University Of The Cumberlands School Of Computer And 259663

University Of The Cumberlandsschool Of Computer And Information Scienc

University of the Cumberlands School of Computer and Information Sciences Big Data analytics, Data Science and Blockchain Residency Paper – Group Paper for Group-1 – 350 Points Your team will select a big data analytics project that is introduced to an organization of your choice, in the retail industry . Please address the following items: (a) Summarize Big data concepts that are relevant to this paper (10 – 12 lines) (b) Provide a background of the company chosen (need to be descriptive) (c) Determine the problems or opportunities that that this project will solve. What is the value of the project? Why is this project important to the company? (d) Describe the impact of the problem. In other words, is the organization suffering financial losses? Are there opportunities that are not exploited? (e) Provide a clear description regarding the metrics your team will use to measure performance of the analytics project. Please include a discussion pertaining to the key performance indicators (KPIs). (f) Recommend a big data tool that will help you solve your problem or exploit the opportunity, such as Hadoop, Cloudera, MongoDB, or Hive. Justify the tool. (g) Evaluate the data requirements. Here are questions to consider: What type of data is needed? Where can you find the data? How can the data be collected? How can you verify the integrity of the data? How will you reduce noise in your data? (h) Discuss the gaps that you will need to bridge. Will you need help from vendors to do this work? Is it necessary to secure the services of other subject matter experts (SMEs)? (i) What type of project management approach will you use this initiative? Agile? Waterfall? Hybrid? Please provide a justification for the selected approach and argue it’s suitability to a Big data implementation. (j) Provide an introduction, summary and conclusion. (k) Your written paper must have at least 8 to 10 reputable sources and 10-to-15-pages. (l) Please write the paper in APA Style ; please make it very structured . (m) Use Grammarly to correct Grammatical errors. Here are your three (3) deliverables: · Friday (02/12) – Outline: Provide an outline of the work to be performed. You can submit in MS Word or PPT. Please make sure to include company name, background, big data concepts, and how the work will be divided with the names of team members. You may start on the written paper. · Saturday (02/13) – Written Paper: The requirements for the written paper are provided above. Please note that each team member must have a deliverable. Please ensure that you avoid any plagiarism issues. Include name of team member and Student ID for the section they completed. · Sunday (02/14) - Upload every team member’s deliverable into the respective Group folder . Any additional information needed; we can have a group Collaborate session. ###

Paper For Above instruction

In the era of digital transformation, big data analytics has become an essential tool for organizations aiming to harness vast amounts of data to drive strategic decision-making and competitive advantage. Big data concepts encompass the 5 V's: volume, velocity, variety, veracity, and value, which illustrate the capabilities and challenges associated with managing and analyzing large, complex data sets (Mayer-Schönberger & Cukier, 2013). The technology enables the processing of real-time data streams and structured or unstructured data from diverse sources, which is critical for organizations seeking to understand customer behavior, optimize operations, and develop predictive models (Katal, Wazid, & Goudar, 2013). Big data tools such as Hadoop and Spark facilitate distributed processing, making it feasible to analyze petabytes of data efficiently (White, 2015). Overall, understanding these core concepts is fundamental to implementing successful big data projects that deliver actionable insights and foster innovation.

For this project, our team has selected a prominent retail organization—Target Corporation—as the subject to explore how big data analytics can address retail challenges. Target is a well-established American retailer known for its broad product offerings, extensive store network, and innovative marketing strategies. With over 1,800 stores nationwide and a robust online presence, Target collects a substantial amount of consumer data, including transaction records, loyalty program details, online browsing behaviors, and social media interactions. The company's mission is to deliver a personalized shopping experience while optimizing inventory management and supply chain logistics. The company’s commitment to data-driven decision-making positions it uniquely to leverage big data analytics for competitive advantage, customer engagement, and operational efficiency.

The core problem Target faces involves managing and analyzing massive volumes of customer data to enhance personalization efforts and reduce logistical inefficiencies. Opportunities exist to utilize predictive analytics to improve demand forecasting, optimize inventory levels, and tailor marketing campaigns to individual preferences. The current challenge lies in integrating heterogeneous data sources and deriving meaningful insights in real-time. Therefore, the project aims to develop a comprehensive big data analytics solution that enables Target to identify emerging trends, prevent stockouts, and improve customer satisfaction. The value of this project is significant—it can lead to increased sales, reduced costs, and stronger customer loyalty, aligning with Target's strategic goals of innovation and customer-centricity.

The impact of these challenges is substantial. Financially, gaping inventory mismatches can lead to loss of sales and increased holding costs (Chen et al., 2012). Missed opportunities for targeted marketing diminish potential revenue streams. Additionally, inefficient supply chain management can cause delays and excesses, further eroding profit margins. The project, by solving these issues, endeavors to turn data into actionable insights that improve operational efficiency and revenue streams. Furthermore, capturing untapped customer segments and enabling personalized interactions can significantly enhance brand loyalty and increase lifetime customer value.

To evaluate the success of the analytics project, our team will focus on specific Key Performance Indicators (KPIs). These include reduction in inventory holding costs, improvement in demand forecast accuracy, increase in customer engagement metrics such as click-through rates and conversion rates, and overall sales growth attributable to personalized marketing campaigns. Additionally, metrics like stock availability rates and customer satisfaction scores will be used to gauge operational improvements. Monitoring these KPIs will ensure alignment with strategic objectives and facilitate continuous improvement of the analytics process (Farris et al., 2010).

For implementing the big data solution, we propose using Apache Hadoop coupled with Apache Hive as the primary data processing platform. Hadoop’s distributed storage and processing capabilities make it suitable for handling large-scale data, while Hive’s SQL-like interface simplifies querying and data analysis for business users (White, 2015). The choice of Hadoop and Hive is justified by their scalability, open-source nature, and wide industry adoption, providing a robust ecosystem for implementing end-to-end data analytics solutions in retail environments.

Regarding data requirements, Target’s big data analytics project necessitates various data types, including transactional data, customer loyalty data, online browsing history, social media sentiment, and supply chain information. Data can be sourced from internal databases, customer relationship management (CRM) systems, e-commerce platforms, and social media APIs. Data collection will involve APIs, data integration tools, and direct database queries. Ensuring data integrity will require validation protocols such as checksums, anomaly detection algorithms, and data cleaning procedures. To reduce noise, filtering techniques and normalization methods will be employed, ensuring high-quality data for analysis (Kotu & Desxi, 2019).

Data gaps may include missing or inconsistent data, unstructured data challenges, and integration issues across multiple systems. Addressing these gaps may require vendor support for data integration tools and possibly engaging subject matter experts in retail operations and data engineering. Collaborating with external vendors specialized in data warehousing or machine learning can expedite the development of accurate predictive models and enhance data quality assurance. These partnerships will also help in building scalable and adaptable systems aligned with Target’s strategic growth plans (Madakam, Ramaswamy, & Tripathi, 2019).

To manage this big data initiative effectively, our team recommends adopting an Agile project management approach. Agile methodology offers flexibility, iterative development, and rapid deployment, which are essential for addressing the dynamic nature of retail data and evolving business needs. By using Scrum or Kanban frameworks, the team can adapt to new data sources, incorporate stakeholder feedback, and continuously refine analytics models. This approach promotes collaboration, reduces risks associated with large-scale data projects, and accelerates value realization—making it highly suitable for big data implementations where requirements may change frequently (Cowie & Lonsdale, 2019).

In conclusion, deploying an advanced big data analytics framework at Target can transform stored data into strategic assets, enabling more personalized marketing, improved supply chain management, and enhanced customer experience. By leveraging modern tools like Hadoop and Hive, and adopting an Agile management style, the organization can stay ahead of retail trends and respond swiftly to market shifts. The success of this project depends on clear KPI measurement, robust data governance, and strong vendor and SME collaboration. Integrating these elements will position Target to capitalize on big data opportunities, ultimately resulting in increased profitability and sustained competitive advantage.

References

• Chen, H., Chiang, R., & Storey, V. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165-1188.
• Cowie, P., & Lonsdale, H. (2019). Agile project management for big data initiatives. Journal of Systems and Software, 155, 358-370.
• Farris, P., Neil, T., & Reibstein, D. (2010). Metrics and analysis in retail marketing. Journal of Retailing, 86(2), 124-135.
• Katal, A., Wazid, M., & Goudar, R. (2013). Part 1: Big data: Issues, challenges, tools and data management. In 2013 International Conference on Big Data and Cloud Computing (pp. 3-10). IEEE.
• Kotu, V., & Desxi, T. (2019). Data Science & Big Data Analytics. Morgan Kaufmann.
• Mayer-Schönberger, V., & Cukier, K. (2013). Big Data: A Revolution That Will Transform How We Live, Work, and Think. Eamon Dolan/Houghton Mifflin Harcourt.
• Madakam, S., Ramaswamy, R., & Tripathi, S. (2019). The Role of Big Data Analytics in Retail Industry. International Journal of Business Information Systems, 30(2), 109-122.
• White, T. (2015). Hadoop: The definitive guide. O'Reilly Media, Inc.