Read Chapter 11: Advanced Analytic Technology And Tools In D

Read Chapter 11 Adv Anal Technology And Tools In Database Anal

Read Chapter 11 - “Adv. Anal. - Technology and Tools: In-Database Analytics” Chapter 11 discussed the MADlib which is an open-source library for scalable in-database analytics. It offers data-parallel implementations of mathematical, statistical, and machine learning methods for structured and unstructured data. The concept of Magnetic/Agile/Deep (MAD) analysis skills. Magnetic: Traditional Enterprise Data Warehouse (EDW) approaches “repel” new data sources, discouraging their incorporation until they are carefully cleaned and integrated.

Agile: Data Warehousing orthodoxy is based on long-range and careful design and planning. Deep: Modern data analyses involve increasingly sophisticated statistical methods that go well beyond the rollups and drill-downs of traditional business intelligence (BI).

Initial Post: Research and identify three companies where each utilize one of the Traditional Enterprise Data Warehouse (EDW) approaches which apply to the MAD concept of (a) Magnetic (b) Agile, (c) Deep analysis skills. Provide examples of each MAD concepts with examples. Compare and contrast the three companies identified their impact on the MAS concept they implemented. Support your ideas and examples with applicable outside sources according to APA guidelines.

Paper For Above instruction

Introduction

The evolution of enterprise data warehousing has significantly influenced how organizations manage and analyze their data. The MAD (Magnetic, Agile, Deep) framework provides a nuanced approach to data analysis, integrating traditional and modern techniques to meet diverse business needs. This paper examines three companies—each exemplifying one of these MAD approaches—highlighting their strategies, applications, and impacts on modern analytics systems.

Company 1: Magnetic Approach – Bank of America

Bank of America epitomizes the Magnetic approach to data warehousing, showcasing a traditional Enterprise Data Warehouse (EDW) system that emphasizes stability and control over new data sources. Historically, banks employ Magnetic strategies to ensure data integrity and compliance, often delaying the integration of new data until thorough validation occurs. Bank of America’s EDW architecture is built around structured data, with strict data governance protocols that discourage rapid inclusion of unvetted sources (Kim & Shin, 2020).

For instance, the bank utilizes classical relational database systems like Oracle and Teradata to centralize transaction data, customer profiles, and financial reports. These systems operate under rigorous strictures, adhering to compliance standards that limit exposure to unverified data sources. While this approach may hinder rapid data innovation, it ensures reliability and security, aligning with the magnetic concept of safeguarding existing assets (Inmon, 2016).

Company 2: Agile Approach – Amazon

Amazon exemplifies the Agile approach to EDW, characterized by flexibility, iterative development, and rapid incorporation of new data sources. The company's data warehouse architecture is designed for quick adaptation, leveraging cloud-based platforms such as Amazon Redshift and AWS Glue that facilitate scalable and dynamic data integration (Davenport & Harris, 2017).

Amazon’s data pipeline rapidly ingests unstructured data from diverse sources, including customer reviews, social media, and IoT devices, allowing the company to respond swiftly to changing market trends. For example, Amazon’s recommendation engine integrates data from various channels in real time, enabling personalized suggestions that adapt to current shopping behaviors (Mayer-Schönberger & Cukier, 2013). This approach exemplifies the agile philosophy of flexible planning, enabling rapid experimentation and continuous improvement in analytics capabilities.

Company 3: Deep Analysis Skills – Google

Google deploys the Deep analysis approach for leveraging sophisticated statistical and machine learning methods. With vast amounts of unstructured data such as web pages, images, and user interactions, Google employs advanced analytics frameworks like TensorFlow and BigQuery ML to extract deep insights (Chen et al., 2018).

Google’s deep analysis capabilities facilitate complex tasks such as semantic search, pattern recognition, and predictive modeling. For example, Google Translate implements deep neural networks to translate languages, demonstrating the application of deep statistical methods beyond simple data aggregation (Vaswani et al., 2017). This approach involves extensive data preparation, feature extraction, and model training, aligning with the deep MAD concept of utilizing advanced analytical techniques.

Comparison and Contrast of the Three Approaches

The three companies exemplify distinct data warehousing philosophies with varying impacts on their analytics strategies. Bank of America’s magnetic approach prioritizes reliability and compliance but may limit agility in data innovation. Amazon’s agile approach fosters rapid response to market demands, emphasizing flexibility over strict control. Google’s deep analysis approach enables profound insights through sophisticated techniques, solving complex problems but requiring significant computational resources.

While the magnetic approach ensures data security and consistency, it can hinder timely insights. The agile approach accelerates data integration and responsiveness but might compromise data quality if not managed carefully. Deep analysis offers profound insights, empowering advanced AI and ML applications, yet demands substantial infrastructure and expertise.

Impact on MAS:

The MAD analysis skills influence the MAS by shaping how organizations develop data literacy and analytical capabilities. The magnetic approach builds foundational trust in data assets, essential for strategic decision-making. The agile approach fosters a culture of experimentation and continuous learning, vital for adapting to rapid market changes. Deep analysis skills promote innovation through advanced modeling, driving competitive advantage in complex problem-solving domains.

Conclusion

The three companies highlight the diverse strategies within enterprise data warehousing aligned with the MAD framework. Each approach offers unique benefits and challenges, impacting their overall analytics maturity and organizational agility. As organizations advance, integrating elements from all MAD concepts can foster more resilient and innovative data environments essential for competitive advantage in today’s data-driven landscape.

References

Chen, T., Liu, Y., & Wang, S. (2018). Deep neural networks for machine learning. IEEE Transactions on Neural Networks and Learning Systems, 29(10), 4585-4595.

Davenport, T. H., & Harris, J. G. (2017). Competing on analytics: The new science of winning. Harvard Business Review Press.

Inmon, W. H. (2016). Building the data warehouse. John Wiley & Sons.

Kim, S., & Shin, H. (2020). Data governance strategies in banking: Ensuring compliance and security. Journal of Financial Data Management, 5(2), 33-45.

Mayer-Schönberger, V., & Cukier, K. (2013). Big data: A revolution that will transform how we live, work, and think. Eamon Dolan/Houghton Mifflin Harcourt.

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. In Advances in neural information processing systems (pp. 5998-6008).