Core Insights - LiveRamp reported a 9% year-over-year increase in total revenue for the quarter ended December 31, 2025, reaching $212 million [7][6] - The company achieved record quarterly operating margin and operating cash flow, with operating income rising to $40 million from $15 million in the prior year [5][7] - Share repurchases totaled $119 million fiscal year-to-date, with 1.4 million shares repurchased in the third quarter [7][6] Financial Performance - Subscription revenue was $158 million, up 9% year-over-year, while Marketplace & Other revenue increased by 8% to $54 million [7][6] - GAAP gross profit was $153 million, reflecting a 9% increase, with a gross margin of 72% [7][6] - Non-GAAP operating income rose by 36% to $62 million, with a non-GAAP operating margin of 29% [7][6] Earnings and Cash Flow - Net earnings for the quarter were $40 million, compared to $11 million in the prior year, resulting in diluted earnings per share of $0.62 [7][6] - Operating cash flow increased to $67 million from $45 million year-over-year [7][6] - Free cash flow for the quarter was also reported at $67 million, up from $45 million [8] Business Developments - The company launched new AI tools in its Data Marketplace and expanded partnerships, notably with Publicis [5][12] - LiveRamp ended the quarter with 140 customers generating over $1 million in annualized subscription revenue, an increase from 125 in the previous year [12][7] - Annualized recurring revenue (ARR) reached $527 million, up 7% compared to the prior year [12][7]

PHILADELPHIA, PENNSYLVANIA / ACCESS Newswire / January 26, 2026 / Datavault AI Inc. (NASDAQ:DVLT) ("Datavault AI" or the "Company"), a leader in data monetization, credentialing, digital engagement and realworld asset ("RWA") tokenization technologies, today announced the execution of a memorandum of understanding with St. John's University in Taipei to support the establishment of the RWA International Research Center (the "Center"). The Center will serve as a core platform for promoting inter- university ...

Core Viewpoint - LiveRamp is set to release its fiscal 2026 third quarter financial results on February 5, 2026, after market close, followed by a conference call to discuss the results [1] Group 1: Financial Results Announcement - The financial results will be released on February 5, 2026, after the financial markets close [1] - A conference call to discuss the results will take place on the same day at 1:30 p.m. PT [1] - The conference call can be accessed via telephone or through a live webcast on the investor relations website [2] Group 2: Company Overview - LiveRamp is a leading data collaboration technology company that empowers marketers and media owners to enhance marketing performance [3] - The company's data collaboration network integrates data across various stakeholders, providing insights and driving growth [3][4] - LiveRamp is recognized for its commitment to neutrality, interoperability, and global scale, helping organizations maximize data value [4]

Core Insights - The infrastructure supporting artificial intelligence is undergoing a significant transformation, driven by the convergence of open formats, AI capabilities, and the unsustainable costs of integrating numerous tools [1][2]. Group 1: Importance of Fundamentals - Basic skills remain crucial as architecture changes can disrupt pipelines, and data quality issues continue to plague organizations, costing an average of $12.9 million annually due to poor data quality [2][11]. - The key challenge by 2026 will not be the existence of these issues but the speed and method of their detection and resolution [2]. Group 2: Metadata Layer as a Battleground - The storage layer competition has concluded with Iceberg, Delta Lake, and Hudi emerging as winners, while Parquet has become the common language for data storage [3][6]. - The focus is shifting upstream to the metadata layer, which is becoming the operational backbone of data management, encompassing data lineage, quality rules, access policies, and business context [6][20]. Group 3: Simplification of Data Stacks - Organizations are experiencing tool fatigue, managing an average of 15 to 30 different tools across various data functions, which is unsustainable [7][9]. - By 2026, the integration process will accelerate, with platforms like Snowflake and Databricks consolidating functionalities to streamline data operations [10]. Group 4: Data Quality as a Business Function - Data quality metrics will shift from engineering-focused indicators to business outcomes, with organizations increasingly linking data pipeline failures to revenue impacts [11][12]. - By 2026, 80% of organizations are expected to deploy AI/ML-driven data quality solutions, emphasizing the need for accountability through data contracts between producers and consumers [12]. Group 5: AI Agents Replacing Dashboards - The traditional model of data observability through dashboards is becoming obsolete, with AI agents expected to take over operational responsibilities by 2026 [13][15]. - These AI agents will be capable of understanding business context, automatically tracing issues, and applying fixes, fundamentally changing the approach to data observability [15]. Group 6: AI Reshaping Data Infrastructure - The initial design of data stacks was for dashboard services, not AI workloads, but AI is now a primary user of data [16]. - By 2026, two types of companies will emerge: AI-native architectures designed for AI workloads and traditional stacks with AI capabilities added later [16]. Group 7: The Rise of Semantic Layers - Semantic layers, previously seen as optional, are becoming essential for AI applications, providing necessary context for data interpretation and ensuring data quality [17]. - These layers serve as a bridge between technical data and business meaning, crucial for AI agents to function effectively [17]. Group 8: Common Theme - A common theme across the predictions is the shift from passive to proactive data infrastructure, where systems will not only store and visualize data but also understand, reason, and act based on interactions [18][19].

Core Viewpoint - The event held in Changsha focused on the theme "Data Empowerment, Multiplying Upwards," showcasing the city's achievements in the data element sector and exploring innovative paths for data value release [1] Group 1: Competition Results - The Changsha preliminary competition attracted 191 teams, with one team advancing to the finals after expert evaluations and presentations [3] - Changsha won 64 awards in the provincial competition, including 21 first prizes, and received the "Pioneer Award" from the Changsha Data Bureau [3] - In the national finals, Changsha secured 10 awards, including 2 first prizes, ranking first among provincial capital cities in terms of both quantity and level of awards [3] Group 2: Policy Insights - A special session was dedicated to policy explanations to facilitate project implementation, with insights from Chen Jianhua, Chief Engineer of the Changsha Data Bureau, on funding and project quality management [3] - Attendees expressed that the policy discussions were highly relevant and provided clear guidance for future project development [3] Group 3: Expert Opinions - Experts highlighted that data empowerment has entered a new phase of both breadth and depth, with Changsha positioned among the top tier nationally due to its clear strategy and solid industrial foundation [5] - As one of the first national data labeling bases, Changsha possesses high-quality data production capabilities and rich industrial heritage, particularly in engineering machinery and audio-visual cultural creation [5] Group 4: Future Outlook - The Changsha Data Bureau plans to deepen market-oriented reforms in data element allocation, focusing on four key areas: enhancing project transformation, exploring data resource utilization, building a development ecosystem, and seizing policy opportunities [6] - The goal is to transition successful competition projects from the "arena" to the "market," encouraging businesses to clarify commercialization paths and application scenarios [6] - The bureau aims to cultivate a batch of data-driven enterprises and develop public data authorization operation demonstration projects by 2026 [6]

Group 1 - The meeting focused on the theme of "linking data value to a new ecosystem," addressing the challenges of "difficult and expensive financing" faced by data-driven enterprises [1] - The Inner Mongolia Data Center shared typical cases of data product pledge loans, providing innovative models for data asset monetization and addressing practical challenges [1] - Financial institutions such as China Merchants Bank, Industrial Bank, and Guangfa Bank introduced tailored financial products for data technology companies, moving beyond traditional asset-based financing [1] Group 2 - The meeting served as a closed-loop design to effectively connect policy benefits, financial resources, and enterprise needs, enhancing the concept of "data empowering the real economy" [2] - The Inner Mongolia Data Center aims to continuously improve the data element circulation service system and optimize the long-term mechanism for government-bank-enterprise collaboration [2]

Core Viewpoint - Datavault AI Inc. has announced a dividend distribution of warrants to purchase shares of its common stock, with a set distribution date of February 21, 2026 [1] Group 1: Dividend Announcement - The board of directors of Datavault AI has established February 21, 2026, as the distribution date for the warrants [1] - The record date for the distribution of the warrants was previously set for January 7, 2026 [1] Group 2: Company Overview - Datavault AI is recognized as a leader in data monetization, credentialing, and digital engagement technologies [1]

Core Viewpoint - The implementation of the "Opinions on Strengthening Data Technology Innovation" marks a new phase in China's systematic layout and collaborative promotion of data technology innovation, aiming to support the high-quality development of digital China, digital economy, and digital society [1][2]. Group 1: Development Goals - By 2027, China aims to establish a number of leading and supporting data technology innovation platforms, initially building a data-driven industrial innovation system with breakthroughs in key technologies and equipment related to data supply, circulation, utilization, and security [2]. - By 2030, key technologies in the data field are expected to reach international leading levels, with an overall leap in the data technology innovation and industrial ecosystem [2]. Group 2: Key Dimensions of Innovation - The "Opinions" focus on three key dimensions: "technical breakthroughs, ecological cultivation, and foundational support," creating a collaborative nurturing system to transition from extensive support to refined cultivation of data technology innovation [2][3]. - Technical breakthroughs are seen as the driving force for innovation, ecological cultivation as the incubator for innovation implementation, and foundational support as the stabilizing factor for sustained innovation [2]. Group 3: Implementation Pathways - The "Opinions" propose a comprehensive and actionable implementation path to address shortcomings in core data technologies, innovation ecosystems, and application of results, emphasizing the importance of a closed-loop system of "research, validation, and transformation" [3]. - Key measures include strengthening technical breakthroughs in data supply, circulation, utilization, and security, as well as promoting efficient transformation of data technology innovation results [3]. Group 4: Innovation Ecosystem - The "Opinions" emphasize the need for an open, collaborative, and multi-faceted innovation ecosystem, proposing systematic measures across five dimensions: innovation platforms, market entities, open-source ecosystems, research systems, and collaborative exchanges [4]. Group 5: Foundational Support - Data technology innovation is recognized as a long-term and systematic project requiring coordinated efforts across funding, talent, standards, and infrastructure for sustainable development [5]. - The "Opinions" outline measures to accelerate the construction of a national integrated computing power network and strengthen the development of key technology standards in the data field [6]. - Four foundational supports—infrastructure, talent, financial policies, and standard systems—are essential for the sustainable development of data technology innovation, addressing core pain points in the application of technological innovations [6].

Group 1: Company Recognition - Teradata has been recognized as a Leader in The Forrester Wave™: Data Fabric Platforms, Q4 2025, evaluated among 14 technology providers based on current offerings, strategy, and customer feedback [1][3] - The report highlights Teradata's proven performance and robust architecture, making it suitable for mission-critical analytical and AI workloads in complex environments [3] Group 2: Evaluation Findings - The evolution of data fabric is driven by Agentic AI, which enables organizations to scale AI across complex ecosystems, allowing for context-aware decisions and real-time adaptation [2] - Teradata received the highest possible scores (5/5) in the Vision and Roadmap criteria, as well as in three Current Offering criteria, including Real-time Performance and Scalability [7] Group 3: Executive Commentary - The Chief Product Officer of Teradata emphasized that being named a Leader reflects the company's commitment to operationalizing AI-driven decision-making across hybrid and multi-cloud environments [3]

Core Viewpoint - Jiangsu Province's Data Bureau has initiated a collection of practical cases for public data applications, aiming to enhance the quality and efficiency of data resource development and utilization by 2025 [1] Group 1: Practical Cases Overview - Seven scenarios have been selected for the sixth batch of public data application cases, covering various fields such as precise investment promotion, inclusive finance, urban governance, social welfare, and rural revitalization [1] - The cases demonstrate local adaptations and practical applications of public data to empower high-quality industrial development [1] Group 2: Specific Cases - Nanjing: AI-enabled industrial chain map for precise investment promotion by Nanjing Longhu Miaoyu Technology Co., Ltd [2] - Nantong: "Jinbei E Credit" to accelerate financing for small and micro enterprises by Qidong Kexin Industrial Investment Development Co., Ltd [2] - Lianyungang: Urban drainage data empowering smart services for pump stations by Lianyungang Yungeng Agricultural Technology Development Co., Ltd [2] - Huai'an: Data empowerment for food safety in elderly care by Wu County Jinqian Supply and Trade Co., Ltd [2] - Yancheng: Employment assistance through data by Jiangsu Shuzhu Big Data Technology Co., Ltd [2] - Yangzhou: "Data Chain Bridge" to break information gaps in inclusive finance by Jiangsu Siyun Technology Co., Ltd [2] - Suqian: Smart agriculture empowering a new ecosystem by Suqian Zhongwu Shuda Data Technology Co., Ltd [2]