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Former CEO of Virgin Entertainment Group Glen Ward Named as Chief Operating Officer of Gojoy

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Ward to focus on driving the company’s strong growth agenda

SILICON VALLEY, Calif., Aug. 13, 2019 (GLOBE NEWSWIRE) — Gojoy, the online marketplace offering users hourly cash rewards through its digital asset Joy Coin, announced today that Glen Ward has been named Chief Operating Officer, effective immediately. Ward joins Gojoy as its gross sales have more than doubled over the last month.

Ward will partner with and support Global CEO, Steven Lin, and be responsible for developing Gojoy’s global retail strategy, as well as drive the company’s incredible growth with a focus on strategy, process, and people. Additionally, he will oversee the company’s US operational and administrative functions, while coordinating Gojoy’s China operations.

Ward brings over three decades of international management experience, working with high-growth companies in China, U.S. and UK. Ward is recognized as a multifaceted leader and for implementing the strategic direction of the Virgin Megastore retail operations and expanding the Virgin brand throughout North America.

CEO Steven Lin added, “[o]ur entrepreneurial and transformational journey calls for complementary leadership in retail and operations. Glen’s strong retail background and track record of success in scaling organizations, will enable us to accelerate our next phase of growth, innovation, and operational excellence. He is a great addition to our executive team and Gojoy globally.”

Commenting on his appointment, Ward stated “I relish the challenge of building the world’s most rewarding online shopping community. By offering the best value and perpetually sharing savings with our customers and business partners, we will shake up the world, and slay a few giants along the way.”

Gojoy is the world’s first blockchain-powered social commerce business. Through the simple act of shopping on Gojoy, every shopper earns Joy Coins for each purchase they make. Coin holders receive hourly rewards which can be used to shop on Gojoy and enjoy the growing success of the Company.

Gojoy’s innovative social e-commerce model has earned global attention as it plans further expansion across lower tier Chinese cities. Non-US users may download Gojoy on WeChat or visit the mobile browser to begin shopping and earning cash rewards. Follow Gojoy on Twitter for the latest updates @ShopGojoy.

About Gojoy:
Gojoy is the only marketplace where every vendor and shopper share in rewards generated from each purchase, on the hour, every hour. Using blockchain technology, Gojoy created Joy Coin, a digital asset earned by shoppers with each purchase, which can be used to make purchases on Gojoy or redeemed as cash. Launched in December 2018, Gojoy has consistently tripled its membership each month with over $8 million in rewards distributed to its users. For more information visit: https://gojoy.com/en/.

A photo accompanying this announcement is available at https://www.globenewswire.com/NewsRoom/AttachmentNg/46942ebf-e2fb-4be2-a90d-1332a163bf53

GlobeNewswire is one of the world's largest newswire distribution networks, specializing in the delivery of corporate press releases financial disclosures and multimedia content to the media, investment community, individual investors and the general public.

Blockchain

AMD and Industry Partners to Develop New Blockchain-based Gaming Platforms

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AMD joins Blockchain Game Alliance, and partners with Robot Cache and ULTRA to promote the development and proliferation of blockchain-based PC gaming

SANTA CLARA, Calif., Dec. 13, 2019 (GLOBE NEWSWIRE) — AMD (NASDAQ: AMD) today announced that it has joined the Blockchain Game Alliance (BGA) and forged partnerships with leading technology providers to help promote the development and proliferation of new blockchain-powered gaming platforms.

The Blockchain Game Alliance is committed to driving awareness and adoption of blockchain technologies within the game industry, providing an open forum for individuals and companies to share knowledge and collaborate, create common standards, establish best practices, and network. As the first major hardware manufacturer to join the BGA, AMD plans to enable alliance members with efficient and high-performance computing technologies for next-generation blockchain-based gaming platforms that could potentially transform the way games are created, published, purchased and played.

AMD also announced partnerships with leading blockchain technology providers, Robot Cache, which launched their online gaming marketplace in June, and ULTRA, which plans to launch its online gaming marketplace in the coming months. Designed to provide optimal cryptographic compute performance with AMD Ryzen™ processors and AMD Radeon™ graphics cards, these marketplaces will provide gamers with new opportunities to buy, sell and share digital video games, as well as offer efficient, new distribution channels for publishers. In addition, Robot Cache will use secure, high-performance AMD EPYC™ processors in the back-end servers powering its platform, and ULTRA will use AMD EPYC™ processors for its blockchain to facilitate block producing.

“Blockchain technology brings broader choice, security and flexibility to both gamers and publishers,” said Joerg Roskowetz, Head of Blockchain Technology, AMD. “Next-generation blockchain game platforms will give gamers access to exclusive online content, and provide new ways for them to truly own it. They will also provide game publishers with new channels to distribute digital game content.”

“The Blockchain Game Alliance is gathering some of the world’s top blockchain innovators and content developers to bring players the best of what this technology has to offer,” said Nicolas Pouard, Blockchain Initiative Director at Ubisoft. “We’re delighted to work with AMD, and other alliance members to determine the role of blockchain in the entertainment experiences of the future.” 

Leading the Blockchain Gaming Charge

Providing incredible compute performance and security for peer-to-peer transactions, AMD is helping to enable the next generation of blockchain-based gaming platforms via:

  • Blockchain Innovation – AMD is at the forefront of the blockchain evolution, providing the underlying compute technology to enable a broad range of new blockchain-powered applications, services and use cases spanning industries ranging from gaming and cloud computing to the Internet of Things, healthcare, and others.
  • Efficient, High-performance CPUs and GPUs – AMD is in a unique position to offer the best combination of high-performance CPUs and GPUs for demanding blockchain workloads.
  • Robust Security – Designed to address today’s increasingly complex and sophisticated security threats, AMD Secure Technology puts protection right on the processor providing an additional layer of robust security.

Supporting Resources

  • Find more information on the Blockchain Game Alliance here
  • Follow AMD on Twitter @AMD 
  • Follow Radeon™ graphics on Twitter
  • Follow Ryzen™ on Twitter

About AMD

For 50 years AMD has driven innovation in high-performance computing, graphics and visualization technologies ― the building blocks for gaming, immersive platforms and the datacenter. Hundreds of millions of consumers, leading Fortune 500 businesses and cutting-edge scientific research facilities around the world rely on AMD technology daily to improve how they live, work and play. AMD employees around the world are focused on building great products that push the boundaries of what is possible. For more information about how AMD is enabling today and inspiring tomorrow, visit the AMD (NASDAQ: AMD) websiteblogFacebook and Twitter pages.

Cautionary Statement

This press release contains forward-looking statements concerning Advanced Micro Devices, Inc. (AMD) including the plans and expected benefits of joining the Blockchain Game Alliance and partnering with blockchain technology providers, which are made pursuant to the Safe Harbor provisions of the Private Securities Litigation Reform Act of 1995. Forward-looking statements are commonly identified by words such as “would,” “intends,” “believes,” “expects,” “may,” “will,” “should,” “seeks,” “intends,” “plans,” “pro forma,” “estimates,” “anticipates,” or the negative of these words and phrases, other variations of these words and phrases or comparable terminology. Investors are cautioned that the forward-looking statements in this document are based on current beliefs, assumptions and expectations, speak only as of the date of this document and involve risks and uncertainties that could cause actual results to differ materially from current expectations. Such statements are subject to certain known and unknown risks and uncertainties, many of which are difficult to predict and generally beyond AMD’s control, that could cause actual results and other future events to differ materially from those expressed in, or implied or projected by, the forward-looking information and statements. Material factors that could cause actual results to differ materially from current expectations include, without limitation, the following: Intel Corporation’s dominance of the microprocessor market and its aggressive business practices may limit AMD’s ability to compete effectively; AMD relies on third parties to manufacture its products, and if they are unable to do so on a timely basis in sufficient quantities and using competitive technologies, AMD’s business could be materially adversely affected; failure to achieve expected manufacturing yields for AMD’s products could negatively impact its financial results; AMD has a wafer supply agreement with GLOBALFOUNDRIES Inc. (GF) with obligations to purchase all of its microprocessor and APU product requirements, and a certain portion of its GPU product requirements, manufactured at process nodes larger than 7 nanometer from GF with limited exceptions. If GF is not able to satisfy AMD’s manufacturing requirements, AMD’s business could be adversely impacted; the success of AMD’s business is dependent upon its ability to introduce products on a timely basis with features and performance levels that provide value to its customers while supporting and coinciding with significant industry transitions; if AMD cannot generate sufficient revenue and operating cash flow or obtain external financing, it may face a cash shortfall and be unable to make all of its planned investments in research and development or other strategic investments; the loss of a significant customer may have a material adverse effect on AMD; AMD’s receipt of revenue from its semi-custom SoC products is dependent upon its technology being designed into third-party products and the success of those products; global economic and market uncertainty may adversely impact AMD’s business and operating results; AMD’s worldwide operations are subject to political, legal and economic risks and natural disasters, which could have a material adverse effect on it; government actions and regulations such as export administration regulations, tariffs, and trade protection measures, may limit AMD’s ability to export AMD’s products to certain customers; AMD’s products may be subject to security vulnerabilities that could have a material adverse effect on AMD; IT outages, data loss, data breaches and cyber-attacks could compromise AMD’s intellectual property or other sensitive information, be costly to remediate and cause significant damage to its business, reputation and operations; AMD’s operating results are subject to quarterly and seasonal sales patterns; AMD may not be able to generate sufficient cash to service its debt obligations or meet its working capital requirements; AMD has a large amount of indebtedness which could adversely affect its financial position and prevent it from implementing its strategy or fulfilling its contractual obligations; the agreements governing AMD’s notes and the Secured Revolving Line of Credit impose restrictions on AMD that may adversely affect AMD’s ability to operate its business; the markets in which AMD’s products are sold are highly competitive; the conversion of the 2.125% Convertible Senior Notes due 2026 may dilute the ownership interest of AMD’s existing stockholders, or may otherwise depress the price of its common stock; uncertainties involving the ordering and shipment of AMD’s products could materially adversely affect it; the demand for AMD’s products depends in part on the market conditions in the industries into which they are sold. Fluctuations in demand for AMD’s products or a market decline in any of these industries could have a material adverse effect on its results of operations; AMD’s ability to design and introduce new products in a timely manner is dependent upon third-party intellectual property; AMD depends on third-party companies for the design, manufacture and supply of motherboards, software and other computer platform components to support its business; if AMD loses Microsoft Corporation’s support for its products or other software vendors do not design and develop software to run on AMD’s products, its ability to sell its products could be materially adversely affected; and AMD’s reliance on third-party distributors and add-in-board partners subjects it to certain risks. Investors are urged to review in detail the risks and uncertainties in AMD’s Securities and Exchange Commission filings, including but not limited to AMD’s Quarterly Report on Form 10-Q for the quarter ended September 28, 2019.

©2019 Advanced Micro Devices, Inc. All rights reserved. AMD, the AMD Arrow logo, Radeon, and combinations thereof are trademarks of Advanced Micro Devices, Inc. Other product names used in this publication are for identification purposes only and may be trademarks of their respective companies.

Contacts:
George Millington
AMD Communications
+1 408-547-7481
George.Millington@amd.com

Jason Schmidt
AMD Investor Relations
+1 408-749-6688
Jason.Schmidt@amd.com

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European Online Payment Fraud & Security Report, 2019 – Online Payment Fraud Rises in Europe and Worldwide

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Dublin, Dec. 13, 2019 (GLOBE NEWSWIRE) — The “Europe Online Payment Fraud and Security 2019” report has been added to ResearchAndMarkets.com’s offering.

Online Payment Fraud Rises in Europe and Worldwide

The volume of online payment fraud loss is on the rise worldwide, projected to more than double by 2023, compared to 2018. In Europe, online retailers in the two largest E-Commerce markets, the UK and Germany, report detection of an increasing number of fraud attempts. In the UK alone more than one-half of card purchase fraud loss stemmed from E-Commerce as of last year. Consequently, digital buyers remain on their guard and, according to surveys cited in this report, many would not purchase from an online store which they believe does not provide a sufficient level of payment security.

Payment Security Regulations to Transform E-Commerce Payments in Europe

The Strong Customer Authentication (SCA) requirements, to apply from September 2019 on, are expected to strengthen the safety of online purchase transactions. At the same time, the spotty level of readiness and awareness of these requirements among merchants and consumers could have a negative effect on the development of E-Commerce in Europe and lead to a considerable loss in economic activity at least in the first year after the regulations take effect.

Questions Answered in this Report

  • What are the top online payment fraud prevention trends in Europe and worldwide?
  • How are the Strong Customer Authentication requirements projected to affect the development of European digital payments?
  • How large are the online payment fraud losses in Europe’s largest E-Commerce market?
  • What measures are taken by online merchants in selected European markets to prevent payment fraud?
  • How important is the consideration of online payment security to digital buyers in Europe?

Companies Mentioned

  • Mastercard Inc.
  • Visa Inc.

Key Topics Covered

1. Management Summary

2. Global Developments

  • Overview of Online Payment Fraud Trends, May 2019
  • Online Payment Fraud Losses, in USD billion, 2018e & 2023f
  • Breakdown of Feelings Consumers Have Towards Card Transaction Declines in Online Shopping, in % of Online Shoppers, by Frequency of Online Shopping, July 2018
  • Breakdown of Most Important Factors in Consumers’ Online Experience, in % of Consumers, 2018
  • Share of Consumers Who Have More Confidence in a Business That Uses Physical Biometrics for Online Security, in %, 2018
  • Share of Respondents Who Would be Willing to Use Fingerprint or Other Biometric to Secure Their Payment Details, in %, 2018
  • Number of Remote Mobile Biometric Transactions, in billions, and Their Share of Total In-Store and Remote Transactions Authenticated via Mobile Biometrics, in %, 2018 & 2023f
  • Breakdown of The Perceived Level of Security of Blockchain Solutions Compared to Conventional IT Solutions, in % of Senior Executives, March 2019
  • Top 10 Blockchain Use Cases, in % of Senior Executives, 2018
  • Spending on Fraud Management Solutions, 2017 & 2023f

3. Europe

3.1. Regional

  • Overview of Strong Customer Authentication Requirements Under PSD2, April 2019
  • Overview of The Strong Customer Authentication Perceptions by Industry Participants, June 2019
  • E-Commerce Merchants’ Readiness to Support Strong Customer Authentication, in %, November 2018
  • Levels of Awareness and Preparation of E-Commerce Merchants to Strong Customer Authentication Requirements, by SMEs and Large Businesses, June 2019
  • Share of Consumers Who Are Unaware of the Strong Customer Authentication Requirements for Online Purchases, in %, June 2019
  • Share of Consumers Who Prefer One-time Passcodes for Authentication, Compared to Fingerprint Recognition, in %, June 2019
  • Barriers to Buying Online, in % of Online Shoppers, July 2018
  • Attitudes to Security of Online Shopping, incl. Payment-Related, in % of Online Shoppers, by Austria, Germany and the UK, April 2018

3.2. UK

  • E-Commerce Fraud Loss on UK-Issued Cards, in GBP million, and Share of Total Card Purchase Fraud Loss, in %, 2013 – 2018
  • Top 10 Reasons for Shopping Cart Abandonment, in % of Online Shoppers, 2018e
  • Breakdown of Consumers’ Perception of Whether They Currently Undergo Enough Security Checks When Making an Online Payment, in % of Consumers, 2018
  • Breakdown of the Preferred Way of Receiving a One-time Passcode to Verify a Payment Transaction, in % of Consumers, 2018
  • Share of Cross-Border E-Commerce Orders Rejected by UK Merchants Due to Suspected Fraud, in % of UK Merchants, 2018

3.3. Germany

  • Share of E-Commerce Merchants Who Faced Fraud or Fraud Attempts in Their Online Shops, in %, 2018
  • Perceived Development of Fraud and Fraud Attempts Over the Past Year, in % of E-Commerce Merchants, 2018
  • Types of Fraud and Fraud Attempts Faced by E-Commerce Merchants in Their Online Stores, in % of E-Commerce Merchants, 2018
  • Measures Taken by E-Commerce Merchants to Prevent Fraud in Their Online Stores, in % of E-Commerce Merchants, 2018
  • Breakdown of Usage of 3D Secure for Credit Card Payments, in % of E-Commerce Sellers, 2017 – 2019
  • Breakdown of the Perceived Change in Shopping Cart Abandonment Rates After Choosing Credit Card Payment as a Result of 3D Secure, in % of E-Commerce Sellers Using 3D Secure, 2019
  • Breakdown of Importance of Payment Topics in Merchants’ Payment Strategy Until 2020, in % of E-Commerce Merchants, 2018
  • Breakdown of Attitude to Paying by Invoice, in % of Online Shoppers, April 2018
  • Most Trusted Mobile Payment Providers, in % of Consumers, August 2018

3.4. France

  • Top 5 Services and Obligations of E-Commerce Merchants in 2019 According to Online Shoppers, in % of Online Shoppers, December 2018
  • Preferred Biometric Authentication Methods in E-Commerce, in % of Online Shoppers, 2019
  • Breakdown of Main Barriers That Make Consumers Hesitant to Purchase Online, in %, by Total Population and Online Shoppers, June 2018
  • Share of 3D Secure Payments, in % of Online Card Payments, April 2011 – April 2018
  • Breakdown of Attitudes Towards Mobile Payments, in % of Internet Users, February 2018

3.5. Spain

  • Breakdown of Factors Most Important in Online Payment Methods, in % of Online Shoppers, October 2018

3.6. Italy

  • Fraudulent Transactions’ Share of E-Commerce Sales, in %, 2017

3.7. Austria

  • Share of Online Shoppers Who Fell Victim to Internet Fraud, in %, and Type of Fraud Experienced, in %, November 2018
  • Factors Taken into Account by Online Shoppers in Order to Avoid Fraud, in % of Online Shoppers, November 2018
  • Share of Online Shoppers Who Experienced Internet Fraud, in %, and Type of Fraud Experienced, in %, December 2018

3.8. Switzerland

  • Share of E-Commerce Merchants Who Faced Fraud or Fraud Attempts in Their Online Shops, in %, 2018
  • Perceived Development of Fraud and Fraud Attempts Over the Past Year, in % of E-Commerce Merchants, 2018
  • Types of Fraud and Fraud Attempts Faced by E-Commerce Merchants in Their Online Stores, in % of E-Commerce Merchants, 2018
  • Measures Taken by E-Commerce Merchants to Prevent Fraud in Their Online Stores, in % of E-Commerce Merchants, 2018

For more information about this report visit https://www.researchandmarkets.com/r/4d3ard

Research and Markets also offers Custom Research services providing focused, comprehensive and tailored research.

CONTACT: CONTACT: ResearchAndMarkets.com Laura Wood, Senior Press Manager press@researchandmarkets.com For E.S.T Office Hours Call 1-917-300-0470 For U.S./CAN Toll Free Call 1-800-526-8630 For GMT Office Hours Call +353-1-416-8900
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Artificial Intelligence of Things (AIoT) Market Research Report 2019-2024 – Embedded AI in Support of IoT Things/Objects Will Reach $4.6B Globally by 2024

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Dublin, Nov. 27, 2019 (GLOBE NEWSWIRE) — The “Artificial Intelligence (AI) in Big Data, Data as a Service (DaaS), AI Supported IoT (AIoT), and AIoT DaaS 2019 – 2024” report has been added to ResearchAndMarkets.com’s offering.

Select Research Findings

  • Embedded AI in support of IoT Things/Objects will reach $4.6B globally by 2024
  • IoT DaaS is growing nearly three times as fast as non-IoT DaaS, with much of it streaming data
  • Structured data market remains greater than unstructured, but the latter will overtake the former
  • AI in industrial machines will reach $415M globally by 2024 with collaborative robot growth at 42.5% CAGR
  • Machine learning will become a key to realize the full potential of big data and IoT in edge computing platforms

This Artificial Intelligence of Things (AIoT) market research provides analysis of technologies, leading companies and solutions. The report also provides quantitative analysis including market sizing and forecasts for AIoT infrastructure, services, and specific solutions for the period 2019 through 2024. It also provides an assessment of the impact of 5G upon AIoT (and vice versa) as well as blockchain and specific solutions such as Data as a Service (DaaS), Decisions as a Service, and the market for AIoT in smart cities.

This research also evaluates various AI technologies and their use relative to analytics solutions within the rapidly growing enterprise and industrial data arena. It assesses emerging business models, leading companies, and solutions. It also analyzes how different forms of AI may be best used for problem solving. The report also evaluates the market for AI in IoT networks and systems. This research provides forecasting for unit growth and revenue for both analytics and IoT. It includes an evaluation of the technologies, companies, and solutions for leveraging big data tools and advanced analytics for IoT data processing. Emphasis is placed on leveraging IoT data for process improvement, new and improved products, and ultimately enterprise IoT data syndication. It includes detailed forecasts for 2019 through 2024.

This research also evaluates the technologies, companies, strategies, and solutions for DaaS. It assesses business opportunities for enterprise use of own data, others data, and a combination of both. It also analyzes opportunities for enterprises to monetize their own data through various third-party DaaS offerings. This research also evaluates opportunities for DaaS in major industry verticals as well as the future outlook for emerging data monetization from 2019 to 2024.

It is important to recognize that intelligence within IoT technology market is not inherent but rather must be carefully planned. AIoT market elements will be found embedded within software programs, chipsets, and platforms as well as human-facing devices such as appliances, which may rely upon a combination of local and cloud-based intelligence.

Just like the human nervous system, IoT networks will have both autonomic and cognitive functional components that provide intelligent control as well as nerve end-points that act like nerve endings for neural transport (detection and triggering of communications) and nerve channels that connect the overall system. The big difference is that the IoT technology market will benefit from engineering design in terms of Artificial Intelligence (AI) and cognitive computing placement in both centralized and edge computing locations.

AI is rapidly making its way into many advanced solutions including autonomous vehicles, smart bots, advanced predictive analytics, and more. Many industry verticals will be transformed through AI integration with enterprise, industrial, and consumer product and service ecosystems. It is destined to become an integral component of business operations including supply chains, sales and marketing processes, product and service delivery and support models. The term for AI support of IoT (or AIoT) is just beginning to become part of the ICT lexicon as the possibilities for the former adding value to the latter are only limited by the imagination.

AI enhances the ability for big data analytics and IoT platforms to provide value to each of these market segments. The use of AI for decision making in IoT and data analytics will be crucial for efficient and effective decision making, especially in the area of streaming data and real-time analytics associated with edge computing networks. Real-time data will be a key value proposition for all use cases, segments, and solutions. The ability to capture streaming data, determine valuable attributes, and make decisions in real-time will add an entirely new dimension to service logic. In many cases, the data itself, and actionable information will be the service.

The convergence of AI and IoT technologies and solutions (AIoT) is leading to thinking networks and systems that are becoming increasingly more capable of solving a wide range of problems across a diverse number of industry verticals. AI adds value to IoT through machine learning and improved decision making. IoT adds value to AI through connectivity, signaling, and data exchange.

AI adds value to IoT through machine learning and improved decision making. IoT adds value to AI through connectivity, signaling, and data exchange.

While early solutions are rather monolithic, it is anticipated that AIoT integration within businesses and industries will ultimately lead to more sophisticated and valuable inter-business and cross-industry solutions. These solutions will focus primarily upon optimizing system and network operations as well as extracting value from industry data through dramatically improved analytics and decision-making processes.

In many cases, the data itself, and actionable information will be the service. AIoT infrastructure and services will therefore be leveraged to achieve more efficient IoT operations, improve human-machine interactions and enhance data management and analytics, creating a foundation for IoT Data as a Service (IoTDaaS) and AI based Decisions as a Service.

IoTDaaS constitutes retrieving, storing and analyzing information and provide customer either of the three or integrated service package depending on the budget and the requirement. New models are emerging to reduce friction across the value chain including enhanced Big Data as a Service (BDaaS) offerings. BDaaS is anticipated to make cross-industry, cross-company, and even cross-competitor data exchange a reality that adds value across the ecosystem with minimized security and privacy concerns.

IoTDaaS offers convenient and cost effective solutions to enterprises of various sizes and domain. IoTDaaS constitutes retrieving, storing and analyzing information and provide customer either of the three or integrated service package depending on the budget and the requirement. AI algorithms enhance the ability for big data analytics and IoT platforms to provide value to each of these market segments. One of the important growth areas for the Data as a Service market is to leverage AI to offer Value-added Data in a Decisions as a Service model.

Big data in IoT is different than conventional IoT and thus will requires more robust, agile and scalable platforms, analytical tools and data storage systems than conventional big data infrastructure. Looking beyond data management processes, IoT data itself will become extremely valuable as an agent of change for product development as well as identification of supply gaps and realization of unmet demands. Big data and analytics will increase in importance as IoT evolves to become more commonplace with the deployment of 5G IoT.

The Massive Machine-type Communications (mMTC) portion of fifth generation cellular networks will facilitate a highly scalable M2M network for many IoT applications, particularly those that do not require high bandwidth. Data generated through sensors embedded in various things/objects will generate massive amounts of unstructured (big) data on real-time basis that holds the promise for intelligence and insights for dramatically improved decision processes.

Big data in IoT is also dissimilar than non-machine related analytics and thus will require more robust, agile and scalable platforms, analytics tools, and data storage systems than conventional infrastructure. Due to this new architecture approach, the need to handle data differently, and the sheer volume of unstructured data, there will be great opportunities for big Data in IoT. Analytics used in IoT will become an enabler for the entire IoT ecosystem as enterprise begins to take advantage of new business opportunities such as syndicating their own data.

AI coupled with advanced big data analytics provides the ability to make raw data meaningful and useful as information for decision-making purposes. The use of AI for decision making in IoT and data analytics will be crucial for efficient and effective decision making, especially in the area of streaming data and real-time analytics associated with edge computing networks. Real-time data will be a key value proposition for all use cases, segments, and solutions. The ability to capture streaming data, determine valuable attributes, and make decisions in real-time will add an entirely new dimension to service logic. In many cases, the data itself, and actionable information will be the service.

These advanced analytics provide the ability to make raw data meaningful and useful as information for decision-making purposes. AI enhances the ability for big data analytics and IoT platforms to provide value to each of these market segments. The use of AI for decision making in IoT and data analytics will be crucial for efficient and effective decision making, especially in the area of streaming data and real-time analytics associated with edge computing networks.

The ability to capture streaming data, determine valuable attributes, and make decisions in real-time will add an entirely new dimension to service logic. In many cases, the data itself, and actionable information will be the service. However, real-time data is anticipated to become a highly valuable aspect of all solutions as a determinant of user behavior, application effectiveness, and identifier of new and enhanced mobile/wireless and/or IoT related apps and services.

In terms of overall AIoT data management, we see three different types of IoT Data:

  • Raw Data: Untouched and Unstructured Data
  • Meta Data: Data about Data
  • Transformed Data: Valued-added Data

AI will be useful in support of managing each of these data types in terms of identifying, categorizing, and decision making.

We see the AIoT market transforming from today’s largely consumer appliance and electronics related approach to one in which AIoT data is highly valued asset wherein companies like SAS provide a utility function in terms of helping enterprise, industrial, and government clients monetize their data. This will likely occur in a Data as a Service market model, which may be segmented in various ways including by Sector including Public Data, Business Data, and Government Data:

  • Public Data consists of Communications and Internet Data (broadcast media, social media, texting, voice, video/picture sharing, etc.), Government Tracked Data (public records such as vehicle and home title, licensing, public resource usage including roadway usage), User Generated Data (consumer and business data made public [may be anonymized or not] such as vehicle usage, appliance data, etc.), and Other Data category.
  • Business Data consists of Enterprise Data and Industrial Data across various industry verticals. This data comes from many different business related activities. Some of this data may be static and/or stored in data lakes. Some of this data may be generated and used in real-time.
  • Government Data is data that the government collects about itself such as Government Services Administration (GSA), essential services (such as public safety), military, homeland security, etc. This is not to be confused the government collecting certain public data (such as highway usage).

It may also be segmented by Source Type. As it is prohibitively difficult to identify all of the sources and source types, we have broadly segmented Source by Machine Data (consumer appliances, vehicles [ cars, trucks, planes, trains, ships, etc. ], robots and industrial equipment, etc.) and Non-machine Data (everything else including people texting/talking/etc., enterprise data collected by humans, etc.).

It is important to note that the DaaS also includes data sourced from a machine (such as from a jet engine) that is not Internet-connected and thus limited in utility without the Internet of Things (IoT) to collect, relay, and provide opportunities for feedback loops. Accordingly, we have also segmented the Data as a Service market by Data Collection Type, which includes IoT DaaS data and Non-IoT DaaS data. Machine Data that does not use IoT, by definition, will not be streaming data or allow for real-time analytics.

Research Benefits

  • Understand the role of AI in Big data and IoT
  • Understand the role and importance of Big Data in IoT
  • Identify key market issues and drivers for Big Data in IoT
  • Identify leading companies for Big Data and Analytics in IoT
  • Understand the emerging vendor ecosystem for Big Data in IoT
  • Identify leading DaaS companies, strategies, and solutions for enterprise
  • Understand the market dynamics for the DaaS market including leading services

Key Topics Covered

Artificial Intelligence of Things: AIoT Market by Technology and Solutions
1. Executive Summary
2. Introduction
3. AIoT Technology and Market
4. AIoT Applications Analysis
5. Analysis of Important AIoT Companies
6. AIoT Market Analysis and Forecasts 2019 – 2024
7. Conclusions and Recommendations

Artificial Intelligence in Big Data Analytics and IoT: Market for Data Capture, Information and Decision Support Services
1. Executive Summary
2. Introduction
3. Overview
4. AI Technology in Big Data and IoT
5. AI Technology Application and Use Case
6. AI Technology Impact on Vertical Market
7. AI Predictive Analytics in Vertical Industry
8. Company Analysis
9. AI in Big Data and IoT Market Analysis and Forecasts 2019 – 2024
10. Conclusions and Recommendations
11. Appendix

Big Data in Internet of Things: IoT Data Management, Analytics, and Decision Making
1. Executive Summary
2. Big Data in Internet of Things
3. Big Data in IoT Business Trends and Predictions
4. Big Data in IoT Vendor Ecosystem
5. Big Data in IoT Market Analysis and Forecasts
6. Key Companies
7. Summary and Conclusions

Data as a Service Market by Enterprise, Industrial, Public and Government Data Applications and Services
1. Executive Summary
2. Data as a Service Technologies
3. Data as a Service Market
4. Data as a Service Strategies
5. Data as a Service Applications
6. Market Outlook and Future of Data as a Service
7. Data as a Service Market Analysis and Forecasts 2019 – 2024
8. Regional DaaS Market Analysis and Forecasts 2019 – 2024
9. Conclusions and Recommendations
10. Appendix

For more information about this report visit https://www.researchandmarkets.com/r/ukr9g6

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