Question: Execution Program Practical Connection Assignment ✓ Solved

Question: Execution Program Practical Connection Assignment

Question: Execution Program Practical Connection Assignment Please relate the course table of contents with roles and responsibilities. Explain how the course is helpful for the mentioned roles and responsibilities.

Roles and Responsibilities:

  • Maintain in-depth knowledge of industry best practices, technologies, architectures and emerging technologies as well enforcing best practices for data management, governance and integration.
  • Creates test plans, automation test scripts, execute regression testing, load testing and prepare performance metrics.
  • Execute tests against written requirements, identify test defects - deviations track to closure.
  • Support strategic planning on architecture and build of data ingestion / federation capabilities to develop highly integrated data model / solutions
  • Develop relevant artifacts including business requirements (BRD), use cases, process flow diagrams, target operating models to effectively rationalize business solutions and future work-streams
  • Participate in data lake governance and compliance processes to ensure all areas of technology are implementing solutions consistent with the target architecture
  • Develops and implement internal communication strategies, plans and tactics that support the business goals and objectives.
  • Provides second line of defense risk oversight of the Operational risk program, including application of operational risk policies/standards, procedures, strategies, material risks, risk reporting routines and metrics
  • Identifying, assessing and reporting progress on Risk and Control Self Assessments (RCSA), including process mapping, identification and assessment of risk, identification of controls, and assessments of control design and effectiveness, identification of themes.
  • Support the design/implementation of advancing and delivering the governance, risk, compliance and oversight program
  • Active participation in high-profile information risk management initiatives.
  • Enhances the financial understanding of business lines, products and segments to aid reporting, forecasting and business decision making.
  • Identify opportunities for business process improvements and develop solutions to promote the seamless delivery of services.
  • Lead/participate in a variety of planned or ad-hoc program initiatives, including project management
  • Responsible for identifying, analyzing, assessing financial impacts, and overseeing strategic results- driven initiatives designed to improve operating efficiency and effectiveness.

Course Description In this course the students explore key data analysis and management techniques, which applied to massive datasets are the cornerstone that enables real-time decision making in distributed environments, business intelligence in the Web, and scientific discovery at large scale. In particular, students examine the map-reduce parallel computing paradigm and associated technologies such as distributed file systems, no-sql databases, and stream computing engines. This highly interactive course is based on the problem-based learning philosophy. Students are expected to make use of technologies to design highly scalable systems that can process and analyze Big Data for a variety of scientific, social, and environmental challenges.

Course Objectives Course Objectives/Learner Outcomes: Upon completion of this course, the student will: • Identify fundamental concepts of Big Data management, Machine Learning and Deep Learning • Become competent in recognizing challenges faced by applications dealing with very large volumes of data as well as in proposing scalable solutions for them. • Be able to understand how Big Data impacts business intelligence, scientific discovery, and our day-to-day life. Learner Outcomes/ Assessments • Learn how to perform research identifying and analyzing datasets via Machine Learning • Build critical thinking skills to develop and apply solutions that achieve strategic and tactical IT-business alignment • Develop professional skills and expertise to advance knowledge in your chosen field or discipline within information technology • Conduct research with professional and ethical integrity • Address complex technical questions and challenge established knowledge and practices in the area • Identify, comprehend, analyze, evaluate and synthesize research • Communicate effectively and employ constructive professional and interpersonal skills • Critically evaluate current research and best practices • Demonstrate IT leadership skills at the team and enterprise levels following tenets of professional, social, and ethical responsibility • Recommend IT strategies that support enterprise mission and objectives Books and Resources The following is recommended as a very good reference. • An Introduction to Statistical Learning with Applications in R, Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani

The cleaned assignment asks you to articulate how a data-centric course maps to varied professional roles spanning data governance, testing, architecture, risk, and business analytics. The answer should demonstrate cross-domain relevance by tying course topics (Big Data management, MapReduce, distributed storage, NoSQL, stream processing) to the responsibilities and decision-making needs enumerated in the Roles and Responsibilities list. The following paper develops that mapping with evidence-informed reasoning and citations to established sources in data management, governance, analytics, and big data technologies.

Paper For Above Instructions

Introduction

The relationship between course content and professional roles in data-driven organizations hinges on a coherent view of data as an organizational asset, governed by standards, architectures, and processes that enable reliable decision-making. The course described emphasizes Big Data management, distributed processing, and scalable architectures, all of which are foundational competencies for the listed roles. By aligning course topics to the roles—from governance and risk oversight to testing, data ingestion, and analytics—the curriculum becomes a practical bridge between theory and enterprise practice. This alignment is supported by established bodies of knowledge and industry practice in data management, analytics, and governance (DAMA-DMBOK2; ISO/IEC standards; Dean & Ghemawat, 2008; James et al., 2013).

Mapping Course Content to Roles and Responsibilities

Data governance, management, and integration: The course’s core focus on Big Data management and distributed architectures directly supports roles tasked with maintaining best practices in data management, governance, and integration. Foundational concepts such as data lineage, metadata management, data quality, and governance frameworks are essential for ensuring consistent, auditable data flows across ingestion, storage, and analytics (DAMA-DMBOK2; Redman, 2008). The coursework’s emphasis on data lake concepts, distributed storage, and NoSQL databases provides the practical tools to implement modern governance models, aligning with the mandate to enforce best practices and to integrate disparate data sources into coherent, governed data ecosystems (Inmon, 2014; Sadalage & Fowler, 2012).

Test planning, automation, and performance metrics: The course’s exposure to distributed processing (MapReduce paradigms, streaming engines) informs the design of robust test plans and performance benchmarking for large-scale data systems. Practitioners can translate concepts of scalability, fault tolerance, and data throughput into test scripts and automated regression suites that measure system behavior under load (Dean & Ghemawat, 2008; White, 2015). This supports the responsibility to create test plans, implement automation, execute regression tests, and report performance metrics with rigor (JSTOR/ACM-style sources support this approach in large-scale data environments).

Requirement-driven testing and defect tracking: The course highlights how to derive test scenarios from distributed system requirements and data-processing pipelines. The alignment with requirement-based testing ensures defects are tracked to closure, as large-scale data workflows are sensitive to data quality, schema evolution, and latency constraints (James et al., 2013; Redman, 2008).

Data ingestion and federation architecture: A central objective is to design data ingestion and federation capabilities that yield highly integrated data models. The course’s treatment of data lakes, distributed file systems, and NoSQL databases provides practical techniques for building scalable ingestion pipelines and federated data models, enabling cross-domain analytics and real-time decision support (Inmon, 2014; Sadalage & Fowler, 2012; Chen, Mao, & Liu, 2014).

Artifacts and business solutions rationalization: The development of BRDs, use cases, process flow diagrams, and target operating models benefits from exposure to analytics and data architecture concepts. A strong theoretical basis in machine learning, data governance, and data management supports the creation of artifacts that justify future work-streams, ensuring alignment with enterprise strategy (Davenport & Dyché, 2013; James et al., 2013).

Governance, risk, compliance, and oversight: The data governance and risk-management content align with responsibilities for governance, risk, and oversight programs. The course provides a foundation for understanding how data governance frameworks, risk policies, and control mechanisms translate into practical oversight activities, including RCSA processes and control design assessments (DAMA-DMBOK2; ISO/IEC 38500; Redman, 2008).

Operational risk and second-line risk oversight: Active participation in risk oversight requires familiarity with operational risk policies and risk reporting. The course’s focus on scalable, reliable data architectures supports resilient information systems, enabling effective risk monitoring and governance reporting (Davenport & Dyché, 2013; James et al., 2013).

Financial understanding and decision support: Enhancing financial understanding across business lines relies on analytics, reporting, and forecasting derived from large data sets. The course’s analytics emphasis—machine learning, statistical learning, and data-driven decision making—provides practitioners with capabilities to interpret financial metrics and support strategic choices (James et al., 2013; Davenport & Dyché, 2013).

Process improvement and program initiatives: The problem-based learning approach mirrors real-world program management by encouraging students to lead or participate in initiatives, plan roadmaps, and drive improvement efforts. This aligns with roles responsible for identifying opportunities, designing processes, and delivering outcomes (DAMA-DMBOK2; James et al., 2013).

Conclusion of mapping: The course, through its emphasis on distributed data platforms, data governance, and analytics, provides a coherent skill set that translates into practical capabilities across governance, testing, architecture, risk, and business decision making. The integration of theory with hands-on, real-world problem solving ensures graduates can contribute effectively to enterprise data programs while aligning with industry best practices and standards (Dean & Ghemawat, 2008; Inmon, 2014; Sadalage & Fowler, 2012).

Discussion of Course Context and Relevance to Roles

The MapReduce paradigm and related technologies (distributed file systems, NoSQL databases, and stream processing engines) are not abstract—these are the structural components used to implement scalable data platforms in modern organizations. For roles focused on data ingestion, governance, and integration, the course provides a practical foundation for designing and operating data pipelines that scale with demand while preserving data quality and lineage. For roles with testing and QA responsibilities, the focus on performance, load testing, and automation aligns with the need to quantify system behavior under stress and to demonstrate system reliability to stakeholders (Dean & Ghemawat, 2008; White, 2015).

From a governance and risk perspective, the syllabus supports the implementation of governance frameworks, risk controls, and oversight mechanisms by equipping practitioners with a solid understanding of data architectures, metadata, and data stewardship concepts. This ensures that data assets are managed in compliance with internal standards and external regulations, reinforcing the “second line of defense” functions described in the roles (DAMA-DMBOK2; ISO/IEC 38500; Redman, 2008).

Finally, the course’s emphasis on analytics and business intelligence connects directly to strategic decision-making. Students learn to translate data insights into actionable business strategies, a core competency for enhanced financial planning and performance management (Davenport & Dyché, 2013; James et al., 2013).

Conclusion

Relating the course contents to the roles listed reveals a clear value proposition: the course equips students with the technical foundations of big data systems, governance and risk considerations, testing and QA practices, and analytics competencies essential to enterprise decision making. The integration of MapReduce, distributed storage, and NoSQL with governance and risk concepts allows graduates to contribute to architecture, data management, compliance, and performance-improvement initiatives in real-world settings. This alignment supports the development of professionals who can navigate the complexities of modern data ecosystems while delivering measurable business outcomes.

References

  1. Dean, J., & Ghemawat, S. (2008). MapReduce: Simplified data processing on large clusters. Communications of the ACM, 51(1), 107-113.
  2. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning with Applications in R. Springer.
  3. Sadalage, P. J., & Fowler, M. (2012). NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence. Addison-Wesley.
  4. Redman, T. C. (2008). Data Quality: The Field Guide. AMACOM.
  5. DAMA International. (2017). DAMA-DMBOK2: Data Management Body of Knowledge. Technics Publications.
  6. Chen, M., Mao, S., & Liu, Y. (2014). Big Data: A Survey. Mobile Networks and Applications, 19(2), 171-209.
  7. Inmon, W. H. (2014). Data Lake Architecture: Designing the Data Lake and Avoiding the Data Swamp. Technics Publications.
  8. White, T. (2015). Hadoop: The Definitive Guide (4th ed.). O'Reilly Media.
  9. Davenport, T. H., & Dyché, J. (2013). Analytics at Work: Smarter Decisions, Better Results. Harvard Business Review Press.
  10. ISO/IEC 38500. (2015). Information technology — Governance of information technology. International Organization for Standardization.

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