While 94% of supply chain leaders say that digital transformation will fundamentally change supply chains in 2018, only 44% have a strategy ready.
66% of supply chain leaders say advanced supply chain analytics are critically important to their supply chain operations in the next 2 to 3 years.
Forecast accuracy, demand patterns, product tracking traceability, transportation performance and analysis of product returns are use cases where analytics can close knowledge gaps.
These and other insights are from The Hackett Group study, Analytics: Laying the Foundation for Supply Chain Digital Transformation (10 pp., PDF, no opt-in). The study provides insightful data regarding the increasing importance of using analytics to drive improved supply chain performance. Data included in the study also illustrate how analytics is enabling business objectives across a range of industries. The study also provides the key points that need to be considered in creating a roadmap for implementing advanced supply chain analytics leading to digital transformation. It’s an interesting, insightful read on how analytics are revolutionizing supply chains in 2018 and beyond.
Key takeaways from the study include the following:
66% of supply chain leaders say advanced supply chain analytics are critically important to their supply chain operations in the next 2 to 3 years. The Hackett Group found the majority of supply chain leaders have a sense of urgency for getting advanced supply chain analytics implemented and contributing to current and future operations. The majority see the value of having advanced analytics that can scale across their entire supplier network.
Improving forecast accuracy, optimizing transportation performance, improving product tracking & traceability and analyzing product returns are the use cases providing the greatest potential for analytics growth. Each of these use cases and the ones that are shown in the graphic below has information and knowledge gaps advanced supply chain analytics can fill. Of these top use cases, product tracking and traceability are one of the fastest growing due to the stringent quality standards defined by the US Food & Drug Administration in CFR 21 Sec. 820.65 for medical products manufacturers. The greater the complexity and cost of compliance with federally-mandated reporting and quality standards, the greater potential for advanced analytics to revolutionize supply chain performance.
Optimizing production and sourcing to reduce total landed costs (56%) is the most important use case of advanced supply chain analytics in the next 2 to 3 years. The Hackett Group aggregated use cases across the four categories of reducing costs, improving quality, improving service and improving working capital (optimizing inventory). Respondents rank improving working capital (optimizing inventory) with the highest aggregated critical importance score of 39%, followed by reducing costs (29.5%), improving service (28.6%) and improving quality (25.75%).
44% of supply chain leaders are enhancing their Enterprise Resource Planning (ERP) systems’ functionality and integration to gain greater enterprise and supply chain-wide visibility. Respondents are relying on legacy ERP systems as their main systems of record for managing supply chain operations, and integrating advanced supply chain analytics to gain end-to-end supply network visibility. 94% of respondents consider virtual collaboration platforms for internal & external use the highest priority technology initiative they can accomplish in the next 2 to 3 years.
The majority of companies are operating at stages 1 and 2 of the Hackett Group’s Supply chain analytics maturity model. A small percentage are at the stage 3 level of maturity according to the study’s results. Supply chain operations and performance scale up the model as processes and workflows are put in place to improve data quality, provide consistent real-time data and rely on a stable system of record that can deliver end-to-end supply chain analytics visibility. Integrating with external data becomes critically important as supply networks proliferate globally, as does the need to drive greater predictive analytics accuracy.
There has been a 14X increase in the number of active AI startups since 2000.
Investment into AI start-ups by venture capitalists has increased 6X since 2000.
The share of jobs requiring AI skills has grown 4.5X since 2013.
These and many other fascinating insights are from Stanford University’s inaugural AI Index (PDF, no opt-in, 101 pp.). Stanford has undertaken a One Hundred Year Study on Artificial Intelligence (AI100) looking at the effects of AI on people’s lives, basing the inaugural report and index on the initial findings. The study finds “that we’re essentially “flying blind” in our conversations and decision-making related to Artificial Intelligence.” The AI Index is focused on tracking activity and progress on AI initiatives, and to facilitate informed conversations grounded with reliable, verifiable data. All data used to produce the AI Index and report is available at aiindex.org. Please see the AI Index for additional details regarding the methodology used to create each of the following graphs.
The following ten charts from the AI Index report provides insights into AI’s rapid growth:
The number of Computer Science academic papers and studies has soared by more than 9X since 1996. Academic studies and research are often the precursors to new intellectual property and patents. The entire Scopus database contains over 200,000 (200,237) papers in the field of Computer Science that have been indexed with the key term “Artificial Intelligence.” The Scopus database contains almost 5 million (4,868,421) papers in the subject area “Computer Science.”
There have been a 6X increase in the annual investment levels by venture capital (VC) investors into U.S.-based Ai startups since 2000. Crunchbase, VentureSource, and Sand Hill Econometrics were used to determine the amount of funding invested each year by venture capitalists into startups where AI plays an important role in some key function of the business. The following graphic illustrates the amount of annual funding by VC’s into US AI startups across all funding stages.
There has been a 14X increase in the number of active AI startups since 2000. Crunchbase, VentureSource, and Sand Hill Econometrics were also used for completing this analysis with AI startups in Crunchbase cross-referenced to venture-backed companies in the VentureSource database. Any venture-backed companies from the Crunchbase list that were identified in the VentureSource database were included.
The share of jobs requiring AI skills has grown 4.5X since 2013., The growth of the share of US jobs requiring AI skills on the Indeed.com platform was calculated by first identifying AI-related jobs using titles and keywords in descriptions. Job growth is a calculated as a multiple of the share of jobs on the Indeed platform that required AI skills in the U.S. starting in January 2013. The study also calculated the growth of the share of jobs requiring AI skills on the Indeed.com platform, by country. Despite the rapid growth of the Canada and UK. AI job markets, Indeed.com reports they are respectively still 5% and 27% of the absolute size of the US AI job market.
Machine Learning, Deep Learning and Natural Language Processing (NLP) are the three most in-demand skills on Monster.com. Just two years ago NLP had been predicted to be the most in-demand skill for application developers creating new AI apps. In addition to skills creating AI apps, machine learning techniques, Python, Java, C++, experience with open source development environments, Spark, MATLAB, and Hadoop are the most in-demand skills. Based on an analysis of Monster.com entries as of today, the median salary is $127,000 in the U.S. for Data Scientists, Senior Data Scientists, Artificial Intelligence Consultants and Machine Learning Managers.
Error rates for image labeling have fallen from 28.5% to below 2.5% since 2010. AI’s inflection point for Object Detection task of the Large Scale Visual Recognition Challenge (LSVRC) Competition occurred in 2014. On this specific test, AI is now more accurate than human These findings are from the competition data from the leaderboards for each LSVRC competition hosted on the ImageNet website.
Global revenues from AI for enterprise applications is projected to grow from $1.62B in 2018 to $31.2B in 2025 attaining a 52.59% CAGR in the forecast period. Image recognition and tagging, patient data processing, localization and mapping, predictive maintenance, use of algorithms and machine learning to predict and thwart security threats, intelligent recruitment, and HR systems are a few of the many enterprise application use cases predicted to fuel the projected rapid growth of AI in the enterprise. Source: Statista.
84% of enterprises believe investing in AI will lead to greater competitive advantages. 75% believe that AI will open up new businesses while also providing competitors new ways to gain access to their markets. 63% believe the pressure to reduce costs will require the use of AI. Source: Statista.
87% of current AI adopters said they were using or considering using AI for sales forecasting and for improving e-mail marketing. 61% of all respondents said that they currently used or were planning to use AI for sales forecasting. The following graphic compares adoption rates of current AI adopters versus all respondents. Source: Statista.
83% Of Enterprise Workloads Will Be In The Cloud By 2020. LogicMonitor’s survey is predicting that 41% of enterprise workloads will be run on public cloud platforms (Amazon AWS, Google Cloud Platform, IBM Cloud, Microsoft Azure and others) by 2020. An additional 20% are predicted to be private-cloud-based followed by another 22% running on hybrid cloud platforms by 2020. On-premise workloads are predicted to shrink from 37% today to 27% of all workloads by 2020.
Digitally transforming enterprises (63%) is the leading factor driving greater public cloud engagement or adoption followed by the pursuit of IT agility (62%). LogicMonitor’s survey found that the many challenges enterprises face in digitally transforming their business models are the leading contributing factor to cloud computing adoption. Attaining IT agility (62%), excelling at DevOps (58%), mobility (55%), Artificial Intelligence (AI) and Machine Learning (50%) and the Internet of Things (IoT) adoption (45%) are the top six factors driving cloud adoption today. Artifical Intelligence (AI) and Machine Learning are predicted to be the leading factors driving greater cloud computing adoption by 2020.
66% of IT professionals say security is their greatest concern in adopting an enterprise cloud computing strategy. Cloud platform and service providers will go on a buying spree in 2018 to strengthen and harden their platforms in this area. Verizon (NYSE:VZ) acquiring Niddel this week is just the beginning. Niddel’s Magnet software is a machine learning-based threat-hunting system that will be integrated into Verizon’s enterprise-class cloud services and systems. Additional concerns include attaining governance and compliance goals on cloud-based platforms (60%), overcoming the challenges of having staff that lacks cloud experience (58%), Privacy (57%) and vendor lock-in (47%).
Just 27% of respondents predict that by 2022, 95% of all workloads will run in the cloud. One in five respondents believes it will take ten years to reach that level of workload migration. 13% of respondents don’t see this level of workload shift ever occurring. Based on conversations with CIOs and CEOs in manufacturing and financial services industries there will be a mix of workloads between on-premise and cloud for the foreseeable future. C-level executives evaluate shifting workloads based on each systems’ contribution to new business models, cost, and revenue goals in addition to accelerating time-to-market.
Microsoft Azure and Google Cloud Platform are predicted to gain market share versus Amazon AWS in the next three years, with AWS staying the clear market leader. The study found 42% of respondents are predicting Microsoft Azure will gain more market share by 2020. Google Cloud Platform is predicted to also gain ground according to 35% of the respondent base. AWS is predicted to extend its market dominance with 52% market share by 2020.
Machine Learning Engineers, Data Scientists, and Big Data Engineers rank among the top emerging jobs on LinkedIn.
Data scientist roles have grown over 650% since 2012, but currently, 35,000 people in the US have data science skills, while hundreds of companies are hiring for those roles.
There are currently 1,829 open Machine Learning Engineering positions on LinkedIn.
Job growth in the next decade is expected to outstrip growth during the previous decade, creating 11.5M jobs by 2026, according to the U.S. Bureau of Labor Statistics.
These and many other insights are from the recently released LinkedIn 2017 U.S. Emerging Jobs Report. LinkedIn has provided an overview of the methodology in their post, The Fastest-Growing Jobs in the U.S. Based on LinkedIn Data. “Emerging jobs” refers to the job titles that saw the largest growth in frequency over that five year period. LinkedIn reports that based on their analysis, the job market in the U.S. is brimming right now with fresh and exciting opportunities for professionals in a range of emerging roles.
Key takeaways from the study include the following:
There are 9.8 times more Machine Learning Engineers working today than five years ago based on LinkedIn’s research, with 1,829 open positions listed on the site today. There are 6.5 times more Data Scientists than five years ago, and 5.5 times more Big Data Developers. The following graphic illustrates the rapid growth of key data scient, machine leanring, big data and full stack developers in addition to sales development and customer success managers.
Software engineering is a common starting point for professionals who are in the top five fasting growing jobs today. The career path to Machine Learning Engineer and Big Data Developer begins with a solid software engineering background. The top five highest growth job typical career paths are shown below:
The skills most strongly represented across the 20 fastest growing jobs include management, sales, communication, and marketing. Additional skills represented across the highest growing jobs include marketing expertise (analytics and marketing automation), start-ups, Python, software development, analytics, cloud computing and knowledge of retail systems.
LinkedIn interviewed 1,200 hiring managers to determine which soft skills are most in-demand and adaptability came out on top. Additional soft skills include culture fit, collaboration, leadership, growth potential, and prioritization.
Big data adoption reached 53% in 2017 for all companies interviewed, up from 17% in 2015, with telecom and financial services leading early adopters.
Reporting, dashboards, advanced visualization end-user “self-service” and data warehousing are the top five technologies and initiatives strategic to business intelligence.
Data warehouse optimization remains the top use case for big data, followed by customer/social analysis and predictive maintenance.
Among big data distributions, Cloudera is the most popular, followed by Hortonworks, MAP/R, and Amazon EMR.
These and many other insights are from Dresner Advisory Services’ insightful 2017 Big Data Analytics Market Study (94 pp., PDF, client accessed reqd), which is part of their Wisdom of Crowds® series of research. This 3rd annual report examines end-user trends and intentions surrounding big data analytics, defined as systems that enable end-user access to and analysis of data contained and managed within the Hadoop ecosystem. The 2017 Big Data Analytics Market Study represents a cross-section of data that spans geographies, functions, organization size, and vertical industries. Please see page 10 of the study for additional details regarding the methodology.
“Across the three years of our comprehensive study of big data analytics, we see a significant increase in uptake in usage and a large drop of those with no plans to adopt,” said Howard Dresner, founder and chief research officer at Dresner Advisory Services. “In 2017, IT has emerged as the most typical adopter of big data, although all departments – including finance – are considering future use. This is an indication that big data is becoming less an experimental endeavor and more of a practical pursuit within organizations.”
Key takeaways include the following:
Reporting, dashboards, advanced visualization end-user “self-service” and data warehousing are the top five technologies and initiatives strategic to business intelligence. Big Data ranks 20th across 33 key technologies Dresner Advisory Services currently tracks. Big Data Analytics is of greater strategic importance than the Internet of Things (IoT), natural language analytics, cognitive Business Intelligence (BI) and Location intelligence.
53% of companies are using big data analytics today, up from 17% in 2015 with Telecom and Financial Services industries fueling the fastest adoption. Telecom and financial services are the most active early adopters, with Technology and Healthcare being the third and fourth industries seeing big data analytics Education has the lowest adoption as 2017 comes to a close, with the majority of institutions in that vertical saying they are evaluating big data analytics for the future. North America (55%) narrowly leads EMEA (53%) in their current levels of big data analytics adoption. Asia-Pacific respondents report 44% current adoption and are most likely to say they “may use big data in the future.”
Data warehouse optimization is considered the most important big data analytics use case in 2017, followed by customer/social analysis and predictive maintenance. Data warehouse optimization is considered critical or very important by 70% of all respondents. It’s interesting to note and ironic that the Internet of Things (IoT) is among the lowest priority use cases for big data analytics today.
Big data analytics use cases vary significantly by industry with data warehouse optimization dominating Financial Services, Healthcare, and Customer/social analysis is the leading use case in Technology-based companies. Fraud detection use cases also dominate Financial Services and Telecommunications. Using big data for clickstream analytics is most popular in Financial Services.
Spark, MapReduce, and Yarn are the three most popular software frameworks today. Over 30% of respondents consider Spark critical to their big data analytics strategies. MapReduce and Yarn are “critical” to more than 20 percent of respondents.
The big data access methods most preferred by respondents include Spark SQL, Hive, HDFS and Amazon S3. 73% of the respondents consider Spark SQL critical to their analytics strategies. Over 30% of respondents consider Hive and HDFS critical as well. Amazon S3 is critical to one of five respondents for managing big data access. The following graphic shows the distribution of big data access methods.
Machine learning continues to gain more industry support and investment plans with Spark Machine Learning Library (MLib) adoption projected to grow by 60% in the next 12 months. In the next 24 months, MLib will dominate machine learning according to the survey results. MLib is accessible from the Sparklyr R Package and many others, which continues to fuel its growth. The following graphic compares projected two-year adoption rates by machine learning libraries and frameworks.
According to IDC, worldwide spending on the IoT is forecast to reach $772.5B in 2018. That represents an increase of 15% over the $674B that was spent on IoT in 2017.
The global IoT market will grow from $157B in 2016 to $457B by 2020, attaining a Compound Annual Growth Rate (CAGR) of 28.5%.
Discrete Manufacturing, Transportation and Logistics, and Utilities will lead all industries in IoT spending by 2020, averaging $40B each.
Bain predicts B2B IoT segments will generate more than $300B annually by 2020, including about $85B in the industrial sector.
Internet Of Things Market To Reach $267B By 2020 according to Boston Consulting Group.
According to IDC FutureScape: Worldwide IoT 2018 Predictions, By the end of 2020, close to 50% of new IoT applications built by enterprises will leverage an IoT platform that offers outcome-focused functionality based on comprehensive analytics capabilities.
The last twelve months of Internet of Things (IoT) forecasts and market estimates reflect enterprises’ higher expectations for scale, scope and Return on Investment (ROI) from their IoT initiatives. Business benefits and outcomes are what drives the majority of organizations to experiment with IoT and invest in large-scale initiatives. That expectation is driving a new research agenda across the many research firms mentioned in this roundup. The majority of enterprises adopting IoT today are using metrics and key performance indicators (KPIs) that reflect operational improvements, customer experience, logistics, and supply chain gains. Key takeaways from the collection of IoT forecasts and market estimates include the following:
The global IoT market will grow from $157B in 2016 to $457B by 2020, attaining a Compound Annual Growth Rate (CAGR) of 28.5%. According to GrowthEnabler & MarketsandMarkets analysis, the global IoT market share will be dominated by three sub-sectors; Smart Cities (26%), Industrial IoT (24%) and Connected Health (20%). Followed by Smart Homes (14%), Connected Cars (7%), Smart Utilities (4%) and Wearables (3%). Source: GrowthEnabler, Market Pulse Report, Internet of Things (IoT), 19 pp., PDF, free, no opt-in.
Bain predicts B2B IoT segments will generate more than $300B annually by 2020, including about $85B in the industrial sector. Advisory firm Bain predicts the most competitive areas of IoT will be in the enterprise and industrial segments. Bain predicts consumer applications will generate $150B by 2020, with B2B applications being worth more than $300B. Globally, enthusiasm for the Internet of Things has fueled more than $80B in merger and acquisition (M&A) investments by major vendors and more than $30B in venture capital, according to Bain’s estimates. Source: Bain Insights: Choosing The Right Platform For The Internet Of Things
The global IoT market is growing at a 23% CAGR of 23% between 2014-2019, enabling smart solutions in major industries including agriculture, automotive and infrastructure. ― Key challenges to growth are the security and scalability of all-new connected devices and the adherence to open standards to facilitate large-scale monitoring of different systems. Source: Export opportunities of the Dutch ICT sector to Germany (25-04-17), PDF, 95 pp., no opt-in
According to Variant Market Research, the Global Internet of Things (IoT) market is estimated to reach $1,599T by 2024, from $346.1B in 2016, attaining a CAGR of 21.1% from 2016 to 2024. Asia-Pacific is predicted to grow at the fastest CAGR over the forecast period 2016 to 2024. The growth is attributed to increasing adoption of IoT in emerging countries such as India and China, high rate of mobile and internet usage, and development of next-generation technologies. Source: Global Internet of Things (IoT) Market: Rising Adoption of Cloud Platform Noticed by Variant Market Research.
Discrete Manufacturing, Transportation and Logistics, and Utilities will lead all industries in IoT spending by 2020, averaging $40B each. Improving the accuracy, speed, and scale of supply chains is an area many organizations are concentrating on with IoT. IoT has the potential to redefine quality management, compliance, traceability and Manufacturing Intelligence. Business-to-Consumer (B2C) companies are projected to spend $25B on IoT in 2020, up from $5B in 2015. The following graphic compares global spending by vertical between 2015 and 2020. Source: Statista, Spending on the Internet of Things worldwide by vertical in 2015 and 2020 (in billion U.S. dollars).
By 2020, 50% of IoT spending will be driven by discrete manufacturing, transportation, and logistics, and utilities BCG predicts that IoT will have the most transformative effect on industries that aren’t technology-based today. The most critical success factor all these use cases depend on secure, scalable and reliable end-to-end integration solutions that encompass on-premise, legacy and cloud systems, and platforms.Source: Internet Of Things Market To Reach $267B By 2020.
The hottest application areas for IoT in manufacturing include Industrial Asset Management, Inventory and Warehouse Management and Supply Chain Management. In high tech manufacturing, Smart Products, and Industrial Asset Management are the hottest application areas. The following Forrester heat Map for 2017 shows the fastest growing areas of IoT adoption by industry. Source: IoT Opportunities, Trends, and Momentum Robert E Stroud CGEIT CRISC.
B2B spending on IoT technologies, apps and solutions will reach €250B ($296.8B) by 2020 according to a recent study by Boston Consulting Group (BCG). IoT Analytics spending is predicted to generate €20B ($23.7B) by 2020. Between 2015 to 2020, BCG predicts revenue from all layers of the IoT technology stack will have attained at least a 20% Compound Annual Growth Rate (CAGR). B2B customers are the most focused on services, IoT analytics, and applications, making these two areas of the technology stack the fastest growing. By 2020, these two layers will have captured 60% of the growth from IoT. Source: Internet Of Things Market To Reach $267B By 2020.
Manufacturers most relied on the Industrial Internet of Things (IIoT) in 2017 to help better understand machine health (32%) on the shop floor, leading to more accurate Overall Equipment Effectiveness (OEE) measurements. Changing how plant maintenance personnel will work and interact with all levels of operation (29.5%) and helping to better prevent and predict shutdowns (27.1%) are the top three use cases of IIoT according to Plant Engineering and Statista.
Improving customer experiences (70%) and safety (56%) are the two areas enterprises are using data generated from IoT solutions most often today. Gaining cost efficiencies, improving organizational capabilities, and gaining supply chain visibility (all 53%) is the third most popular uses of data generated from IoT solutions today. 53% of enterprises expect data from IoT solutions to increase revenues in the next year. 53% expect data generated from their IoT solutions will assist in increasing revenues in the next year. 51% expect data from IoT solutions will open up new markets in the next year. 42% of enterprises are spending an average of $3.1M annually on IoT. Source: 70% Of Enterprises Invest In IoT To Improve Customer Experiences.
McKinsey Global Institute estimates IoT could have an annual economic impact of $3.9T to $11.1T by 2025. Their forecast scenario includes diverse settings and use cases including factories, cities, retail environments, and the human body. Factories alone could contribute between $1.2T to $3.7T in IoT-driven value. Source: McKinsey & Company, What’s New With The Internet of Things?
Business Intelligence Competency Centers (BICC), R&D, Marketing & Sales and Strategic Planning are most likely to see the importance of IoT. Finance is considered among the least likely departments to see the importance of IoT. The study also found that sales analytics apps are increasingly relying on IoT technologies as foundational components of their core application platforms.These and many other insights are from Dresner Advisory Services’ 2017 Edition IoT Intelligence Wisdom of Crowds Series study. The study defines IoT as the network of physical objects, or “things,” embedded with electronics, software, sensors, and connectivity to enable objects to collect and exchange data. The study examines key related technologies such as location intelligence, end-user data preparation, cloud computing, advanced and predictive analytics, and big data analytics. Please see page 11 of the study for details regarding the methodology.
Manufacturing, Consulting, Business Services and Distribution/Logistics are IoT industry adoption leaders. Conversely, Federal Government, State & Local Government are least likely to prioritize IoT initiatives as very important or critical. IoT early adopters are most often defining goals with clear revenue and competitive advantages to drive initiatives. Manufacturing, Consulting, Business Services and Distribution/Logistics are challenging, competitive industries where revenue growth is often tough to achieve. IoT initiatives that deliver revenue and competitive strength quickly are the most likely to get funding and support. Source: Dresner Advisory Services’ 2017 Edition IoT Intelligence Wisdom of Crowds Series study.
IoT advocates or early adopters say location intelligence, streaming data analysis, and cognitive BI to deliver the greatest business benefit. Conversely, IoT early adopters aren’t expecting to see as significant of benefits from data warehousing as they are from other technologies. Consistent with previous studies, both the broader respondent base and IoT early adopters place a high priority on reporting and dashboards. IoT early adopters also see the greater importance of visualization and end-user self-service. Source: Dresner Advisory Services’ 2017 Edition IoT Intelligence Wisdom of Crowds Series study.
Business Intelligence Competency Centers (BICC), Manufacturing and Supply Chain are among the most powerful catalysts of BI and IoT adoption in the enterprise. The greater the level of BI adoption across the 12 functional drivers of BI adoption defined in the graphic below, the greater the potential for IoT to deliver differentiated value based on unique needs by area. Marketing, Sales and Strategic Planning are also strong driver areas among IoT advocates or early adopters. Source: Dresner Advisory Services’ 2017 Edition IoT Intelligence Wisdom of Crowds Series study.
IoT early adopters are relying on growing revenue and increasing competitive advantage as the two main goals to drive IoT initiatives’ success. The most successful IoT advocates or early adopters evangelize the many benefits of IoT initiatives from a revenue growth position first. IoT early adopters are more likely to see and promote the value of better decision-making, improved operational efficiencies, increased competitive advantage, growth in revenues, and enhanced customer service when BI adoption excels, setting the foundation for IoT initiatives to succeed. Source: Dresner Advisory Services’ 2017 Edition IoT Intelligence Wisdom of Crowds Series study.
The most popular feature requirements for advanced and predictive analytics applications include regression models, textbook statistical functions, and hierarchical clustering. More than 90% of respondents replied that these three leading features are “somewhat important” to their daily use of analytics. Geospatial analysis (highly associated with mapping, populations, demographics, and other Web-generated data), recommendation engines, Bayesian methods, and automatic feature selection is the next most required series of features. Source: Dresner Advisory Services’ 2017 Edition IoT Intelligence Wisdom of Crowds Series study.
74% of IoT advocates or early adopters say location intelligence is critical or very important. Conversely, only 26% of the overall sample ranks location intelligence at the same level of importance. One of the most promising use cases for IoT-based location intelligence is its potential to streamline traceability and supply chain compliance workflows in highly regulated manufacturing industries. In 2018, expect to see ERP and Supply Chain Management (SCM) software vendors launch new applications that capitalize on IoT location intelligence to streamline traceability and supply chain compliance on a global scale. Source: Dresner Advisory Services’ 2017 Edition IoT Intelligence Wisdom of Crowds Series study.
According to IDC FutureScape: Worldwide IoT 2018 Predictions, by the end of 2020, close to 50% of new IoT applications built by enterprises will leverage an IoT platform that offers outcome-focused functionality based on comprehensive analytics capabilities. By 2021, 75% of enterprises with a positive IoT ROI will use tactical analytics applications to reduce operating costs, but the 25% that successfully invest strategically in a decision architecture will increase their revenue share. Source: IDC FutureScape: Worldwide IoT 2018 Predictions.
The global IoT market is projected to grow to $661.74B by 2021. The Industrial IoT market is expected to grow to $123.8B by 2021, and the IoT Cloud Market is estimated to grow to $7.15B by Source: IoT Growth: A Forecast.
WiFi and Bluetooth low energy (BLE) are top contenders as preferred IoT connectivity mechanisms. However, long-range, wide-area networks (LoRaWAN) and narrowband IoT (NB-IoT) are equally poised to give a tough fight to WiFi and BLE vendors. Data analytics, correlation, and pattern recognition capabilities at point-of-data creation prove to be a key decision factor in vendor evaluation. Source: IDC Survey Reveals Significant Impact of Internet of Things Initiatives on IT Infrastructure.
According to IDC, worldwide spending on the Internet of Things (IoT) is forecast to reach $772.5B in 2018, an increase of 14.6% over the $674B that will be spent in 2017. IoT hardware will be the largest technology category in 2018 with $239B going largely toward modules and sensors along with some spending on infrastructure and security. Services will be the second largest technology category, followed by software and connectivity. Source: IDC Forecasts Worldwide Spending on the Internet of Things to Reach $772 Billion in 2018.