Data Interpretation, Analysis and Visualization: Finding the Right Tool for the Job - Essay Sample

Abstract

There has been a vast confusion between companies based on data interpretation, its analysis techniques, and visualization tools to use. It creates difficulty in understanding which tools and techniques to use depending on the type of data, industry domain, and the type of results desired to conclude anything since different approaches can also lead to vague or false positives or might lead to its accuracy as compared to one another. It becomes hard to decide what suits best. This paper will address this issue and how the difficulty can lead to unnecessary errors where the data looks vague. In this paper, it will discuss where the difference lies between data analytics and data visualization and how information is being interpreted and analyzed and the steps involved in each of the processes. The main idea is to determine the feasibility of these methods and interpreting their style of results. However, I believe many programming languages will get more accuracy. There have been cases where visualizations have made it possible to perceive it differently, especially when it comes to more business decisions than of technical choices. This paper will discuss these techniques concerning feasibility and requirement metrics and helps in getting a better understanding of the methods to get to the desired results, though both data visualization and data analytics go hand in hand since if only one is involved it becomes time-consuming and if the other is removed it becomes less accurate.

Is your time best spent reading someone else’s essay? Get a 100% original essay FROM A CERTIFIED WRITER!

Save 25%

Introduction

Background

Data visualization and interpretation of data are needed in today's business world. It allows the business executives to make decisions quicker through those visualizations and dashboards in a shorter span of time with the information they have on it. It is a part of business intelligence to know how the business is being affected and to propose strategy, their after-effects, threats, and opportunities, which is why historical data is needed to mark huge trends on the dashboards. For this, a variety of data visualization software packages are used for those who plan to work in the current business environment. Students want to get enrolled in business school and learn various statistical and data analysis, due to the growing number of employment opportunities which specializes in analyzing the data with the help of numerous methods being used by companies. These tools are well researched and maintain high level of efficiency and accuracy in Academic and corporate World (Pattanaik,2017).

Problem Statement

Research requires numerous methods to analyze the data for its purpose to conclude problem-solving, optimization, system modeling, data analysis or interpretation, etc. In typical research or a professional assignment, there is some point where there is a need to use an analytical tool to make some progress in working before concluding a statement. However, a wrong selection of a method or a tool may lead to misleading results or false positives. It is a fact that for any research problem or a situation, a researcher must select an analytical tool or a methodology to support his statements and choose the one most suitable for his topic. "But usually, if we hold a hammer than all problems seem to be a nail to us." The tool selection should not be based on our expertise, a recommendation from colleagues, or the latest trend found in the literature, internet, etc.(Pattanaik,2017).

"The meaning of the term Analytical tools refers to all the mathematical, statistical, heuristics and algorithms, modeling and simulations and soft computation based non-physical or non-tangible approaches for problem-solving, optimization, data interpretation, decision making, and analysis." Depending on the complexity and the size of the problem or chunk of data, either a tool, programing language, or a manual calculation is required to arrive at the inference(Pattanaik,2017).

Researchers at different levels of skillset, there may be researchers who are more compliant with the tools, others may be in the statistical calculation, and the rest can be in a programming language, etc. However, if they are unsure of tool selection, then several sources come into their help since either of the approaches might give them different results depending on the software complexity, time is taken to run the program, features, etc. (Pattanaik,2017). "Sometimes, colleagues or co-scholars discuss the problem to suggest some tools from their knowledge and expertise. The researchers then start to google to find out the substance behind the suggestions and search some similar alternatives in their comfort zone" (Pattanaik,2017). Ultimately it is often noticed that the researcher must take the last call in the selection of the tool. The main aim of providing illustrative examples using software tools, methodologies, codes is to bridge the gap between theoretical knowledge and practical application from the theoretical perspective. "Selection of tool for a research problem will be more prudent by understanding the application procedure on software or otherwise" (Pattanaik,2017).

It becomes too time-consuming these days to test a tool or software by designing and implementing to get the desired results and again use a different tool to validate and compare the results depending on the type of results, time consumption, complexity, easy understand ability, and fulfilling the purpose of the study. The scenarios are different, as well as the kind of data that would help in figuring out what type of analytics is essential, which becomes a problem sometimes in case of any urgency for a specific study.

Purpose Statement

Data is one of the most valuable assets of any organization. Still, data alone is worthless if there are no supporting technologies used in mining it, processing it, organizing it, and analyzing it. The value in data is not in each byte, bit, or data field, but is in the results which are obtained from it and which are insightful. Through this, it has become possible for a variety of business intelligence solutions to collect different data from across various departments and serve many purposes (Wood R. (2018). Due to reasons of efficiency and particularity, tying all these solutions to a single business intelligence tool is the most appropriate thing to do as it would lead to more accurate, broad, and complete delivery of the business performance.

Visualization makes the data simple and easily understandable by the human brain hence making them more aware of the data performance metrics. Data visualization does not change the data itself, but there is a need for further analysis. It is not easy to consume information presented through two-dimensional tables since the mind tends to polish over them scanning for highest and lowest values hence missing the details entailed in between. That is not a problem with data visualization. Its problem is quite the opposite since the visuals are, in most cases, compelling as the pictures of the metrics in question are drawn literally (Wood R. (2018).

Research work is classified as exploratory, descriptive, diagnostic, and hypothesis testing (Pattanaik,2017). Further, there will be comparisons between two different approaches like descriptive and analytical, applied and fundamental, quantitative and qualitative, conceptual and empirical, etc.(Pattanaik,2017). Regardless of the type of research work, a varied degree of research accuracy proven by various analytical tools. For example, "the statistical design of experiments drastically reduces the experimental cost, effort and time while keeping the research inference unchanged." The need for improvement, prediction, pattern findings, search for the best solution, multi-criteria decision making, hypothesis testing, solving problems using heuristics, etc. will always remain the central aspect of any typical research activity. Hence, selecting the best suitable analytical tool from numerous alternatives is an essential step for the success of the research.

Since all professionals need to utilize the best tools for their tasks, individuals incorporate learning from their own experiences of their careers while inexperienced look for guidance through research and learning (Brittain, Cendon, Nizzi &Pleis,2018). Many journals offer their preferences based on tool popularity, costs, community support, data handling, easy to understand features of the software, time taken by the programs to run (Brittain, Cendon, Nizzi &Pleis,2018). Sometimes articles often include biased qualifiers to market that tool, which are not measurable enough. The potential tools for scientists differ a lot. The initial selection is based on public information as well as tools from the methodologist university (Brittain, Cendon, Nizzi &Pleis,2018). The public information includes research from renowned sites for various data scientific-analytical research projects and articles. If a researcher searches on google, he will find numerous websites that have journals, articles, blogs, video links which provide such information and knowledge of advantages of each tool and methods. "An article by KDNuggets included Python, R, and SAS in the top 4 tools for analytics and data mining" (Brittain, Cendon, Nizzi &...