Journal of Stock & Forex Trading
Open Access

What Role Does Data Analysis Play in Research?

Sohel Vazeer^{* *}Correspondence: Sohel Vazeer, Department of Computer Information Systems, Nanchang University, Jiangxi, China, Email:

Author info »

Description

Although there are many different groups, organizations, and experts who approach data analysis, most of them may be summed up in one broad definition. In order to gain relevant, valuable information that helps business decision-making, data analysis is the act of changing, processing, and cleaning raw data. The process provides valuable information and insights that reduce the risks involved in making decisions. These insights and statistics are usually displayed in charts, pictures, tables, and graphs. By evaluating what has occurred in the past or what would occur if we take that action, we may see a fundamental example of data analysis every time we make a decision in our daily life. In its most basic form, this procedure entails considering the past or future and making a decision in light of that evaluation [1]. A large portion of an analyst's job involves sorting through data. That is the exact definition of an analyst. However, the Information Age of today regularly generates a data that is sufficient to overwhelm even the most devoted analyst. Therefore, data analysis is essential in transforming this information into a more precise and useful form, which facilitates the analysts' work. Descriptive statistics, inferential analysis, and quantitative analysis are only a few of the many methods available to users of data analysis. Data analysis techniques and methods are two phrases that some experts interchangeably employ. Sometimes people even include the previously mentioned "data analysis types" in the mix, here we want to distinguish between the many types of data analysis and their diverse applications [2].

Importants of data analysis

Here is a list of the key factors why data analysis is so crucial in today's corporate environment.

Better customer targeting: You don't want to spend your company's limited time, money, and resources creating advertising campaigns that are aimed at demographics that are only marginally interested in the products and services you provide. You can decide where to focus your advertising efforts by using data analysis [3].

Will have a better understanding of target customer: Data analysis keeps track of how well your products and marketing initiatives are doing among your target market. Data can help your business understand more about the spending habits, disposable money, and most likely interests of your target market. Businesses can use this information to forecast the number of commodities they will require, set prices, and decide the duration of advertising campaigns.

Reduce operational costs: Data analysis reveals which sections of your company require more funding and resources, as well as which should be trimmed back or completely removed because they are not producing [4].

Better problem-solving techniques: Decisions made with knowledge are more likely to be effective. Data offers information to businesses. This progression's direction is clear. Companies use data analysis to help them make informed decisions and steer clear of expensive mistakes [5].

Receive more accurate data: Data is essential for making educated judgments, but there are other factors to consider as well. The relevant data must be correct. With the help of data analysis, businesses can collect pertinent, precise information that is then utilized to develop future marketing strategies, business plans, and realign the company's vision or mission.

References

1. Goodnight J. The forecast for predictive analytics: hot and getting hotter. Statistical Analysis and Data Mining: Data Sci. J. 2011 Feb;4(1):9-10.

   [Crossref][Google scholar].

2. Chalutz Ben-Gal H. An ROI-based review of HR analytics: practical implementation tools. Pers. Rev. 2019 Aug 23;48(6):1429-48.

   [Google scholar]

3. Flouris I, Giatrakos N, Deligiannakis A, Garofalakis M, Kamp M, Mock M. Issues in complex event processing: Status and prospects in the big data era. J. Syst. Softw. 2017 May 1;127:217-36.

   [Crossref][Google scholar].

4. Favaretto M, De Clercq E, Elger BS. Big Data and discrimination: perils, promises and solutions. A systematic review. J. Big Data. 2019 Dec;6(1):1-27.

   [Google scholar].

5. Liébana-Cabanillas F, Singh N, Kalinic Z, Carvajal-Trujillo E. Examining the determinants of continuance intention to use and the moderating effect of the gender and age of users of NFC mobile payments: A multi-analytical approach. Inf. Technol. Manag. 2021 Jun;22:133-61.

   [Google scholar].