Shamnad Mohamed Shaffi

The marketing analytics landscape is being transformed by the convergence of Generative AI and Advanced Attribution Models. Generative AI enables the creation of unique, personalized content, revolutionizing customer engagement and campaign optimization [3 - 6]. Advanced Attribution Models provide unprecedented insights into the complex customer journey, tracking the impact of touch points and channels on conversion rates [7 - 10]. This article explores the integration of these cutting - edge technologies, demonstrating how organizations can harness their synergies to drive measurable improvements in marketing performance [1 - 2]. Through case studies and analysis, the study examines the practical applications, challenges, and strategic implications of this transformative approach [11 - 12]. The findings offer valuable insights for practitioners, data scientists, and leaders, providing a roadmap to leverage Generative AI and Advanced Attribution Models to revolutionize marketing strategies, enhance customer experiences, and achieve sustainable growth in the digital age [1 - 2]

Real-time big data analysis is already disrupting healthcare by changing the way decisions are made, treatment given, and organizations and processes managed. Using technologies like AI, IoT, and cloud computing, healthcare organizations can analyze large data sets alerting patients to diseases, picking appropriate treatments, and determining where resources will be essential. In this article, the author explores the progress, opportunities, and issues of real-time analytics in the healthcare industry and the sort such as predictive analytics, patient supervision, and telemedicine implementation. Despite the ethical issues, data privacy and system integration issues remain potent barriers, trends such as precision medicine and digital health mapping of the world present the implementation of a brave new world in the delivery of healthcare. For this, there is a need for innovation through embracing technology, sector collaboration and, most importantly, proper regulation in order to foster and promote secure usage for people

The growing dependence of data for decision-making has made BI a necessity for companies. However, BI systems mostly like the traditional model where they cannot process or analyze the volumes of data generated every day. The revolutionary AI analytics has spun the approach, leveraging AI such as machine learning and natural language processing to offer real-time insights, predictive forecasts, and action applications. Through the discussion, this research explores BI data milieu changes in AI analytics, distinctions, and its various implementations throughout industries. This research also provides a light to the challenges revolving around AI driven BI setups and suggestion on different ways to assuage them. By swelling the AI analytics, which is desirable because of competition and leads to a significant improvement in efficiencies and decision-making progress for the organization

Enterprise Content Management (ECM) and Data Governance are essential for organizations to manage, secure, and optimize data assets efficiently. This paper presents a structured framework for implementing ECM and data governance policies using an imaginary company, Teleware, as a case study. The study explores common challenges faced by enterprises, including data integration, quality, accessibility, and regulatory compliance. The proposed framework includes an assessment of the existing information infrastructure, identification of regulatory requirements, and enhancement of content management processes. A phased implementation approach is introduced, incorporating best practices in metadata management, data security, and risk mitigation. The adoption of a next-generation data governance platform ensures improved data quality, workflow efficiency, and enterprise-wide accessibility. Agile methodologies are leveraged to streamline policy execution, ensuring adaptability to evolving business needs. This framework serves as a strategic guide for organizations aiming to establish a data-driven culture, enhance compliance, and optimize decision-making through effective content and data governance

This paper presents a comprehensive strategy for ensuring compliance with the California Consumer Privacy Act (CCPA) and securing sensitive customer data within the organization. It focuses on evaluating and strengthening the current information security infrastructure, addressing potential risks, and enhancing privacy policies and training programs. The project aims to meet current and future state-level privacy compliance requirements while safeguarding Personal Identifiable Information (PII), Customer Proprietary Network Information (CPNI), and Payment Card Information (PCI). Key recommendations include risk assessments, the implementation of secure access controls, centralized data management, and network security measures. By adhering to established standards like NIST 800-30, the organization aims to mitigate risks, ensure regulatory compliance, and create a resilient data privacy framework that supports business growth and customer trust.

Enterprises today face increasing cybersecurity threats that necessitate robust digital forensics and risk mitigation strategies. This paper explores these challenges through an imaginary case study of an organization, a global identity management and data analytics company handling vast customer data. Given the critical nature of its data assets, EP has established a dedicated digital forensics team to detect threats, manage vulnerabilities, and respond to security incidents. This study outlines an approach to cybersecurity, including proactive threat anticipation, forensic investigations, and compliance with regulations like GDPR and CCPA. Key threats such as social engineering, insider risks, phishing, and ransomware are examined, along with mitigation strategies leveraging AI and machine learning. By detailing security framework, this paper highlights best practices in digital forensics, incident response, and enterprise risk management. The findings emphasize the importance of continuous monitoring, policy enforcement, and adaptive security measures to protect sensitive data and ensure business continuity in an evolving threat landscape