Artificial Intelligence
authID.AI Announces Results for Q2 2021
DENVER, Aug. 05, 2021 (GLOBE NEWSWIRE) — authID.AI (www.authid.ai) [OTCQB:AUID] today announced its results for the second quarter ended June 30, 2021. The Company, previously known as Ipsidy, made strategic changes in its branding, leadership and board of directors, and continued to expand its channel partner network.
The authID platform eliminates the need for passwords by delivering secure biometric identity proofing and authentication solutions to a broad array of verticals including finance, healthcare, sharing economy, online gaming and other large data-sensitive industries. The strengthened firm is poised to capture greater share of the growing market for password alternatives and cyber threat solutions.
Financial Results for the Three Months and Six Months Ended June 30, 2021
- Total revenue for the three months ended June 30, 2021 increased to $0.6 million from $0.3 million for the same period ended June 30, 2020. Total revenue for the six months in 2021 increased to $1.2 million from $1.1 million for the same period in 2020.
- Net loss for the three months ended June 30, 2021 decreased by 12% to $3.1 million as compared to $3.5 million for the three months ended June 30, 2020. Net loss for the six-month period in 2021 decreased by 24% to $5.5 million from $7.3 million for the same period in 2020.
- Basic and diluted net loss per share for the three months ended June 30, 2021 improved by 25% to $0.15 cents from $0.20 cents for the three months ended June 30, 2020. Basic and diluted net loss per share for the six months ended June 30, 2021 improved by 33% to $0.28 cents from $0.42 cents for the same period ended June 30, 2020.
- Adjusted EBITDA loss for the three-month periods ended June 30, 2021 and June 30, 2020 was unchanged at $1.4 million. Adjusted EBITDA loss for the six-month period ended June 30, 2021 was $2.6 million as compared to $2.7 million for the same period in 2020.
- Effective June 14, 2021, the Company completed a 1-for-30 reverse stock split of common stock in order to make it easier for investors to trade in our stock and as a necessary step before the Company’s common stock can be admitted to listing on a national exchange.
- During the six months ended June 30, 2021, the Company received voluntary conversion notices to exchange approximately $6.2 million of convertible notes and a majority of the accrued interest into approximately 1.2 million (post-split) shares of common stock. This conversion was the primary reason for the reduction in Total liabilities outstanding from $9.3 million as of December 31, 2020 to $3.7 million as of June 30, 2021.
Refer to Table 1 for reconciliation of net income to Adjusted EBITDA (a non-GAAP measure).
The Company continued to execute upon its strategy of expanding the authID Channel Partner Network. Various agreements were executed with integration efforts underway or nearing completion with fintech companies, technology solutions providers, and payment processors who will deploy authID’s trusted identity verification and seamless FIDO2 passwordless login services with their customer portfolios in order to mitigate identity fraud, account takeover and phishing. The highlights for the six-month period ending June 30, 2021 include:
- Rebranded as authID.ai with a new ticker symbol of AUID to best capture our vision for the future of biometric authentication and our mission to replace passwords.
- Appointed a new management team with deep industry experience and success spearheading significant growth at market-leading technology companies. Tom Thimot joined authID as Chief Executive Officer and Board Director, and Tripp Smith joined as President and Chief Technology Officer in order to propel the Company’s next phase of growth.
- Completed integration to Temenos core banking platform making the full complement of authID’s identity verification and authentication solutions easily available to the Temenos global portfolio. The services will be launched in the third quarter with a North American bank committed to providing a secure frictionless onboarding and passwordless banking experience for their international clientele.
- Selected by CU NextGen, a leading provider of next generation technology solutions and platforms to US-based credit unions. CU NextGen is leveraging authID’s biometric identity authentication solution to transform the way its credit union clients recognize their members—both digitally and in person. CU NextGen has completed integration to authID with its first credit union and looks forward to rolling out authID’s services to additional clients across the United States.
- Selected by US payment processor, On The Fly POS, who will deliver authID’s trusted identity verification and seamless passwordless login services with its card-present and payment gateway merchant portfolios.
- Expanded our partner network in the Americas, signing an extension to our agreement with Inetum (a division of the global IT services company formerly known as Gfi), covering the North and Central American regions. The Agreement also provides access to Mexico’s national identity registry, through Inetum, which when integrated to authID will enhance our identity verification services.
- Signed an agreement with BPSmart, an innovative technology provider servicing North America and Latin America, to integrate the authID identity services into their chat-based AI-Powered, Digital Assistant Platform.
- Added through a US channel partner various sharing-economy and ecommerce providers, including auto-rentals and property management companies, who are leveraging our biometric identity proofing services to help reduce identity fraud in digital customer onboarding.
“In the last month, we began the process of launching our exciting new authID brand with a vision to help every organization ‘Recognise their Customers” without friction or loss of privacy,” said Tom Thimot, CEO of authID.ai. “To realize our mission to eliminate passwords and become the preferred platform for biometric authentication, our new leadership team will drive success by leveraging channel partnerships as a force multiplier. The authID team will also launch our campaign to service the US market with new self-service capabilities aimed at quickly onboarding thousands of small and medium-sized businesses, who can benefit from our low-friction identity proofing and authentication services. We are confident that our product and go to market strategies will increase the demand for authID’s software as the market realizes that matching a customer biometric to access an account is more secure with less friction than traditional passwords and one-time passwords via SMS.”
Additional analysis of the Company’s performance can be found in “Management’s Discussion and Analysis of Financial Condition and Results of Operations” included in the Quarterly Report on Form 10-Q for the three and six months ended June 30, 2021 filed at www.sec.gov and posted on the Company’s investor relations website.
About authID.ai:
authID.ai (Ipsidy Inc.) (OTCQB:AUID) (authID.ai). Recognise Your Customer™. Our mission is to eliminate all passwords, and to be the preferred global platform for biometric identity authentication. The authID.ai Identity as a Service (IDaaS) platform delivers a suite of biometric identity proofing and authentication solutions that establish security and trust in today’s digital world. Our vision is to enable every organization to “Recognise Your Customer” instantly, without friction or loss of privacy, powered by the most sophisticated biometric and artificial intelligence technologies.
authID.ai is headquartered in the United States and has operating subsidiaries: MultiPay in Colombia, www.multipay.com.co; Cards Plus in South Africa, www.cardsplus.co.za; Ipsidy Enterprises in the U.K. and Ipsidy Perú S.A.C. Further information on authID.ai can be found at www.authID.ai or contact us at [email protected].
authID.ai can be found at www.authID.ai.com or contact us at [email protected].
Contacts:
| Tom Thimot, CEO | [email protected] |
| Stuart Stoller, CFO | [email protected] |
| +1 (516) 274-8700 | |
| Media: | |
| Emily Porro, Senior Vice President, Makovsky | [email protected] |
Notice Regarding Forward-Looking Statements.
Information contained in this announcement may include “forward-looking statements.” All statements other than statements of historical facts included herein, including, without limitation, those regarding the financial position, business strategy, plans and objectives of management for future operations of both Ipsidy Inc. dba authID.ai and its business partners, future service launches with customers, the outcome of pilots and new initiatives and customer pipeline are forward-looking statements. Such forward-looking statements are based on a number of assumptions regarding authID.ai present and future business strategies, and the environment in which authID.ai expects to operate in the future, which assumptions may or may not be fulfilled in practice. Implementation of some or all of the new services referred to is subject to regulatory or other third party approvals. Actual results may vary materially from the results anticipated by these forward-looking statements as a result of a variety of risk factors, including the risk that implementation, adoption and offering of the service by customers, consumers and others may take longer than anticipated, or may not occur at all; changes in laws, regulations and practices; changes in domestic and international economic and political conditions, the as yet uncertain impact of the Covid-19 pandemic and others. Additional risks may arise with respect to commencing operations in new countries and regions, of which authID.ai is not fully aware at this time. See the Company’s Annual Report on Form 10-K for the Fiscal Year ended December 31, 2020 filed at www.sec.gov for other risk factors which investors should consider. These forward-looking statements speak only as to the date of this announcement and cannot be relied upon as a guide to future performance. authID.ai expressly disclaims any obligation or undertaking to disseminate any updates or revisions to any forward-looking statements contained in this announcement to reflect any changes in its expectations with regard thereto or any change in events, conditions or circumstances on which any statement is based.
Non-GAAP Financial Information.
The Company provides certain non-GAAP financial measures in this statement. Management believes that Adjusted EBITDA, when viewed with our results under GAAP and the accompanying reconciliations, provides useful information about our period-over-period results. Adjusted EBITDA is presented because management believes it provides additional information with respect to the performance of our fundamental business activities and is also frequently used by securities analysts, investors and other interested parties in the evaluation of comparable companies. We also rely on Adjusted EBITDA as a primary measure to review and assess the operating performance of our company and our management team in connection with our executive compensation. These non-GAAP key business indicators, which include Adjusted EBITDA, should not be considered replacements for and should be read in conjunction with the GAAP financial measures.
We define Adjusted EBITDA as GAAP net loss adjusted to exclude: (1) interest expense, (2) interest income, (3) provision for income taxes, (4) depreciation and amortization, (5) stock-based compensation expense (stock options and restricted stock) and (6) certain other items management believes affect the comparability of operating results. Please see Table 1 below for a reconciliation of Adjusted EBITDA to net income (loss), the most directly comparable financial measure calculated and presented in accordance with GAAP.
Table 1
Reconciliation of Net Loss to Adjusted EBITDA
| For the Quarter Ended | For the Six Months Ended | ||||||||||||||
| June 30, 2021 |
June 30, 2020 |
June 30, 2021 |
June 30, 2020 |
||||||||||||
| Net loss | $ | (3,057,610 | ) | $ | (3,462,448 | ) | $ | (5,547,609 | ) | $ | (7,298,869 | ) | |||
| Add Back: | |||||||||||||||
| Interest Expense | 256,550 | 310,153 | 553,988 | 489,203 | |||||||||||
| Debt extinguishment – loss/(gain) | (485,760 | ) | – | (485,760 | ) | 985,842 | |||||||||
| Warrant exercise inducement expense | – | 366,795 | – | 366,795 | |||||||||||
| Severance cost | – | 426,175 | – | 426,175 | |||||||||||
| Other expense/(income) | (6,121 | ) | (24,713 | ) | (7,658 | ) | (34,666 | ) | |||||||
| Depreciation and amortization | 314,317 | 321,987 | 624,146 | 647,331 | |||||||||||
| Taxes | 2,354 | 3,592 | 9,542 | 12,466 | |||||||||||
| Impairment loss | – | 163,822 | – | 1,035,629 | |||||||||||
| Stock compensation | 1,623,547 | 460,883 | 2,261,126 | 629,993 | |||||||||||
| Adjusted EBITDA (Non-GAAP) | $ | (1,352,723 | ) | $ | (1,433,754 | ) | $ | (2,592,225 | ) | $ | (2,740,101 | ) | |||
IPSIDY INC. (dba authID.ai) AND SUBSIDIARIES
CONDENSED CONSOLIDATED BALANCE SHEETS
| June 30, | December 31, | |||||||
| 2021 | 2020 | |||||||
| (unaudited) | ||||||||
| ASSETS | ||||||||
| Current Assets: | ||||||||
| Cash | $ | 1,805,414 | $ | 3,765,277 | ||||
| Accounts receivable, net | 201,939 | 72,986 | ||||||
| Current portion of net investment in direct financing lease | 76,661 | 72,682 | ||||||
| Inventory | 138,781 | 254,951 | ||||||
| Other current assets | 650,308 | 237,769 | ||||||
| Total current assets | 2,873,103 | 4,403,665 | ||||||
| Property and Equipment, net | 148,493 | 97,829 | ||||||
| Other Assets | 136,163 | 240,223 | ||||||
| Intangible Assets, net | 3,941,547 | 4,527,476 | ||||||
| Goodwill | 4,183,232 | 4,183,232 | ||||||
| Net investment in direct financing lease, net of current portion | 382,671 | 422,021 | ||||||
| Total assets | $ | 11,665,209 | $ | 13,874,446 | ||||
| LIABILITIES AND STOCKHOLDERS’ EQUITY | ||||||||
| Current Liabilities: | ||||||||
| Accounts payable and accrued expenses | $ | 2,103,954 | $ | 2,665,132 | ||||
| Notes payable obligation, current portion | 4,632 | 5,947 | ||||||
| Capital lease obligation, current portion | 30,763 | 39,232 | ||||||
| Convertible debt | 662,000 | – | ||||||
| Deferred revenue | 461,083 | 237,690 | ||||||
| Total current liabilities | 3,262,432 | 2,948,001 | ||||||
| Capital lease obligation, net of current portion | – | 10,562 | ||||||
| Notes payable, net of discounts and current portion | 485,760 | 487,339 | ||||||
| Convertible debt | – | 5,800,976 | ||||||
| Other liabilities | – | 47,809 | ||||||
| Total liabilities | 3,748,192 | 9,294,687 | ||||||
| Commitments and Contingencies (Note 13) | ||||||||
| Stockholders’ Equity: | ||||||||
| Common stock, $0.0001 par value, 1,000,000,000 shares authorized; 21,363,027 and 19,642,401 shares issued and outstanding as of June 30, 2021 and December 31, 2020, respectively | 2,137 | 1,964 | ||||||
| Additional paid in capital | 111,493,973 | 102,651,304 | ||||||
| Accumulated deficit | (103,781,760 | ) | (98,234,151 | ) | ||||
| Accumulated comprehensive income | 202,667 | 160,642 | ||||||
| Total stockholders’ equity | 7,917,017 | 4,579,759 | ||||||
| Total liabilities and stockholders’ equity | $ | 11,665,209 | $ | 13,874,446 | ||||
IPSIDY INC. (dba authID.ai) AND SUBSIDIARIES
CONDENSED CONSOLIDATED STATEMENTS OF OPERATIONS
(Unaudited)
| Three Months Ended June 30, |
Six Months Ended June 30, |
|||||||||||||||
| 2021 | 2020 | 2021 | 2020 | |||||||||||||
| Revenues: | ||||||||||||||||
| Products and services | $ | 565,165 | $ | 306,692 | $ | 1,141,078 | $ | 1,085,630 | ||||||||
| Lease income | 12,616 | 14,427 | 25,702 | 29,278 | ||||||||||||
| Total revenues, net | 577,781 | 321,119 | 1,166,780 | 1,114,908 | ||||||||||||
| Operating Expenses: | ||||||||||||||||
| Cost of Sales | 156,905 | 61,798 | 373,049 | 417,521 | ||||||||||||
| General and administrative | 3,049,700 | 2,389,794 | 4,977,626 | 3,872,916 | ||||||||||||
| Research and development | 347,173 | 190,339 | 669,183 | 620,740 | ||||||||||||
| Impairment loss | – | 163,822 | – | 1,035,629 | ||||||||||||
| Depreciation and amortization | 314,590 | 321,987 | 624,419 | 647,331 | ||||||||||||
| Total operating expenses | 3,868,368 | 3,127,740 | 6,644,277 | 6,594,137 | ||||||||||||
| Loss from operations | (3,290,587 | ) | (2,806,621 | ) | (5,477,497 | ) | (5,479,229 | ) | ||||||||
| Other Expense: | ||||||||||||||||
| Warrant exercise inducement expense | – | (366,795 | ) | – | (366,795 | ) | ||||||||||
| Extinguishment of debt – gain (loss) | 485,760 | – | 485,760 | (985,842 | ) | |||||||||||
| Other income | 6,121 | 24,713 | 7,658 | 34,666 | ||||||||||||
| Interest expense, net | (256,550 | ) | (310,153 | ) | (553,988 | ) | (489,203 | ) | ||||||||
| Other income (expense), net | 235,331 | (652,235 | ) | (60,570 | ) | (1,807,174 | ) | |||||||||
| Loss before income taxes | (3,055,256 | ) | (3,458,856 | ) | (5,538,067 | ) | (7,286,403 | ) | ||||||||
| Income Tax Expense | (2,354 | ) | (3,592 | ) | (9,542 | ) | (12,466 | ) | ||||||||
| Net loss | $ | (3,057,610 | ) | $ | (3,462,448 | ) | $ | (5,547,609 | ) | $ | (7,298,869 | ) | ||||
| Net Loss Per Share – Basic and Diluted | $ | (0.15 | ) | $ | (0.20 | ) | $ | (0.28 | ) | $ | (0.42 | ) | ||||
| Weighted Average Shares Outstanding – Basic and Diluted | 20,248,868 | 17,441,164 | 20,003,913 | 17,473,583 | ||||||||||||
Note: Following the reverse stock split in June 2021, net loss per share and weighted average shares outstanding were adjusted to reflect the lower number of common stock outstanding.
IPSIDY INC. (dba authID.ai) AND SUBSIDIARIES
CONDENSED CONSOLIDATED STATEMENTS OF CASH FLOWS
(Unaudited)
| Six Months Ended June 30, |
||||||||
| 2021 | 2020 | |||||||
| CASH FLOWS FROM OPERATING ACTIVITIES: | ||||||||
| Net loss | $ | (5,547,609 | ) | $ | (7,298,869 | ) | ||
| Adjustments to reconcile net loss with cash flows from operations: | ||||||||
| Depreciation and amortization expense | 624,419 | 600,978 | ||||||
| Stock-based compensation | 2,261,126 | 629,993 | ||||||
| Extinguishment of note payable | – | 985,481 | ||||||
| Warrant exercise inducement expense | – | 366,795 | ||||||
| Amortization of debt discounts and issuance costs | 237,435 | 214,668 | ||||||
| Impairment losses | – | 1,059,495 | ||||||
| Forgiveness of note payable | (485,760 | ) | – | |||||
| Changes in operating assets and liabilities: | ||||||||
| Accounts receivable | (127,930 | ) | (23,217 | ) | ||||
| Net investment in direct financing lease | 35,371 | 31,796 | ||||||
| Other current assets and other assets | (308,479 | ) | 21,984 | |||||
| Inventory | 113,870 | 374,366 | ||||||
| Accounts payable and accrued expenses | 644,649 | 1,056,433 | ||||||
| Deferred revenue | 223,393 | 28,810 | ||||||
| Other liabilities | (47,809 | ) | – | |||||
| Net cash flows from operating activities | (2,377,324 | ) | (1,951,287 | ) | ||||
| CASH FLOWS FROM INVESTING ACTIVITIES: | ||||||||
| Purchase of property and equipment | (78,325 | ) | (2,394 | ) | ||||
| Investment in other assets including work in progress | (10,829 | ) | (124,870 | ) | ||||
| Other assets | – | 13,462 | ||||||
| Net cash flows from investing activities | (89,154 | ) | (113,802 | ) | ||||
| CASH FLOWS FROM FINANCING ACTIVITIES: | ||||||||
| Proceeds from issuance of convertible note payable | – | 1,510,000 | ||||||
| Payment of debt issuance costs | – | (104,800 | ) | |||||
| Proceeds from sale of common stock, | – | 200,000 | ||||||
| Proceeds from exercise of warrants | – | 283,950 | ||||||
| Proceeds from payroll protection loan | 485,760 | 485,760 | ||||||
| Payments on notes payable | (2,892 | ) | – | |||||
| Principal payments on capital lease obligation | (19,224 | ) | (19,487 | ) | ||||
| Net cash flows from financing activities | 463,644 | 2,355,423 | ||||||
| Effect of Foreign Currencies | 42,971 | (42,465 | ) | |||||
| Net Change in Cash | (1,959,863 | ) | 247,869 | |||||
| Cash, Beginning of the Period | 3,765,277 | 567,081 | ||||||
| Cash, End of the Period | $ | 1,805,414 | $ | 814,950 | ||||
| Supplemental Disclosure of Cash Flow Information: | ||||||||
| Cash paid for interest | $ | 8,779 | $ | 5,296 | ||||
| Cash paid for income taxes | $ | 9,853 | $ | 12,466 | ||||
| Modification of warrants issued with convertible debt | $ | – | $ | 95,223 | ||||
| Exchange of notes payable and accrued interest for convertible notes payable | $ | – | $ | 2,662,000 | ||||
| Warrant exercise with a subscription receivable | $ | – | $ | 965,033 | ||||
| Settlement of accounts payable with issuance of common stock | $ | – | $ | 8,270 | ||||
| Conversion of convertible notes payable and accrued interest to common stock | $ | 6,232,340 | $ | – | ||||
| Settlement of accounts payable with issuance of stock options | $ | 349,376 | $ | – | ||||
For additional information, see Form 10-Q for the quarterly period ended June 30, 2021 filed at www.sec.gov.
Artificial Intelligence
Fingerprint Sensor Market worth $5.9 billion by 2029 – Exclusive Report by MarketsandMarkets™
CHICAGO, May 27, 2024 /PRNewswire/ — The Fingerprint Sensor Market is projected to grow from USD 4.2 billion in 2024 and is estimated to reach USD 5.9 billion by 2029; it is expected to grow at a Compound Annual Growth Rate (CAGR) of 7.0% from 2024 to 2029 according to a new report by MarketsandMarkets™. The rise in number of identity threats is one of the key factors leading to emergence of fingerprint technologies. Identity theft refers to the illegal acquisition of an individual’s personal or financial details to perpetrate fraud, including unauthorized transactions. It occurs through various methods and inflicts harm on victims’ credit, finances, and reputation.
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Browse in-depth TOC on “Fingerprint Sensor Market” 100 – Tables60 – Figures200 – Pages
Fingerprint Sensor Market Report Scope:
Report Coverage
Details
Market Revenue in 2024
$ 4.2 billion in 2024
Estimated Value by 2029
$ 5.9 billion
Growth Rate
Poised to grow at a CAGR of 7.0%
Market Size Available for
2020–2029
Forecast Period
2024–2029
Forecast Units
Value (USD Million/Billion)
Report Coverage
Revenue Forecast, Competitive Landscape, Growth Factors, and Trends
Segments Covered
By technology, sensor technology, type, end-use application and Region
Geographies Covered
North America, Europe, Asia Pacific, and Rest of World
Key Market Challenge
Sensor performance limitations
Key Market Opportunities
Increased adoption of IoT-based biometric technology
Key Market Drivers
Rise in number of identity threats leading to emergence of fingerprint technologies
Area & Touch sensors in the sensor type segment is expected to witness higher CAGR during the forecast period.
Area & touch sensors segment is expected to witness a higher CAGR during the forecast period in the fingerprint sensor market. These sensors activate upon physical contact with an object or individual and are more sensitive compared to traditional buttons or manual controls. They provide users with a seamless and responsive experience, making them ideal for modern electronic devices where sleek design and user convenience are paramount.
The 2D segment in the fingerprint sensor market is expected to capture higher share during the forecast period.
2D sensor technology captures a person’s fingerprint pattern, including ridges and valleys, in two dimensions using a single plane of lasers to measure the X and Y dimensions. This technology is primarily utilized for detection and ranging tasks. Due to its affordability, 2D sensor technology remains popular among OEMs, especially in mobile devices requiring compact sensors to meet the preferences of end users. Key players such as Synaptics Incorporated (US), Fingerprints (Sweden), and Shenzhen Goodix Technology Co., Ltd. (China) offer 2D sensors for various consumer electronics and applications.
Ultrasonic is expected to witness the highest CAGR in the fingerprint sensor market during the forecast period.
Ultrasonic fingerprint sensors utilize sound waves to penetrate the outer layers of the skin, enabling the capture of three-dimensional (3D) details and distinct fingerprint characteristics like ridges and sweat pores. Unlike current capacitive touch-based fingerprint technologies, ultrasonic sensors can read both the epidermal and dermal layers of the skin, allowing for the capture of fine details. Consequently, these sensors can accurately read wet fingers or those with ruptured or damaged skin. The continuous advancements in sensing technology are expected anticipated to propel the growth of the ultrasonic fingerprint sensor market. In January 2022, vivo (China) announced the 1000 9 Pro smartphone with Qualcomm’s 3D Sonic Max ultrasonic fingerprint sensor. The new smartphone is based on Snapdragon 8 Gen 1 processor. The Qualcomm 3D Sonic Max in the IQ00 9 Pro enables a super-fast one-tap fingerprint registration process.
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North America is expected to hold the second largest share of the fingerprint sensor industry during the forecast period.
North America is driven by the increasing adoption of biometric authentication across various sectors, particularly consumer electronics, government, and commercial domains. The US Department of Homeland Security (DHS) has been actively promoting the use of biometric technologies, including fingerprint sensors, for enhancing border security and immigration processes. The DHS’s Biometric Entry-Exit Program has facilitated the deployment of fingerprint scanners at various ports of entry, contributing to the market’s growth in the region. According to November 2023 statistics by US Department of Homeland Security (DHS), 1,018,349 persons obtained lawful permanent resident status in year 2022 compared to 740,002 persons in 2021, which is 37.6% y-o-y increase.
Key Players
Major vendors in the fingerprint sensor companies include Shenzhen Goodix Technology Co., Ltd. (China), Fingerprints (Sweden), Synaptics Incorporated (US), Apple Inc. (US) NEXT Biometrics (Norway), Novatek Microelectronics Corp. (Taiwan), Qualcomm Technologies, Inc. (US) among others.
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Artificial Intelligence
Cloud Analytics Market worth $118.5 billion by 2029- Exclusive Report by MarketsandMarkets™
CHICAGO, May 27, 2024 /PRNewswire/ — The market for cloud analytics has a promising future because of the acceptance of real-time analytics, the expansion of big data and IoT, and the integration of AI and machine learning. Its adoption across multiple industries will be further accelerated by improved security, edge computing integration, and user-friendly platforms that prioritise data-driven decision-making and customised customer experiences.
The Cloud Analytics Market is estimated to grow from USD 35.7 billion in 2024 to USD 118.5 billion in 2029, at a CAGR of 27.1% during the forecast period, according to a new report by MarketsandMarkets™. Cloud analytics revolutionizes data storage and analysis by harnessing the power of the cloud. By storing and analyzing data in the cloud, businesses can extract actionable insights crucial for both SMEs and large enterprises. This approach facilitates identifying patterns, predicting future outcomes, and gaining valuable insights. Cloud analytics offers an opportunity to consolidate data and convert it into actionable intelligence while reducing procurement and maintenance costs. It involves utilizing both cloud-stored data and the rapid computing power of the cloud for faster analytics. However, with cloud infrastructure, organizations gain access to scalable, secure, and efficient data storage and processing solutions, enabling them to meet the demands of big data and drive innovation.
Browse in-depth TOC on “Cloud Analytics Market”
333 – Tables 66 – Figures342 – Pages
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Scope of the Report
Report Metrics
Details
Market size available for years
2019–2029
Base year considered
2023
Forecast period
2024–2029
Forecast units
USD Billion
Segments Covered
Offering, Data Type, Data Processing, Vertical, and Region
Geographies covered
North America, Europe, Asia Pacific, Middle East & Africa, and Latin America
Companies covered
IBM (US), SAS Institute (US), Oracle (US), Google (US), Microsoft (US), Teradata (US), Salesforce (US), AWS (US), NetApp (US), Qilk (US), Sisense (US), SAP (Germany), Atos (France), Altair (US), Microstrategy (US), Tibco Software (US), Hexaware Technologies (India), Zoho (India), Rackspace Technology (US), Splunk (US), Cloudera (US), Domo (US), Hewlett Packard Enterprise (US), Incorta (US), Tellius (US), Rapyder (US), Hitachi Vantara (US), Board International (Switzerland), Ridge (Israel), Jaspersoft (US), Yellowfin (Australia), Deonodo (US), GoodData (US), Thoughtspot (US), and Infogain (US)
By offering the services segment to account for higher CAGR during the forecast period
Services segment in the Cloud Analytics Market have experienced remarkable growth in the Cloud Analytics Market, fueled by the increasing adoption of data-driven decision-making across industries. These services offer businesses the capability to analyze vast amounts of data stored in the cloud swiftly and efficiently, enabling them to extract valuable insights for strategic planning, optimization, and innovation. With the scalability and flexibility of cloud infrastructure, analytics services can accommodate diverse data types and analytical workloads, empowering organizations to derive actionable intelligence from their data in real time. As businesses continue to prioritize agility and competitiveness, the demand for cloud analytics services is expected to soar, driving further innovation and expansion in the Cloud Analytics Market landscape.
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By Type, advanced analytics solution is expected to hold the largest market size for the year 2024
The surge in advanced analytics adoption within the Cloud Analytics Market is reshaping the landscape of data-driven decision-making. Organizations across diverse sectors are increasingly turning to advanced analytics solutions hosted on cloud platforms to extract deeper insights from their data. This trend stems from the growing realization that traditional analytics methods are no longer sufficient to cope with the complexities of modern data ecosystems. Advanced analytics, powered by machine learning algorithms, predictive modeling, and AI, offer unparalleled capabilities to uncover hidden patterns, forecast trends, and optimize business processes. By leveraging the scalability, flexibility, and cost-effectiveness of cloud infrastructure, businesses can access powerful analytics tools without the burden of hefty upfront investments in hardware and software. As a result, the Cloud Analytics Market is witnessing rapid expansion, fueled by the transformative potential of advanced analytics in driving innovation, enhancing operational efficiency, and gaining a competitive edge in today’s data-driven economy.
By Vertical, Healthcare & Life Sciences is projected to grow at the highest CAGR during the forecast period
The healthcare and life sciences sector is experiencing a transformative shift with the emergence of cloud analytics. This technology integrates vast amounts of data from various sources, including electronic health records, wearable devices, and genomic information, to derive meaningful insights and drive informed decision-making. Cloud analytics offers scalability, flexibility, and cost-effectiveness, enabling organizations to efficiently manage and analyze massive datasets that were previously challenging to handle. By leveraging advanced analytics techniques such as machine learning and predictive modeling, healthcare providers and life sciences companies can enhance patient care, optimize clinical workflows, and accelerate drug discovery processes. Moreover, cloud-based analytics facilitates collaboration among researchers, clinicians, and stakeholders, fostering innovation and driving advancements in personalized medicine and population health management. As the industry continues to embrace digital transformation, cloud analytics stands as a cornerstone for unlocking the full potential of data-driven healthcare and life sciences initiatives.
Asia Pacific is expected to grow at the highest CAGR during the forecast period
The Asia Pacific region is experiencing a significant surge in the adoption of cloud analytics, reshaping how businesses make data-driven decisions. Companies spanning various industries are embracing cloud-based analytics platforms to optimize operations, foster innovation, and gain actionable insights. Additionally, the ubiquitous nature of mobile devices and internet connectivity has heightened the demand for real-time analytics accessible from anywhere. Governments and enterprises recognize the strategic value of harnessing analytics to maintain competitiveness in the global marketplace. Consequently, investments in cloud analytics technologies and talent development are escalating, positioning the Asia Pacific region as a pivotal player in the global cloud analytics landscape.
Top Key Companies in Cloud Analytics Market:
The significant cloud analytics software and service providers include IBM (US), SAS Institute (US), Oracle (US), Google (US), Microsoft (US), Teradata (US), Salesforce (US), AWS (US), NetApp(US), Qilk(US), Sisense (US), SAP (Germany), Atos (France), Altair (US), Microstrategy (US), Tibco Software (US), Hexaware Technologies (India), Zoho (India), Rackspace Technology (US), Splunk (US), Cloudera (US), Domo (US), Hewlett Packard Enterprise (US), Incorta (US), Tellius (US), Rapyder (US), Hitachi Vantara (US), Board International (Switzerland), Ridge (Israel), Jaspersoft (US), Yellowfin (Australia), Deonodo(US), GoodData(US), Thoughtspot (US), and Infogain (US). These companies have used organic and inorganic growth strategies such as product launches, acquisitions, and partnerships to strengthen their position in the Cloud Analytics Market.
Recent Developments:
In January 2024, Salesforce unveiled fresh Commerce Cloud tools leveraging generative AI and data-driven insights, enhancing every customer interaction for heightened loyalty and revenue growth.In February 2024, IBM and Wipro strengthened their collaboration to offer clients expanded AI services and support. This collaboration aims to utilize AI technologies to tackle various business challenges and foster innovation across industries.In June 2023, Salesforce and Google formed a partnership. The partnership between Salesforce and Google focuses on integrating Google’s AI capabilities into Salesforce’s products, enhancing their analytics offerings.In May 2023, Microsoft launched Microsoft Fabric Data Analytics which is a cutting-edge platform tailored for the AI era, facilitating seamless integration of analytics and AI capabilities into data processing workflows. It streamlines data ingestion, preparation, and analysis, enabling organizations to derive actionable insights efficiently.In August 2022, Teradata introduced VantageCloud Lake, a cloud-native service designed to simplify data lakes for businesses, enabling seamless management and analytics of vast data sets.Inquire Before Buying@ https://www.marketsandmarkets.com/Enquiry_Before_BuyingNew.asp?id=959
Cloud Analytics Market Advantages:
Platforms for cloud analytics offer scalable resources, making it simple for companies to modify their requirements for storage and processing power in response to demand. Because of its adaptability, businesses may manage different data volumes without having to make costly infrastructure investments.Businesses can lower their capital hardware and software costs by utilising cloud analytics. Pay-as-you-go cloud service pricing allows businesses to optimise their total IT expenditure by only paying for the resources they really utilise.Compared to on-premises systems, cloud analytics solutions may be implemented and deployed far more quickly. This speedy deployment shortens the time to insight, allowing companies to begin data analysis and value extraction right away.Platforms for cloud-based analytics enable remote access to analytics tools and data from any location with an internet connection. Team members can collaborate on data projects regardless of location thanks to this accessibility, which fosters teamwork.Advanced analytics features like machine learning, artificial intelligence, and predictive analytics are frequently included in cloud analytics solutions. Organisations can gain deeper insights and make better decisions with the aid of these technologies.Businesses can analyse data as it is generated with the use of cloud analytics solutions, which provide real-time data processing and analytics. Applications that need instant insights, like fraud detection, operational monitoring, and customer engagement, depend on this real-time capacity.Platforms for cloud analytics can easily interface with a range of data sources, such as databases, data lakes, outside apps, and Internet of Things gadgets. A unified perspective of corporate operations and thorough data analysis are guaranteed by this integration capabilities.Report Objectives
To define, describe, and predict the Cloud Analytics Market by offering, data type, data processing, vertical, and regionTo describe and forecast the Cloud Analytics Market, in terms of value, by region—North America, Europe, Asia Pacific, Middle East & Africa, and Latin AmericaTo provide detailed information regarding major factors influencing the market growth (drivers, restraints, opportunities, and challenges)To strategically analyze micro markets with respect to individual growth trends, prospects, and contributions to the overall Cloud Analytics MarketTo profile key players and comprehensively analyze their market positions in terms of ranking and core competencies, along with detailing the competitive landscape for market leadersTo analyze competitive developments such as joint ventures, mergers and acquisitions, product developments, and ongoing research and development (R&D) in the Cloud Analytics MarketTo provide the illustrative segmentation, analysis, and projection of the main regional marketsBrowse Adjacent Markets: Analytics Market Research Reports & Consulting
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Artificial Intelligence
PolyU research finds improving AI large language models helps better align with human brain activity
HONG KONG, May 27, 2024 /PRNewswire/ — With generative artificial intelligence (GenAI) transforming the social interaction landscape in recent years, large language models (LLMs), which use deep-learning algorithms to train GenAI platforms to process language, have been put in the spotlight. A recent study by The Hong Kong Polytechnic University (PolyU) found that LLMs perform more like the human brain when being trained in more similar ways as humans process language, which has brought important insights to brain studies and the development of AI models.
Current large language models (LLMs) mostly rely on a single type of pretraining – contextual word prediction. This simple learning strategy has achieved surprising success when combined with massive training data and model parameters, as shown by popular LLMs such as ChatGPT. Recent studies also suggest that word prediction in LLMs can serve as a plausible model for how humans process language. However, humans do not simply predict the next word but also integrate high-level information in natural language comprehension.
A research team led by Prof. Li Ping, Dean of the Faculty of Humanities and Sin Wai Kin Foundation Professor in Humanities and Technology at PolyU, has investigated the next sentence prediction (NSP) task, which simulates one central process of discourse-level comprehension in the human brain to evaluate if a pair of sentences is coherent, into model pretraining and examined the correlation between the model’s data and brain activation. The study has been recently published in the academic journal Sciences Advances.
The research team trained two models, one with NSP enhancement and the other without, both also learned word prediction. Functional magnetic resonance imaging (fMRI) data were collected from people reading connected sentences or disconnected sentences. The research team examined how closely the patterns from each model matched up with the brain patterns from the fMRI brain data.
It was clear that training with NSP provided benefits. The model with NSP matched human brain activity in multiple areas much better than the model trained only on word prediction. Its mechanism also nicely maps onto established neural models of human discourse comprehension. The results gave new insights into how our brains process full discourse such as conversations. For example, parts of the right side of the brain, not just the left, helped understand longer discourse. The model trained with NSP could also better predict how fast someone read – showing that simulating discourse comprehension through NSP helped AI understand humans better.
Recent LLMs, including ChatGPT, have relied on vastly increasing the training data and model size to achieve better performance. Prof. Li Ping said, “There are limitations in just relying on such scaling. Advances should also be aimed at making the models more efficient, relying on less rather than more data. Our findings suggest that diverse learning tasks such as NSP can improve LLMs to be more human-like and potentially closer to human intelligence.”
He added, “More importantly, the findings show how neurocognitive researchers can leverage LLMs to study higher-level language mechanisms of our brain. They also promote interaction and collaboration between researchers in the fields of AI and neurocognition, which will lead to future studies on AI-informed brain studies as well as brain-inspired AI.”
Media ContactMs Annie WongSenior Manager, Public AffairsTel: +852 3400 3853Email: [email protected]
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