No Plagiarism And APA Format Required: What Is Big Data Why

No Plagarism And Apa Format Required1 What Is Big Data Why Is It Imp

Identify and explain what Big Data is, its significance, and its sources. Discuss your perspectives on the future of Big Data, considering whether it might be replaced or overshadowed by other technologies or concepts. Define Big Data analytics and differentiate it from standard analytics approaches. Describe the critical success factors necessary for effective Big Data analytics implementation. Highlight the major challenges that organizations might face when adopting Big Data analytics systems.

Examine the specific problem of customer service cancellations and how it threatened the business viability of AT. Analyze the technical obstacles posed by the data's nature and characteristics in AT’s scenario. Explain the concept of sessionizing, its purpose, and why it was essential for AT’s data analysis. Review existing research on customer churn models, focusing on the variables commonly used in those studies, and compare those to the approach used in the AT case.

Identify other prominent Big Data analytics platforms besides Teradata Vantage capable of conducting the types of analysis discussed in AT’s case. Include examples such as Apache Hadoop, Spark, or others, referencing relevant sections that detail platform capabilities.

Paper For Above instruction

Big Data refers to the vast volumes of structured and unstructured data generated at high velocity from various sources. The importance of Big Data lies in its potential to provide insightful patterns and trends that can significantly influence decision-making processes across industries. Organizations leverage Big Data for improved operational efficiency, enhanced customer experience, and competitive advantage. The primary sources of Big Data include social media interactions, transaction records, sensor data from Internet of Things (IoT) devices, and logs from digital platforms (Manyika et al., 2011). As data generation continues to accelerate exponentially, understanding Big Data’s relevance becomes more crucial than ever.

The future of Big Data appears promising, with its integration into emerging technologies such as artificial intelligence and machine learning. While some speculate that Big Data might eventually be replaced or diminished by more advanced or efficient data handling techniques, it is more likely to evolve rather than fade. The advent of quantum computing and enhanced data processing frameworks might redefine how Big Data is handled, but the need to analyze large datasets will persist. Consequently, Big Data could transform into even more sophisticated analytics tools that provide real-time insights and predictive capabilities (McKinsey Global Institute, 2016).

Big Data analytics involves examining richly voluminous data sets to uncover hidden patterns, correlations, and insights that inform strategic decisions. Unlike traditional analytics, which often relies on smaller, structured datasets and descriptive statistics, Big Data analytics can handle large-scale, complex, and varied data types through distributed computing systems. This enables organizations to perform predictive analytics, prescriptive analytics, and real-time analytics, leading to more dynamic decision-making processes (Chen, Chiang, & Storey, 2012).

Critical success factors for Big Data analytics include strong executive support, a clear strategic vision, data governance frameworks, skilled analytics teams, and advanced technological infrastructure. Ensuring data quality and security is essential, as is fostering a data-driven culture within the organization. The alignment of analytics initiatives with business goals maximizes value creation from Big Data efforts (Katal, Wazid, & Goudar, 2013).

Several challenges accompany Big Data adoption. Technical challenges include managing data variety, ensuring data quality, and integrating disparate data sources. Organizational barriers involve a lack of skilled personnel, resistance to change, and aligning analytics with decision-making processes. Ethical considerations, such as data privacy and security, also pose significant hurdles. Additionally, the high cost of infrastructure and maintenance can impede some organizations from fully exploiting Big Data's potential (Hashem et al., 2015).

Customer service cancellation problems at AT exemplified how losing customers threatened the company's survival. High cancellation rates led to revenue declines and increased marketing costs for customer retention. The challenge was to understand the underlying causes and prevent future cancellations through targeted engagement strategies and predictive models.

One of the major technical hurdles was dealing with the high-volume, high-velocity, and high-variety nature of AT’s data. This complexity required robust data storage and processing capabilities. Data heterogeneity, incomplete records, and noisy data further complicated analysis efforts. Efficiently integrating and cleaning this data to create meaningful insights was a significant challenge.

Sessionizing refers to segmenting a user’s interactions into sessions, or coherent blocks of activity, to better analyze user behavior. It was necessary for AT to sessionize its data to accurately model user journeys and identify patterns leading to cancellations. Sessionization enabled more precise analysis of customer interactions over time, facilitating targeted interventions.

Research on customer churn models typically involves variables such as customer demographics, service usage patterns, complaint history, and engagement metrics. Many studies employ machine learning algorithms like decision trees, support vector machines, or neural networks (Coussement & Van den Poel, 2008). Compared to these, AT’s approach might incorporate novel session-based variables and real-time data streams to enhance prediction accuracy, emphasizing temporal behavior patterns.

Besides Teradata Vantage, several other Big Data platforms are well-suited for complex analytical tasks. Apache Hadoop provides scalable storage and processing power. Apache Spark offers fast, in-memory data analysis capabilities ideal for real-time analytics. Cloud-based platforms like Amazon Web Services (AWS) and Google BigQuery also support large-scale data processing and analytics workflows, making them suitable alternatives for organizations pursuing similar analytics goals (Zikopoulos et al., 2012).

References

• Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to decision making. MIS Quarterly, 36(4), 1165-1188.
• Coussement, K., & Van den Poel, D. (2008). Churn prediction in subscription services: A case study. Journal of Interactive Marketing, 22(3), 51-62.
• Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U. (2015). The role of big data in Smart Grid. Energy, 90, 288-299.
• Katal, A., Wazid, M., & Goudar, R. H. (2013). Big data: Issues, challenges, tools, and applications. Advanced Computing and Communications, 1(2), 404-409.
• Manyika, J., Chui, M., Brown, B., et al. (2011). Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute.
• McKinsey Global Institute. (2016). The data revolution: Big data, open data, data-driven decision making, and the future of data. McKinsey & Company.
• Zikopoulos, P., Eaton, C., deRoos, D., et al. (2012). Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data. McGraw-Hill.