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As we close out 2024, the pace of data visualization innovation will continue to accelerate. For B2B businesses, the ability to transform complex data into actionable insights is now a necessity rather than a luxury. Central to this evolution is Artificial Intelligence, which is reshaping dashboards and data visualizations to enable organizations to make faster, more impactful decisions. Looking ahead, 2025 is set to be a defining year for large language models (LLMs), real-time analytics, and advanced machine learning algorithms that will elevate AI-driven data visualizations. Here’s a toolk

As the technological landscape is rapidly evolving, the integration of Internet of Things (IoT) technologies into engineering systems is not just a trend—it’s a necessity. The global industries are facing increasing pressures to enhance efficiency, reduce operational costs, and maintain competitive advantages, IoT emerges as a transformative force, particularly within Telecom and Utility sectors. IoT is not a stand-alone technology. The efficiency of connected devices not only depends on but thrives when the complimentary technologies are implemented tactfully. Amalgamation of Data & Analytic

It must be noted that the existence of lakehouse architectures has brought some substantial changes in the data architecture landscape. In this evolution process, organizations are still struggling on how to handle complex and diverse data management, to which the answer is the lakehouse model. Lakehouses can be viewed as a better integration of data lakes and data warehouses to provide improved data management systems. This blog post delves into the further evolution of lakehouse architecture and explains its main concepts, recent developments, and transformation of today’s data management.

Balancing AI-driven innovation with sustainability requires a multifaceted approach, leveraging advanced technologies like computational storage drives, distributed computing, and expanded memory via CXL. These solutions can significantly reduce the energy consumption of AI infrastructure while maintaining high performance and operational efficiency. By addressing the challenges associated with power consumption and adopting innovative storage and processing technologies, data centers can achieve their sustainability goals and support the growing demands of AI and ML applications.

The holiday season brings a significant surge in demand for customer service, driven by the rise of online shopping. Businesses increasingly turn to artificial intelligence (AI) and automation to bridge the gap in support during these high-pressure periods. With AI-powered tools like chatbots and automated systems, companies can handle demand spikes effectively without compromising performance. Let’s explore key strategies for scaling AI in customer service to ensure smooth operations during the holiday rush. The holiday shopping season, particularly Cyber Week, generates massive web traffic.

Banks suffered an astounding $485.6 billion loss to fraud and scams last year, highlighting the urgent need for them to outpace criminals. Fraud analytics plays a crucial role in enabling banks to transition from merely reacting to fraud to proactively preventing it. Explore how fraud analytics helps detect and prevent various types of fraud, minimizing financial losses and improving customer trust and satisfaction. Fraud analytics blends artificial intelligence (AI), machine learning, and predictive analytics for advanced data analysis.

Dev Nag is the Founder and CEO of QueryPal. He was previously the Founder and CTO at Wavefront, which was backed by Sequoia Capital and acquired by VMware. At VMware, he served in the Office of the CTO and launched VMware’s flagship AIOps product. He previously held engineering leadership roles at Google, eBay, and PayPal. Dev holds more than a dozen patents in machine learning and security. He published academic papers in computational biology and medical informatics at Stanford, where he received two degrees.

Artificial intelligence (AI) is a relatively new field that has rapidly evolved into a major influence on the strategic direction of organizations. Its significance extends far beyond automation, enhancing complex decision-making processes. AI is both a risk and a tool for managing risk—a paradox that organizations must confront as they navigate the landscape of 2024 and beyond. While AI is often associated with task automation, it also plays a critical role in improving decision-making. AI empowers change across various domains, from social to informational, by automating time-consuming proc

Generative AI – a technology wonder of modern times – has revolutionized our ability to create and innovate. It also promises to have a profound impact on every facet of our lives. Beyond the seemingly magical powers of ChatGPT, Bard, MidJourney, and others, the emergence of what’s known as RAG (Retrieval Augmented Generation) has opened the possibility of augmenting Large Language Models (LLMs) with domain-specific enterprise data and knowledge. RAG and its many variants have emerged as a pivotal technique in the realm of applied generative AI, improving LLM reliability and trustworthiness.

Data breaches and cyber threats are becoming increasingly common in this digital era, and protecting valuable information is the top priority for data-driven organizations. To curb the constant issues of data being compromised, lost, and misused, a Data Protection Officer (DPO) and their teams can implement a data loss prevention (DLP) strategy and tools that will continuously monitor and analyze data to identify potential violations of security policies and stop them from evolving. In this article, we will take a closer look at the seven steps of DLP strategies and tools that will help in en

In this digital world, data is an important asset; however, organizations are searching for storage solutions that will help them manage big data’s volume, latency, resiliency, and data access requirements. Traditionally, companies used existing tech stacks that delivered the same capabilities as a warehouse or lake but had adjustments in handling massive amounts of semi-structured data. These approaches often resulted in high costs and data duplication across all businesses. The emergence of data lake houses as a hybrid data architecture aims to deliver better benefits as it eliminates data

Delivering an improved digital employee experience (DEX) has become a top priority for many enterprise IT leaders, as it directly influences productivity, employee morale, and other critical aspects of business success. However, many organizations still lack the necessary visibility into their IT ecosystems to fully understand how digital tools impact employee experiences and productivity. This gap often hinders efforts to effectively manage the digital workplace and provide employees with an exceptional experience.

Synthetic data can be defined as data that is not acquired through actual occurrences or interactions but rather created fake data. It is specifically intended to mimic the characteristics, behaviors and organizations of actual data without copying them from actual observations. Although there exist a myriad of approaches to generating synthetic data, its generation might use simple rule-based systems or even more complicated methods, such as Machine Learning based on GANs. It is aimed at creating datasets which are as close as possible to real data, yet not causing the problems connected wit

Generative AI has swiftly become popular among marketers and has the potential to grow to a $1.3 trillion industry in the next 10 years. OpenAI’s ChatGPT is just one growth example—rocketing to over 100 million users in just two months of its release. Many have hailed generative AI as a process-changing tool that can quickly produce swaths of content with minimal human intervention, drastically scaling content production. That’s the claim anyway. But as AI becomes more prevalent, its use in content production opens several questions — does generative AI actually produce quality content? Can i

The tech industry is synonymous with innovation, rapid changes, and the introduction of cutting-edge products. While the field offers exciting opportunities, the realities often diverge from the glamorous image portrayed in movies. Behind the scenes, professionals face extended work hours, relentless deadlines, and high expectations. These pressures are particularly pronounced for men in senior roles, where societal norms discourage expressing emotions, leading to unique mental health challenges. Fortunately, advancements in AI are providing effective tools to help men manage stress and maint

Artificial Intelligence (AI) has evolved from a mere buzzword to the driving force behind a transformative wave of digital innovation. As we approach 2025, AI's integration into business strategies is reshaping industries and paving the way for groundbreaking advancements. From machine learning to natural language processing (NLP), digital transformation is experiencing a seismic shift. Let’s explore how AI powers this transformation and revolutionizes business operations. AI fuels the future! Dive into how it powers efficiency, enhances CX, and transforms industries in the digital era of 202

In recent years, computer vision (CV) has appeared as a transformative technology that reshapes the landscape of numerous industries by allowing machines to analyze and understand visual information around them. According to tech leaders, computer vision is often referred to as the eyes of artificial intelligence (AI), which makes it a transformative technology that not only revolutionizes the industries that adapted it but also becomes a cornerstone for the advancement of AI. With more technological advancements, the convergence of CV with IoT, big data analytics (BDA), and automation has gi

Paige O’Neill is Chief Marketing Officer at Seismic where she leads all marketing functions including corporate, product, field, partner, and customer marketing. She is a data-driven marketer with a track record of propelling growth, delivering outstanding results, and spearheading thought leadership within the Customer Experience industry across her 30+ years of experience. Prior to Seismic, Paige served as CMO at public and private software companies including Sitecore, Prysm, SDL, and Aravo Solutions. She also spent nearly a decade at Oracle in product marketing and public relations roles.

Picture a world where your professional services business operates like a well-oiled machine, effortlessly balancing resources, predicting project outcomes, and communicating with precision. This isn’t a far-off dream—it’s the reality that Artificial Intelligence (AI) is bringing to the professional services industry right now. As the lines between human expertise and technological capabilities blur, AI is emerging as the secret weapon for firms looking to surge ahead in a fiercely competitive market. By leveraging AI-powered Professional Services Automation (PSA) software, firms can optimize

Generative AI (GenAI) has the potential to transform enterprise operations by driving automation, boosting efficiency, and fostering innovation. However, its implementation is not without challenges, particularly around data privacy and security. According to Gartner's Generative AI 2024 Planning Survey, 39% of data and analytics leaders identify data protection and privacy as major concerns. What fuels these challenges? Traditional data management practices, characterized by fragmented data sources and siloed governance protocols, are proving inadequate in the era of Large Language Models (L