Categoria: Management

Throughout 2024, data analytics continued to play a crucial role in driving data-based decision-making for B2B companies, while investment in advanced data technologies surged. These new tools offer deeper insights, predictive capabilities, and more sophisticated analytics to improve governance. The tools listed below are equipped with powerful features across BI, predictive modeling, natural language processing, and governance, reshaping the landscape of B2B data analytics.

Here are our expert reviews on the top five data analytics tools that defined 2024 and how each one can help businesses leverage data more effectively.

Key Trends Shaping Data Analytics in 2024

Before diving into the tools, let’s look at some major trends in data analytics for 2024. AI and machine learning have moved from innovative add-ons to essential components that support real-time insights and forecasting. Data quality and governance have become even more significant, with more companies prioritizing structured data environments for better compliance and integrity.

Natural language processing (NLP) has unlocked new capabilities in handling structured and unstructured data, such as analyzing customer sentiment and predicting market trends. The tools in our list excel in seamless integration, AI capabilities, and enhanced governance, addressing these trends.

Tool #1: Power BI — Leading in Visualization

Overview:

Microsoft’s Power BI retains its top position in data visualization and reporting due to its strong integration and user-friendly interface. It makes complex data accessible, empowering B2B teams to make informed decisions.

Key Features:

Seamless Integration with Microsoft Suite: Easy data extraction, manipulation, and analysis.

AI-Infused Predictive Analytics: Predictive models based on past data trends for future insights.

Enhanced Governance and Compliance: Features such as role-based access and data governance, essential for regulated industries.

Best Use Cases:

Highly effective for sales and operations teams looking for real-time analytics. B2B companies can use Power BI’s KPI and data modeling features to optimize customer service metrics and sales funnels.

The best data analytics tool depends on a company’s specific needs, such as scalability, integration ease, and advanced analytics like NLP or machine learning. Tools like Power BI and Tableau lead in visualization, while Looker Studio offers a seamless cloud-native experience. SAS shines in compliance-focused environments, and ML tools offer robust predictive capabilities.

As we look to 2025, AI-driven analytics, real-time decision-making, and embedded data tools are set to continue their rapid growth. Each of these five tools laid the groundwork in 2024 and will continue shaping the data analytics landscape in the years ahead.

To Know More, Read Full Article @ https://ai-techpark.com/top-5-data-analytics-tools-2024/

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Data Governance and Security Trends in 2024

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Danielle, you have an impressive background in both academia and professional services. Could you start by sharing your journey and what led you to your current role at Caseware as VP, Head of Analytics and AI?

My journey to my current AI and analytics role at Caseware hasn’t been typical, but it has given me the perfect balance of expertise to understand our customers, who are auditors and accountants looking to use technology to improve their daily workflows. At the beginning of my career, I followed my mother’s advice to take an accounting class in college. She insisted that understanding financial matters was crucial, no matter what career path I chose.

Once I entered the accounting world, I followed a fairly traditional career path. I also went on to earn a Master of Science in Accountancy from the University of Virginia. However, I found that while I can understand and work within traditional accounting frameworks, I also bring a different, non-linear approach to problem-solving, which ultimately led me to the world of software, analytics, and AI within the accounting industry.

My ability to bridge the gap between traditional accounting and innovative technologies has definitely positioned me well for my current leadership role at Caseware, and I am enjoying leveraging both my accounting expertise and my unconventional thinking to drive advancements in analytics and AI for our customers.

AI is transforming many industries, but why do you believe professional services, particularly audit and accounting, are uniquely positioned to benefit from AI advancements?

Our profession is highly regulated with an exceptionally high expectation for precision. This has historically made it difficult to adopt new technologies, as we can’t afford to ‘fail fast’ in auditing. The complex, judgment-intensive tasks we perform are not conducive to automation through previous technological innovations. As a result, the accounting profession has often been perceived as a technology laggard, not due to unwillingness, but because of the stringent requirements for reliability and accuracy in our work.

Generative AI represents a fundamental shift. Unlike previous technologies that focused primarily on automation, generative AI is designed to work alongside humans to enhance our capabilities rather than replace them. This aligns well with our existing processes, which already incorporate extensive human review and validation.

What are some common misconceptions about AI in the context of professional services, and how can these be addressed?

AI, like human professionals, isn’t perfect, and that’s why it’s essential to have a robust review process in place. The misconception that all AI is a ‘black box’ and therefore is categorically unusable is simply not true. Standards, including new International ethical guidelines, recognize the potential of AI and provide frameworks for responsible use. It’s not about avoiding AI entirely, but rather about ensuring proper oversight and validation to make the most of its capabilities to underpin the stability of markets in the best way we can.

To Know More, Read Full Interview @ https://ai-techpark.com/aitech-interview-with-danielle-supkis-cheek/

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The holiday season is just around the corner, bringing with it Black Friday, the year’s largest shopping event. While this period offers exciting opportunities for both retailers and shoppers, it also brings increased risks. Shoppers eagerly anticipate discounts, but hackers and fraudsters see the surge in online and in-store traffic as an opportunity to strike. Blockchain technology, initially developed for cryptocurrency, is now becoming a powerful tool for improving security in various sectors, including retail. With retailers and payment processors turning to blockchain, they aim to protect customer information and quickly detect and block fraudulent activities during Black Friday sales.

When the volume of transactions skyrockets, cybercriminals target both online platforms and physical stores. In 2023, Adobe reported that consumers spent $9.2 billion on Black Friday, and the figure is expected to rise this year. As online shopping continues to grow faster than in-store purchases, the need for a more secure framework becomes even more urgent, especially to counter threats like payment fraud, phishing, and identity theft.

How Blockchain Ensures Secure Shopping on Black Friday

Blockchain technology offers a decentralized, tamper-resistant ledger that transforms how transactions are handled. Unlike traditional databases with centralized servers, blockchain records data across multiple nodes, making it much harder for hackers to manipulate. This innovation is already strengthening security for Black Friday shopping in several ways:

Decentralized Data Protection

Traditional online retailers store customer data in centralized systems, which are vulnerable to breaches. In contrast, blockchain distributes transaction records across a network of nodes. This decentralized model ensures that even if one node is compromised, customer information remains secure across the network, reducing the risk of large-scale data theft.

Enhanced Payment Security

Blockchain-based payment platforms provide an extra layer of security through mechanisms like “proof of work” or “proof of stake,” which authenticate transactions and prevent double-spending. These systems make it extremely difficult for unauthorized parties to alter transactions or engage in fraudulent activities. Retailers and financial institutions are increasingly adopting blockchain to ensure faster, more secure payments that cannot be reversed fraudulently.

Transparent Records to Prevent Fraud

Blockchain’s transparent structure makes it a powerful tool against fraud. Each transaction is verified and recorded by multiple participants on the network, ensuring that tampering attempts are quickly detected. This is especially useful against chargeback fraud, where a customer disputes a legitimate purchase to claim a refund. With blockchain, every transaction’s full history is readily available, making it easy to spot and address fraudulent behavior.

To Know More, Read Full Article @ https://ai-techpark.com/black-friday-shopping-security/

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Cybersecurity breaches are increasingly common among small and medium businesses (SMBs), making them ideal targets for cybercriminals. Due to limited budgets, lack of expertise, and the misconception of being “too small to be targeted,” many SMBs operate with minimal cybersecurity defenses. Unfortunately, this vulnerability is exactly what cyber attackers rely on, exploiting weak defenses through relentless attacks and sophisticated phishing campaigns. In India alone, ransomware attacks on websites surged by 261% this year, with insurance companies often left covering the damage.

This raises an important question: How are organizations that specialize in risk management being blindsided by cyber threats? The answer lies in inadequate security practices. Many businesses lack critical defenses such as multi-factor authentication (MFA), phishing-resistant employee training, reliable backups, and endpoint detection and response (EDR) systems. Additionally, the rapid shift of SMBs to cloud platforms introduces complex configurations that exceed the technical capacity of many small businesses.

Insurance Companies and Cyber Risk Management

Recognizing the growing risks, insurers are tightening their standards for cyber insurance policies. Companies now need to meet stricter requirements, such as multi-step authentication (e.g., verification codes via email or phone), routine security scans, and offline data backups, to qualify for coverage.

However, insurers face challenges in evaluating cyber risk. Without access to comprehensive insights from businesses' security systems, insurance companies struggle to assess the true level of exposure. This reactive, “outside-looking-in” approach slows incident response efforts, as forensic teams must first reconstruct pre-breach system conditions. At the same time, businesses without a solid cybersecurity framework increase uncertainty for insurers, forcing them to reconsider how they assess and manage cyber risk.

The integration of cybersecurity and insurance efforts creates a win-win-win scenario for all stakeholders. Insurance companies benefit from fewer claims and improved financial performance, SMBs enjoy better protection and more favorable policy terms, and end-users gain enhanced data security.

By fostering partnerships between insurers and cybersecurity providers, the industry can shift from reactive risk management to proactive prevention. This collaborative approach will not only help mitigate the growing ransomware threat but also create a more resilient digital environment for businesses and consumers alike.

To Know More, Read Full Article @ https://ai-techpark.com/role-of-insurance-in-cyber-defense-enhancement/

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The healthcare sector is undergoing rapid transformation, driven by a deeper understanding of patient needs and the adoption of cutting-edge technologies. At the forefront of this evolution are digital biomarkers and AI/ML, which serve as critical enablers of next-generation healthcare solutions. This paper examines the historical development, impact, and future trajectory of these technologies to explore how they are reshaping the modern healthcare ecosystem.

The Evolution of Biomarkers in Healthcare

Traditionally, biomarkers have played a crucial role in diagnosing diseases, predicting their progression, and evaluating treatment responses. Conventional biomarkers, such as blood tests and imaging, paved the way for the digital evolution of healthcare.

Digital biomarkers utilize technology for real-time data collection and analysis, offering more dynamic insights into health trends. With the widespread adoption of wearable devices and mobile health apps, digital biomarkers are becoming essential tools in patient care, revolutionizing how health professionals monitor and manage individual well-being.

Understanding Digital Biomarkers: A Comprehensive Approach

Definition and Classification

Digital biomarkers refer to objective, measurable physiological or behavioral data captured through digital tools. These markers can range from simple metrics, like daily step counts, to more complex indicators, such as heart rate variability measured through wearables. Unlike traditional biomarkers, which rely on lab tests, digital biomarkers offer real-time insights into a patient’s health status.

Types of Digital Biomarkers

Wearable Device Biomarkers: Smartwatches and fitness trackers measure activity levels, sleep quality, and heart rate.

Electronic Health Records (EHRs): Incorporating digital biomarkers into EHRs enables long-term tracking, improving care coordination and outcomes.

Mobile Health Applications: These apps monitor health metrics and provide personalized recommendations, fostering greater patient engagement.

Integrating Digital and Traditional Biomarkers

The integration of digital biomarkers with traditional ones, such as genetic data, offers a comprehensive view of patient health. For instance, combining genomic insights with wearable data allows healthcare providers to identify risk factors early, enabling timely interventions and personalized care.

To Know More, Read Full Article @ https://ai-techpark.com/digital-biomarkers-ai-ml/

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Dev, can you start by sharing the journey that led you to establish QueryPal and what inspired you to focus on transforming customer support through AI-powered ticket automation?

The journey to QueryPal began with my experiences at Google and PayPal, where I saw firsthand the challenges of scaling customer support. I realized that while AI was transforming many industries, customer support remained largely unchanged. The inspiration came from seeing how Large Language Models (LLMs) could understand and generate human-like text. I knew we could leverage this technology to revolutionize customer support, making it more efficient and effective. QueryPal was born from the vision of creating an AI system that could understand customer inquiries at a deep level and provide accurate, helpful responses at scale.

How has AI enhanced the accuracy of customer support responses at QueryPal, and what role does it play in improving response times and customer satisfaction?

AI has dramatically enhanced the accuracy of customer support responses at QueryPal. Our advanced natural language understanding allows us to comprehend the nuances of customer inquiries, including context and intent. This leads to more precise and relevant responses. Moreover, our AI can access and synthesize information from vast knowledge bases in seconds, providing comprehensive answers faster than any human could. This improvement in both accuracy and speed has led to significant increases in customer satisfaction scores for our clients. We’re also in the early stages of researching Causal AI, which could enable our system to understand cause-and-effect relationships in customer issues, potentially allowing it to reason about novel situations it hasn’t explicitly seen in training data.

Personalized customer support is a significant advancement in customer service. Can you explain how AI-powered systems at QueryPal tailor responses to individual customer inquiries?

Personalization in QueryPal’s AI system operates on multiple levels. First, it considers the customer’s context, including channel metadata. Second, it analyzes the specific language and tone of the current inquiry. Finally, it takes into account how past responses for similar questions have satisfied customers. By combining these factors, our AI can tailor responses that not only answer the specific question but also address potential underlying concerns, use appropriate language and tone, and even anticipate follow-up questions. Personalization in QueryPal’s AI system is already advanced, but we’re excited about the potential of Agentic AI. We’re in the process of integrating this technology, which could allow our system to handle complex, multi-step tasks with minimal human specification. In the future, it might be able to understand the broader context of a customer’s journey, anticipate needs, and even take proactive steps to resolve issues before they escalate.

To Know More, Read Full Interview @ https://ai-techpark.com/aitech-interview-with-dev-nag/

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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.

AI as a Catalyst for Transformation

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 processes and driving efficiency. Additionally, AI offers deeper insights to management teams than ever before.

In finance, for example, AI models outperform traditional methods by evaluating a broader set of factors to assess credit risk, predict market trends, detect fraud, and identify optimal investments. Similarly, in healthcare, AI enables early diagnosis and increases diagnostic accuracy, transforming how medical treatments are managed. These examples demonstrate that AI not only mitigates risks but also reshapes operational behavior, opening new avenues for efficiency and effectiveness.

Machine Learning’s Role in Enterprise Risk Management

Machine learning—one of the most crucial AI fields—plays a vital role in Enterprise Risk Management (ERM). By learning from data and detecting patterns beyond human observation, machine learning is particularly useful in industries like cybersecurity, where threats are constantly evolving. AI systems also monitor network activities in real-time, providing alerts for suspicious events to prevent breaches.

According to Gartner’s 2024 report, companies leveraging AI-based risk management tools saw a 30% reduction in data breach incidents. This statistic emphasizes AI’s ability to prevent risk events. Moreover, as data protection laws become stricter, AI helps organizations maintain compliance through precise monitoring and reporting mechanisms.

The dual role of AI as both a risk and a risk management tool defines the modern business landscape. Organizations that recognize AI’s strategic value and incorporate it into their planning will be well-positioned to thrive. The improvements in decision-making, efficiency, and risk forecasting that AI offers are too significant to ignore.

However, these opportunities come with responsibility. Companies must adopt ethical AI practices and ensure robust data protection to avoid negative societal impacts. Failure to address these issues could have serious consequences, not only for businesses but also for society as a whole.

Ultimately, the question is not whether to adopt AI but how to implement it sustainably and responsibly. Leaders with a vision for ethical AI usage will not only mitigate risks but also unlock new opportunities previously beyond reach. As business environments continue to evolve rapidly in 2024 and beyond, organizations that fail to adapt will fall behind. Integrating AI as both a tool and a mandate is essential for any innovative organization looking to succeed.

To Know More, Read Full Article @ https://ai-techpark.com/ai-is-both-a-risk-and-a-tool/

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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. Most recently, a technique known as Graph RAG has been getting a lot of attention, as it allows generative AI models to be combined with knowledge graphs to provide context for more accurate outputs. But what are its components and can it live up to the hype?

What is Graph RAG and What’s All the Fuss About?

According to Gartner, Graph RAG is a technique to improve the accuracy, reliability and explainability of retrieval-augmented generation (RAG) systems. The approach uses knowledge graphs (KGs) to improve the recall and precision of retrieval, either directly by pulling facts from a KG or indirectly by optimizing other retrieval methods. The added context refines the search space of results, eliminating irrelevant information.

Graph RAG enhances traditional RAG by integrating KGs to retrieve information and, using ontologies and taxonomies, builds context around entities involved in the user query. This approach leverages the structured nature of graphs to organize data as nodes and relationships, enabling efficient and accurate retrieval of relevant information to LLMs for generating responses.

KGs, which are a collection of interlinked descriptions of concepts, entities, relationships, and events, put data in context via linking and semantic metadata and provide a framework for data integration, unification, analytics and sharing. Here, they act as the source of structured, domain-specific context and information, enabling a nuanced understanding and retrieval of interconnected, heterogeneous information. This enhances the context and depth of the retrieved information, which results in accurate and relevant responses to user queries. This is especially true for complex domain-specific topics that require a deeper, holistic understanding of summarized semantic concepts over large data collections.

To Know More, Read Full Article @ https://ai-techpark.com/graph-rags-precision-advantage/

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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 enhancing the security of your IT structures.

Seven-Step Framework in Deploying DLP Strategy

If any business is handling sensitive data and operating in a regulated environment or suffers from repeated cybersecurity threats, it’s time that needs to add DLP strategies.

Proofpoint’s 2024 data loss landscape report indicates that 84.7% of enterprises have encountered data loss, with an average of 15 incidents per organization per year. This implies the importance of appropriately implementing DLP strategies.

Therefore, without any further ado, let’s understand the seven-step strategic framework of DLP:

Identify and Classify Your Data

To protect data effectively, DPOs need to know the exact type of data that they need to work on.

With the help of data discovery tools such as Informatica, Spectral, and Osano, data discovery administrators will scan the data repositories and report on findings, providing visibility of what needs to be protected. These tools further use regular expressions for their searches; they are very flexible but can be complicated to fine-tune.

After implementing data discovery, data administrators can use data classification software such as Varonis, Fortra Digital Guardian, and Imperva which will help them control users’ data access and avoid storing sensitive data in any unsure locations, reducing the risk of data leaks and data loss.

Use Data Encryption

In the data-centric world, encryption provides a two-step security measure that involves converting data into code that is only deciphered with a decryption key.

Organizations that deal with extremely sensitive forms of data are required to follow data security standards and regulations, including the Payment Card Industry Data Security Standard (PCI DSS) and the General Data Protection Regulation (GDPR). If an organization fails to comply with encrypting sensitive data, it can result in regulatory non-compliance and can lead to costly data breaches and legal penalties.

Therefore, to safeguard, data professionals can use different data encryption tools such as IBM Security Guardium Encryption, Thales CipherTrust, and Sophos SafeGuard Encryption, which add complex mathematical algorithms to data and transform it into a random series of characters that are indecipherable without the suitable decryption key.

To Know More, Read Full Article @ https://ai-techpark.com/data-loss-prevention-techniques-of-2024/

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Data has always been the backbone of business operations, highlighting the significance of data and analytics as essential business functions. However, a lack of strategic decision-making often hampers these functions. This challenge has paved the way for new technologies like data fabric and data mesh, which enhance data reuse, streamline integration services, and optimize data pipelines. These innovations allow businesses to deliver integrated data more efficiently.

Data fabric can further combine with data management, integration, and core services across multiple technologies and deployments.

This article explores the importance of data fabric architecture in today’s business landscape and outlines key principles that data and analytics (D&A) leaders need to consider when building modern data management practices.

The Evolution of Modern Data Fabric Architecture

With increasing complexities in data ecosystems, agile data management has become a top priority for IT organizations. D&A leaders must shift from traditional data management methods toward AI-powered data integration solutions to minimize human errors and reduce costs.

Data fabric is not merely a blend of old and new technologies; it is a forward-thinking design framework aimed at alleviating human workloads. Emerging technologies such as machine learning (ML), semantic knowledge graphs, deep learning, and metadata management empower D&A leaders to automate repetitive tasks and develop optimized data management systems.

Data fabric offers an agile, unified solution with a metadata-driven architecture that enhances access, integration, and transformation across diverse data sources. It empowers D&A leaders to respond rapidly to business demands while fostering collaboration, data governance, and privacy.

By providing a consistent view of data, a well-designed data fabric improves workflows, centralizes data ecosystems, and promotes data-driven decision-making. This streamlined approach ensures that data engineers and IT professionals can work more efficiently, making the organization’s systems more cohesive and effective.

Know More, Read Full Article @ https://ai-techpark.com/data-management-with-data-fabric-architecture/

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