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Posts from the ‘Analytics’ Category

The State Of IoT Intelligence, 2018

  • Sales, Marketing and Operations are most active early adopters of IoT today.
  • Early adopters most often initiate pilots to drive revenue and gain operational efficiencies faster than anticipated.
  • 32% of enterprises are investing in IoT, and 48% are planning to in 2019.
  • IoT early adopters lead their industries in advanced and predictive analytics adoption.

These and many other fascinating insights are from Dresner Advisory Services’ latest report,  2018 IoT Intelligence® Market Study, in its 4th year of publication. The study concentrates on end-user interest in and demand for business intelligence in IoT. The study also examines key related technologies such as location intelligence, end-user data preparation, cloud computing, advanced and predictive analytics, and big data analytics. “While the market is still in an early stage, we believe that IoT Intelligence, the means to understand and leverage IoT data, will continue to expand as organizations mature in their collection and leverage of sensor level data,” said Howard Dresner, founder, and chief research officer at Dresner Advisory Services. 70% of respondents work at North American organizations (including the United States, Canada, and Puerto Rico). EMEA accounts for about 20%, and the remainder is distributed across Asia-Pacific and Latin America. Please see pages 11, 15 through 18 of the study for specifics regarding the methodology and respondent demographics.

Key insights gained from the study include the following:

  • Sales, Marketing and Operations are most active early adopters of IoT today. Looking to capitalize on IoT’s potential to gain real-time customer feedback on products’ and services’ performance, Sales and Marketing lead all departments in their prioritizing IoT’s value in the enterprises. 12% of Operations leaders say that IoT is critical to attaining their goals. Executive Management and Finance have yet to see the value that Sales, Marketing and Operations do.

  • Manufacturers see IoT as the most critical to achieving their product quality, production scheduling and supply chain orchestration goals. Insurance industry leaders also view IoT as critical to operations as their business models are now concentrating on automating inventory and safety management. Insurance firms also track vehicles in shipping and logistics fleets to gain greater visibility into how route operations can be optimized at the lowest possible risk of accidents. Financial Services and Healthcare are the next most interested in IoT with Higher Education and Business Services assign the lowest levels of importance by industry.

  • Investment in IoT analytics, application development and defining accurate, reliable metrics to guide development is the most critical aspect of IoT adoption today. Investments in the data supply chain including data capture, movement, data prep, and management is the second-most critical area followed by investments in IoT infrastructure.  Analytics, application development, and accurate, reliable metrics guiding DevOps are consistent with the study’s finding that early adopters have an excellent track record adopting and applying advanced and predictive analytics to challenging logistical, operations, sales, and marketing problems.

  • IoT early adopters or advocates prioritize dashboards, reporting, IoT use cases that provide data streams integral to analytics, advanced visualization, and data mining. IoT early adopters and the broader respondent base differ most in the prioritization of IT analytics, location intelligence, integration with operational processes, in-memory analysis, open source software, and edge computing. The data reflects how IoT early adopters quickly become more conversant in emerging technologies with the goal of achieving exponential scale across analytics and IoT platforms.

  • The criticality of advanced and predictive analytics to all leaders surveyed is at an all-time high. Attaining a (weighted-mean) importance score of 3.6 on a 5.0 scale, advanced and predictive analytics is today considered “critical” or “very important” to a majority of respondents. Despite a mild decline in 2017, importance sentiment (the perceived criticality of advanced and predictive analytics) is on an uptrend across the five years of our study. Mastery of advanced and predictive analytics is a leading indicator of IoT adoption, indicating the potential for more analytics pilots and in-production IoT projects next year.

  • The most valuable features for advanced and predictive analytics apps include support for a range of regression models, hierarchical clustering, descriptive statistics, and recommendation engine support. Model management is important to more than 90% of respondents, further indicating IoT analytics scale is a goal many are pursuing. Geospatial analysis (highly associated with mapping, populations, demographics, and other web-generated data), Bayesian methods, and automatic feature selection is the next most required series of features.

  • Access to advanced analytics for predictive and temporal analysis is the most important usability benefit to IoT adopters today. Second is support for easy iteration, and third is a simple process for continuous modification of models. The study evaluated a detailed set of nine usability benefits that support advanced and predictive activities and processes. All nine benefits are important to respondents, with the last one of a specialist not being required important to a majority of them at 70%.

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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

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.

IoT Market Predicted To Double By 2021, Reaching $520B

  • Bain predicts the combined markets of the Internet of Things (IoT) will grow to about $520B in 2021, more than double the $235B spent in 2017.
  • Data center and analytics will be the fastest growing IoT segment, reaching a 50% Compound Annual Growth Rate (CAGR) from 2017 to 2021.
  • IoT customers are planning and executing more proof of concept pilots, with many balancing their expectations regarding broader adoption.
  • Cloud Service Providers (CSP) are emerging as influential providers of IoT services, consulting and analytics for enterprises, leaving smaller opportunities for other providers in niche industries.
  • Security, integration with existing technology and uncertain returns on investment are the three biggest barriers to great IoT adoption in the enterprise.
  • Bain sees the need for vendors to concentrate on a few core industries with greater intensity to deliver more targeted industry solutions.

Enterprises adopting IoT are finding that vendors aren’t making enough progress on lowering the most significant barriers to adoption in the areas of security, ease of integration with existing information technology (IT), operational technology (OT) systems and uncertain returns on investment. As a result, enterprises are extending their expectations of when their use cases will reach scale and delivered results. These and many other fascinating findings are from Bain’s latest IoT research brief, Unlocking Opportunities in the Internet of Things. The PDF is downloadable here (PDF, 12 pp, no opt-in).

Additional key takeaways the research brief include the following:

  • The combined markets of the Internet of Things (IoT) will grow to about $520B in 2021, more than double the $235B spent in 2017. Data center and analytics will be the fastest growing IoT segment, reaching a 50% Compound Annual Growth Rate (CAGR) from 2017 to 2021. System integration, data center and analytics, network, consumer devices, connectors (or things) and legacy embedded systems are the six core technology and solution areas of the IoT market. The following graphic compares the CAGR of each area in addition to defining the worldwide revenue for each category.

  • Enterprises are still optimistic about IoT’s business value and potential to deliver a positive ROI; however many are planning less extensive IoT implementations by 2020. Bain finds that enterprises are still running more proofs of concept than they were two years ago. They’ve also discovered that more customers are considering trying out new use cases: 60% in 2018 compared with fewer than 40% in 2016.

  • Security, integration with existing technology and uncertain returns on investment are the three biggest barriers to great IoT adoption. Bain found that enterprises would buy more IoT devices and pay up to 22% more on average for them if security concerns were addressed. Integration continues to be a barrier to greater IoT adoption as well. Bain found that vendors haven’t simplified the integration of IoT solutions into business processes or IT and OT as much as enterprises have expected. The report calls for vendors to invest in learning more about typical implementation challenges in their customers’ industries so they can suggest more strategic, end-to-end solutions.

  • IoT vendors including CSPs generating the most sales are concentrating on two to three industries to scale the depth of their expertise quickly.  More than 80% of vendors still target four to six industries which makes it difficult to reach an expertise and knowledge scale that wins new clients. Bain finds that when vendors and CSPs concentrate on two or three domains, they gain mastery of specific markets faster and can provide insights to enterprises more effectively. Gaining expertise in two to three core industries is also an excellent differentiation strategy for vendors and CSPs who compete against price-driven IoT service providers.

  • Interest in remote monitoring and real-time monitoring is flourishing in IoT making this one of the fastest-growing use case categories. Being able to monitor production systems to the machine or asset level remotely and having the option to turn the data stream into a real-time source of knowledge is a fast-growing area of IoT adoption today. Based on interviews with manufacturers the popularity of Overall Equipment Effectiveness (OEE) is growing, fueled by the options available for remote and real-time monitoring of production assets. Bain discovered that industrial equipment leader ABB bundles remote monitoring into its connected robotics systems and connected low-voltage networks, which allows customers to troubleshoot and quickly identify issues requiring greater attention.
  • Cloud Service Providers (CSP) are emerging as influential providers of IoT services, consulting and analytics for enterprises, leaving smaller opportunities for other providers in niche industries. Amazon Web Services (AWS) and Microsoft Azure have emerged as the dominant CSP leaders of the fast-moving global market for IoT software and solutions. Bain finds that CSPs are successful in lowering barriers to IoT adoption, allowing for simpler implementations and making it easier to try out new use cases and scale up quickly. The study finds that the broad horizontal services provide little optimization for industry-specific applications, leaving a significant opportunity for industry solutions from systems integrators, enterprise app developers, industry IoT specialists, device makers and telecommunications providers.

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.

Where Business Intelligence Is Delivering Value In 2018

  • Executive Management, Operations, and Sales are the three primary roles driving Business Intelligence (BI) adoption in 2018.
  • Dashboards, reporting, end-user self-service, advanced visualization, and data warehousing are the top five most important technologies and initiatives strategic to BI in 2018.
  • Small organizations with up to 100 employees have the highest rate of BI penetration or adoption in 2018.
  • Organizations successful with analytics and BI apps define success in business results, while unsuccessful organizations concentrate on adoption rate first.
  • 50% of vendors offer perpetual on-premises licensing in 2018, a notable decline over 2017. The number of vendors offering subscription licensing continues to grow for both on-premises and public cloud models.
  • Fewer than 15% of respondent organizations have a Chief Data Officer, and only about 10% have a Chief Analytics Officer today.

These and many other fascinating insights are from Dresner Advisory Service’s  2018 Wisdom of Crowds® Business Intelligence Market Study. In its ninth annual edition, the study provides a broad assessment of the business intelligence (BI) market and a comprehensive look at key user trends, attitudes, and intentions.  The latest edition of the study adds Information Technology (IT) analytics, sales planning, and GDPR, bringing the total to 36 topics under study.

“The Wisdom of Crowds BI Market Study is the cornerstone of our annual research agenda, providing the most in-depth and data-rich portrait of the state of the BI market,” said Howard Dresner, founder and chief research officer at Dresner Advisory Services. “Drawn from the first-person perspective of users throughout all industries, geographies, and organization sizes, who are involved in varying aspects of BI projects, our report provides a unique look at the drivers of and success with BI.” Survey respondents include IT (28%), followed by Executive Management (22%), and Finance (19%). Sales/Marketing (8%) and the Business Intelligence Competency Center (BICC) (7%). Please see page 15 of the study for specifics on the methodology.

Key takeaways from the study include the following:

  • Executive Management, Operations, and Sales are the three primary roles driving Business Intelligence (BI) adoption in 2018. Executive management teams are taking more of an active ownership role in BI initiatives in 2018, as this group replaced Operations as the leading department driving BI adoption this year. The study found that the greatest percentage change in functional areas driving BI adoption includes Human Resources (7.3%), Marketing (5.9%), BICC (5.1%) and Sales (5%).

  • Making better decisions, improving operational efficiencies, growing revenues and increased competitive advantage are the top four BI objectives organizations have today. Additional goals include enhancing customer service and attaining greater degrees of compliance and risk management. The graph below rank orders the importance of BI objectives in 2018 compared to the percent change in BI objectives between 2017 and 2018. Enhanced customer service is the fastest growing objective enterprises adopt BI to accomplish, followed by growth in revenue (5.4%).

  • Dashboards, reporting, end-user self-service, advanced visualization, and data warehousing are the top five most important technologies and initiatives strategic to BI in 2018. The study found that second-tier initiatives including data discovery, data mining/advanced algorithms, data storytelling, integration with operational processes, and enterprise and sales planning are also critical or very important to enterprises participating in the survey. Technology areas being hyped heavily today including the Internet of Things, cognitive BI, and in-memory analysis are relatively low in the rankings as of today, yet are growing. Edge computing increased 32% as a priority between 2017 and 2018 for example. The results indicate the core aspect of excelling at using BI to drive better business decisions and more revenue still dominate the priorities of most businesses today.
  • Sales & Marketing, Business Intelligence Competency Center (BICC) and   Executive Management have the highest level of interest in dashboards and advanced visualization. Finance has the greatest interest in enterprise planning and budgeting. Operations including manufacturing, supply chain management, and services) leads interest in data mining, data storytelling, integration with operational processes, mobile device support, data catalog and several other technologies and initiatives. It’s understandable that BICC leaders most advocate end-user self-service and attach high importance to many other categories as they are internal service bureaus to all departments in an enterprise. It’s been my experience that BICCs are always looking for ways to scale BI adoption and enable every department to gain greater value from analytics and BI apps. BICCs in the best run companies are knowledge hubs that encourage and educate all departments on how to excel with analytics and BI.

  • Insurance companies most prioritize dashboards, reporting, end-user self-service, data warehousing, data discovery and data mining. Business Services lead the adoption of advanced visualization, data storytelling, and embedded BI. Manufacturing most prioritizes sales planning and enterprise planning but trails in other high-ranking priorities. Technology prioritizes Software-as-a-Service (SaaS) given its scale and speed advantages. The retail & wholesale industry is going through an analytics and customer experience revolution today. Retailers and wholesalers lead all others in data catalog adoption and mobile device support.

  • Insurance, Technology and Business Services vertical industries have the highest rate of BI adoption today. The Insurance industry leads all others in BI adoption, followed by the Technology industry with 40% of organizations having 41% or greater adoption or penetration. Industries whose BI adoption is above average include Business Services and Retail & Wholesale. The following graphic illustrates penetration or adoption of Business Intelligence solutions today by industry.

  • Dashboards, reporting, advanced visualization, and data warehousing are the highest priority investment areas for companies whose budgets increased from 2017 to 2018. Additional high priority areas of investment include advanced visualization and data warehousing. The study found that less well-funded organizations are most likely to lead all others by investing in open source software to reduce costs.

  • Small organizations with up to 100 employees have the highest rate of BI penetration or adoption in 2018. Factors contributing to the high adoption rate for BI in small businesses include business models that need advanced analytics to function and scale, employees with the latest analytics and BI skills being hired to also scale high growth businesses and fewer barriers to adoption compared to larger enterprises. BI adoption tends to be more pervasive in small businesses as a greater percentage of employees are using analytics and BI apps daily.

  • Executive Management is most familiar with the type and number of BI tools in use across the organization. The majority of executive management respondents say their teams are using between one or two BI tools today. Business Intelligence Competency Centers (BICC) consistently report a higher number of BI tools in use than other functional areas given their heavy involvement in all phases of analytics and BI project execution. IT, Sales & Marketing and Finance are likely to have more BI tools in use than Operations.

  • Enterprises rate BI application usability and product quality & reliability at an all-time high in 2018. Other areas of major improvements on the part of vendors include improving ease of implementation, online training, forums and documentation, and completeness of functionality. Dresner’s research team found between 2017 and 2018 integration of components within product dropped, in addition to scalability. The study concludes the drop in integration expertise is due to an increasing number of software company acquisitions aggregating dissimilar products together from different platforms.

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

The Best Big Data Companies And CEOs To Work For In 2018

Forbes readers’ most common requests center on who the best companies are to work for in analytics, big data, data management, data science and machine learning. The latest Computer Reseller News‘ 2018 Big Data 100 list of companies is used to complete the analysis as it is an impartial, independent list aggregated based on CRN’s analysis and perspectives of the market. Using the CRN list as a foundation, the following analysis captures the best companies in their respective areas today.

Using the 2018 Big Data 100 CRN list as a baseline to compare the Glassdoor scores of the (%) of employees who would recommend this company to a friend and (%) of employees who approve of the CEO, the following analysis was completed today. 25 companies on the list have very few (less than 15) or no Glassdoor reviews, so they are excluded from the rankings. Based on analysis of Glassdoor score patterns over the last four years, the lower the number of rankings, the more 100% scores for referrals and CEOs. These companies, however, are included in the full data set available here. If the image below is not visible in your browser, you can view the rankings here.

 

The highest rated CEOs on Glassdoor as of May 11, 2018 include the following:

Dataiku Florian Douetteau 100%
StreamSets Girish Pancha 100%
MemSQL Nikita Shamgunov 100%
1010 Data Greg Munves 99%
Salesforce.com Marc Benioff 98%
Attivio Stephen Baker 98%
SAP Bill McDermott 97%
Qubole Ashish Thusoo 97%
Trifacta Adam Wilson 97%
Zaloni Ben Sharma 97%
Reltio Manish Sood 96%
Microsoft Satya Nadella 96%
Cloudera Thomas J. Reilly 96%
Sumo Logic Ramin Sayar 96%
Google Sundar Pichai 95%
Looker Frank Bien 93%
MongoDB Dev Ittycheria 92%
Snowflake Computing Bob Muglia 92%
Talend Mike Tuchen 92%
Databricks Ali Ghodsi 90%
Informatica Anil Chakravarthy 90%

 

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.

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