In the modern data-driven world, an enterprise needs to turn raw data into actionable insights for success. This paper serves as a guide on mastering data modeling techniques through Snowflake SQL, one of the most reputed cloud data platforms. Its focus is on preparing, transforming, and modeling data to enable advanced analytics and aid business decision-making.

Artificial intelligence, or AI, has impacted almost every area of business operations with a focus on sales. Modern customer insights provided by AI have impacted the methods of selling, which over the course of years were known as traditional, resulting in customer-oriented decisions, improved communication with the customer, and subsequently an increase in revenue. Concentrating on customer analytics as the primary means of enhancing sales strategies, this paper investigates the application of AI in sales operations.

In today's data-driven landscape, organizations increasingly rely on robust analytics tools to make informed decisions. ThoughtSpot, with its AI-driven search and analytics capabilities, offers a powerful platform for data visualization and analysis. However, users often encounter challenges when conducting pre and post analysis, particularly in comparing metrics over distinct time periods. This paper explores strategies to maximize ThoughtSpot's capabilities for effective pre and post analysis by addressing common limitations, such as data aggregation, visualization techniques, and user interface constraints.

The well-being and mental health of healthcare workers are essential to the overall functionality of healthcare systems, yet they are often at risk due to the demanding nature of the profession. AI technologies, through predictive analytics, wearable devices, and natural language processing, offer continuous monitoring and early detection of mental health risks such as burnout.

As cybersecurity threats evolve, organizations are increasingly exploring the shift to passwordless authentication as part of their security strategies. Passwords are often a weak link in an organization’s security chain, vulnerable to phishing attacks, credential theft, and poor management practices. This paper outlines the enterprise journey toward adopting a passwordless framework, emphasizing the significance of eliminating passwords, how such a transition can be planned and implemented, and the key benefits of adopting passwordless security.

As information and software intensify in avionics, the right safety and reliability features become critical. As avionics systems become more advanced, simple software verification techniques can no longer be used to ascertain system safety. The adoption of Hardware-In-the-Loop (HIL) testing has become a real improvement in the confirmation procedures of embedded systems especially in avionics. This approach replicates real life situations while further physically identifying the software compatibility on actual hardware interfaces and is comparatively more realistic as well.

The growing complexity of corporate operations needs the use of innovative technology to improve efficiency and value. ServiceNow, a major digital workflow platform, uses artificial intelligence (AI) to improve business processes across a variety of industries. This thesis looks into the potential of ServiceNow AI to provide concrete business value by automating workflows, improving decision-making and increasing customer experiences. ServiceNow's platform integrates AI capabilities, allowing enterprises to streamline operations, cut costs, and drive innovation.

Artificial Intelligence (AI) is reshaping the landscape of medical education, offering innovative tools for personalized learning, real-time simulations, and enhanced diagnostic training. However, integrating AI into medical education brings critical ethical challenges, particularly in data privacy and patient confidentiality. This paper explores the delicate balance between leveraging AI-driven educational tools and safeguarding the privacy rights of patients and students.

Despite being key institutions in both national and state security functions, federal agencies handle incredibly massive amounts of sensitive data, making them a high value vector for insider threats. This demonstrates how insider threats often evade traditional security mechanisms that fail to detect malicious activity in real-time and mitigate risk effectively in a timely manner. A real-time insider threat detection model using machine learning for anomaly detection in federal systems therefore is proposed in this paper.

Decision makers have realized how important real-time processing and analysis of large volumes of data is in the current dynamic technological environment for every field. This change is known as ‘digital alchemy’ and is in operation through the use of highly developed artificial intelligence software tools to analyze raw data into business intelligence. This paper specifically examines the processes for such a change, discussing technological support, machine learning algorithms, data preparation processes, and AI applications for real-time analysis.

The healthcare sector is shifting. The demand for healthcare services is gradually growing, the number of patients opting for remote care is surging, and the recruitment of specialists is increasingly becoming challenging. These changes make relying on traditional healthcare delivery methods such as in-person care nearly impossible. HMIs provide novel approaches for providers to address emerging challenges in the sector. The technologies facilitate the delivery of services to many patients, even with limited staff, allow the delivery of care services virtually, and enhance the overall quality of service delivery.

Although traditional fraud detection and prevention methods have been integral in reducing fraud and losses in the Medicaid programs, these methods have weaknesses such as inability to process large quantities of data, the inability to provide real-time monitoring of claims processing, the lack of predictive capabilities, and limitations in leveraging unstructured data. Integration of AI in Medicaid fraud mitigation bridges these limitations. The technology is advanced in data processing, allowing scalability of fraud investigations, real-time monitoring of claims, and forecasting of potential frauds.

Predictive maintenance (PdM) is an essential component of modern industrial operations, especially with the fourth Industrial Revolution (Industry 4.0). Traditional PdM relies on either rule-based algorithms or deep learning neural nets to predict downtimes, increasing uptime and productivity. Traditional PdM faces a lot of challenges owing to lack of sufficient high-quality data, leading to a high number of false positives. With the recent advancements in generative AI (GenAI) a new set of enablers have come forward which can enable higher quality PdM models enabled through advanced simulations and synthetic data generation.

In today’s rapidly evolving financial landscape, the adoption of Artificial Intelligence (AI) and Machine Learning (ML) technologies, coupled with the deployment of Business Rules Management Systems (BRMS), has transformed how financial transactions are conducted, monitored, and secured.

The increasing demand for intelligent systems capable of handling complex workflows has driven innovations in multi-agent AI platforms. Phi Data, an open-source framework, and LangChain, a dynamic toolchain for LLM-powered applications, offer comprehensive toolkits for building, deploying, and monitoring multi-agent systems. This paper explores the potential of both platforms, focusing on the design and implementation of a financial analysis multi-agent system. By integrating real-time data retrieval, web search capabilities, and robust large language models (LLMs), the systems effectively analyze stock trends, summarize analyst recommendations, and provide actionable insights.

Financial fraud analysis has emerged as a top priority for the financial industry due to the growing intricacy and number of transactions. Even though traditional approaches are quite useful in earlier settings, they promise little when detecting complex and evolutionary fraudulent patterns. AI and ML are buzzwords transforming the fraud detection domain by providing better features like real-time anomaly detection and further predictive analysis. This paper focuses on how artificial intelligence and machine learning can be incorporated into fraud detection in the financial payment system.

In today's technological landscape, the role of a cloud database administrator overseeing both SQL and NoSQL databases has become vital to meet the escalating demands of contemporary organizations. Primarily, a thorough expertise in cloud platforms alongside a strong grasp of SQL and NoSQL cloud solutions forms the cornerstone for effective management, scaling, and maintenance of data storage infrastructures. Additionally, possessing a comprehensive knowledge of essential aspects such as cloud architecture, database design, data migration methods, performance optimization, and security protocols is of utmost importance.

The healthcare industry faces an unprecedented challenge: harnessing the exponential growth of data to improve patient outcomes. This research explores the crucial convergence of Artificial Intelligence (AI) and Database Administration (DBA) in addressing this challenge.

We acknowledge that the threat continues to change, and therefore, there is a need to use new technologies to support structures in cyber security. Cyber threat intelligence solutions supported by Artificial Intelligence (AI) are the innovative solutions implemented to identify, analyse and prevent cyber threats in advance. The current article offers a detailed review of threats with the help of AI-based solutions, focusing on the issue of monitoring and responding capabilities.