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Most AI workloads are deployed in private cloud or on-premises environments, driven by data locality and compliance needs. AI a primary driver in IT modernization and data mobility AI’s demand for data requires businesses to have a secure and accessible data strategy. Cost, by comparison, ranks a distant 10th.
In 2025, data management is no longer a backend operation. It has become a strategic cornerstone for shaping innovation, efficiency and compliance. This article dives into five key data management trends that are set to define 2025. This reduces manual errors and accelerates insights.
In today’s data-driven world, large enterprises are aware of the immense opportunities that data and analytics present. Yet, the true value of these initiatives is in their potential to revolutionize how data is managed and utilized across the enterprise. Take, for example, a recent case with one of our clients.
Oren Yunger is an investor at GGV Capital , where he leads the cybersecurity vertical and drives investments in enterprise IT, datainfrastructure, and developer tools. When it comes to meeting compliance standards, many startups are dominating the alphabet. It makes sense that startups want to tackle compliance first.
With its unparalleled flexibility, rapid development and cost-saving capabilities, open source is proving time and again that it’s the leader in data management. But as the growth in open source adoption increases, so does the complexity of your datainfrastructure.
Orsini notes that it has never been more important for enterprises to modernize, protect, and manage their IT infrastructure. Justin Giardina, CTO at 11:11 Systems, notes that the company’s dedicated compliance team is also a differentiator. At 11:11 Systems, we go exceptionally deep on compliance,” says Giardina. “At
However, trade along the Silk Road was not just a matter of distance; it was shaped by numerous constraints much like todays data movement in cloud environments. Merchants had to navigate complex toll systems imposed by regional rulers, much as cloud providers impose egress fees that make it costly to move data between platforms.
Data is the lifeblood of the modern insurance business. Yet, despite the huge role it plays and the massive amount of data that is collected each day, most insurers struggle when it comes to accessing, analyzing, and driving business decisions from that data. There are lots of reasons for this.
Migration to the cloud, data valorization, and development of e-commerce are areas where rubber sole manufacturer Vibram has transformed its business as it opens up to new markets. Data is the heart of our business, and its centralization has been fundamental for the group,” says Emmelibri CIO Luca Paleari.
A high hurdle many enterprises have yet to overcome is accessing mainframe data via the cloud. Mainframes hold an enormous amount of critical and sensitive business data including transactional information, healthcare records, customer data, and inventory metrics.
In 2025, insurers face a data deluge driven by expanding third-party integrations and partnerships. Many still rely on legacy platforms , such as on-premises warehouses or siloed data systems. Step 1: Data ingestion Identify your data sources. First, list out all the insurance data sources.
However, the increasing integration of AI and IoT into everyday operations also brings new risks, including the potential for cyberattacks on interconnected devices, data breaches, and vulnerabilities within complex networks. Securing these technologies is paramount in a region where digital infrastructure is critical to national development.
Generative AI is a major investment and requires a substantial commitment in infrastructure and talent, Manry says. Do we have the data, talent, and governance in place to succeed beyond the sandbox? These, of course, tend to be in a sandbox environment with curated data and a crackerjack team. How confident are we in our data?
By eliminating time-consuming tasks such as data entry, document processing, and report generation, AI allows teams to focus on higher-value, strategic initiatives that fuel innovation. Similarly, in 2017 Equifax suffered a data breach that exposed the personal data of nearly 150 million people.
From prompt injections to poisoning training data, these critical vulnerabilities are ripe for exploitation, potentially leading to increased security risks for businesses deploying GenAI. Data privacy in the age of AI is yet another cybersecurity concern. This puts businesses at greater risk for data breaches.
Data sovereignty and the development of local cloud infrastructure will remain top priorities in the region, driven by national strategies aimed at ensuring data security and compliance. The Internet of Things will also play a transformative role in shaping the regions smart city and infrastructure projects.
It demands a robust foundation of consistent, high-quality data across all retail channels and systems. AI has the power to revolutionise retail, but success hinges on the quality of the foundation it is built upon: data. The Data Consistency Challenge However, this AI revolution brings its own set of challenges.
A modern data and artificial intelligence (AI) platform running on scalable processors can handle diverse analytics workloads and speed data retrieval, delivering deeper insights to empower strategic decision-making. They are often unable to handle large, diverse data sets from multiple sources.
Additionally, leveraging cloud-based solutions reduced the burden of maintaining on-premises infrastructure. For instance, AT&T launched a comprehensive reskilling initiative called “Future Ready” to train employees in emerging technologies such as cloud computing, cybersecurity, and data analytics. Contact us today to learn more.
Just because you’re a startup doesn’t mean you can be careless with the data you’re handling, but enterprise-grade compliance and privacy used to be prohibitively expensive for small teams. However, meeting governance, risk and compliance (GRC) standards and proving that you’ve done so used to be very expensive.
The reasons include higher than expected costs, but also performance and latency issues; security, data privacy, and compliance concerns; and regional digital sovereignty regulations that affect where data can be located, transported, and processed. So we carefully manage our data lifecycle to minimize transfers between clouds.
Enterprise applications have become an integral part of modern businesses, helping them simplify operations, manage data, and streamline communication. However, as more organizations rely on these applications, the need for enterprise application security and compliance measures is becoming increasingly important.
Data sovereignty and local cloud infrastructure will remain priorities, supported by national cloud strategies, particularly in the GCC. Adopting multi-cloud and hybrid cloud solutions will enhance flexibility and compliance, deepening partnerships with global providers.
Few CIOs would have imagined how radically their infrastructures would change over the last 10 years — and the speed of change is only accelerating. To keep up, IT must be able to rapidly design and deliver application architectures that not only meet the business needs of the company but also meet data recovery and compliance mandates.
Drawing from current deployment patterns where companies like OpenAI are racing to build supersized data centers to meet the ever-increasing demand for compute power three critical infrastructure shifts are reshaping enterprise AI deployment. Here’s what technical leaders need to know, beyond the hype.
Data sovereignty has emerged as a critical concern for businesses and governments, particularly in Europe and Asia. With increasing data privacy and security regulations, geopolitical factors, and customer demands for transparency, customers are seeking to maintain control over their data and ensure compliance with national or regional laws.
tagging, component/application mapping, key metric collection) and tools incorporated to ensure data can be reported on sufficiently and efficiently without creating an industry in itself! Infrastructure architecture: Building the foundational layers of hardware, networking and cloud resources that support the entire technology ecosystem.
This is true whether it’s an outdated system that’s no longer vendor-supported or infrastructure that doesn’t align with a cloud-first strategy, says Carrie Rasmussen, CIO at human resources software and services firm Dayforce. He advises using dashboards offering real-time data to monitor the transformation.
CIOs manage IT infrastructure and foster cross-functional collaboration, driving alignment between technological innovation and sustainability goals. These frameworks extend beyond regulatory compliance, shaping investor decisions, consumer loyalty and employee engagement.
The challenge, however, will be compounded when multiple agents are involved in a workflow that is likely to change and evolve as different data inputs are encountered, given that these AI agents learn and adjust as they make decisions. This opens the door for a new crop of startups, including AgentOps and OneReach.ai. IT employees?
Savvy IT leaders, Leaver said, will use that boost to shore up fundamentals by buttressing infrastructure, streamlining operations, and upskilling employees. “As 40% of highly regulated enterprises will combine data and AI governance. That, in turn, will put pressure on technology infrastructure and ops professionals.
An agentic era needs a platform that brings AI, data, and workflows together, and that should be an open, connected, enterprise-ready platform, said ServiceNows chief innovation officer Dave Wright in a press conference last week. ServiceNow said it expects the new model to be available in Q2 this year.
Dice compared salary data from those who identified as experts in these skillsets to those who reported using the skills regularly, uncovering a premium for expert-level tech professionals with these skillsets. As one of the most sought-after skills on the market right now, organizations everywhere are eager to embrace AI as a business tool.
The variety of tasks in business workflows and the need for greater accuracy are driving the shift towards specializedmodelsfine-tuned on specific functions or domain data, says Sumit Agarwal, an analyst at Gartner who helped author the report. Microsofts Phi, and Googles Gemma SLMs. Googles Gemma 3, based on Gemini 2.0,
In 2024 alone, the average cost of a data breach rose by 10% 1 , signaling just how expensive an attack could become. The risk of cybersecurity lapses, data breaches, and the resulting penalties for regulatory non-compliance have made it more important than ever for organizations to ensure they have a robust security framework in place.
AI in Action: AI-powered contract analysis streamlines compliance checks, flags potential risks, and helps you optimize spending by identifying cost-saving opportunities. AI in Action: AI streamlines integration by assessing system compatibility, automating data migration, and reducing downtime associated with your software deployments.
As organizations continue to implement cloud-based AI services, cloud architects will be tasked with ensuring the proper infrastructure is in place to accommodate growth. Organizations have accelerated cloud adoption now that AI tools are readily available, which has driven a demand for cloud architects to help manage cloud infrastructure.
There are two main considerations associated with the fundamentals of sovereign AI: 1) Control of the algorithms and the data on the basis of which the AI is trained and developed; and 2) the sovereignty of the infrastructure on which the AI resides and operates.
Many are prioritising investments in emerging technologies like AI, digital security, and data analytics. These capabilities demand a reliable, scalable computing infrastructure, and the cloud often marks the first step. VMware hyperconverged infrastructure was central to our cloud offering.
As enterprises navigate complex data-driven transformations, hybrid and multi-cloud models offer unmatched flexibility and resilience. Heres a deep dive into why and how enterprises master multi-cloud deployments to enhance their data and AI initiatives. The terms hybrid and multi-cloud are often used interchangeably.
Focused on digitization and innovation and closely aligned with lines of business, some 40% of IT leaders surveyed in CIO.com’s State of the CIO Study 2024 characterize themselves as transformational, while a quarter (23%) consider themselves functional: still optimizing, modernizing, and securing existing technology infrastructure.
AI agents , powered by large language models (LLMs), can analyze complex customer inquiries, access multiple data sources, and deliver relevant, detailed responses. In this post, we guide you through integrating Amazon Bedrock Agents with enterprise data APIs to create more personalized and effective customer support experiences.
However, enterprise cloud computing still faces similar challenges in achieving efficiency and simplicity, particularly in managing diverse cloud resources and optimizing data management. Enterprise IT struggles to keep up with siloed technologies while ensuring security, compliance, and cost management.
GRC certifications validate the skills, knowledge, and abilities IT professionals have to manage governance, risk, and compliance (GRC) in the enterprise. With companies increasingly operating on a global scale, it can require entire teams to stay on top of all the regulations and compliance standards arising today.
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