest growing technologies between 2015 and 2020. Source: Statista.

  • A decentralized general-merchandise retailer that used Big Data to create performance group clusters saw sales grow 3% to 4%. Big Data is the catalyst of a retailing industry makeover, bringing greater precision to localization than has been possible before. Big Data is being used today to increase the ROI of endcap promotions, optimize planograms, help to improve upsell and cross-sell sales performance and optimize prices on items that drive the greatest amount of foot traffic. Source: Use Big Data to Give Local Shoppers What They Want, Boston Consulting Group, February 8, 2018.

  • 84% of enterprises have launched advanced analytics and Big Data initiatives to bring greater accuracy and accelerate their decision-making Big Data initiatives focused on this area also have the greatest success rate (69%) according to the most recent NewVantage Venture Partners Survey. Over a third of enterprises, 36%, say this area is their top priority for advanced analytics and Big Data investment. Sources: NewVantage Venture Partners Survey and Statista.

Additional Big Data Information Sources:

4 Pain Points of Big Data and how to solve them, Digital McKinsey via Medium, November 10, 2017

53% Of Companies Are Adopting Big Data Analytics, Forbes, December 24, 2017

6 Predictions For The $203 Billion Big Data Analytics Market, Forbes, Gil Press, January 20, 2017

Analytics Comes of Age, McKinsey Analytics, January 2018 (PDF, 100 pp.)

Big Data & Analytics Is The Most Wanted Expertise By 75% Of IoT Providers, Forbes, August 21, 2017

Big Data 2017 – Market Statistics, Use Cases, and Trends, Calsoft (36 pp., PDF)

Big Data and Business Analytics Revenues Forecast to Reach $150.8 Billion This Year, Led by Banking and Manufacturing Investments, According to IDC, March 14, 2017

Big Data Executive Survey 2018, Data and Innovation – How Big Data and AI are Driving Business Innovation, NewVantage Venture Partners, January 2018 (PDF, 18 pp.)

Big Data Tech Hadoop and Spark Get Slow Start in Enterprise, Information Week, March 20, 2018

Big Success With Big Data, Accenture  (PDF, 12 pp.)

Gartner Survey Shows Organizations Are Slow to Advance in Data and Analytics, Gartner, February 5, 2018

How Big Data and AI Are Driving Business Innovation in 2018, MIT Sloan Management Review, February 5, 2018

IDC forecasts big growth for Big Data, Analytics Magazine. April 2018

IDC Worldwide Big Data Technology and Services 2012 – 2015 Forecast, Courtesy of EC Europa (PDF, 34 pp.)

Midyear Global Tech Market Outlook For 2017 To 2018, Forrester, September 25, 2017 (client access reqd.)

Oracle Industry Analyst Reports – Data-rich website of industry analyst reports

Ten Ways Big Data Is Revolutionizing Marketing And Sales, Forbes, May 9, 2016

The Big Data Payoff: Turning Big Data into Business Value, CAP Gemini & Informatica Study, (PDF, 12 pp.)

The Forrester Wave™: Enterprise BI Platforms With Majority Cloud Deployments, Q3 2017 courtesy of Oracle

How Zero Trust Security Fuels New Business Growth

Bottom Line: Zero Trust Security (ZTS) strategies enabled by Next-Gen Access (NGA) are indispensable for assuring uninterrupted digital business growth, and are proving to be a scalable security framework for streamlining onboarding and systems access for sales channels, partners, patients, and customers of fast-growing businesses.

The era of Zero Trust Security is here, accelerated by NGA solutions and driven by the needs of digital businesses for security strategies that can keep up with the rapidly expanding perimeters of their businesses. Internet of Things (IoT) networks and the sensors that comprise them are proliferating network endpoints and extending the perimeters of growing businesses quickly.

Inherent in the DNA of Next-Gen Access is the ability to verify the user, validate the device (including any sensor connected to an IoT network), limit access and privilege, then learn and adapt using machine learning techniques to streamline the user experience while granting access to approved accounts and resources. Many digital businesses today rely on IoT-based networks to connect with suppliers, channels, service providers and customers and gain valuable data they use to grow their businesses. Next-Gen Access solutions including those from Centrify are enabling Zero Trust Security strategies that scale to secure the perimeters of growing businesses without interrupting growth.

How Zero Trust Security Fuels New Business Growth  

The greater the complexity, scale and growth potential of any new digital business, the more critical NGA becomes for enabling ZTS to scale and protect its expanding perimeters. One of the most valuable ways NGA enables ZTS is using machine learning to learn and adapt to users’ system access behaviors continuously. Insights gained from NGA strengthen ZTS frameworks, enabling them to make the following contributions to new business growth:

  1. Zero Trust Security prevents data breaches that cripple new digital business models and ventures just beginning to scale and grow. Verifying, validating, learning and adapting to every user’s access attempts and then quantifying their behavior in a risk score is at the core of Next-Gen Access’ DNA. The risk scores quantify the relative levels of trust for each system user and determine what, if any, additional authentication is needed before access is granted to requested resources. Risk scores are continuously updated with every access attempt, making authentication less intrusive over time while greatly reducing compromised credential attacks.
  2. Securing the expanding endpoints and perimeters of a digital business using NGA frees IT and senior management up to focus more on growing the business. In any growing digital business, there’s an exponential increase in the number of endpoints being created, rapidly expanding the global perimeter of the business. The greater the number of endpoints and the broader the perimeter, the more revenue potential there is. Relying on Next-Gen Access to scale ZTS across all endpoints saves valuable IT time that can be dedicated to direct revenue-producing projects and initiatives. And by relying on NGA as the trust engine that enables ZTS, senior management will have far fewer security-related emergencies, interruptions, and special projects and can dedicate more time to growing the business. A ZTS framework also centralizes security management across a digital business, alleviating the costly, time-consuming task of continually installing patches and updates.
  3. Zero Trust Security is enabling digital businesses globally to meet and exceed General Data Protection Regulation (GDPR) compliance requirements while protecting and growing their most valuable asset: customer trust. Every week brings new announcements of security breaches at many of the world’s most well-known companies. Quick stats on users affected, potential dollar loss to the company and the all-too-common 800 numbers for credit bureaus seem to be in every press release. What’s missing is the incalculable, unquantifiable cost of lost customer value and the millions of hours customers waste trying to avert financial chaos. In response to the need for greater oversight of how organizations respond to breaches and manage data security, the European Union (EU) launched General Data Protection Regulation (GDPR) which goes into effect May 25, 2018. GDPR applies not only European organizations, but also to foreign businesses that offer goods or services in the European Union (EU) or monitor the behavior of individuals in the EU. The compliance directive also states that organizations need to process data so in a way that “ensures appropriate security of the personal data, using appropriate technical and organizational measures,” taking into account “state of the art and the costs of implementation.”

Using an NGA approach that includes risk-based multi-factor authentication (MFA) to evaluate every login combined with the least privilege approach across an entire organization is a first step towards excelling at GDPR compliance. Zero Trust Security provides every organization needing to comply with GDPR a solid roadmap of how to meet and exceed the initiative’s requirements and grow customer trust as a result.

Conclusion

Next-Gen Access enables Zero Trust Security strategies to scale and flex as a growing business expands. In the fastest growing businesses, endpoints are proliferating as new customers are gained, and suppliers are brought onboard. NGA ensures growth continues uninterrupted, helping to thwart comprised credential attacks, which make up 81% of all hacking-related data breaches, according to Verizon.

83% Of Enterprises Are Complacent About Mobile Security

  • 89% of organizations are relying on just a single security strategy to keep their mobile networks safe.
  • 61% report that their spending on mobile security had increased in 2017 with 10% saying it had increased significantly.
  • Just 39% of mobile device users in enterprises change all default passwords, and only 38% use strong two-factor authentication on their mobile devices.
  • Just 31% of companies are using mobile device or enterprise mobility management (MDM or EMM).

These and many other insights are from the recently published Verizon Mobile Security Index 2018 Report. The report is available here for download (22 pp., PDF, no opt-in). Verizon commissioned an independent research company to complete the survey in the second half of 2017, interviewing over 600 professionals involved in procuring and managing mobile devices for their organizations. Please see page 20 of the study for additional details on the methodology.

The study found that the accelerating pace of cloud, Internet of Things (IoT), and mobile adoption is outpacing enterprises’ ability to scale security management, leaving companies vulnerable. When there’s a trade-off between the expediency needed to accomplish business performance goals and security, the business goals win the majority of the time. 32% of enterprises are sacrificing security for expediency and business performance, leaving many areas of their core infrastructure unsecured. Enterprises who made this trade-off of expediency over security were 2.4x as likely to suffer data loss or downtime.

Key takeaways from the study include the following:

  • 79% of enterprises consider their employees to be the most significant security threat. The study points out that it’s not due to losing devices, inadvertent security errors or circumventing security policies. It’s the threat of employees using their secured access for financial or personal gain. 58% of senior management leaders interviewed view employees with secure access as the most significant threat. Security platforms that can stop credential attacks using risk assessment models predicated on behavioral pattern matching and analysis by verifying an employee’s identity are flourishing today. One of the leaders in this field is Centrify, who espouses Zero Trust Security. The following graphic from the study shows the priority of which actors enterprise leaders are most concerned about regarding threats, with employees being the most often mentioned.

  • 32% of enterprises have sacrificed security for expediency and business performance leading to 45% of them suffering data loss or downtime. The study found that companies who sacrificed security were also 2.4x more likely to have experienced data loss or downtime as a result of a mobile-related security incident. For the 68% who prioritized security over expediency, just 19% had suffered data loss or downtime.

  • 89% of enterprises are relying on just a single security practice to keep their mobile networks safe. Verizon’s study found that the majority of enterprises are relying on just one security practice to protect their networks. 55% have two in place, and just 14% have four. Of the four security practices, only 39% change all default passwords. Just under half (47%), encrypt the transmission of sensitive data across open, public networks. The following graphic from the study illustrates the percentage of enterprises who have between 1 and all four security practices in place.

  • Just 49% of enterprises have a policy regarding the use of public WiFi, and even fewer (47%) encrypt the transmission of sensitive data across open, public networks. A startling high 71% of respondents use public Wi-Fi networks for work tasks, despite their companies prohibiting their use. Taking risks with unsecured Wi-Fi networks for expediency and business performance being done at the expense of security supports a key finding of this study. Nearly one in three (32%) of enterprises are sacrificing security for expediency and business performance, including accessing unsecured Wi-Fi networks. The following infographic from the study explains a few of the many security threats inherent in the design and use of public Wi-Fi networks.

 

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