Significance And Challenges Of Big Data Research In Retail Industry Essay.

Discuss about the Significance challenges of big data research.

The entertainment industry mainly specializes in providing the customers with films, music and books that are based on audio. Most of the companies operate on a global manner and the base of customers is particularly high in countries like Germany, United States, the United Kingdom, France, Japan, India and Australia (Wamba et al. 2015).

Most of the customers purchase the products individually such as the films, audio books and the songs along with the packages that are up for subscription. The subscription package allows the customers in downloading a number of songs and films with the help of internet for a limited period (Kim, Trimi and Chung 2014). An example of this would be the system that is prevalent in the UK. The customers can go for the Premier Package where they can download 50 films and books along with 100 songs for a period of one month at a price of £150. The customers can also read a book or watch a film at one-tenth of the price as well.

The use of online streaming from the internet is also done by the customers for whom most of the companies have delivery channels consisting of cable TV operator, mobile phone and the internet. The customers have the option of choosing their method of payment such as the subscriptions based on an annual or a monthly system of debit. Most of the companies purchase the products in bulk quantities from the record companies at a decided price, which is then used by the customers by paying a price as well. This helps the companies in maintaining their margin of profit as well (Kaisler et al. 2013).   

Most of the companies take the help of various applications so that the business activities can be carried out in a smoother and efficient manner. This involves the use of .NET-based system that is developed in a customized manner along with trading through multimedia, subscription management and sales order processing. The companies also take the help of enterprise resource planning (ERP) system as well (Jin et al. 2015). 

The retail industry is one of the first industries that invest hugely in integration and the collection of data of customers in the data warehouse. It helps the retail companies in taking better decisions, as the data is available to the organization regarding the customers. Since most of the decision makers do not have the ability in accessing the data whenever required, most of the companies hire consultants so that it can help in solving the data needs on a short-term manner (Marr 2015). In most of the cases, it can be seen that the data that is extracted is from the same source and is accessed by different departments without any strategy of information delivery. This had negatively affected the organization, as the reports that were being presented by the managers based on the various sources of data were lacking the integration (Hilbert 2016).

Rationale of the Study

The strategies and the needs of the business help in building the business intelligence and the warehouse of the data. The data warehouse is a technique in data analysis that helps in supporting the decision of the businesses by encouraging the managers so that the examination of the data can be carried out in a better way (Segarra et al. 2016). The repository way of collecting the data helps in effectively measuring the effect of the different combinations, which is inclusive of the supply chain, preference of the customers, geographic and demographic features as well, which helps in assisting the process of customer retention by the analysts. The retailers can make use of the levers such as assortment, allocation and replenishment of the products along with the pricing and promotion of the products so that it can help in optimizing the performance of the companies (Amoore and Piotukh 2015).

This particular research will help in outlining the theories that are used by the industries as well as in academics to understand the function of data warehousing in the relevant industries. The process of research will help in focusing on the development and the design of the data warehouse along with business intelligence that is inclusive of the analysis of data and presenting it in a better way so that te tools of reporting can be used in an efficient manner. The system of business intelligence will be impossible without the use of data warehouse. 

The research process will be based on the following aims and objectives:

1. To examine the important of data warehouse along with system of business intelligence in the entertainment industry
2. To develop the data warehouse and the business intelligence system in the entertainment industry
3. To examine the decision tools that will help the decision makers in taking better decisions

1. How to measure the importance of data warehouse and business intelligence in the entertainment industry?
2. What are the tools that need to be developed for data warehousing and business intelligence system?
3. How can the decision makers take better decisions through the help of various tools?

According to Kimball et al. (2015), Business Intelligence (BI) helps in delivering the accurate information that will be useful for the decision makers in taking appropriate decisions within a specific time so that the decisions can be taken in an effective manner.

Data warehouse is the system that that helps in consolidating and retrieving the data in a periodical manner from the source so that it can be used for the purpose of analysis. The process of updates is done in batches and not during the transaction process on a daily manner.

Kaisler et al. (2013) was of the view that Data Mart is a part of the data warehouse, which helps in storing the data within the electronic repository and is not part of the organization with respect to the daily operations that are taking place. These data is applied within a particular area of the organization.

Scope of Research

Jin et al. (2015) was of the opinion that Online Analytic Processing (OLAP) is a technology that helps in managing, storing and inquiring of the data so that it can help in supporting the various uses of business intelligence.

Extract, Transformation and Load (ETL) is a system that consists of various processes in cleaning, transforming, combining, archiving and structuring the data so that it can help in using the data in the data warehouse. 

Segarra et al. (2016) stated that Data warehousing is the process that helps in the collection of data so that it can be stored in the database of the managers and can be integrated at different time so that it can help in the process of decision making.  The data that is collected from the various operations that are taking place within the retail companies can be stored and reconciled in the central repository so that it can help in extracting the information for the decision to be taken in a better way.

According to Amoore and Piotukh (2015), Data warehouse is the conglomeration of all the different data marts that are used within the retailing industries and the storage of information is done in through a dimensional model. The data marts help in delivering the objectives of business that are present in the different departments within the organization. Data mart is a subset of the data warehouse, which helps in representing the process of business through the star schemes that are available within the retailing organization.

Marr (2015) was of the view there are three levels that are present in the process of data modeling, which consists of high-level modeling that includes the features of entities, relationships and the relationships that are present with the entity. The second level consists of the mid-level modeling where the data is set by the departments and the third level that is low-level modeling helps in optimizing the performance within the organizations.

Kim, Trimi and Chung (2014) was of the view that the physical data is created using the midlevel model of data and extending it by using the physical and the main characteristics of the model. The data model that is physical in nature consists of series of tables, which are also known as relational tables.

Kimball et al. (2015) was of the view that it is a term that helps in analyzing the tools that are present in data. It helps the businesses in generating better information so that it can lead to effective decision making and in turn result in better profits for the companies. It helps the companies in analyzing the profitability ratio for the products along with the analysis of market and customers. The companies are also able to forecast and plan the products accordingly and analyze the channels of distribution so that it can help in effective marketing of the products and reach maximum number of customers for increasing the level of profits (Mackey and Gass 2015).

Aim and Objectives

It is an organized process through which the proposal is proceeded with. It gives the researcher a better point of view regarding the way of continuing with the process of research. it also enables the researcher in understanding the various techniques that are required in collecting and conducting the research process (Taylor, Bogdan and DeVault 2015).

There are mainly three types of investigation process that is taken in to the proposal of the research, which are as follows:

• Exploratory
• Descriptive
• Explanatory

The following process of research will be based on the descriptive type of investigation, as the researcher will be using various concepts and theories so that it can help in understanding the profitability of the retail organization by using the data warehousing technique. 

The method of collecting the data is divided in to primary and secondary sources, which helps the researcher in continuing with the process of research (Flick 2015).  The data that is primary in nature consists of the interviews and the surveys that are conducted by the researcher. The secondary sources of data consist of the books, journals and the websites of the companies so that the necessary documents can be accessed (Glesne 2015).  

The researcher will face some difficulties in collecting the information, as it can be manipulated and can be done in an incorrect manner. These hindrances have to be taken in to account by the researcher while conducting the process of research (Brinkmann 2014).

The researcher has to maintain certain restrictions so that it can help in conducting the research in an organized manner. The Data Protection Act, 1998 states that the researcher has to maintain privacy and the names of the participants cannot be disclosed for the the process of research. If the research breaches the Act, then he will be liable to bear the consequences of it. 

Reference List

Amoore, L. and Piotukh, V., 2015. Life beyond big data: Governing with little analytics. Economy and Society, 44(3), pp.341-366.

Brinkmann, S. (2014). Interview. In Encyclopedia of Critical Psychology (pp. 1008-1010). Springer New York.

Flick, U. (2015). Introducing research methodology: A beginner's guide to doing a research project. Sage.

Glesne, C. (2015). Becoming qualitative researchers: An introduction. Pearson.

Hilbert, M., 2016. Big data for development: A review of promises and challenges. Development Policy Review, 34(1), pp.135-174.

Jin, X., Wah, B.W., Cheng, X. and Wang, Y., 2015. Significance and challenges of big data research. Big Data Research, 2(2), pp.59-64.

Kaisler, S., Armour, F., Espinosa, J.A. and Money, W., 2013, January. Big data: Issues and challenges moving forward. In System Sciences (HICSS), 2013 46th Hawaii International Conference on (pp. 995-1004). IEEE.

Kim, G.H., Trimi, S. and Chung, J.H., 2014. Big-data applications in the government sector. Communications of the ACM, 57(3), pp.78-85.

Kimball, R., Ross, M., Mundy, J. and Thornthwaite, W., 2015. The kimball group reader: Relentlessly practical tools for data warehousing and business intelligence remastered collection. John Wiley & Sons.

Mackey, A., & Gass, S. M. (2015). Second language research: Methodology and design. Routledge.

Marr, B., 2015. Big Data: Using SMART big data, analytics and metrics to make better decisions and improve performance. John Wiley & Sons.

Segarra, L.L., Almalki, H., Elabd, J., Gonzalez, J., Marczewski, M., Alrasheed, M. and Rabelo, L., 2016. A Framework for Boosting Revenue Incorporating Big Data. Journal of Innovation Management, 4(1), p.39.

Taylor, S. J., Bogdan, R., & DeVault, M. (2015). Introduction to qualitative research methods: A guidebook and resource. John Wiley & Sons.

Wamba, S.F., Akter, S., Edwards, A., Chopin, G. and 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, pp.234-246.