Introduction

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.

Week one

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.

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.