Tech News

Unlock Business Value with GenAI Through a Data Semantic Approach

Published

10 months ago

October 7, 2025

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.

Check out our previous post NMK Interactive 2025 Sets New Middle East AV Benchmark

Related Topics:#featured cybersecurity Dubai Technology IT News Latest Technology News MEA Middle East Middle East Technology

Up Next

Bulwark & E-7 Cyber To Boost Cybersecurity In Middle East

Don't Miss

NMK Interactive 2025 Sets a New Benchmark for AV Experiences in the Middle East

Click to comment

Tech News

Snowflake powers KSA’s Zahid Group’s data and AI transformation to unlock enterprise value

Published

9 hours ago

August 12, 2026

Integrator Web-Editor

Zahid Group, one of Saudi Arabia’s leading diversified business groups spanning heavy equipment, energy, transport and manufacturing, and others, has selected Snowflake, the AI Data Cloud company, as the strategic foundation for its enterprise data and AI transformation, giving the Group a scalable way to strengthen governance, advance AI adoption, and elevate customer experiences enterprise-wide. The relationship was formalized during a signing ceremony at Zahid Business Park in Jeddah, launching a multi-year investment that will strengthen productivity and support technology-led growth in line with Saudi Vision 2030.

With Snowflake, Zahid Group’s Digital Solutions Division is unifying data from across its business streams and departments on a governed, secure, centralized and scalable lakehouse platform. A modern business intelligence ecosystem will give leaders faster access to information, enabling them to spend less time reconciling reports and more time acting on insights. The next phase will use Snowflake Cortex AI to enable employees to engage with data through natural language, reducing reliance on traditional reporting and shortening the path from question to decision.

Snowflake’s adoption at Zahid Group comes as Saudi Arabia accelerates its goal of becoming the Middle East’s leading AI infrastructure and technology hub. The Council of Ministers has designated 2026 as the Year of AI, and PwC estimates that AI could contribute around 12.4% of the country’s GDP by 2030.

Before adopting Snowflake, Zahid Group’s data was dispersed across multiple systems, requiring extensive manual consolidation and resulting in reporting inconsistencies that slowed decision-making. As an early proof of value, the Caterpillar Helios initiative demonstrated the power of secure, real-time data sharing through Snowflake. Building on this foundation, Zahid Group is now extending Snowflake’s capabilities across its digital ecosystem, enabling real-time data streaming and integration with core platforms such as Infor and Salesforce.

This connected architecture has standardized critical reporting processes, reducing month-end reporting cycles from days to hours, and in many cases minutes. By eliminating manual effort and improving data consistency, it provides leaders with timely, trusted insights that support faster decision-making and strengthen a culture of data-driven innovation.

Suzan Sadek, Group IT Manager, Zahid Group, said: “Data is one of the most valuable assets of the digital economy. By choosing Snowflake, we are building a trusted and scalable data foundation that enables AI-driven innovation, faster decision-making, and improved customer experience. This transformation strengthens Zahid Group’s competitiveness, while supporting Saudi Arabia’s Vision 2030 ambition to create a data-driven economy.”

Michel Nader, General Manager for the Middle East, Turkey & Africa, Snowflake, said: “Zahid Group is demonstrating how trusted data can become the foundation for enterprise AI at scale. Snowflake brings information closer to customers while providing leading AI capabilities to enable digital transformation across Zahid’s operating environment. We are proud to support the company’s next phase too, where employees can access trusted insights faster and strengthen the Group’s ability to create lasting and scalable value across its businesses.”

Looking ahead, Zahid Group will expand Snowflake’s platform’s role across the Group, extending governed data products, advanced analytics and AI capabilities into more business functions to deliver measurable value for customers, partners and employees.

Tech News

PNY Technologies Joins LEAP 2026 with the Latest AI Technologies – Riyadh, Saudi Arabia | 31 August to 3 September | Booth H3-D10

Published

1 day ago

August 11, 2026

Integrator Web-Editor

PNY Technologies announces its participation in LEAP 2026, taking place in Riyadh from 31 August to 3 September, where it will present its latest AI infrastructure and accelerated computing solutions.

The showcase will feature the technologies behind PNY’s AI infrastructure portfolio, from the AI Enterprise Factory to the latest NVIDIA RTX PRO and GeForce graphics solutions, together with networking and infrastructure technologies.

Visitors will also be able to explore the PNY AI Factory Digital Twin Configurator, which allows users to design and configure their own AI factory using digital twins and NVIDIA Omniverse.

PNY will also host live demonstrations developed in collaboration with its technology partners, including SOMOD, DDN, INFINIARC, VERTIV, and F5.

The company’s participation in LEAP 2026 reflects its continued commitment to supporting AI innovation across the Middle East and helping shape the technologies powering the region’s digital transformation.

The PNY team, including regional representatives, will be on-site throughout LEAP 2026 to meet with visitors, partners, and members of the media at Hall H3, Booth D10. Attendees wishing to arrange a briefing or interview are welcome to submit a request below.

Contact request: https://forms.pny.eu/pny-at-leap-2026/

Press contact: sverdier@pny.com / mhamdouche@pny.com

Tech News

Globant Introduces Glob.AI, Reinventing Technology Services for the AI Era

Published

1 day ago

August 11, 2026

Integrator Web-Editor

Globant (NYSE: GLOB), a global company focused on driving enterprise reinvention through AI, today introduces Glob.AI, a new AI-native tech services model that fundamentally changes how enterprises access, purchase and deploy AI services. Available online through a self-service model, Glob.AI gives organizations access to enterprise-grade, high quality AI Pods (service units run by a set of AI agents and supervised by humans). Companies are charged for what they produce -per output or per consumption- never per seat or per hour.

“For over 20 years, Globant has stayed ahead of every major technology shift, and AI is no different. Glob.AI is our next step: the one-stop shop where our AI Pods live, each specialized by task and industry. Traditional AI adoption drives token consumption far beyond what efficient output requires, and the real cost is the wasted tokens plus the unstructured, manual supervision of AI. AI Pods introduce a smarter model, running the right AI through parallel agents, loops, workflows, and deterministic processes, representing the true state of the art of what AI can deliver today,” said Martín Migoya, CEO and co-founder of Globant.

Glob.AI brings together AI-native velocity with Globant’s 23 years of enterprise delivery baked into the governance layer. AI agents do the heavy lifting, with Globant’s experts supervising their outputs and guiding every step. Glob.AI works to standards–documented, tested, secure, built for enterprise scale. It’s the speed AI promises, with the rigor and quality that businesses need, by including:

Well-defined, deterministic, and repeatable processes, supervised end to end by experts
Optimized AI usage and transparent pricing: customers pay strictly for expert-validated outputs and/or real consumption — not hours, seats, AI hallucinations, retries, or wasted cycles.
At least 30% more productive than the typical engineer-plus-AI approach
Full AI sovereignty and governance: total flexibility over which models customers use and where they run them, with zero client data ever used to train external models.
Token consumption secured in a client’s proprietary Token Vault, providing full traceability and compounding a client’s institutional knowledge over time.

“AI is not just making the same projects faster, it is making thousands of projects viable that never were before,” said Guibert Englebienne, co-founder of Globant. “Until today, enterprises could not buy technology services this way: instantly, transparently, paying only for results. That is the shift Glob.AI delivers, and the strong demand for AI Pods shows enterprises are ready for it.”

Organizations joining the waiting list at Glob.AI will gain early access to a comprehensive catalog of agentic workflows capable of building and deploying solutions for enterprise platforms and specialized industry use cases. The AI Pods in the offering also include partnerships with major organizations including Anthropic, AWS, Vercel, OpenAI, Adobe, Azure, Google Cloud Platform, SAP and Salesforce.

Traditional enterprise tech services require months of discovery, RFPs, and procurement cycles before the work begins. Glob.AI changes that model entirely. Users can log in and immediately start building enterprise grade software. Tech delivery becomes a live, continuous service rather than a fixed-term project — every improvement and release happening on the platform, fully visible to the client, in real time.

Globant released AI Pods in mid-2025, and they are already in use by several Fortune 500 organizations across media, entertainment, professional services, and finance, delivering early results that include:

FIFA experiencing a 20% efficiency increase in throughput generation while maintaining or improving quality rates
LALIGA deploying AI across key functions in three months
YPF reducing contract timelines by up to 40%
PharmaMar achieving 15x faster insights in oncology research
A leading commercial bank completing a COBOL migration in 2 months versus 14 as projected with a traditional approach