Tag: #data

New AI Model Improves Personalized Blood Glucose Prediction for Type 1 Diabetes

Jeonbuk National University Researchers Develop an AI Model For Personalized Blood Glucose Monitoring

The hybrid model integrates three components to address key challenges and was rigorously evaluated, paving way for accurate blood glucose prediction

Patients with Type 1 diabetes (T1D) require accurate and consistent monitoring of their blood glucose levels. Over the past decade, AI models have been explored to tackle this challenge; however, inter-patient variability and large data volumes remain key challenges. In a new study, researchers present BiT-MAML, a model-agnostic algorithm aimed at personalized blood glucose prediction of patients with T1D. This approach overcomes the limitations of existing models and enables precise predictions in real clinical settings.

Type 1 diabetes (T1D) is an autoimmune condition in which the body’s own immune system attacks insulin-producing cells. As a result, patients with T1D must closely monitor their blood glucose (BG) levels and rely on insulin injections or pumps. Even small miscalculations or oversights can lead to unregulated blood sugar levels, leading to potentially life-threatening complications.

Continuous glucose monitoring (CGM) systems have emerged as a promising tool for predicting and forecasting BG levels. Over the past decade, researchers have explored artificial intelligence (AI) models for improving the prediction accuracy of CGM systems. However, differences in physiology between patients and poor adaptation for new users persist to challenge the widespread adoption of this technology in real-world settings. In addition, traditional models often focus on either short-term or long-term glucose patterns, but not both.

In an attempt to address these issues, a research team led by Professor Jaehyuk Cho from the Department of Software Engineering at Jeonbuk National University in South Korea, have developed an innovative model, named BiT-MAML, aimed at tackling inter-patient variability in BG prediction. Explaining further, Prof. Cho says, “BG dynamics are not uniform across all patients. The physiological patterns of an elderly patient are vastly different from those of a young adult.” Adding further, he says, “Our model demonstrates how this variability can be accounted for by developing more personalized models.” Their findings were published in Scientific Reports on August 20, 2025. 

BiT-MAML (where “BiT-“ stands for Bidirectional LSTM-Transformer” and “MAML” stands for “Model-Agnostic Meta-Learning”) uses hybrid architecture combining two deep learning models: bidirectional long-short-term memory (Bi-LSTM) and Transformer. Bi-LSTM processes time-series BG data bidirectionally, precisely capturing short-term patterns. Simultaneously, the transformer, utilizing a multi-head attention approach, efficiently models long-term patterns, capturing complex day-to-day and lifestyle-based cyclical variations. During training, the researchers applied a meta-learning approach known as Model-Agnostic Meta-Learning (MAML) that helps the model quickly adapt to new and diverse patients using only a small amount of training data by learning from a wide range of patient examples. 

To test model performance, the researchers adopted a Leave-One-Patient-Out Cross-Validation (LOPO-CV) scheme. “In simple terms, we train the AI on five patients, then test it on the sixth patient it has never seen before,” explains Prof. Cho. “This is effective for assessing the model’s ability to generalize to unseen patients.” 

The model demonstrated significantly reduced prediction error compared to conventional models. Notably, the prediction error varied from an excellent 19.64 milligram/decilitre (mg/dL) for one patient to a challenging 30.57 mg/dL for another. While these results represent a clear improvement over the standard LSTM models, they also highlight the persistent difficulty of managing inter-patient variability in real-world settings. “Our study shows how AI-based BG prediction models should be evaluated to improve both trust and model performance,” concludes Prof. Cho. “Addressing this challenge will contribute to the development of effective CGM models that can serve diverse patients with T1D, from children to elderly.”

These findings attest to the fact that the development of effective personalized BG prediction requires the use of advanced AI models incorporating robust evaluation methods that can transparently report the full spectrum of performance.

Reference

Title of original paper: Personalized blood glucose prediction in type 1 diabetes using meta-learning with bidirectional long short term memory-transformer hybrid model

Journal: Scientific Reports

DOI: 10.1038/s41598-025-13491-5  

Qualcom invests €500K to launch new AI practice

Qualcom, a leading Irish provider of IT and cybersecurity services, today announces that it is investing €500,000 to launch its new artificial intelligence (AI) practice. This investment will span three years and, in the continued expansion of its team, Qualcom plans to hire four AI specialists within this timeframe.

The new practice will support secure AI adoption for Irish organisations and enable them to align with evolving regulatory requirements. The investment includes a new partnership with AI infrastructure provider NROC and, as part of this, Qualcom will provide a full wraparound service to secure and manage customers’ AI environments, using NROC’s technology. The funding also includes the training and upskilling of new team members, as well as AI training for Qualcom’s existing managed services and infosec teams.

In turn, the new practice will further enable Qualcom to deliver AI-powered solutions that will secure customers’ Microsoft data, and to provide ultra-secure managed services to businesses. Qualcom has also developed a comprehensive AI policy framework designed to help organisations to incorporate AI tools such as Microsoft Copilot and ChatGPT into their daily operations, while safeguarding sensitive data and ensuring compliance,

The company is launching the new dedicated practice in response to heightened demand among customers for AI solutions, services, and capabilities to drive business growth and remain competitive.

This investment comes as Qualcom celebrated 30 years in business in 2025. The company recently announced that it has boosted the headcount within its support centre by 33%, and enhanced facilities at its Dublin headquarters to equip the business for continued growth.

David Kinsella, Technical Director, Qualcom, said: “This investment in our people, platforms, and capabilities reflects our commitment to supporting customers as they navigate both the opportunities and risks of AI. As we look ahead to the next three years, there’s no doubt that the use and applications of AI will continue to grow exponentially. The launch of the new practice will enable us to adapt quickly in line with industry demand, delivering right first-time services that are fully compliant and maximise IT uptime for businesses in Ireland. We’re looking forward to working closely with customers as we support the secure rollout of AI tools to help them to keep pace with their competitors.”

Why Penetration Testing Companies Are Essential for Modern Cybersecurity

In a digital economy where data is one of the most valuable assets an organization owns, the ability to detect vulnerabilities before attackers do has become a strategic necessity. Penetration testing companies help organizations uncover hidden security weaknesses by simulating real-world cyberattacks against applications, infrastructure, and networks, allowing businesses to strengthen defenses before malicious actors exploit those gaps.

Why penetration testing has become essential

Cybersecurity threats have grown more sophisticated and persistent in recent years. Enterprises no longer face only opportunistic hackers; they must also defend against organized cybercriminal groups, state-sponsored attackers, and automated attack tools that scan the internet continuously for vulnerabilities.

Traditional security tools—such as firewalls, antivirus software, and intrusion detection systems—play an important role, but they cannot identify every weakness. Many vulnerabilities stem from misconfigurations, insecure code, overlooked access controls, or complex interactions between systems.

Penetration testing addresses this challenge by applying the mindset and techniques of attackers. Security professionals attempt to exploit vulnerabilities in a controlled environment, demonstrating exactly how an attack could unfold and what business impact it might have. Instead of theoretical risks, companies receive practical insight into real security gaps.

What penetration testing companies actually do

Professional penetration testing providers offer a range of services designed to assess different layers of an organization’s technology stack. These services typically include:

Network penetration testing
This type of assessment focuses on internal and external network infrastructure. Testers attempt to exploit weaknesses in routers, servers, firewalls, or network protocols to gain unauthorized access.

Web application testing
Modern organizations rely heavily on web platforms. Penetration testers evaluate applications for vulnerabilities such as SQL injection, cross-site scripting, insecure authentication mechanisms, and flawed session management.

Mobile application security testing
As mobile apps increasingly handle sensitive data and financial transactions, specialized testing ensures they are protected against reverse engineering, insecure APIs, and data leakage.

Cloud security assessments
With many businesses migrating workloads to the cloud, penetration testing helps identify configuration errors, excessive permissions, and exposed services that could allow attackers to move laterally within cloud environments.

Social engineering testing
Some engagements also evaluate human vulnerabilities through phishing simulations or other social engineering techniques. These tests help organizations measure employee awareness and identify training gaps.

The methodology behind effective penetration testing

High-quality penetration testing is structured and systematic rather than random hacking attempts. Professional testers typically follow a standardized methodology that includes several stages.

  1. Reconnaissance and information gathering
    Security specialists collect publicly available information about the target organization, its infrastructure, domains, and technologies. This stage helps testers map potential entry points.
  2. Vulnerability identification
    Automated tools and manual analysis are used to identify weaknesses in software, configurations, and systems.
  3. Exploitation
    Testers attempt to exploit discovered vulnerabilities in order to determine whether they can gain access, escalate privileges, or extract sensitive information.
  4. Post-exploitation analysis
    This phase evaluates how far an attacker could move within the environment after gaining initial access.
  5. Reporting and remediation guidance
    Perhaps the most important stage is the final report, which includes detailed findings, severity ratings, proof-of-concept evidence, and clear recommendations for remediation.

The goal is not only to expose vulnerabilities but also to provide organizations with actionable guidance to improve their overall security posture.

How businesses benefit from penetration testing

Organizations that invest in regular penetration testing gain several advantages beyond simple vulnerability detection.

First, testing helps reduce the risk of costly data breaches. A single cyber incident can lead to financial losses, regulatory penalties, operational disruption, and reputational damage.

Second, penetration testing supports regulatory compliance. Many industries—including finance, healthcare, and e-commerce—require periodic security assessments to meet standards such as PCI DSS, ISO 27001, or HIPAA.

Third, it improves internal security maturity. When development and infrastructure teams receive detailed feedback from testers, they gain a deeper understanding of secure architecture and coding practices.

Finally, penetration testing strengthens customer trust. Demonstrating that systems are regularly tested by independent experts signals a strong commitment to protecting user data.

Choosing the right penetration testing partner

Not all security providers deliver the same level of expertise or value. When selecting a penetration testing company, organizations should consider several factors.

Technical expertise is critical. Experienced testers should hold recognized certifications such as OSCP, CEH, or CREST, and have proven experience with modern technologies including cloud platforms, APIs, and containerized environments.

Methodology and transparency also matter. Reputable firms clearly explain their testing process, scope, and reporting structure before the engagement begins.

Industry experience can significantly improve the quality of testing. Providers familiar with sectors like fintech, healthcare, or logistics understand common threat patterns and regulatory expectations.

Actionable reporting is another key factor. Security reports should translate technical findings into clear business risks and remediation steps that engineering teams can realistically implement.

The growing role of penetration testing in modern cybersecurity

As digital ecosystems expand, the attack surface of organizations grows with them. Cloud services, APIs, IoT devices, and remote work infrastructure all introduce new potential entry points for attackers.

Because of this complexity, cybersecurity can no longer rely solely on defensive monitoring tools. Businesses must proactively search for weaknesses in the same way adversaries do. Regular penetration testing has therefore evolved from a niche security service into a core component of modern cyber risk management.

Organizations that integrate testing into their security lifecycle—especially during software development and infrastructure changes—can detect vulnerabilities earlier and reduce remediation costs significantly.

In this environment, companies increasingly turn to specialized security partners to strengthen their defenses. Andersen penetration testing company services, for example, are often integrated into broader cybersecurity and software engineering initiatives, enabling businesses to identify vulnerabilities early, validate the resilience of their systems, and continuously improve their security posture as their digital products evolve.

How Live Entertainment Technology Is Changing Traditional Table Games

If you’ve spent any time wandering through the quiet, prestigious streets of Mayfair, you know that the atmosphere of a high-end gaming room is nearly impossible to bottle. It’s the sound of a shuffled deck, the weighted click of a chip, and that unspoken nod between a dealer and a regular. For a long time, the digital world simply couldn’t compete with that. But things have shifted. We’ve moved far beyond the clunky, cartoonish graphics of the early internet. Today, the tech driving live entertainment is doing something quite remarkable: it’s making the screen disappear.

While 4K streaming is certainly a treat for the eyes, the most significant change isn’t found in pixel counts alone—it’s in the depth of the immersion. With the integration of Augmented Reality (AR), you’re no longer just looking at a video feed of a table. You’re seeing digital overlays that track every card movement and betting pattern in real-time. It’s a bit surreal, honestly. You might be sitting on your sofa, but the visual data makes the game feel more transparent than ever.

Bridging the Gap to the Physical Floor

I’ve often wondered if a digital interface could ever truly replicate the “soul” of a physical club. Interestingly, advances in live dealer casino technology are often compared to the experience offered in physical venues across Mayfair, specifically in how they prioritize the human element. The dealers aren’t just there to flip cards; they are trained entertainers and facilitators.

High-speed, low-latency 5G has been the real hero here. Without it, the “live” part of the experience would be a stuttering mess. Now, the interaction is instantaneous. When you ask a question or place a late bet, the response is immediate. This lack of lag creates a kind of psychological bridge. Before you know it, your brain stops treating the screen like a “game” and starts treating the whole thing like a genuine event. It’s a strange shift. This seamlessness happens because of several layers of tech humming away in the background—stuff you’d never notice unless it broke.

Take Optical Camera Recognition, for instance. It’s basically the “eyes” of the operation, instantly translating a physical card shuffle into digital data. Then you have the cinematography. It isn’t just a static webcam anymore; automated cameras now pivot and zoom based on where the action is, much like how your own eyes would dart around a table in a real room. Some setups are even experimenting with haptic feedback, where your phone gives a tiny, tactile buzz to mimic the vibration of a roulette ball hitting the pocket. It sounds small, but those little touches really pull you in.

Why It Matters Beyond the Fun

It isn’t all just bells and whistles, though. I’ve noticed that as the tech gets more sophisticated, the people running the show have to be more responsible, too. It’s a bit of a double-edged sword. There’s a lot of talk about how AI monitors these games now. While that sounds a bit “Big Brother,” it’s actually there to spot patterns of risky behavior that a human eye might miss. I think it’s a positive step.

It’s how regulations drive responsible online casino gaming that really defines the current era. By using data to ensure players are staying within their limits, the industry is trying to prove it can be both high-tech and high-standard. It’s about longevity, not just a quick thrill.

What do you think about this digital shift? Does the convenience of a high-tech live stream ever truly beat the feeling of a night out in a classic London venue, or is the technology finally getting close enough to call it a draw? I’d love to hear your thoughts on whether you prefer the haptic buzz of a phone or the weight of a premium gaming chip.

 

How Smart Vehicle Technology and Real Time Data Are Reshaping Road Safety and Driver Accountability

Modern vehicles are no longer isolated mechanical machines. They operate as connected platforms equipped with sensors, software, and communication tools that collect and process real time data. Automakers now integrate advanced driver assistance systems, onboard diagnostics, and cloud connectivity to enhance safety and performance. These technologies actively monitor speed, braking patterns, lane positioning, and surrounding traffic conditions to reduce human error and support informed driving decisions.

This transformation reflects a broader shift within the mobility sector. Vehicles now function as part of a digital ecosystem that includes mobile applications, traffic infrastructure, and telematics services. Real time data exchange allows drivers to receive alerts, optimize routes, and respond to road hazards more efficiently. As this technology becomes standard rather than optional, it shapes expectations around safety, transparency, and accountability on the road.

Manufacturers also collaborate with software developers and telecommunications providers to strengthen connectivity reliability. Over the air updates improve system performance without requiring physical servicing, while cloud platforms store anonymized performance data to refine future safety features. This continuous improvement cycle ensures that vehicles evolve long after purchase. As hardware and software operate together, connected mobility systems create an environment where prevention and informed response replace reactive measures.

Technology and Accountability After a Road Collision

According to www.accidentjusticepro.com, a car accident is not only a moment of physical impact. It triggers insurance claims, liability assessments, potential legal action, and safety reviews that can extend for months. Traditionally, fault determination relied heavily on eyewitness accounts, physical damage inspection, and police reports. These methods often produced conflicting narratives, especially in complex multi vehicle collisions. Today, connected vehicle systems and digital recording tools provide a structured layer of evidence that reshapes how a car accident is evaluated from both legal and technical perspectives.

When a car accident occurs, event data recorders capture pre impact speed, braking input, seatbelt usage, airbag deployment timing, and steering direction. Telematics systems log GPS positioning and vehicle behavior in real time. This information can confirm whether a driver attempted evasive action, exceeded speed limits, or ignored automated safety warnings. Insurance providers and legal professionals increasingly rely on this data to resolve disputes more efficiently. While the collision itself remains a serious and often disruptive event, technology reduces ambiguity in its aftermath and introduces measurable accountability into what was once largely subjective analysis.

Artificial Intelligence in Risk Detection

Artificial intelligence has expanded the capabilities of vehicle safety systems. Advanced algorithms analyze patterns from millions of driving scenarios to detect potential risks in real time. Lane departure warnings, adaptive cruise control, automatic emergency braking, and pedestrian detection systems operate by interpreting sensor inputs within fractions of a second. These features reduce reaction time gaps that often contribute to roadway incidents.

Beyond in vehicle systems, AI also supports traffic management platforms. Cities deploy smart traffic signals and predictive analytics to monitor congestion and adjust flow dynamically. This broader infrastructure integration reduces bottlenecks and high risk intersections. By combining vehicle intelligence with smart city frameworks, the transportation ecosystem becomes more responsive and data driven, contributing to safer road environments overall.

Machine learning models continue to improve as they process larger volumes of driving data. Developers refine algorithms to account for diverse weather conditions, road surfaces, and traffic behaviors. As a result, safety systems adapt more effectively to real world variability. Continuous algorithm training strengthens predictive accuracy and enhances driver assistance reliability without increasing complexity for the user.

Telematics and Behavioral Insights

Telematics systems collect ongoing driving data, including acceleration patterns, braking intensity, and cornering behavior. Fleet operators and insurers use this information to evaluate driving performance and encourage responsible habits. Drivers receive feedback through mobile dashboards, allowing them to identify areas for improvement and reduce risky behaviors over time.

This data driven approach promotes accountability without constant supervision. Rather than relying solely on post incident assessments, telematics shifts attention toward prevention. Businesses that manage vehicle fleets benefit from reduced operational risks, while individual drivers gain greater awareness of how their habits influence safety outcomes. The growing adoption of telematics reflects the broader digital transformation within transportation technology.

Behavioral analytics platforms also support customized training initiatives. Organizations can identify consistent risk indicators and design targeted coaching programs to address them. Over time, this structured feedback loop encourages measurable improvement in driving standards. Telematics therefore functions not only as a monitoring tool but also as a practical mechanism for long term risk reduction and performance enhancement.

Cybersecurity and Data Integrity in Modern Vehicles

As vehicles become increasingly connected, cybersecurity becomes a critical priority. Protecting sensitive driving data and preventing unauthorized system access is essential to maintain trust in digital mobility platforms. Manufacturers invest in encryption protocols, secure software updates, and network monitoring to reduce vulnerabilities. Without strong safeguards, connected systems could expose drivers to privacy risks or operational disruptions.

Data integrity also affects accountability. Accurate records must remain tamper resistant to ensure fairness in assessments and investigations. Reliable cybersecurity frameworks support the legitimacy of digital evidence and protect both drivers and service providers. As connected vehicles continue to evolve, maintaining robust security standards remains central to sustaining confidence in smart transportation technologies.

Security architecture now incorporates multi layer defenses that isolate critical vehicle functions from external communication channels. Regular penetration testing and vulnerability assessments strengthen system resilience against emerging threats. By embedding security principles into design rather than treating them as afterthoughts, manufacturers protect both operational stability and data credibility. This proactive approach reinforces trust in connected vehicle ecosystems.

A New Standard for Road Responsibility

The integration of smart vehicle systems, real time analytics, and connected infrastructure has redefined how responsibility is evaluated on the road. Decisions are no longer based solely on testimony or fragmented observations. Instead, comprehensive datasets provide structured insight into driving behavior and vehicle performance. This shift supports more objective evaluations and encourages higher safety standards across the mobility sector.

Looking ahead, continued innovation in sensor technology, AI modeling, and infrastructure connectivity will further refine how road incidents are prevented and assessed. As technology advances, drivers, manufacturers, insurers, and regulators must collaborate to balance innovation with ethical data practices. Smart mobility systems are not simply convenience features. They represent a structural change in how road safety and accountability are approached in a digitally connected world.

As adoption expands, expectations around transparency and measurable responsibility will continue to rise. Stakeholders across the transportation industry will rely more heavily on verified digital records and predictive systems to guide policy and operational standards. The result is a mobility landscape shaped by data driven evaluation and continuous improvement. Smart vehicle technology has established a durable framework that reshapes how safety, performance, and accountability coexist on modern roads.

 

Dell PowerEdge XR9700 Brings Cloud RAN and AI to Harsh Edge Environments

Dell Technologies introduces the Dell PowerEdge XR9700 server, a first of its kind closed-loop liquid-cooled, fully-enclosed, ruggedized server engineered to run Cloud RAN and edge AI workloads in unprotected outdoor environments. Designed to mount on utility poles, rooftops and building exteriors, the PowerEdge XR9700 brings high performance computing into dense urban areas, remote locations, and space-constrained facilities where traditional data center infrastructure cannot reach.

Why it matters

Telecommunications operators and those working at the edge often struggle to deploy compute due to lack of power and space. The PowerEdge XR9700 solves this, delivering high performance compute directly at the point of need in an ultra-compact, zero-footprint IP66-rated enclosure that’s sealed from the elements. For telecommunications operators, it provides a flexible, software-defined alternative to traditional RAN solutions, supporting Cloud RAN and Open RAN processing at the cell site. At the same time, the platform can run edge and AI applications directly where data is created and consumed.

Built for Extreme Conditions

Designed to withstand the harshest environments, this platform’s ultra-compact IP66-rated enclosure and GR-3108 Class 4 certification delivers reliable, quiet performance in environments exposed to extreme temperatures, dust and moisture. Closed-loop liquid cooling with a thermal management architecture maintains consistent operation across a temperature range of -40°C to 46°C (-40°F to 115°F) and withstands direct solar radiation, all in a compact 15-liter form factor suitable for mounting on utility poles, rooftops and building sides. This zero-footprint design brings telecom and edge workloads to locations where only traditional radio solutions could previously operate.

Performance that Scales

Powered by the Intel Xeon 6 SoC with integrated Intel vRAN Boost technology and Intel AMX technology, the PowerEdge XR9700 delivers the processing power and fronthaul connectivity to support up to 15 5G sectors in a single server. While optimized for Cloud RAN, the platform’s flexibility allows operators to run edge and AI workloads based on network architecture and service requirements.

As part of the Dell PowerEdge XR-Series, the XR9700 integrates with Dell’s existing management tools and software stack. Integrated Dell Remote Access Controller (iDRAC) provides remote visibility and control for zero-touch provisioning (ZTP), while compatibility with the same Cloud RAN software validated on the PowerEdge XR8720t simplifies certification and accelerates telecom deployments.

Andrew Vaz, vice president, Dell Technologies: “Operators and enterprises shouldn’t have to compromise when deploying compute in challenging environments. The Dell PowerEdge XR9700 brings Cloud RAN, Open RAN, and edge AI capabilities to places they’ve never been able to go before, opening up new possibilities for network expansion and edge applications.”

 Availability

The Dell PowerEdge XR9700 will be globally available 2H CY 2026.

Additional resources

  • Find out more about the Dell PowerEdge XR9700.
  • Learn more about Dell Open Telecom Ecosystem Lab (OTEL) AI-assisted telecom testing and validation.
  • Connect with Dell on X and LinkedIn

About Dell Technologies
Dell Technologies (NYSE: DELL) helps organizations and individuals build their digital future and transform how they work, live and play. The company provides customers with the industry’s broadest and most innovative technology and services portfolio for the AI era.

Valentine’s Day spend shows strong growth in key luxuries

Despite a modest overall dip in Valentine’s Day spending (-14%) last Saturday, several categories spiked as romantic consumers shifted their focus to luxury treats and quality time together.

Bank of Ireland’s debit and credit card spending data for the full day of February 14th versus Valentine’s Day last year shows strong increases across pubs, jewellery, hotels and restaurants. The data highlights that while shoppers spent less on traditional gifts such as flowers and cards, they were more willing to invest in a special night out.

Jewellery spending also surged by 51% on the day itself, suggesting that more people left things last-minute for gifts this year. Hospitality also benefitted this year. Pubs saw the most dramatic rise, up 51%, although this was likely a mixture of ‘romance and rugby’ with the Ireland versus Italy rugby game landing last Saturday too.  Restaurant spending was up 22% compared to Valentine’s Day last year and hotel stays rose 11%, with a strong appetite for romantic dining and overnight stays.

While some traditional categories such as flowers, experiences and perfumes recorded declines, the data highlights a clear shift in consumer preference with less emphasis on single‑use gifts and more investment in shared enjoyment.

Gerardo Larios Rizo, Head of Hospitality Sector, Bank of Ireland said: “Our Valentine’s Day data shows that while overall spending was slightly softer, people were still determined to make the day special. Instead of splashing out on single‑use gifts, consumers shifted to special moments such as a romantic dinner, a hotel stay or even celebrating ‘romance and rugby’ in their local pub. While some romantics shopped ahead, the spike in jewellery sales on the day itself suggests a rush of last-minute panic-buying this year.”

Bank of Ireland card spending – Feb 14th 2026 versus Feb 14th 2025

  • Pubs (+51%)
  • Jewellery (+51%)
  • Restaurants (+22%)
  • Hotels (+11%)
  • Gift Websites (+4%)
  • Chocolates flat year on year
  • Flowers (-33%)
  • Cards (-28%)
  • Perfume (-6%)

The Unseen Engine: How Enterprise Storage Is Powering Business Innovation in Ireland

In the pursuit of digital transformation, businesses often spotlight their cutting-edge applications, their multicloud strategies, or their latest AI models. Yet, behind each of these advancements lies a powerful, unseen engine: the enterprise storage platform. Ivor Buckley, Field CTO, Dell Technologies Ireland tells us more below 

Once regarded as a back‑end system, enterprise storage has become a strategic platform that underpins innovation. As Irish organisations race to modernise services, comply with regulation and compete internationally, the way they store, protect, and govern data is turning into a fundamental differentiator.

Today’s IT leaders face a significant challenge. They must support an ever-expanding portfolio of workloads, from critical business databases to cloud-native applications and data-intensive AI projects. All this must be achieved within the constraints of tight budgets and limited staffing. The sheer volume of data being created and managed is staggering; global data generation is expected to reach 393.9 ZB by 2028 as per IDC. This explosion of information puts immense pressure on infrastructure that was not designed for this scale or complexity resulting in data foundations under strain

According to the latest Dell Innovation Catalyst Study, 48% of Irish organisations are prioritising data readiness for AI related workload, while 66% say they are still in their early or mid-stage of their AI/GenAI journey. This underscores a reality that organisations want to innovate but their data foundations and current storage systems are not fully equipped.

From Data Silo to Intelligent Hub

The perception of enterprise storage as a mere commodity is outdated. Modern platforms have become intelligent hubs that automate complex tasks and unlock new efficiencies. By integrating machine learning and advanced analytics, today’s storage systems can proactively optimise workload placement, predict performance bottlenecks before they occur, and simplify management tasks that once consumed countless hours.

This shift is relevant in Ireland, where businesses from multinationals to SMEs are accelerating digital transformation under the National AI Strategy. A study Dell undertook found that 96% of Irish organisations face challenges when it comes to identifying, preparing, and using data for AI/GenAI uses cases, with 40% struggle to integrate AI systems with existing IT infrastructure. Intelligent storage platforms directly address these pain points by reducing complexity and improving data accessibility without creating new data silos

For Irish businesses planning to expand their e-commerce operations and presence, a modern storage platform can intelligently prioritise these diverse workloads, ensuring that customer-facing applications remain responsive while they have high-speed access, they need to train their models that maintain the strategic initiatives that drive business growth.

Bridging Private Cloud and Multicloud for Seamless Innovation

In today’s digital landscape, businesses are increasingly faced with the decision to operate within a private cloud, adopt a multicloud environment, or find a balance between the two. Enterprise storage serves as the reliable backbone for these evolving strategies, delivering the infrastructure needed to provide both security and agility at scale.

For Irish businesses relying on private cloud infrastructure, enterprise storage provides robust data protection, predictable performance, and the confidence that sensitive information remains under their control.  As organisations here in Ireland expand further into multicloud setup, seamless data mobility becomes essential not just for storing data but also for making it accessible and secure wherever it resides.

According to the Dell study, 46% of local organisations plan to modernise their IT with intelligent infrastructure, and another 46% aim to optimise workload placement across edge, core, and cloud environments.

The right storage platform is central to both goals: it can synchronise data across environments, break down silos and help ensure that everyday operations remain stable even as new services and AI projects come online.

This reflects a clear shift towards hybrid architecture, a trend mirrored in Ireland’s public-sector digital transformation and the country’s growing cloud smart enterprise landscape.

Crucially, enterprise storage also addresses security, and compliance demands unique to both private and multicloud models. By providing unified management and strong governance features, these platforms make it easier for businesses across Ireland to implement consistent security policies and adhere to regulatory requirements. The result is an IT environment that’s not only flexible and responsive but also protected, adhering to regulation and aligned with business goals.

Fuelling the Future of AI and Analytics

Perhaps the most significant driver of storage innovation today is AI. AI and machine learning workloads are incredibly data-hungry, requiring massive datasets to be fed to powerful processors without delay. A bottleneck in the storage layer can bring an entire AI initiative to a standstill.

Modern enterprise storage platforms are engineered to meet these demands, delivering the high throughput and low latency needed to fuel advanced analytics. A healthcare provider, for instance, might use AI to analyse medical images to detect diseases earlier. This process requires rapid access to petabytes of high-resolution image data. An intelligent storage system ensures that this data is readily available, accelerating the model training process and ultimately improving patient outcomes.

One of the most significant developments in this space is the emergence of the data lakehouse – a modern data architecture that blends the flexibility of a data lake with the performance and governance of a data warehouse.

Rather than forcing organisations to move and duplicate data repeatedly into different silos, a Data Lakehouse strategy is about bringing AI to the data. By minimising unnecessary data movement and providing a single point of access, it helps address some of the biggest blockers to AI projects: fragmented data, inconsistent governance, and slow time‑to‑insight.

Modern Enterprise Storage Has Become the Unseen Engine of Digital Innovation

The journey of enterprise storage reflects the broader story of technological progress. What was once a simple utility has become a strategic enabler for Cloud, AI and data-driven services, quietly powering the applications and insights that define modern business. By embracing automation, enabling seamless data mobility, and delivering the performance needed for next-generation workloads, enterprise storage has become the unseen engine of digital innovation.

Irish businesses are operating in one of Europe’s most dynamic digital economies and the opportunity is clear. Ireland’s National AI Strategy aims to see 75% of Irish enterprises using cloud, AI, and data analytics by 2030. To fully realise this potential, businesses must proactively evaluate, adopt, and integrate these advanced solutions into their Cloud Operating Model. This isn’t just about keeping up, it’s about unlocking new levels of efficiency, innovation, and competitiveness. By investing in vital storage infrastructure, businesses of all sizes can simplify data management, scale with confidence, and accelerate their AI journey for the next wave of AI-driven transformation.

How AI-Powered Data Annotation is Transforming Computer Vision in Irish Tech Companies

Computer vision is powering everything across Ireland’s fast-growing tech ecosystem, from advanced manufacturing and smart retail to fintech security. Data annotation sits at the core of these intelligence systems. Keep reading to understand how Irish tech companies are improving accuracy and accelerating model training as AI-powered annotation systems become scalable and precise.

Data Annotation Trends in Irish Tech Companies

Many Irish tech companies in the early computer vision development relied on small teams, mostly in-house, to label videos and images manually. These processes were inconsistent, slow and expensive, especially during scaling or when datasets reach the millions. Now, companies are relying on AI-powered data annotation to reshape their workflow. By combining human validation with automated pre-labelling, providers like the oWorkers team offer support in handling large-scale datasets with great precision and speed. This is a hybrid approach that allows both established businesses and startups to train their vision models with great efficiency without compromising quality.

Data annotation plays an essential role in system training, since even the most sophisticated AI model is as accurate as the data it trains from. Irish companies are taking advantage of well-annotated datasets for different sectors like retail analytics, fintech, health tech and smart cities to power fraud prevention, facial recognition, predictive maintenance and object detection. AI-powered tools are gaining popularity since they reduce human errors, speed up turnaround and guarantee consistent labelling standards across different projects. Because of that, organisations can scale their computer vision solutions confidently, improve model performance and shorten development cycles in competitive global markets.

How AI-Powered Annotation Elevates Models Accuracy

Companies cannot achieve accurate computer systems by chance; they should build them on precisely labelled data. Improving model accuracy and developing AI-driven platforms for Irish tech organisations is directly tied to the consistency and quality of annotation processes.

Machine Learning Pre-Labelling

Machine learning models are used by AI-powered annotation tools to automatically create initial labels for videos and image frames. This pre-labelling technique helps companies reduce workloads and accelerate dataset preparation. The only work annotators have is to review and refine already generated tags, segmentation masks and/or bounding boxes instead of starting from scratch. For Irish companies working under pressure, this means quicker deployment and faster iterations of computer vision solutions.

Human Validation (In the Loop)

Human experience and expertise remain vital even though automation alone speeds up workflows. Human-in-the-loop validation guarantees that any AI-generated annotation is checked for edge cases, context and nuance. Skilled reviewers in this approach handle complex scenarios, correct inaccuracies and maintain dataset consistency. This is a perfect combination of precision and speed, which results in a stronger model performance and reliable training data.

Bias Reduction and Feedback Loops

AI-assisted annotation systems “grow” over time through a well-structured feedback loop. This means that corrections made by human annotators are returned to the systems to refine future output. Because of that, companies can boost efficiency while identifying and minimising bias in datasets. Reducing bias, especially for Irish tech companies like healthcare, finance and smart cities, is vital for fairness, long-term trust and compliance.

Conclusion

AI-enhanced data annotation is taking centre stage in computer vision innovation in Ireland‘s tech companies. These organisations can develop reliable, scalable and more accurate AI systems by combining human expertise with intelligent automation.