OIM7502-B Business Data Analytics

Assignment 1 – (OIM7502-B Business Data Analytics)

Literature Review

According to Mihaela Laura ,2018 in the recent few years there has been a certain number of changes happening in the Data Analysis sector in case of the business. As it was found that in the past few years the explosion of interest happened in case of the big data analysis in case of the business. The role of data analytics is very much important as it helps to optimize the performance of an organization. The introduction of this kind of data analytics helps to make appropriate business decisions which helps to analyses the customer satisfaction based on the products and the services.  The scope in this field of Business Analytics is growing in a rapid way which helps to improve or enhance the mainstream of a business of any kind. This kind of exploitation is associated with the proper focus, skillful people and best managemental promise. On the basis of the studies, it was also found that the total growth rate of this kind of Database analytical support system will grow in a rapid way in the upcoming few years. It was also found that a proper kind of decision making is also very much associated with this kind of product-based feature. The addition of this kind of Business Analytics helps to reduce the risks which are associated with decision making factors of the business. The main working principle of such data analysts is to emphasize those data analytics techniques in the form of the research. The role of a Data Analyst is to help the Organizations to understand their customers, to personalize the Organizational behavior-based content and to create the content-based strategies to develop new kinds of products as per the requirement. There are mainly four kinds of Business Analytics that are present in the market such as Descriptive Analytics, Prescriptive Analysis, Diagnostic Analytics and Predictive Analysis.

OIM7502-B Business Data Analytics 1

Figure 1: Types of Data Analytics in Business (Source: https://towardsdatascience.com/why-data-analytics-is-gaining-hype-in-the-21st-century-b7b1ca289f09)

According to Shahriar Akter,2021 Organizations use company analysis to make data-driven decisions. Company examination gives businesses a great outline and understanding of how organizations can become more effective, and these experiences will empower such business upgrades and mechanize their cycles. It is not surprising that information-driven organizations, as well as those that use business investigation, outperform their competitors. The reason for this is that the knowledge gained with the help of the business investigation enables them to comprehend the reasons why specific outcomes are generated, investigate more successful business kind of processes, and even forecast the likelihood of specific outcomes. Business investigation also provides adequate assistance and inclusion for organizations hoping to make the best proactive decisions. Business investigation also enables organizations to mechanize their entire dynamic interaction in order to convey ongoing responses when required. The actual benefit which is associated with the business examination is that it helps to aid in the acquisition of fundamental business knowledge. In general, it helps to accomplish this by introducing the necessary information. This goes a long way toward determining a more effective, yet simple, dynamic. One area of business investigation that aids any organization in achieving immediate success is proficiency. Business examinations have played an important role in assisting businesses in increasing their productivity since their inception. Business investigation examines a large volume of information quickly and in such a way that it can be dissected. This enables organizations to make the best decisions. In whole the addition of the data analyst in the business helps to provide a bunch of the vital information which are very much essential or very much required to optimize the growth of the business in each and every segment. As in the present time there are a lot of software’s are out there which can collect, analyses and provide the data in a very short period of time and much accurately so due to that reason, the results generated more cost effectively and efficiently as well.

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Figure 2: Process of Data Analytics (Source: https://towardsdatascience.com/why-data-analytics-is-gaining-hype-in-the-21st-century-b7b1ca289f09)

According to Soraya Sedkaoui ,2018 The study of dissecting raw information to make decisions about that data is known as information examination. A large number of information examination strategies and cycles have been computerized into mechanical cycles and calculations that work over raw data for human use. This kind of the study of dissecting raw information to make decisions about that data is very much important in case of the business and is known as information which is associated with the examination procedure. Information investigation is a broad term that encompasses many different types of data analysis. Any type of data can be subjected to information investigation methods in order to gain knowledge that can be used to further develop things. Patterns and measurements that would otherwise be lost in a sea of data can be uncovered using information examination procedures. This data could then be used to improve cycles and increase a company’s or framework’s overall productivity. The rise of massive information and testing organizations has completely altered the business jungle gym. Massive data and the use of information investigation are becoming increasingly common, particularly in organizations looking for new approaches to cultivate more intelligent capacities and address issues in powerful cycles. Working with massive amounts of data and employing a progression of information examination methods necessitates strong multidisciplinary abilities and knowledge of “measurements, econometrics, software engineering, information mining, law, and business ethics, among other things.”

OIM7502-B Business Data Analytics 3

Figure 3: Characteristics of Data Analysis (Source: http://gildan-bonus-content.s3.amazonaws.com/GIL2586_BigData/GIL2586_BigData_BonusPDF.pdf)

According to & Samuel Fosso Wamba 2020, this is the fourth current uprising time where colossal data greatly influences connections, since the commotion of affiliations, stages, people and progressed movement have changed the determinants of firms’ new turn of events and reality. A relentless gigantic progress for huge data has been gained from scholastics what’s more educated authorities, since enormous data appraisal prompts basic data and development of innovative headway of endeavors and affiliations, changing economies in bordering, public and overall level. In that particular circumstance, data science is depicted as the mix of major standards that advance information and data acquired from data. The methodologies and applications are used to look at major data to help associations in understanding their present situation additionally in settling on better decisions on time. Nowadays, the colossal increment of data through the Internet of Things (industrious choice of related contraptions, sensors besides phones) has added to the rising of a “data driven” period, where massive data examination are used in each space, for example, Health care, Agriculture, energy and improvement of establishment, sports, monetary viewpoints and affirmation, food and transportation and every world economy. The improvement of open information is an obvious model from one side of the world to the next, while huge amounts of information moving out of the data comes from information assessment processes. In that specific situation, most affiliations are assembling, overseeing and disengaging information for key business choices influencing colossal information. It is associated with the capacity to make it  due to the obliteration and return again is irrefutably vital for affiliations and is portrayed in a  huge amount of resources. The possibilities which are associated with the colossal information assessment are tremendous and the advantages which are associated with the driven affiliations are fundamental determinants of the evaluated power and development execution. Regardless, there are huge obstacles to taking on a data driven technique and getting enormous information through epic extents of data.

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Figure 4: Credit Risk Framework “(Source: http://gildan-bonus-content.s3.amazonaws.com/GIL2586_BigData/GIL2586_BigData_BonusPDF.pdf)”

Research Scenario

In the present time as the technologies are developing in each and every sector so due to that reason, in this era of digital evaluation most of the Organizations are trying to implement the advanced sort of technologies in order to improve their business and to promote it worldwide. With the addition of this kind of the new technologies and with the increasing amount of data of an individual Organization it is getting very hard to use the traditional set of approaches to evaluate the all-total data analysis process. This particular research is based on the Organization named “Orion star sports and outdoors”.  They are having different types of product line from the children to adults in the segments like sports products for children, different kinds of clothes and shoes, different other outdoor accessory products and many more. Being having such huge number of accessible products and for its proper distribution all round the world it is very much important to use different kinds of the Data analysis method so that, they can analyze about the areas they have to focus on as the implementation of such data can reduce costs, increase benefit, and improve usability, resulting in extraordinary business growth. For the Organization like the Orion star the implementation of such help to gain a better understanding of consumer preference, which can assist them in developing extra compelling marketing systems.

Implementation of the SAS Software Scenario

OIM7502-B Business Data Analytics 1.1

 In this article, humans will discuss five key ways that information examination is critical to a complicated business improvement procedure. In this particular research-based paper a Company’s Orion database has been evaluated with the help of the SAS Horizon. SAS software is considered as a command driven software package which is mainly used in the case of data visualization and to perform different kinds of statistical analysis. In short one can say that, to compare the rate of the growth and to note down certain changes in the marketing field it is very much essential to implement things like this to devolve a proper understanding about the future growth.

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Compared to any other software’s that are available in the market the software called has more advanced analytics data structure. The generated data is more associated with the data management system, a new kind of database intelligence and it is also very much helpful in order to predict any kind of predictive data analytics. The SAS horizon can be used both with the help of the SAS programming language and with the help of the different kinds of graphical interference. The most important thing is that, with the help of this kind of the SAS software it can access any kind of dataset of any format.

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The datasets include different types of SAS tables, Database files and other different kinds of the Microsoft excel tables. The use and the implementation of such software like the SAS leaves a very important effect on the business-based platforms. The use of such software-based platforms helps to manage and helps to manipulate the existing number of datasets and make certain amounts of changes in the dataset where it is necessary. While conducting a business it is very much important to check all the important aspects which are associated with this to analyst and to evaluate the performance base criteria.

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This kind of data analytics can be done for both the Organization and the Employees point of view.  Organization management teams have to provide objective data on staff members’ subtleties, and how they are going to perform as well as drawing in themselves to be developed by the organization. As a result, an explanatory index contains relevant data, and their actual quality is considered, which will be investigated with a knowledge agreement. SAS programming will be used in one such investigation to break down the information with tables, statistics, and diagrams. In the preceding section, a few methods of different experts are shown in order to fully understand their discernment in terms of information rational with AI innovation. In the case of the Country_Lookup a total 238 number of the observation has been done with respect to the two main variables named as Country_Key and the Country_Name.

OIM7502-B Business Data Analytics 1.5

According to the analysts, information is considered as good material that necessitates a few stages to improve the depiction of the investigative process. These stages include data gathering, information inspection, data rebuilding, data disposal, data investigation, and so on. The bridge step is crucial when trying to gather raw data. If there is an error in gathering information or if the information is insufficient, the as a whole investigation may result in disappointment. As a result, the process of collecting data is an essential and crucial stage of the investigation.

OIM7502-B Business Data Analytics 1.6

On the basis of the above attached picture, it was found that, the Outdoors product line is considered as the most preferred among the other variables. On addition to that in case of the Product _line dataset comes the Clothes and shoes and different other kinds of the sports activities. The crude information had to be precise enough just to allow the investigation interaction to take place. In the following stage, primitive data is acquired for examination and removal of irrelevant information from the informational collection. Furthermore, decisions are commonly assumed to be the foundation of interesting and informative outcomes.

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As a result, unseemly outcomes may be weak in an independent direction. Whether there is any struggle in selecting various parts of a business strategy, a data arrangement can give better results to trying to make any advances. Researchers have also found that companies gain a huge benefit from making moves if a specific circumstance occurs in the business.  The deployment of such data analysis helps to Lead Generation, Value addition, to improve the process of the supervision and to develop different kinds of the Prospective kind of planning according to the requirement or as per the current market-based scenario which is associated with that particular kind of business.. The SAS horizon is arguably considered as one of the most widely used statistical software for both business and the industry purpose. With the help of the SAS tool the generated datasets can be easily retrieved which in the long run will definitely help analyses those statistical data structures.

OIM7502-B Business Data Analytics 1.8

Being such an analytical based software it is also associated with Data Modelling, forecasting and the proper kind of analysis of the data with the help of different kinds of Machine Learning and AI. The estimated total selling of any product plays a very important role, as this the result of this kind of the data analysis helps the Organization of Orion star sports and outdoors to know its actual position in the word market and also helps to find out the areas in which the Company is needed to implement basis changes so that it can improve its rate of success in the upcoming years.

OIM7502-B Business Data Analytics 1.9

On the other hand the dataset named as the Employment payroll is associated with the sections like the Employee ID , the gender of the employee , the estimated salary of each of them, the date of birth , the date of joining of that each individual employee, the marital status of both of them and the number of the dependents as well.  In here, a total of 425 datasets have been provided on the basis of which the total analysis has been done. Furthermore, with respect to the Employee info the associated datasets are the address of each of them and the tagged phone numbers of them. The other dataset which is associated with the total data analytical process are of the products which are sold by the Organizations, the category of those products, the groups which are associated with them, the tagged name of them, to which country it is needed to supply, the ID and the name of the individual supplier. The output results are also needed to be generated with the help of the leveling process.  In case of the Employee_payroll dataset a total 424 number of the Observations have been done with respect to the eight number of variables named as the Birth_Date, Employee_ID, Dependents,Employee_Gender,Employee_Hire_Date, Martial_Status, Salary and Employee_Hire_Date.

OIM7502-B Business Data Analytics 1.10

With the help of the SAS kind of the software the data analytical values can be generated more accurately. As for this particular Organization the given datasets are associated with a huge amount of data is it will be very much difficult to analyses all those things with the help of traditional sort of methods, due to these different kinds of the Data Analytical software’s like SAS are used to give a clear bit of idea about the situation of the Organization according to the current market scenario, the sectors where the Organization need to more focus on and about the growth the Organization in the upcoming years. Furthermore, on the basis of the comparison in between the Customer_Gender and the Customer_Age_Group it was found that, in case of the Male the maximum amount of the customer is of the age group of 31-45 years, later comes the age group of 61-75 years.46 – 60 years and 15- 30 years of age group respectively.

On the other hand, for the Female customers the total number of the Customer age group is of 15-30 years ,31- 45 years, 46-60 years and 61- 75 years respectively.

OIM7502-B Business Data Analytics 1.11

Recommendation for SAS Based Platform 

SAS is a software application that is used for statistical data analysis and data management. It is only available for Windows-based working frameworks. It is widely acknowledged as one of the most widely used measurable programmer systems in both academia and industry. SAS is a set of software tools for analyzing data. It easily integrates with SQL and Connect, allowing SAS to use a social network of insights as nothing more than a data source. A It’s cloud-based, powerful, and adaptable examination process that converts raw data into common, time, and restricted formats. It may have been tailored to specific corporate difficulties, such as preventing blackmail, identifying risks, comprehending client wants, and strengthening distribution networks. Because it provides a wide range of object parts, such as asset assessment, IoT exams, autonomously coordination, and financial matters, SAS has become particularly popular in dissecting and fathoming consumer desires and requirements. The SAS, on the other hand, has a number of challenges. Because the equations are not open source, they are not accessible to the general public.

Reference List

Journals

Raguseo, E. and Vitari, C., 2018. Investments in big data analytics and firm performance: an empirical investigation of direct and mediating effects. International Journal of Production Research, 56(15), pp.5206-5221.

Smys, S. and Joe, C.V., 2019. Big data business analytics as a strategic asset for health care industry. Journal of ISMAC, 1(02), pp.92-100.

Mikalef, P., Pappas, I.O., Krogstie, J. and Giannakos, M., 2018. Big data analytics capabilities: a systematic literature review and research agenda. Information Systems and e-Business Management, 16(3), pp.547-578.

Clayton, P.R. and Clopton, J., 2019. Business curriculum redesign: Integrating data analytics. Journal of Education for Business, 94(1), pp.57-63.

Akter, S., Michael, K., Uddin, M.R., McCarthy, G. and Rahman, M., 2020. Transforming business using digital innovations: The application of AI, blockchain, cloud and data analytics. Annals of Operations Research, pp.1-33.

Sivarajah, U., Irani, Z., Gupta, S. and Mahroof, K., 2020. Role of big data and social media analytics for business to business sustainability: A participatory web context. Industrial Marketing Management, 86, pp.163-179.

Fay, M. and Kazantsev, N., 2018. When smart gets smarter: How big data analytics creates business value in smart manufacturing.

Dong, J.Q. and Yang, C.H., 2020. Business value of big data analytics: A systems-theoretic approach and empirical test. Information & Management, 57(1), p.103124.

Grover, V., Chiang, R.H., Liang, T.P. and Zhang, D., 2018. Creating strategic business value from big data analytics: A research framework. Journal of Management Information Systems, 35(2), pp.388-4

Mikalef, P., Pappas, I., Krogstie, J. and Pavlou, P.A. eds., 2020. Big data and business analytics: A research agenda for realizing business value. Elsevier.

Yeung, J., Wong, S., Tam, A. and So, J., 2019, July. Integrating Machine Learning Technology to Data Analytics for E-Commerce on Cloud. In 2019 Third World Conference on Smart Trends in Systems Security and Sustainablity (WorldS4) (pp. 105-109). IEEE.

Chuang, S.Y., Sahoo, N., Lin, H.W. and Chang, Y.H., 2019. Predictive maintenance with sensor data analytics on a Raspberry Pi-based experimental platform. Sensors, 19(18), p.3884.

McCarthy, R., Ceccucci, W., McCarthy, M. and Halawi, L., 2019. Alpha Insurance: A Predictive Analytics Case to Analyze Automobile Insurance Fraud using SAS Enterprise Miner. Information Systems Education Journal, 17(2), p.20.

Patil, S., Nemade, V. and Soni, P.K., 2018. Predictive modelling for credit card fraud detection using data analytics. Procedia computer science, 132, pp.385-395.

Hassan Zadeh, A., Schiller, S., Duffy, K. and Williams, J., 2018. Big Data and The Commoditization of Analytics: Engaging First-Year Business Students with Analytics. E-Journal of Business Education & Scholarship of Teaching, 12(1), p.120.

Chawla, G., Bamal, S. and Khatana, R., 2018. Big Data Analytics for Data Visualization: Review of Techniques. International Journal of Computer Applications, 182(21), pp.37-40.

 

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