Part A
This part emphasizes on the use of Big Data and its applications. Students are required to demonstrate research in the area of Big Data and its solutions.
Over the past several years the retail sector has been bombarded with challenges. Brick-and Mortar stores have closed in record numbers and continue to do so. Indeed virtually every member of the entire industry is at high risk. They are vexed by several threats ranging from increased competition from online super-shopper sites such as Amazon to a plethora of more brand-specific problems, such as lack of differentiators, pricing pressures, muddled marketing messaging, poor businesses processes, online pricing/brand comparison sites, customer reviews on their own websites, vulnerability to customer social messaging, and an overall disconnection with buying public. One of the retail sectors is Alana’s Retail Clothing House (ARCH). ARCH former strategies in merchandising, merchandise selection, pricing models, customer service, and marketing are no longer working as effectively as they have in the past. They are planning to introduce a new product into the market. However, in recent times the company has made some bad investment decisions which in turn have impacted their profit bottom line. Thus the senior management requires a thorough analysis of every new product that is introduced to the market. As a senior Data analyst, you have been appointed to advise the senior management on the feasibility of the new product.
Your task is to suggest ways how Big Data Solutions can help Alana’s Retail Clothing House. You need to write a report to the senior management on Product selection and pricing, using Big Data to develop new pricing models, to find better ways to get more, better, and cleaner customer data which can help profitability.
Part B:
You are required to select and examine an existing company that is currently using business intelligence and decision support systems using Big Data as part of its business strategy. In order for you to meet the requirements of this Part B, you need to answer the following questions:
• Discuss in detail what kind of Big Data online or offline is being used for the business. How the organization of chose the right Big Data application for their business.
• Discuss in detail what Big Data tools and techniques are used by the organization to support its business operations or processes. Provide examples on how those tools and techniques are used in the organization.
• What improvements/enhancements can be observed from this company as compared to other companies within the same industry when using Big data? What are the Business and Organizational impact using Big Data?
• How do you think Big Data solutions provide values to business intelligence to your chosen company and its customers?
• What are the main factors that influence the success of business intelligence in the company?
In retail sector, big data expedites the analysis of behavior of in-store purchase in real time. With this kind of facility to have quick insight into demand scenario, retail stores can manage their stock levels, prices, merchandise and also able to maximize sales.Big data provides the ability to store huge volume of data set, which is beyond the capacity of commonly used software tools to curate, capture, process and manage a large volume of data within a desired amount of time. With a shift form few dozen terabytes to petabytes of data, size of Big data is increasing day by day. Big data is nothing but a set of technologies and techniques which is made up of new forms of integration to reveal a large volume of data values from a large set of data that are of massive scale, complex and diverse in nature. Architecture of a big data technology is having five layers- Presentation layer, application layer, Storage layer and integration layer.
Big data provides the ability to store huge volume of data set, which is beyond the capacity of commonly used software tools to curate, capture, process and manage a large volume of data within a desired amount of time. With a shift form few dozen terabytes to petabytes of data, size of Big data is increasing day by day. Big data is nothing but a set of technologies and techniques which is made up of new forms of integration to reveal a large volume of data values from a large set of data that are of massive scale, complex and diversein nature(Zhao, Fan and Hu, 2014).
Architecture of a big data technology is having five layers- Presentation layer, application layer, Storage layer and integration layer. For process management and workflow, presentation layer provides primary interface. Application layer is the place, where modeling, analysis and business intelligence are executed(Chugh and Grandhi, 2013).The storage and processing layer store and process a huge volume of unstructured and structured data either in real time or in non-real time. Lastly, the integration layer work as an interface between the several enterprise data sources across an organization and helps to move data in, out and throughout the architecture of big data(Bernardino and Ribeiro, 2011).
Market research has been conducting based so many products since a century. Importance of market research is increasing gradually in order to determine the market strength of a particular product.
While retail sectors are in concern, big data explores the analysis of behavior of in-store purchase in real time. With this kind of facility to have quick insight into demand scenario, retail stores can manage their stock levels, prices, merchandise and also able to maximize sales(Vera-Baquero, Colomo-Palacios and Molloy, 2013).
With the incorporation ofBig data and BI (Business Intelligence) tools, ARCH can have the ability of massive processing capability to produce the large data sets and helps to analyze the market trend with respect to a particular product. This in turn helps the company to uncover the cost-reducing opportunities(Bi and Cochran, 2014). In this context ARCH will have a great impact in three areas, due to the incorporation of Big data and BI tools, such as-
Customer intimacy: organizations are in rush to gather customer data form interactive websites, third-party data banks, government and online communities. That is where Big data put a great impact to strengthen the corporate strategy. As a social-media Platform Facebook is telling about 30 billion contents are shared by users daily(Duan and Xiong, 2015). On the other hand it is now possible to bring the customer contact and social media feeds in a one place. With the further advancement of the analytical tools, it becomes possible to allow more effective, efficient and effective tool with least processing cost. Hence it can help to develop new insights rapidly(Chugh and Grandhi, 2013).
It is a big opportunity for ARCH, if its owners are thinking to build a web portal to incorporate the E-commerce in the business model. Big data help to customize product offers in real time environment. For example, AMAZON has adopted this technique since few years by displaying the related products with the purchased products. In offer giving offline advertisement alsoBigdata assists in ad placement by analyzing which TV programs will deliver the greatest impact for several segments of customer(De Ville, 2006).
New Big data opportunities are coming up across the globe to gather past and related data from many sources whether it is an online data or offline data. The weak side of a business scenario comes up when data collection is something hard-to-find. With concept of Big data what was once proprietary information, now can be accessed from public to private resources. After this data collection, it is possible for ARCH to generate revenue by analyzing the market of their product. Even customers buying behavior will also help ARCH to improve their product and they can offer dynamic pricing along with different content. For example, Facebook runs lots of experiments everyday with a set of users. These users are seeing different features than others. Even Amazon offers different content along with dynamic pricing applicable for several customers and adjustments are made appropriately(Duan and Xiong, 2015).
Before something starts deteriorating it is needed to take efficient steps to fix. In previous scenario of ARCH allows information to be rustin storage area and that was a waste of opportunity. Supply chain data offers several information-rich interactions, RFID (Radio Frequency Identification) for capturing the physical movement of products. This scope allows getting information on manufacturing sites, customers, suppliers and asset movement, revenue, customers in real time.Allowing customer information and their purchased item of ARCH is not everything but the way it is seen from the perspective of customers is important.I.e. when a promotion ofARCH’s substitute product will take place in the market, then it will reveal the actual customer preferences(GlenysVahn, 2014).
With the incorporation of Big data and BI (Business Intelligence) tools, ARCH can have the ability of massive processing capability to produce the large data sets and helps to analyze the market trend with respect to a particular product. This in turn helps the company to uncover the cost-reducing opportunities.
Conclusion:
With concept of Big data what was once less known information, now can be accessed from public to private resources. With the further advancement and incorporation of analytical tools, it becomes possible to allow more effective and efficient tool with least processing cost. With the mining techniques, such as, classification allows to segregate collected data as per the similarities. With Classification Rule Mining, it became possible to indentify frequency of a particular product and even it becomes possible to find other frequent product that is coming frequently with a most frequent product. That in turn helps to mine the buying behavior of customers. This scope allows getting information on manufacturing sites, customers, suppliers and asset movement, revenue, customers in real time which helps the top most person of company to take decision. All those in turn help ARCH and its organizational heads to take effective decisions.
Introduction:
Business Intelligence tools are kind of application software, which are designed for retrieving, analyzing, transforming and making report data for business intelligence. These kinds of tools generally used to read and analyze data, which have been stored previously in data warehouses. Categories of BI tools are: Querying and Reporting software, spreadsheets, OLAP, data mining, local information systems, Data warehousing and Digital dashboards (Heinrichs, Lim and Hudspeth, 2002). All the big data tools and techniques help to get store, retrieve and modify data easily those stored in physical locations and virtual spaces, such as cloud. This tools techniques are use to track the product market and revenue growth from a remote location, as data can be stored on virtual space. Hence it can be concluded that Big data is nothing but the large data set to analyze, revel the pattern among those gathered data specially related to human behavior.
With the incorporation of DSS standard and BI (Business Intelligence) tools, Levi Strauss can have the ability of massive processing capability to produce the large data sets and helps to analyze the market trend with respect to a particular product. This in turn helps the company to uncover the cost-reducing opportunities(Huang et al., 2014).Supply chain data offers different information using RFID (Radio Frequency Identification) technology to capture the physical movement of products. This scope allows getting information on customers, suppliers and asset movement, revenue, customers in real time.
1. Levi has adopted this technique since few years by displaying the related products with the purchased products. In offer giving offline advertisement also Big data assists in ad placement by analyzing which TV programs will deliver the greatest impact for several segments of customer (Duan and Xiong, 2015). On the other hand Levi DSSs allows manager of Levi to take decision and also make it easy for managers to examine the performance of the distributors, supplier and manufacturer over supply chain management. Levi is Involved in offline retail to measure and ultimately optimize brick-and-mortar of retail stores. Data those are stored in an offline storage are linked with the data warehouse. Tools those are used in Big data fetch records form offline storage and make analysis over those data. Before making analysis upon those data, data are fetched and stored using offline storage or in a virtual storage space such as clouds.
While we are considering the data collection of Levi in the form of online or offline, Levi Strauss manage to collect their big data from online source through their official FB page and through the official sites of Levi. If offline data collection of Levi is considered, it shows their offline customer care service when a customer arrives to their store. Customer’s opinion makes a huge different towards the required improvement and opinion on the existing system engaged in production and customer service(Kirkpatrick, 2013).
2. Jeanswear giant LS& Co has chosen Capgemini for the development of an integrated data warehouse and BI platform to analyze the data, which are flowing within the company. Being one of the world’s largest apparel companies, LS&CO leverages multiple analytics and BI systems in order to support its wholesale and retail business units. According to the new deal Capgemini will be working with Teradata and SAP to implement the SAP BusinessObject, BI platform 4.0. This is designed so to provide a global, single and broad-based view of the data flow of the organization. Hence it can help Levi to drive revenue and also allowsmaking decrement in costs(Leeuwen and Timmermans, 2004).
3. According to the vice president, application services, Tammy Amirault, Levi’s continued success on global business depends on Levi’s capability to give quick reaction to the movement of data across the enterprise (Leonard, 2013). Which in turn helps in determining customer’s buying behavior. Customer data are collected and analyzed with respect to the substitute product. It is now possible to bring the customer contact and social media feeds in a one place. With the further advancement of the analytical tools, it becomes possible to allow more effective, efficient and tool with least processing cost (Kirkpatrick, 2013).
4. With the big data solution data are stored and fetched in/form virtual and physical storage medium. Those data are gathered, analyzed, and lastly classified to know the actual preference of the customers and even it is possible to understand their buying behavior. Examining theses customers’ preferences and their behavior companies can take their valuable decisions for their future benefits. Even customers can get better shopping experience while they are purchasing materials online. For example users can maintain their account and update their wish list as per their preferences that in turn helps a business organization to know present market trend.
5. With the incorporation of Big data it became possible to incorporate the BI tools and other analytic tools to analyze the market of a particular product. Even it became possible to incorporate the some value added services and offers as per the most frequently sold products. On the other hand companies are now a day became able to understand strategies of other companies to sale the substitute products due to the online presence of the customer data, their preferences and stock report. These stock reports are easy to maintain and easy to fetch the customer records along with the revenue report due to the incorporation of data mining and other analytics methodologies.
Conclusion:
Now a day Organizations are in rush to gather customer data form interactive websites, third-party data banks, government and online communities in order to collect a huge amount of data on customer information, their preference while choosing substitute product. That is where Big data put a great impact to strengthen the corporate strategy. For example, Facebook is telling about 30 billion contents are shared by users daily. On the other hand it is now possible to bring the customer contact and social media feeds in a one place. The weak side of a business scenario comes up when data collection is something hard-to-find. With concept of Big data what was once proprietary information, now can be accessed from public to private resources. With the further advancement of the analytical tools, it becomes possible to allow more effective and efficient tool with least processing cost.Supply chain data offers different information along with RFID (Radio Frequency Identification) technology an organization capture the physical movement of products. This scope allows getting information on manufacturing sites, customers, suppliers and asset movement, revenue, customers in real time which helps the top most person of company to take decision. Hence it can help to develop new insights rapidly. It is also possible by incorporating data mining with the Big data to know the buying pattern while a large volume of data on customerand product and revenue movement are fetched. With the mining techniques, such as, classification allows to segregate collected data as per the similarities. With Classification Rule Mining, it became possible to indentify frequency of a particular product and even it becomes possible to find other frequent product that is coming frequently with a most frequent product. That in turn helps to mine the buying behavior of customers.
References
Bernardino, J. and Ribeiro, P. (2011). Open source Business Intelligence: an alternative to proprietary tools. International Journal of Electronic Business, 9(3), p.219.
Bi, Z. and Cochran, D. (2014). Big data analytics with applications. Journal of Management Analytics, 1(4), pp.249-265.
Chugh, R. and Grandhi, S. (2013). Why Business Intelligence?. International Journal of E-Entrepreneurship and Innovation, 4(2), pp.1-14.
De Ville, B. (2006). Decision trees for business intelligence and data mining. Cary, NC: SAS Institute.
Duan, L. and Xiong, Y. (2015). Big data analytics and business analytics. Journal of Management Analytics, pp.1-21.
Glenys Vahn, G. (2014). BUSINESS ANALYTICS IN THE AGE OF BIG DATA. Business Strategy Review, 25(3), pp.8-9.
Heinrichs, J., Lim, J. and Hudspeth, L. (2002). Teaching Strategic Marketing Models with Web-Based Business Intelligence Tools: Innovative Guided Marketing Analysis. Journal of Marketing Education, 24(2), pp.135-142.
Huang, R., Sato, A., Tamura, T., Ma, J. and Yen, N. (2014). Towards next-generation business intelligence: an integrated framework based on DME and KID fusion engine. Multimedia Tools and Applications.
Kirkpatrick, R. (2013). Big Data for Development. Big Data, 1(1), pp.3-4.
Leeuwen, J. and Timmermans, H. (2004). Recent advances in design and decision support systems in architecture and urban planning. Dordrecht: Kluwer.
Leonard, P. (2013). Customer data analytics: privacy settings for ‘Big Data’ business. International Data Privacy Law, 4(1), pp.53-68.
Mayer-Schönberger, V. and Cukier, K. (2013). Big data. Boston: Houghton Mifflin Harcourt.
Ohlhorst, F. (2013). Big data analytics. Hoboken, N.J.: John Wiley & Sons.
Rohweder, J. (2005). Middle Mississippi River Decision Support System. Reston, Va.: U.S. Dept. of the Interior, U.S. Geological Survey.
Vera-Baquero, A., Colomo-Palacios, R. and Molloy, O. (2013). Business Process Analytics Using a Big Data Approach. IT Prof., 15(6), pp.29-35.
Zhao, J., Fan, S. and Hu, D. (2014). Business challenges and research directions of management analytics in the big data era. Journal of Management Analytics, 1(3), pp.169-174.
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