The big data analysis as an essential aspect in business intelligence

Table of Contents

Introduction. 1

Literature Review.. 2

Implications of Big Data Analysis. 3

Research Objectives: 3

Research Questions. 4

Review of Methodology. 4

Research Gap. 5

Conclusion. 5

Bibliography. 7


In this modern and mechanized world, the data is widespread. The information is available in open access in huge quantity. So, it is relevant to analyses the data and refine it so, that the business could get the exact data which is required (Booth, 2019).Correspondingly, the considerable technology has made it significant to sort out the information which is needed for the business. Today there is a revolution on social media dedicated towards the paradigm shifts of data availability, data analysis and data sources. Various operational business strategies focus on massive data availability. They refine the data and arrange it as according to the business requirement. There are multiple data analysis channels which sort the data and use the data for business intelligence purposes. The big data analysis has become an essential aspect in business intelligence today since the business intelligence practices enforce the business to get the most appropriate data from authenticated and reliable resources. The data is developed, analyzed and accessed. Business intelligence analyses the various data sources and scans the exact form of data that will drive the precise outcomes to form the study (Hart, 2018).

Business intelligence (B.I.)aims to improve business decisions and improve the effectiveness in the business standards through the excellent performance of the business organization. Business intelligence (B.I.) is a broad subject that focuses on the data structure, data availability and the data structure that will develop the complete construction of the data and analyses the figures that ensure data availability (Booth, 2019).Hence the data is available for effective decision making and business development.

Literature Review

There is various form of data sources such as questionnaires, interviews, structured data sources, unstructured data sources, complex data, statistical data sources, technical data, transparent data information etc. these are all sources of data which are available to the end-users. The data is available, and the users make effective and efficient use of these data sources (Bielski, 2017).

Studies have shown that there are many data users. A study showed that there are almost 5 million tweets per day, Facebook has more than 1 million clicks in an hour, Twitter receives more than 500 million tweets every hour, thousands of pictures are posted on Pinterest every hour, flicker has more 20 billion views this means that every Huan on the earth could be given two images afloat from flicker. So, social media has advanced. Today the growing trend of social media and the news spread on social media has a direct positive influence on the business world. The direction of youtube, bloggers, tic tuckers, Web, the other social media web sites have raised the trend of business economies. There are practical and efficient business trends that are based on available data (Mrdalj, 2019).

A survey conducted in 2018 showed that 38% of business organizations prefer to analyses their reports, assignments and make effective decisions for the business. 85% of business organizations rely on the business challenges raised through the data sources. Similar, businesses seek information for future trends, further business trends and upcoming business trends through these data sources. Since social media is advanced for data, so companies investigate the authenticated and most reliable sources of data availability. So, the implication of data is made through sound and dependable sources of data analysis (Hart, 2018).


The literature further showed that the data is obtained through surveys, through the internet, through research and other many sources that make effective and efficient uses of the data sources. So, the data availability and analysis of data are the domain which is less researched. There is very less research on the various sources of data collection. (Briggs, 2017)

Implications of Big Data Analysis

This study aims to probe into various methods of data collection, data analysis, data sources, and analyzing the multiple sources of data availability that define data availability. Research is required to identify the causes of data availability. B.I. Helps to understand numerous domains of data availability. B.I. Targets significant data sources as well as the forms of data that will be useful for the business. So, here is the active role of modern-day B.I. That targets the implication of significant data sources on the impacts of data management (Kar, 2018). The study will probe into the following research questions:

Research Objectives:

  • To investigate the significant factors for Implications of big data analysis
  • To see the impact of Business Intelligence (B.I.)
  • To probe the Implications of big data analysis on business intelligence
  • To investigate the future result of big data analysis on business intelligence

Research Questions

  • What are the significant factors for Implications of big data analysis
  • What is the result of Business Intelligence (B
  • .I.)
  • What are the implications of big data analysis on business intelligence
  • What is the future outcome of big data analysis on business intelligence

Review of Methodology

The methodology is the backbone of eth study. It decides the except the path of the study. This chapter of research shows what will be the direction of the study and what type of data it will drive. Three are many tools for data collection. Qualitative and quantitative techniques of research data collection. In this study, quantitative research techniques will be undertaken. Since the quantitative data analysis drives the exact outcomes for the study. The quantitative research targets the numerical form of data outcomes. It analyses the data and gets the respondents to feedback in the most appropriate way (Booth, 2019). The quantitative techniques include various methods, and in this study questionnaires, structured interviews will be undertaken. Since these are continent methods of data collection within the limited timeframe. So, surveys will be developed through articles. Questionnaire variables will be adopted from other sources. This will improve the validity and reliability of the questionnaires. The researcher will produce structured interviews. Since no specific journal promotes the formal interview sources. So, Researchers will develop formal interviews. The study tools will raise the effectiveness, reliability and validity of the research (Kar, 2018).

Research Gap

Today with the globalization impacts, the data sources are heterogeneous the data is available in bulk. The researchers must refine the data through various sources that will make the information effectively and efficiently use the data. Business intelligence (B.I.) aims to improve data to make it useful and efficient for business use. Further, Business intelligence (B.I.) aims to develop new challenges and new opportunities for various data sources in the business. Today, Business intelligence (B.I.) seeks to develop the most refined form of data that will be best suitable for the company. Hence the study will describe in detail about the research on business intelligence and the business data development (Graves, 2018).


In the end, it could be stated that Business intelligence (B.I.) makes the effective and efficient use of data from the data sources. The data is obtained from the day to day business operations that will make meaningful use of the data. The information is collected to perform business operations effectively. The information is collected, scanned, refined and analyzed for practical business use. So, B.I. has become an essential subject in research today. This study will investigate the developmental aspect of Business intelligence.


Rocha, M. K. (2017). Introducing Students to Business Intelligence: Acceptance and Perceptions of OLAP Software. Issues in Informing Science & Information Technology, 12–18.

Mrdalj, S. (2019). Would Cloud Computing Revolutionize Teaching Business Intelligence Courses? Issues in Informing Science & Information Technology.

Hart, M. E. (2018). System Brings Business Intelligence to Desktop. ABA Banking Journal.

Briggs, L. (2017). Magnum B.I.: Using Powerful Business Intelligence Tools, Two Districts Are Drilling Down to the Finer Points of Student Data, Where the Most Revealing Insights Lie. T H E Journal (Technological Horizons In Education),.

Graves, P. (2018). Need It Now! Banks Are Getting Serious about Business Intelligence. They Have to, to Cope with Market and Regulatory Demands. ABA Banking Journal.

Bielski, L. (2017). Picturing Strategy with Numbers? Improved Financial Reporting and Wider Use of Business Intelligence Help CFOs Fix Broken Financial Processes. ABA Banking Journal.

Geanie W. Hignite, M. A. (2018). An Analysis of Chinese Business Students’ Performance on a Test of Emotional Intelligence. College Student Journal.

Michael A. Moses, D. R. (2019). A Comparative Analysis of the Emotional Intelligence Levels of American and Chinese Business Students. College Student Journal.

Booth, C. B. (2019). The Return of Artificial Intelligence: Is A.I. Finally Ready for Business? The McKinsey Quarterly.

Kar, S. L. (2018). Business Performance Management: A Road Map for Utilities. Management Quarterly.

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