1 What are the operational challenges that may appear with the use of data analytics in the proposed idea? 2 Is there any training that shall be arranged and provided to the operational team members on these tools? 3 What parameters shall be used to validate the operational feasibility of the idea? 4 What are the operational goals that shall be achieved? 5 What are the tangible benefits that will be attained with the successful implementation of the proposed business idea? 6 What are the intangible benefits that will be attained with the successful implementation of the proposed business idea? 7 What are the operational constraints that may be present? Analyzing the Issues Big Data analytics is being used by the RTR Company in many of the business operations and activities. However, there are a few issues that may be associated with the use of the technology in the proposed business idea. Also, the idea of a perfect dress includes numerous variables that may increase the complexity levels. There are numerous customers associated with the business firms. These customers vary in terms of the age group, work profile, standard of living, etc. These factors can have major influences in the customer purchase power and preferences. There are over 500 brands that the RTR Company is associated with. With these many choices, it is certain that the choices of the customers will vary . The use of the Big Data technologies can be ineffective and inadequate with the insufficient data sets. The large volumes of data will be necessary so that the preferences and patterns can be identified accordingly. The large-scale analysis will be effective with the involvement of massive data pieces . Possible Alternatives The issues illustrated above can be resolved by considering and including the response of the customers along with obtaining the product insights and operational insights. These will enable the RTR Company to understand the use and validity of the business idea. The business insights will also be obtained based on the collective information gathering and analysis . The shirt-term goals defined for the organization is the determination of the customer preferences and characteristics associated with the idea of a perfect dress. Based on these factors, the profiles and the sales strategy of the organization will be developed. The advanced data analytics will provide the ability to recognize the patterns and trends. The involvement of insufficient data will result in the inaccurate analytics and the inferences made will also be inaccurate. There are various sources of data that can be used and targeted by the business firms. Social media platforms are significant data sources that shall be used to gather the data sets. The existing customers and the potential customers of the organization are active on the social media platforms. These platforms can provide the organization will elaborate understanding of the customer preferences, fashion trends, and the customer demands . Resources and Timeframe Big Data tools, such as Hadoop will be necessary to manage the huge volumes of information and for the advanced analytics of the data sets. The information will be gathered from a variety of sources and all of these varied data sets will be included in the Big Data tools for analysis. The human resources will also be involved in the project. The Data Engineers and Data Scientists along with the Data Analysts will be involved in the process of gathering, analysis, and extraction of the data . The overall duration for the project will be 8 months. Monitoring and review procedures will be essential to achieve the successful outcomes. These shall include the security and privacy checks and compliance verifications. These will be necessary to ensure the misuse of the data gathered is prevented. The actual and estimated values will also be mapped in the monitoring processes to make sure that there are no gaps. Recommendations & Conclusion RTR Company shall consider and follow the recommendations listed below. The collective analysis from the customer insights, product insights, and operational insights shall be used so that the adequate business decisions are made Compliance checks and verifications shall focus on the legal compliance, security compliance, and ethical compliance. Any of the gaps or loopholes shall be reported immediately Trainings on the Big Data tools and platforms shall be enabled and provided to the resources engaged in the RTR project. Skill-assessment of the resources shall be performed before allocating the responsibilities to the resources. Data analytics is being used by the business firms all across the globe. There are numerous benefits provided by Big Data and the analytical tools to the business firms. RTR Company shall also make use of the Big Data tools to carry out the advanced data analysis. The business idea of identifying a perfect dress for the customers can be successful only when the detailed insights to the customer requirements and viewpoints are obtained. The other aspects, such as product insights and operations insights will also be crucial to make the business decisions. The determination of the preferences and trends will be possible only when the organization succeeded in collecting adequate information and applied advanced analytics to recognize the associated patterns. References  C. Catlett and R. Ghani, “Big Data for Social Good,” Big Data, vol. 3, no. 1, pp. 1–2, Mar. 2015, doi: 10.1089/big.2015.1530.  Y. Du, “Data Analytics and Applications in the Fashion Industry: Six Innovative Cases,” 2019. Accessed: Jul. 01, 2020. [Online]. Available: https://digitalcommons.uri.edu/cgi/viewcontent.cgi?article=1007&context=tmd_major_papers.  T. Hartung, “Making Big Sense From Big Data,” Frontiers in Big Data, vol. 1, Oct. 2018, doi: 10.3389/fdata.2018.00005. C. L. Remondino, “Fashion Industry as a Big Data Enterprise for Sustainability,” Current Trends in Fashion Technology & Textile Engineering, vol. 3, no. 4, Mar. 2018, doi: 10.19080/ctftte.2018.03.555616. S. S. Kim and Y. S. Kim, “Study on Recognitions of Luxury Brands by Using Social Big Data,” Fashion & Textile Research Journal, vol. 18, no. 1, pp. 1–14, Feb. 2016, doi: 10.5805/sfti.2016.18.1.1. J.-Y. DAI, “The Application of Big Data in the Fashion Industry,” DEStech Transactions on Social Science, Education and Human Science, no. esem, Jun. 2018, doi: 10.12783/dtssehs/esem2018/23880. V. Quevedo, “Fashion is Big Business,” Journal of Textile Science & Fashion Technology, vol. 4, no. 4, Feb. 2020, doi: 10.33552/jtsft.2020.04.000595. M. DuBreuil and S. Lu, “Traditional vs. big-data fashion trend forecasting: an examination using WGSN and EDITED,” International Journal of Fashion Design, Technology and Education, vol. 13, no. 1, pp. 68–77, Jan. 2020, doi: 10.1080/17543266.2020.1732482. T. Hartung, “Making Big Sense From Big Data,” Frontiers in Big Data, vol. 1, Oct. 2018, doi: 10.3389/fdata.2018.00005. N. Verma, D. Malhotra, and J. Singh, “Big data analytics for retail industry using MapReduce-Apriori framework,” Journal of Management Analytics, pp. 1–19, Feb. 2020, doi: 10.1080/23270012.2020.1728403. A. Hussain and A. Roy, “The emerging era of Big Data Analytics,” Big Data Analytics, vol. 1, no. 1, Jul. 2016, doi: 10.1186/s41044-016-0004-2. S. S. Kumar and Ms. V. Kirthika, “Big Data Analytics Architecture and Challenges, Issues of Big Data Analytics,” International Journal of Trend in Scientific Research and Development, vol. Volume-1, no. Issue-6, pp. 669–673, Oct. 2017, doi: 10.31142/ijtsrd4673.
Subject Name: Computer Science
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