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MSCA 32005 Real Time Analytics

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MSCA 32005 Real Time Analytics

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Course Code: MSCA32005
University: University Of Illinois At Chicago

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Question:

Justification of significance of Big Data in the chosen industries Big Data opportunities illustrated with examples of two chosen industries Big Data challenges illustrated with examples of two chosen industriesBig data Analytics current techniques and technologies The value added to each chosen industry by the Big Data initiative from building new capabilities and facilitate decision-makers An interesting, well written summary of the main points. An excellent final comment on the subject, based on the information provided.

Answer:

Introduction
The recent world is changing at an extremely fast pace with the help of emerging technologies. Several newer forms of applications, softwares and tools would be used for the purpose of data analysis, capturing of data, storing, visualization and sharing of data from one component of the industry to another. The industries also make use of sensors, which are used for producing and collecting data. The increasing number of electronic devices and the use of these devices by individuals, business and industries are helping in the generation of huge amount of data. With the emerging rate of data, Big Data has become an increasing interest of issue within researchers and companies who are conducting a thorough research on the particular field of study (Demirkan & Delen, 2013).
Big Data is also able to represent various sets of data that would be collected from a vast number of sources. This huge amount of data is being produced in massive amounts with high level of frequency. The traditional methods and tools, which were used to process data are not able to adequately process the data as the big data collected from several sources is in huge amounts (Bharadwaj et al., 2013). The excessive rise of big data has made a major impact on the industries. The innovations within the investing in data are much needed in order to explore and thus understand the position of market and behaviour of customers.  
The report focuses on Amazon and Starbucks, which make use of big data technologies within their own business strategies in order to understand the business processes and customer decisions. The use of big data by industries would also be helpful for predicting the decisions and nature of the customers. This would also help in better recommending of better products for the customers for their satisfaction and also incurring better outcomes for the business within the industries (Provost & Fawcett, 2013).
Aspects of Big Data within Amazon  
Significance of Big Data within Amazon – Big Data plays a major role with the management of logistics and management of supply chain. The company has an unrivalled data bank based on the behavioural pattern of customer purchasing habits. With the help of big data, Amazon is able to build a recommended system that would be able to efficiently suggest better products to their customers and thus build a healthy relationship for the business and increase the growth of business processes (Chen, Mao & Liu, 2014). The implication of big data within Amazon has helped the organisation in evolving over the years and improving the recommender engine. This has tremendously helped to increase the level of perfection. Amazon uses the click-stream data of customers and data based on historical purchases. This data is further processed and then hence customized results are displayed on the web pages that would be browsed by the customer (Wamba et al., 2015).
With the help of big data, Amazon is able to compete within the existing market. Amazon has also implemented a remote service of computing, with the use of Amazon Web Services (AWS) to offer vast number of services. Big data services are used within AWS in order to offer various tools for collecting data, computation and analysis of data added with collaboration and sharing of data (Newcombe et al., 2015).
Big Data Opportunities for Amazon – The Company aspires to be one of the best customer-centric based company. That mainly focus on selection wide products and provide an excellent form of customer experience. The benefits of the impact of big data within Amazon for collecting customer information is that they are able to translate the gathered information into action. Big Data provides opportunities for the business of Amazon beyond the making of products (Watson, 2014).
The recommendation engines that are based within the company are based on the technology of big data. They are able to analyse the decisions of the customers and thus suggest better modes of options. Amazon depends entirely on the decision of their customers. Big data provides a wide range of operations for the business of Amazon. The engines based on recommendation help in simplifying the vital task of prediction of the needs of the customer. This helps in increasing the opportunities for business for introducing new features within their business application and thus providing such recommendations for their customers that they would be able to satisfy the variant needs of the customers (Walunj & Sadafale, 2013).
In the past years, it has been noticed that Amazon has shifted from an e-commerce website to one of the biggest online website that offers much more than just offering products to their customers. The company would focus massively on the use of big data, which would change the opportunities for business and thus providing a healthy business environment.
The example of the opportunities of Big Data is Amazon Fresh. This is one of the example that Amazon would be moving into a much larger market and thus make use of data-driven logistics expertise for the purpose of creation and capturing value based on customer decisions (Trkman, Budler & Groznik, 2015).  
Big Data Challenges within Amazon – With the implementation within newer technologies such as Big Data, there are numerous challenges that are being faced by the organization. The innovation of big data is still into the stages of maturation as still the technology is incubating. There are various gaps within the field of big data, which are needed to be addressed. Data Scientists within this field of the organization are working hard in order to focus entirely on the patterns and numbers within the use of machine learning (Chen & Zhang, 2014).
There are shortage of skills within the organization. The ecosystem of Big Data is moving at a tremendous pace, which would mean that the developers have to adapt to newer technologies in order to keep up with the pace of technologies. The concerns of cost of implementing the newer technologies is also a matter of concern (Provost & Fawcett, 2013). Amazon has to invest much more within big data so as to compete within the existing market. Security is also a high matter of concern, which should be strongly addressed for the purpose of securing the data of the customers within the data lake. There should be proper methods of encryption, which would be efficient enough to deal with the various aspects of the business processes. As Amazon deals with several payment facilities, thus the methods of encryption within the payment modes should be secured and revised properly so as to ensure that there would be no chances for the hacking of confidential data of customers.   
Example of the challenges faced by Amazon would mainly affect within the healthcare industry such as the industry should be able to meet the HIPAA compliance. It would need a complex form of understanding that would not just be limited to securing the data within the AWS servers but also would be able to secure the computers, copiers and printers within the workplace (Hashem et al., 2015).  
Big data Analytics Current Techniques and Technologies within Amazon – Big Data makes use of AI based technology and Big Data in order to accelerate the level of profits. They have introduced newer forms of products, technologies and techniques for the satisfaction of customers and increase the efficiency of business processes. They have introduced a newer product, which is named as Echo. This implemented technology is a major jump within the inline technology. Big data make use of high rate of analytics with the help of high level of algorithms, which are meant to calculate and solve various level of complexities.
The algorithms designed and trained by the developers at Amazon are able to dictate the best form of applications, which would help in deciding the best form of actions. Amazon collects various forms of data from their customers when they navigate through the website, the mobile application when they spent time during the browsing of each of the pages. The retail market make use of external sets of data that would include the census data for the purpose of collecting demographic details. The core business of Amazon is mainly handled within the central data warehouse. They mainly consist of the servers of Amazon Web Services or Hewlett-Packard that mainly run Oracle and based on Linux. The data lake within Amazon servers are able to store huge bulks of data in a secure form that might range from gigabytes to exabytes. The data would be analysed with a wide form of selection based on analytic engines and tools (Mathew & Varia, 2014).
Within the AWS servers, Amazon is also able to host public data sets at no cost facilities. Much of the available sets of data could be used and integrated within the solutions based on AWS cloud. The developers at Amazon utilize the public data for mapping the Human Genome Project. Amazon makes use of tools such as NoSQL and relational databases and Amazon EMR clusters, stream and log processing in order to handle the big data analytics. The use of NoSQL within Amazon helps in the scaling of horizontal frames and thus is helpful for avoiding various kinds of major operations within the data. NoSQL leads to better form of flexibility within their operations. Amazon also leverages the use of Athena, which is a particular kind of tool that would be helpful for a data analyst in order to search for interactive kind of queries within the AWS public cloud platform. Amazon Athena is also a form of severless based query service and does not require any kind of underlying infrastructure of computing.    
Value added within Amazon by Big Data Initiative – In the recent times, the complexity and size of data that are being analysed would be based on the newer form of technologies. In order to gain much more value from the Big Data initiative, AWS helps in providing the best form of secure, scalable, cost-effective and comprehensive portfolio of services (Kim, Trimi & Chung, 2014). This would majorly help for building data lake within the cloud, analyse the entire sets of data that would also include the data from IoT devices with a wide form of analytical based approaches that might also include machine learning technologies. With the help of big data initiatives within the business sectors, there are many organizations who are able to run their data lakes and analytics within their AWS servers.
These form of initiatives within the latest form of technologies helps Amazon in building newer form of capabilities for the growth of the business sector. The implementation of the newer form of value based technologies within Amazon also helps in simplifying for making of decisions for the decision-makers. The help of these technologies would also help for the company for delivering the best form of services to their clients within less possible time.  
Aspects of Big Data within Starbucks  
Significance of Big Data within Starbucks – Big Data has made a major impact within every form of business. The business processes within Starbucks with the help of Big Data is mainly used for leveraging the use of data based on customer reviews. Starbucks deals with over 90 million transactions in a single week at around 25,000 stores all around the world. The coffee giant is making use of Big Data technologies in order to help the company in the areas of digital marketing, business and sales decisions (Liebowitz, 2013).
Big Data is used within the organization in order to enable them to make accurate predictions and decisions within the business operations. The amount of processed data within Starbucks is increasing at a tremendous pace. It has been estimated by the organization that the amount of collected data would be projected to double within the recent two years until 2020. The underlying technology based on big data has enabled in transforming the business decisions into data-driven decisions (Sanders, 2015).   
With the help of Big Data, Starbucks is able to deliver better productivity within the business processes. The Big Data technology would be helpful for the management team for discovering newer areas within the business for scope of improvement and also help the employees to be much more focused on customer-centric decisions.
Big Data Opportunities for Starbucks – With the implementation of Big Data within the business processes of Starbucks, it has been seen that there is a tremendous level of productivity within the business. The data that is collected from various sources is an important resource for business processes that would help in revealing various pathways and opportunities for the scale of improvement within their served products. Starbucks uses Big Data to help in the determination of opening of newer franchises within a certain location, which would be viable for the growth of the business (Schmarzo, 2013). This decision is mainly based on vital information such as the demographics of area, the locations and behaviour of customers. The assessment of this data helps the organization in estimating the success of the business from their chosen locations.
Big Data is being widely used by the organization in order to understand the varied choices of products based on different customers. This huge amount of collected data is stored in their varied database units, which is later analysed by data analysts within the organization. The data collected would help the company in understanding the tastes of customers and thus they are able to suggest better and new products. They would also be able to provide discount coupons and enticing offers for their customers based on their purchase history (GalbRaith, 2014).
A recent example for the increasing opportunities of business with the help of Big Data includes the fact that Starbucks offers special discount offers for their customers on Thursday. Based on the purchased items, the company sends coupons to their customers on their smartphones, which could be redeemed at the store during their purchase.
Big Data Challenges within Starbucks – Although Big Data is a much needed technology within Starbucks, yet the company is faced with newer form of challenges. These form of challenges sometimes poses problem for the organization in meeting up to the business expectations. Starbucks makes use of their personal continuous stream in order to purchase data. It has been noted that with there is a little problem for the organization for integrating newer data sets. The governance of data is also much needed for ensuring that the information would be enough in order to integrated readily (Frizzo-Barker et al., 2016).
Starbucks is also facing a major challenge in figuring the location of their newer stores. With the help of Big Data and the massive use of data analysis, it is reliable to locate a particular area. The major challenge faced by the organisation is to deal with the local government for granting permission for setting up their store. The other challenges faced would include the increasing cost of setting up of new technologies within the existing business process (Morabito, 2014). Starbucks also faces challenges with the security of data of their customers, which are stored within their data servers. In the recent times, with a lot of impact about the breaches of data, it is very essential to secure the data of the customers in order to maintain a strong and healthy relationship with their customers. The data kept within their servers should be highly secured by the security professionals such that there would be no breach within the internal data of the customers.   
 Example of a challenge faced by Starbucks is the rapid emergence of other coffee companies that includes Blue Bottle Coffee and Stumptown Coffee Roasters. They are using better use of data in order to attract customers, which is posing a threat to the business of Starbucks.
Big data Analytics Current Techniques and Technologies within Starbucks – The recent growth of Big Data within the business procedures has helped the company in understanding the pattern of decisions of their customers. With the help of data analytics, Starbucks has improved the efficiency of business by optimising their process of inventory, minimising the amount of waste and thus improve productivity (Westerman, Bonnet & McAfee, 2014).
One of the current techniques used by Starbucks is the use of one important tool that is named as Atlas. It is one of the best mapping and a platform based on business intelligence. This tool has been helpful for collecting data based on changeable weather conditions so as to react according to the choice preferences of their customers based on hot or cold beverages when necessary. The new form of intelligence software has played a pivotal role in picking up new locations of stores (Chua & Banerjee, 2013). Starbucks evaluates various sites that are primarily based on key metrics that includes traffic patterns, links of transportation, density of populations and average income levels of the population. The company has also invested a lot into deploying the best tools, which would be helpful in analysing and deciding the best choices of their customers. Various recommendation engines that are inbuilt within the application would be helpful in understanding the choice of customers and suggest them with better recipes and provide them with additional offers such that the customers would be choose Starbucks as their favourite destination (Kane, 2015). Starbucks obtains the data from Atlas, which is a kind of business intelligence platform and a mapping technique that is mainly developed by Esri. The collected data would help in discovering the various forms of potential based on the location of a particular store. This kind of system also takes account of different kinds of factors such as patterns of traffic, demographics, proximity and density of population.
Starbucks also provides loyalty cards to their customers. These cards are specifically provided in order to track the individual buying data of specific individuals. It has been estimated that the company has nearly 6 million people within their loyalty card program. These profiles are mainly used by the company for allowing the organization to view the products that are sold by them and to the particular demographic. Hence, they are used for understanding the trends of purchasing and thus they are able to set the various timelines in order to send promotional offers and mobile coupons (Haskova, 2015). These are generally meant for targeting customers and attracting them for purchasing from their store.
Value added within Starbucks by Big Data Initiative – The demand planning process would be able to add value within the Big Data initiative of Starbucks. The use of cognitive capabilities predictive method of analysis would be able to gain much more insights within the business. The commitment towards using the data as a form of competitive advantage and thus able to work on visualization of that information. The cost of storing the big amounts of data based on the perception of customers and other market analysis is extremely low. The understanding of advanced form of data analytics and the implications on the business processes would be helpful for the organization. The organization makes use of various tools based on big data for targeting a particular initiative and thus leverages the analytics in a practical manner (Ross & Blumenstein, 2013).
The processing of data often leads towards better level of outcomes and opportunities. The use of big data initiatives would also help in automating the aspect of data processing and save considerable amount of effort and time, thus leading to increased profits within the organization. The impact of big data has added much value within the business aspect of Starbucks. The use of Big Data technologies and AI is affordable for the organization and is thus helped in eradicating the burden of transitioning the various insights into useful actions (Turban et al., 2015).
Conclusion
Based on the above discussion about the use of Big Data within Amazon and Starbucks, it could be concluded that the role of big data technologies plays a major impact within the growth of the business within these industries. Hence, the various form of challenges within the sector of business should be met with proper form of remedial measures. On the basis of the discussion, it could be concluded that Amazon helps in providing various capabilities across different networks, software and business processes in order to meet with the strictest form of requirements. There are several partner companies, which have to be satisfied so as to maintain a healthy relationship.  Big Data has opened a pool of opportunities for the growth of the business of the company. The company makes use of several form of tools and methods in order to deal with the various upcoming challenges and adoption of wide level of opportunities for the impact on the business. The widespread network of Starbucks is a major benefit for the organization as more customers would be able to visit their stores and thus they would be able to generate much amount of data from their reviews and deliver high quality of service. This would also help in maintaining a healthy relationship with their customers.
References
Bharadwaj, A., El Sawy, O., Pavlou, P., & Venkatraman, N. (2013). Digital business strategy: toward a next generation of insights.
Chen, C. P., & Zhang, C. Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275, 314-347.
Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile networks and applications, 19(2), 171-209.
Chua, A. Y., & Banerjee, S. (2013). Customer knowledge management via social media: the case of Starbucks. Journal of Knowledge Management, 17(2), 237-249.
Demirkan, H., & Delen, D. (2013). Leveraging the capabilities of service-oriented decision support systems: Putting analytics and big data in cloud. Decision Support Systems, 55(1), 412-421.
Frizzo-Barker, J., Chow-White, P. A., Mozafari, M., & Ha, D. (2016). An empirical study of the rise of big data in business scholarship. International Journal of Information Management, 36(3), 403-413.
GalbRaith, J. R. (2014). Organizational design challenges resulting from big data.
Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U. (2015). The rise of “big data” on cloud computing: Review and open research issues. Information Systems, 47, 98-115.
Haskova, K. (2015). Starbucks Marketing Analysis. CRIS-Bulletin of the Centre for Research and Interdisciplinary Study, 2015(1), 11-29.
Kane, G. C. (2015). Enterprise social media: Current capabilities and future possibilities. MIS Quarterly Executive, 14(1).
Kim, G. H., Trimi, S., & Chung, J. H. (2014). Big-data applications in the government sector. Communications of the ACM, 57(3), 78-85.
Lapitskiy, I. (2018). Number of Starbucks Stores Globally, 1992-2018 – COMSTAT Data Hub. Retrieved from https://comstat.comesa.int/infographics/kchdsge/number-of-starbucks-stores-globally-1992-2018
Liebowitz, J. (Ed.). (2013). Big data and business analytics. CRC press.
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Provost, F., & Fawcett, T. (2013). Data science and its relationship to big data and data-driven decision making. Big data, 1(1), 51-59.
Ross, P., & Blumenstein, M. (2013). Cloud computing: the nexus of strategy and technology. Journal of Business Strategy, 34(4), 39-47.
Sanders, N. R. (2015). Big data driven supply chain management. A Framework for Implementing Analytics and Turning Information Into Intelligence, 2014.
Schmarzo, B. (2013). Big Data: Understanding how data powers big business. John Wiley & Sons.
Trkman, P., Budler, M., & Groznik, A. (2015). A business model approach to supply chain management. Supply Chain Management: An International Journal, 20(6), 587-602.
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Wamba, S. F., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015). How ‘big data’can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, 234-246.
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