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
65% OF ANALYSTS SAY AI WORKS BEST WHEN THE LOGIC IS MANAGED AT THE BUSINESS LEVEL, ALTERYX RESEARCH FINDS
Alteryx, Inc., an AI-ready data and analytics company, today released its “2026 State of Data Analysts in the Age of AI” report, revealing that while AI is becoming central to business decision-making, human oversight remains critical to ensuring AI-generated outcomes are trusted and actionable. The research found that analysts spend nearly four hours per week validating and correcting AI-generated outputs, while poor data quality and governance continue to undermine AI and analytics initiatives. The findings also show that AI works best when the people closest to the business stay involved, with 65% of analysts saying AI and agent-based systems are most productive when the logic is managed at the business level. As organizations accelerate toward more agentic AI systems, the need for trusted data, governed logic and workflows, and human oversight continues to grow.
Key Findings at a Glance:
- 96% of data analysts are actively using AI tools in their roles
- 47% of failed AI and analytics projects are attributed to poor data quality or governance
- 65% of analysts say AI and agent-based systems are most productive when the logic is managed at the business level
- Data analysts spend an average of 5.7 hours per week preparing and cleaning data, and an additional 3.7 hours per week checking and correcting AI outputs
- Only 3% prefer fully autonomous AI without routine human involvement, while 46% favor a human-in-the-loop approach
The findings point to a broader shift in how organizations are operationalizing AI. As businesses move from experimentation to deploying AI in core workflows and decision-making, trust increasingly depends on more than model performance alone. Analysts and operations teams play a critical role because they maintain business logic, governance standards, and operational context that help AI systems produce reliable and actionable outcomes.
Human Oversight Still Remains Central in the Age of Agentic AI
As AI becomes a bigger part of an analyst’s day-to-day work, the impact goes beyond simple productivity gains. Businesses are quickly adopting more advanced AI capabilities, like agentic AI, but, on the contrary, analysts are now spending more time reviewing, validating, and guiding AI-generated work. Over half (59%) expect to use AI agents to generate insights within the next year, and many are already using them to draft communications (59%) and manage workflows (54%).
Even as AI takes on a larger role in data-to-insight workflows, analysts remain closely involved because they are ultimately accountable for the quality, accuracy, and reliability of the outcomes. Nearly half (46%) prefer a human-in-the-loop approach where AI systems require human approval before taking action, while only 3% are comfortable with fully autonomous AI. The findings suggest that as AI becomes more embedded in business processes, trust, oversight, and human judgment remain essential to ensuring outputs are accurate, explainable, and aligned with business needs.
“AI is already influencing how businesses make decisions every day, but our research highlights a reality many organizations are now confronting: trust matters just as much as speed,” said Andy MacMillan, CEO at Alteryx. “The people closest to the business play a critical role because they understand the logic, rules, and operational context behind decisions, whether that’s pricing models, compliance requirements, or operational thresholds, and that business logic is constantly evolving. AI can accelerate work, but organizations still need governed workflows and human oversight to ensure outcomes are visible, understandable, repeatable, and auditable across the organization.”
Data Challenges Continue to Limit AI Success
Behind every successful AI initiative is a strong data foundation, and many organizations are still struggling to get there. Even as AI adoption grows, ongoing issues with data quality, access, and governance continue to slow progress and limit AI effectiveness. Analysts say either poor data quality or governance is responsible for nearly half (47%) of failed AI and analytics projects, making it the biggest barrier to AI success.
Most (79%) analysts believe their data is ready for AI at scale, yet the day-to-day reality looks much different. Analysts still spend an average of nearly 6 hours each week preparing and cleaning data, plus nearly another 4 hours reviewing and correcting AI-generated outputs, checking for issues such as incorrect calculations, inconsistent metrics, or responses that don’t align with company policies and definitions. Governance concerns are also rising, with access control and data exposure (42%) ranking as the top issue, followed closely by regulatory compliance (41%). These findings show that as companies push AI deeper into business operations, the people closest to the business increasingly need to provide the context AI relies on, including not just clean data, but also the business logic, workflows, policies, and governance that shape how decisions are made and acted on.
AI Becomes Core to Business Decision-Making
AI is quickly becoming part of everyday business decision-making. Nearly all analysts surveyed (96%) say they use AI tools in their work every day, and organizations are already seeing the impact. Among IT leaders, 85% report noticeable gains in employee productivity, while 79% say AI is helping teams make decisions faster.
As AI adoption grows, AI-generated insights are carrying more weight across the business. Half (50%) of analysts and 62% of IT leaders say that most or almost all business-critical decisions are now influenced by AI insights.
But generating insights faster doesn’t always make decisions easier. The biggest challenge organizations face is helping business leaders understand and trust AI-generated outputs, with 43% saying interpreting and explaining AI insights remains a key barrier. At the same time, companies continue embedding AI into core technologies like cloud data warehouses (40%) and business intelligence tools (39%), making AI an increasingly central part of how businesses operate.
The Evolving Role of the Data Analyst
Analysts increasingly see AI as a collaborator that changes how work gets done, not a replacement for human expertise. In fact, 82% say automation is making them more effective by helping them work faster and focus on higher-value tasks.
As AI becomes more embedded in everyday operations, the role of the analyst is evolving from producing insights to guiding how AI systems operate. Over the next five years, 40% believe changing skill requirements will have the biggest impact on their responsibilities, while 36% point to the growing importance of real-time analytics. The findings suggest that analysts and operational teams will play an increasingly important role in defining, validating, and evolving the business logic AI systems rely on to deliver trusted, repeatable outcomes. This includes the rules, calculations, and operational processes that determine how the business actually runs, whether it’s updating tax rules in different countries, changing sales commission structures, adjusting supply chain thresholds, or applying compliance and pricing policies as conditions evolve.
Tech News
HOLCIM UAE OFFICIALLY LAUNCHES ECOCYCLE® TO ADVANCE CIRCULAR CONSTRUCTION
Holcim UAE officially launched ECOCycle® at the Make It In The Emirates event at ADNEC Centre, Abu Dhabi, marking a landmark moment in the country’s journey toward smarter, more sustainable construction. ECOCycle uses Holcim’s advanced circular technology to accelerate change, building cities from cities and closing the loop in construction.
The UAE generates enormous volumes of construction demolition materials every year, accounting for an estimated 70% to 75% of the nation’s total solid waste. ECOCycle directly addresses this challenge by transforming this into new, high-quality building materials, giving discarded resources a second life rather than sending them to landfill. ECOCycle, Holcim’s circularity technology platform, guarantees a minimum of 10% up to 100% recycled construction demolition materials in every labeled product, with no compromise on quality or performance.
Speaking at the launch, Ali Said, CEO of Holcim UAE and Oman, said: “With ECOCycle, we’re building cities from cities, closing the loop in construction and helping our customers achieve their ambitious circularity goals – by providing building materials and solutions that carry this label, with no compromise on quality and performance. At the same time, we’re reducing the use of primary materials, conserving natural resources, and minimizing the volume of materials sent to landfill.”
The concept is simple but powerful. Instead of extracting new raw materials for every construction project, ECOCycle recovers and reprocesses materials from old structures, feeding them back into the construction cycle. The result is a genuinely closed-loop system that reduces waste, conserves natural resources, and supports the UAE’s ambition to divert 75% of waste from landfill.
This is not an untested idea. Holcim has already used this technology across multiple markets worldwide, including in France where – in a world first – an entire residential building was constructed using 100% recycled concrete. The UAE launch brings that proven track record to this region for the first time.
ECOCycleproducts can contribute to internationally recognized green building certifications, giving developers, architects, and contractors confidence that they are building responsibly. From foundations to facades, ECOCycle is how Holcim turns the cities of today into the building materials of tomorrow, building cities from cities.
Tech News
BOLT EXPANDS INTO THE UAE CAPITAL
Dubai Taxi Company PJSC (“DTC”), the leading provider of mobility services in Dubai, and its strategic partner Bolt today announced the entry of Bolt’s ride-hailing services in Abu Dhabi, marking a significant step in the partnership’s expansion across the UAE.
The expansion builds on strong e-hailing momentum across the DTC–Bolt strategic partnership. In 2025, DTC reported a 24% year-on-year increase in e-hailing activity across its taxi and limousine segments, supported by continued fleet expansion and growing customer adoption of digital booking channels.
Bolt will initially launch limousine services where customers in Abu Dhabi will be able to access ride-hailing services backed by a huge network of fleet owners, drivers, and vehicles. This will be followed by taxi services in weeks to follow.
Vasilis Hadjiaslanis, General Manager of Bolt UAE, said: “Abu Dhabi is a natural next step for Bolt in the UAE. We have seen exceptional demand for reliable, app-based mobility, and this milestone gives residents and visitors in the capital access to a service that is fast, convenient, and built around their needs. We are proud to be on this journey alongside our partners at DTC, and we look forward to continuing to grow our presence across the UAE.”
That momentum carried into Q1 2026, with e-hailing activity rising a further 9% year-on-year, reflecting the continued resilience of app-based mobility and the long-term growth potential of digital transport services in the UAE.
The expansion also relies on the partnership’s growth in Dubai, where Q1 2026 saw the integration of 1,823 National Taxi vehicles into the Bolt platform. Broadening Bolt’s UAE footprint and strengthens its role in supporting the country’s evolving ecosystem, shaping how residents, visitors, and businesses move across cities.
Driven by this high demand, Bolt expansion into Abu Dhabi reinforces DTC’s commitment to delivering more accessible mobility solutions for residents, visitors, and businesses nationwide, and support the UAE’s wider shift toward smart mobility.
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