Table of Contents
This paper mainly explains the contents learned every week in detail through the weekly reflection. Thus, four topics are covered here:
1. Business Data Analytics
2. Data-driven decision making
3. Data analytic vs. Business research
4. Digging into data with data visualisation
These four topics will expose the role of Data in business operations. In the era of big data, the organisations' development has an increasingly strong demand for data because it can disclose information to help enterprises analyse, discover problems and find solutions. On the other hand, the concept and meaning of visualised data are also mentioned in the following part. This paper tries to reflect on the knowledge they have learned and their understanding of relevant knowledge through the use of Diep strategy:
1. Describe objectively what you
2. Interpret the insight
3. Evaluate what you have learned
4. Plan how this learning will be applied in practice
The weekly feedback base on the Reading Material, Lecture and Tutorial. An accurate description of what I have learned through these four topics; With the support of lecture and literature, I will explain my views, evaluate my understanding of the four topics and judgment of their value, and how to put them into practice inexperience.
In the first week of this semester, we learned the importance of data. As a student with a design background, my expectation for future courses is to maintain good design ability and extend my ability as much as possible. In particular, new knowledge can expand and enhance my understanding of design. My data expertise comes from my undergraduate study of communication design, which enables me to have an early version of the importance of applying data to the practical design scheme, ranging from typesetting to core decision making. The most important data collection is mainly from personal research or small questionnaire survey in the network environment, which is also the first step of the design project.
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This week's course makes me understand that data is complex, diverse and has the symbol of information disclosure (RMIT 2021). According to our needs, it can parse the important content behind these symbols or messages in different forms or ways (Bowne-Anderson 2018). Displays practical information that can be visualised and easily read. Therefore, data analysis is more than simply collecting information. Reading the article of Black (2019) makes me understand that data means potential problems and demands, development trends and more value from the business analysis within enterprises to the era of big data with the information explosion. Data collection and research in the business industry has gradually shown its importance and advantages, and it drives the development of the enterprise, business planning and decision-making (Sahay 2018). Every product released by the enterprise, every decision has the shadow of data analysis.
However, for a learning stage student without a formal job, these theories are all too macro. Last year, my friend and I considered a partnership to conduct a cross-border e-commerce business on pet supplies. At the very beginning, the problem we encountered was how to choose products effectively. In the front, we searched through pet chain stores and the Internet, which products are more popular with people, but this is not enough. In a market environment full of beautiful things, the most important thing is that we must have a competitive product. This is difficult for us to make decisions at the beginning stage. I once proposed a questionnaire survey to my partner, but my team member soon refuted it. Because the market already wants you to show what the customer wants. We need to collect more data in the existing market to help us identify the competitive products. Whether it is big data or business analysis, it may be crucial for me in the future. At the present stage, data is vital to help me find that people's demand for pet products must be of better quality, reasonable price, and value in the market.
This week's focus is on decision making and data. Decision-making is a way for enterprises to make decisions regarding the company to company, project and management. It is divided into three levels: operational, managerial and strategic decision making. Decision-making at these three levels affects the enterprise from grassroots building and core business operations to managers evaluating potential solutions to its operation problems and setting the right overall strategy and objectives (RMIT 2021). Data-driven decisions can help organizations better measure and improve situations. This concept means that enterprises need to access a large amount of complex information through different channels in the era of big data. Therefore, enterprises need employees to have good judgment to evaluate and analyze relevant data. Shah, Horne & Capella (2012) point out that employees who maintain a sceptical attitude are more likely to make correct decisions and make a more rational judgment and analysis. In general, enterprises should focus on training employees to integrate data information into decision-making more effectively and learn to think critically about the accuracy, deviation and quality of data (Shah, Horne & Capella 2012; Menon & Thompson 2016).
In my opinion, the theory proposed by Shah, Horne & Capella (2012) is in line with the elements of modern enterprises' sustainable strategic development. As we learned last week about the importance of data, enterprise development needs to combine data feedback. Still, at the same time, it needs to have critical thinking to analyze data. Managers and teams should more carefully consider the information required to solve problems and avoid mistakes caused by factors such as overconfidence and bias (MacGarvie & McElheran 2018). And strategically think about how to apply the most effective and most minor data to enterprise decisions and actions (Menon & Thompson 2016). Menon & Thompson (2016) put forward four steps: define, integrate, explore and test, avoid the influence of invalid information, and transform the value of valuable data to the greatest extent. Reading Shah and MacGarive's articles, I found that both pieces suggest that we keep a 'clear head' when confronted with large amounts of data. It means that we need to learn to think and listen to so-called 'opposing' points of view. At the same time, thinking backwards about the possibility of assuming that our opinion is wrong helps us build our critical thinking skills. This new concept will significantly impact my future career, greatly avoiding my overly one-sided and more rational analysis in a large amount of data.
I learned about data analysis and decision-making before earlier weeks, which is data meaning and how it works and affects business decision-making. This week, through the study of business research, I have a new understanding of this new concept and the knowledge I have learned before. First of all, I think business research is a prerequisite for decision making. In this week's Lecture, the definition of business research in Schindler's article (2021) is a systematic inquiry that provides information and information 'is quoted Insights to guide a specified financial decision '. Specific systematic investigation refers to the achievement of organizational goals in the following five steps (Schindler 2021):
1. Clarify the research question.
2. Design the research.
3. Collect and prepare the data.
4. Analyze and interpret the data.
5. Report insights and recommendations.
In this sense, the first step, clarify a research question, means that you need to be clear about the 'problem' and what information is required to solve it. Therefore, the question is divided into four parts: management, research, investigative, and measurement questions. My understanding of this theory is to divide the problem into several small problems, explore and make hypotheses step by step, and analyze additional information or data that need to be collected.
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Teams may mislocate the problem in the first step, but I think this is also a reverse assumption. When we face a problem, we also need to think about the impact and result of solving this problem. The wrong problem can help us to find better problems that can be erased or solved (Sullivan, J 2014). We need to keep critical thinking for the next three steps and make the most accurate analysis with the most effective and most minor data.
The systematic study of the sampling survey this week is also beneficial for my future career and technology because communication design requires in-depth research on customers' target audience in many cases. Different sampling survey methods are suitable for different situations. A good grasp of this knowledge will enable me to locate the target masses' needs more accurately.
With the development of times and technology, people's work efficiency improves, and visual data serves as the main driver to assist people in information transmission (Samuel 2015). As a student with a design background, I often need to use data visualization to display my design works in the projects I have studied in the past few years. For example, in the research part of the project, I have to design and display essential data characteristics to support my views and concepts. Therefore, when I learn this week's content, I find that it resonates with my past knowledge, and I understand more about it.
However, I often get into trouble in this part because too much data and information collection will affect my judgment. As a result, data collection may not be sufficiently targeted. Samuel (2015) points out that knowing the crowded goal at all times will help you figure out how to present the visual data. Visualization needs to revolve around the skill level of the audience (Stikeleather, J 2013). I agree with Samuel about the concept of data as an additional tool to support one's argument. It can prove the rationality and correctness of one's ideas or concepts. Also, it can be used as a highlight to help people remember or focus on important information. Before, I only knew Excel in a course about IT, but IT was far from what I learned this week.
In my past understanding, how data visualization depends on the type of data you want to show, such as trends or weights. Several products' data is more suitable for using a line chart to show its movement with the time change. For the proportion of several products at the time point, use a pie chart, etc. However, the ultimate purpose of these visualizations is to catch the audience's eye, and how to choose the visualized data depends on what you want to express objectively and unbiased through it (Stikeleather, J 2013). This is one of the strategies Stikeleather mentioned in her article in the Harvard Business Review. In my past projects, the numbers I have presented to suit my point of view may not be objective enough. Therefore, in future projects, I must always maintain an objective mindset and avoid giving 'selective' data. Not only would I lose the trust of the audience, but it would also mean that my opinions were not correct enough.
In the end, I learned a lot from the classes in the past few weeks. I have been in contact with some of the Bachelor course content, but only in the "know". Through the study of these weeks, I have a deeper "understand" of these theories and concepts. People value data in terms of revealing problems, trends, and validity. Decision-makers need to have critical and rational thinking in the face of data before making correct decisions. While using data visualization with an objective attitude means that people can improve data analysis efficiency, which also serves as evidence to support arguments. In short, the data itself does not have any meaning, only people put the data in the right place, and the right way to parse the data can highlight the significance of the data.
RMIT 2021, ‘Week 1: Introduction to business data analytics’, lecture notes, ECON1555, RMIT University, viewed 8 April 2021, < https://rmit.instructure.com/courses/77962/pages/week-1-lecture-and-tutorial-materials?module_item_id=3074488>.
Bowne-Anderson, H 2018, 'Your data literacy depends on understanding the types of data and how they’re captured', Harvard Business Review Digital Article, pp. 1-5.
Black, K 2019, ‘Business Analytics and Statistics’, First ed. Milton, Qld: John Wiley & Sons Australia, Chapter 1.
Sahay, A 2020, ‘Business Analytics: A Data-Driven Decision Making Approach for Business’, Volume 2, Business Expert Press, New York, NY, Chapter 2.
Shah, S, Horne, A & Capellá, J 2012, 'Good data won’t guarantee good decisions', Harvard business review, vol. 90, no. 4, pp. 23-25.
Menon, T & Thompson, L 2016, ‘How to make better decision with less data, Harvard Business Review Digital Articles, pp. 2-5.
RMIT 2021, ‘Week 2: Data Driven Decision Making’, lecture notes, ECON1555, RMIT University, viewed 8 April 2021, < https://rmit.instructure.com/courses/77962/pages/week-2-introduction-and-to-do-list?module_item_id=3111548>.
Sullivan, J 2014, ‘Getting the Right Data Scientists Asking the ‘Wrong’ question’, Harvard Business Review Digital Article.
Schindler, P. (2021). Business research methods, 14th edition. McGrawhill-Hill/Irwin, NY.
RMIT 2021, ‘Week 3: Data Analytics vs. Business Research’, lecture notes, ECON1555, RMIT University, viewed 8 April 2021, < https://rmit.instructure.com/courses/77962/pages/week-3-lecture-and-tutorial-materials?module_item_id=3118396>.
Samuel, A 2015, ‘How to Give a Data-Heavy Presentation, Harvard Business Review Digital Articles, pp. 2–5.
Stikeleather, J 2013, ‘How to Tell a Story with Data,Harvard Business Review Digital Articles, 1–2.
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