Tech News
Unlock Business Value with GenAI Through a Data Semantic Approach
By Robert Thanaraj, Sr Director Analyst at Gartner
Semantic representations of information are crucial for the functionality of large language models (LLMs), which is fuelling a heightened focus on semantics within data and analytics (D&A) and AI.
Data silos become entrenched and limit an organization’s capacity to draw insights from its data. Without understanding the relationships within data, the individual pieces of information become less useful.
Semantic approaches facilitate a shared understanding of business terms and their interrelationships, which is vital for providing the necessary context for generative AI (GenAI). In a Gartner survey on the evolution of data management, 44% of the respondents from AI-ready organizations reported that semantic alignment is a key factor in assessing the AI readiness of their data.
D&A leaders can enhance and expand their semantic understanding by leveraging emerging technologies such as knowledge graphs and augmented data catalogs, thereby unlocking greater value from their information resources.
What is Data Semantics?
Data semantics refers to the meaning and interpretation of data within a business-specific context, as opposed to focusing on the physical representation of data through a data dictionary or a business glossary. It involves understanding what a data element represents, how it should be used, and its relationships with other data elements. Without this understanding, data is of limited use for AI use cases.
Semantic modeling is a practice of connecting technical metadata with business metadata.
A business glossary serves as the foundation for all things “semantic,” documenting the meanings of business-related terms. When the semantics and rules of a business glossary are well-understood, it leads to better data quality, easier integration and greater usefulness, supporting interactions with LLMs. The glossary also supports business goals like reducing costs and managing risks by making definitions clear, consistent and easy to trace back to their sources.
Top Recommendations for D&A leaders
- -Upskill your data engineers with semantic modeling techniques such as the use of knowledge graphs in building business ontologies.
- -Introduce DataOps practices to “deliver value from data” more easily, quickly and broadly. Take a people-, product- and governance-centric approach.
- -Invest in converged data management platforms. Establish a platform engineering team that produces platform services for platform tenants.
Key Benefits of Data Semantics
Leveraging and governing semantics effectively enables:
- –Improved Data Understanding: Both people and applications gain a unified view of data and its structure. For example, if several medical e-commerce sites use consistent relationships between terms, applications can extract and aggregate information across these sites to support user queries or serve as input for other applications.
- –Knowledge Reuse: Relationships uncovered by one group can be reused or built upon by others for new use cases, allowing previously identified connections to be embedded in future work.
- –Enhanced Accuracy with LLMs: Incorporating knowledge graphs into the training and inference processes of LLMs serves as a factual base (i.e., data and metadata source) for mitigating errors and hallucinations.
- –Enhanced Interoperability and Innovation: By adopting semantic modeling, organizations open themselves to a wider range of use cases and enable more effective data interchange.
Link Data from Different Sources to Derive Data Relationships
Semantic reconciliation plays a crucial role in effectively linking data from different sources. It is also essential for inferring relationships between disparate datasets. Without a clear understanding of the relationships, correlations and distinctions among the meanings of data from different modalities such as text, videos, images and structured data, organizations cannot fully realize the potential of their data assets.
Modern semantic tools use algorithms to find connections in data. These tools recommend the best ways to clean, organize and analyze information. They also track where data comes from and how it is used for better governance.
With augmented data discovery, algorithms automatically detect correlations, segments, clusters, outliers and relationships, presenting the most statistically significant and relevant results. By using these semantic approaches, organizations can connect information from different sources, uncover relationships and gain valuable insights that drive better decisions.
In business ecosystems, the degree of openness is driven by members’ strategies, common goals and shared interests. For example, governments, nongovernmental organizations, charities and community groups can collaborate on health or public policy issues, or in open-source developer communities. This creates an opportunity for exploiting the knowledge of data and the meaning of data in terms of what can be applied to several digital business moments.
Lastly Think Data Semantics Before Introducing Large Language Models
Organizations are spearheading transformative initiatives to implement large language models in order to transform their operations. However, data and analytics leaders often rush to integrate LLM capabilities without first ensuring these tools are aligned with real business outcomes. To maximize value, it’s essential to connect LLMs with robust semantic frameworks.
Knowledge graphs are a powerful foundation for leveraging LLMs in business contexts. These machine-readable data structures capture semantic knowledge about both physical and digital entities. These worlds include entities and their relationships, which adhere to a network of nodes and links forming the graph data model.
LLMs can streamline the creation of ontologies, which define categories and relationships within data. By using “few-shot” learning prompts—providing just a handful of examples—users can guide LLMs to generate base ontologies in open formats that suit their needs. These initial frameworks can then be refined for greater detail as required.
Additionally, LLMs support ontology mapping by helping users align entities and relationships across different datasets or systems. With targeted prompts and sample mappings, organizations can extract relevant connections from their data and improve accuracy through iterative refinement.
By adopting large language models alongside semantic representations like knowledge graphs and ontologies, organizations position themselves for faster deployment of advanced analytics solutions that deliver meaningful business value.
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Tech News
VAST Data Partners with Google Cloud to Enable Enterprise AI at Scale Across Hybrid Cloud Environments
VAST Data, the AI Operating System company, today announced an expanded partnership with Google Cloud, the first fully managed service for the VAST AI Operating System (AI OS), enabling customers to deploy the AI OS and extend a unified global namespace across hybrid environments. Powered by the VAST DataSpace, enterprises can seamlessly connect clusters running in Google Cloud and on-premises locations, eliminating complex migrations and making data instantly available wherever AI runs.
Enterprises want to run AI where it performs best, but data rarely lives in one place and migrating can take months and costs millions. Fragmented storage and siloed data pipelines make it hard to feed the AI accelerators with consistent, high-throughput access and every environment change multiplies governance and compliance burdens.
VAST and Google Cloud address this challenge by making data placement a choice rather than a constraint. In this recorded demonstration, VAST showcased the power of the VAST DataSpace to connect clusters across more than 10,000 kilometers, linking one in the United States with another in Japan. This configuration delivered seamless, near real-time access to the same data in both locations while running inference workloads with vLLM, enabling intelligent workload placement so organizations can run AI models on TPUs in the US and GPUs in Japan without duplicating data or managing separate environments.
“Together with Google Cloud, VAST is building a unified data and computing environment that extends to wherever a customer wants to compute and unleashes the potential of AI by unlocking access to all data everywhere,” said Jeff Denworth, Co-Founder at VAST Data. “Delivered as a managed AI Operating System on Google Cloud, customers can go from zero to production in minutes – we’re turning hybrid complexity into a single, intelligent fabric that provides fast access to data, regardless of where it resides to accelerate time to value for agentic AI.”
“Bringing VAST AI Operating System to Google Cloud Marketplace will help customers quickly deploy, manage, and grow the data solution on Google Cloud’s trusted, global infrastructure,” said Nirav Mehta, Vice President, Compute Platform at Google Cloud. “VAST can now securely scale and support customers on their digital transformation journeys.”
Powering Google Cloud TPUs with seamless data access and near-local performance
Recent performance results also show how the VAST AI Operating System connects seamlessly to Google Cloud Tensor Processing Unit (TPU) virtual machines, integrating directly with Google Cloud’s platform for large-scale AI. In testing with Meta’s Llama-3.1-8B-Instruct model, the VAST AI Operating System delivered model load speeds comparable to some of the best options available in the cloud, while maintaining predictable performance during cold starts.
These results confirm that the VAST AI OS is not just a data platform but a performance engine designed to keep accelerators fully utilized and AI pipelines continuously in motion.
“The VAST AI OS is redefining what it means to move fast in AI, delivering model load speeds comparable to cloud-native alternatives while providing the full power of an advanced, enterprise-grade AI platform,” said Subramanian Kartik, Chief Scientist at VAST Data. “This is the kind of acceleration that turns idle accelerators into active intelligence, driving higher efficiency and faster time to insight for every AI workload.”
With VAST on Google Cloud, customers can benefit from:
- Deploy AI in Minutes, Not Months: Organizations can run production AI workloads on Google Cloud today against existing on-premises datasets without migration planning, transfer delays, or extended compliance cycles. Using VAST DataSpace and intelligent streaming, they can present a consistent global namespace of data across on-prem and Google Cloud instantly.
- Reduce Data-Movement Costs: Stream only the subsets that models require to avoid full replication and reduce egress – cutting footprint and redirecting budget from data movement to AI innovation with infrastructure that is future-ready for the demanding AI pipelines in genomics, structural biology, and financial services.
- Maximize Google Cloud Innovation with Flexible Data Placement: Choose what to migrate, replicate, or cache to Google Cloud while keeping one namespace and consistent governance by applying unified access controls, audit, and retention policies everywhere to simplify compliance and reduce operational risk. Leverage VAST DataStore and VAST DataBase to unify prep, training, inference, and analytics without rewiring pipelines.
- TPU-Ready Data Path: Feed TPU VMs over validated NFS paths with optimized model loading and metadata-aware I/O, delivering fast, consistent warm-start performance and predictable behavior during cold-starts.
- Build on a Unified Platform: The VAST AI Operating System delivers a DataStore, DataBase, InsightEngine, AgentEngine and DataSpace that scales across on-premises and Google Cloud environments and adapts to changing business needs without architectural rewrites, enabling data scientists to use a variety of access protocols with a single solution.
Tech News
AUKEY PARTNERS WITH THE BROOKLYN NETS FOR AN ELECTRIFYING NBA SEASON
AUKEY, a leading innovator in cutting-edge tech accessories, is proud to announce a multiyear partnership with the NBA’s Brooklyn Nets, beginning this 2025-26 NBA season. This collaboration is AUKEY’s first sports partnership, marking an exciting milestone for their expansion and reflecting their ongoing commitment to delivering high-quality, innovative technology experiences to a global audience.
Through this partnership, AUKEY will team up with the Brooklyn Nets to engage fans both on and off the court. Together, they’ve launched a non-commercial, limited-edition wireless power bank, the MagFusion M 5000 Brooklyn Nets Co-Branded Edition, combining the team’s bold identity with cutting-edge wireless charging technology.
Fans can participate in AUKEY’s social media giveaway activities for a chance to win one on Instagram and Facebook, keeping their energy flowing anytime, anywhere while enjoying exciting game moments.

MagFusion M 5000 Brooklyn Nets Co-Branded Edition
“We’re thrilled to partner with the Brooklyn Nets, a team that embodies creativity, resilience, and the spirit of New York,” said Jackey Li, CEO at AUKEY US. “At AUKEY, we power every moment with strength, endurance, and an unbreakable drive to keep innovating. The Nets share that same unstoppable spirit and we look forward to sharing that spirit of innovation and energy with basketball fans worldwide.”
AUKEY’s work with the Nets will extend in-arena at Barclays Center for the team’s home games, as well as on the team’s social media channels. This partnership represents a fusion of tech, sport, and culture and together, AUKEY and the Brooklyn Nets aim to unlock more power in every moment, from the court to the community, keeping fans charged for what’s next.
Tech News
GCC COMPANIES ACHIEVE 30-SECOND PAYROLL PROCESSING WITH 100 PER CENT ACCURACY USING ADVANCED HRMS, REVEALS GREYTHR
Companies across the GCC region have experienced higher workforce management efficiency using advanced AI-powered HRMS, reporting 100 per cent accuracy and stronger compliance with GCC labour regulations, reveals a recent survey conducted by greytHR, the leading full-suite Human Resource Management System (HRMS) platform. Notably, organisations with around 1000 employees could complete their payroll processing in just about 30 seconds using the innovative platform.
The findings point to an exponential shift within the GCC HR landscape, where organisations are embracing intelligent automated HR operations amid evolving labour regulations, hybrid work models, and the rise of multi-country workforces. The company’s data shows that 75 per cent of GCC companies are first-time HR automation adopters, while 24 per cent have migrated from legacy systems, highlighting the ongoing regional transition towards fully digitised, compliance-ready HR frameworks.
greytHR is powering this digital shift through its robust cloud-based infrastructure and AI-powered tools, which simplify the entire hire-to-retire employee lifecycle, from recruitment and onboarding to core HR, leave, attendance, payroll, performance, exit and engagement.
Girish Rowjee, Co-founder and CEO of greytHR, said, “At greytHR, we believe that ‘people’ are the primary pillar of any business. A company’s growth relies on the dedication and hard work of its employees. As a result of this belief, we built our HRMS to make employee lifecycle management simpler, more transparent, and more connected within the HR ecosystem. Our goal is to help organisations reinvent how they manage and support their workforce through intelligent, people-focused automation in today’s digital world.”
Through it’s a highly intelligent and unified system, greytHR has been continuously addressing the region’s distinctive challenges and maximising impact through efficient workforce management.
Sayeed Anjum, Co-Founder & CTO, greytHR, said: “As companies expand across borders and hybrid work models become the norm, HR leaders face issues such as manual payroll errors, fragmented systems and limited automation, which can directly impact compliance, employee satisfaction, and productivity. Our platform is tailored to address these pain points and the region’s unique needs by serving as an intelligent, unified system that simplifies all stages of workforce management. This further aligns with our broader vision of creating measurable impact for companies and transforming the regional HR ecosystem through digitisation.”
He further stated: “Currently, IT & ITeS, Business, and Financial Service sectors lead in HRMS adoption, at 19 per cent, 15 per cent and 10.5 per cent respectively, highlighting the vital role of technology-driven and service-oriented businesses in catalysing the ongoing digital HR revolution.”
greytHR offers built-in compliance features tailored to GCC nations, including automated GPSSA deductions, multi-country payroll capabilities, and real-time analytics. Moreover, its intuitive interface and modular architecture make it accessible to businesses of all sizes, from startups to large enterprises.
The company showcased these advanced offerings at the recent HR Summit & Expo 2025, held in Dubai, highlighting its commitment to supporting the region’s evolving workforce needs. As GCC continues to position itself as a global business hub, greytHR remains steadfast in its efforts to positively shape the future of the regional HR industry.
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