Interviewer.
Hello everyone, today we have invited Jingbo Zhou, who works for one of the Big Four accounting firms in the world, to join us for an exclusive interview with a CDA holder. As a data analyst, can you give us an overview of what you do?
Guest.
My name is Jingbo Zhou, I graduated from St. Petersburg State University and currently work at Deloitte China as a data analyst. My job is to study the company's business trends and exceptions over time and make recommendations.

Interviewer: 

When doing company data analysis, what do you usually start from?

Guest: 

I usually start from the perspective of macro thinking classification. For example, for the current state of a company's operations, I would research from finance, sales, operations, human resources, and products to better understand the company's overall situation.

Interviewer.
What are the general aspects of doing research on the current state of a company's business?
GUEST.
When doing research on the current state of a company's business, we generally start with the following aspects.
Financial indicators: We will collect the company's financial data, including operating income, profit, cash flow, etc., and analyze these indicators to understand the company's financial situation.
Market indicators: We will collect the company's market data, including market share, customer structure, competitors, etc., and analyze these indicators to understand the company's market position.
Product indicators: We will collect the company's product data, including product sales, quality issues, customer satisfaction, etc., and analyze these indicators to understand the company's product status.
By analyzing the above aspects, we can get a comprehensive understanding of the company's business status and develop reasonable suggestions for improvement.

Interviewer.
Are the research methods the same for different industries? Are there any additional research directions for the accounting industry?
GUEST.
The research methods are not exactly the same for different industries, but the basic analysis process is similar. For example, we all have to collect and organize data, conduct analysis and model building, and finally draw conclusions.
For the accounting industry, we need to pay special attention to research in areas such as accounting and financial statement analysis. We have to conduct in-depth analysis of financial data in the context of accounting standards and company's actual situation in order to provide reliable information and recommendations for company's decision making. We will also focus on emerging research areas in the accounting industry, such as the application of artificial intelligence in the accounting field and big data analysis of financial statements. By exploring these areas, we can provide companies with a more advanced and efficient approach to financial analysis.
In our research work, we use various tools and methods, such as statistical analysis, machine learning model building, etc. Through these tools and methods, we can analyze data at a more granular level, thus providing more valuable information and recommendations for company decisions. We will also continue to improve our skills and experience through exchanges with industry peers to make greater contributions to the company's development.

Interviewer.
If there is an overall decrease in turnover this month and the data is abnormally attributed, how should it be analyzed?
GUEST.
First of all, we have to consider the problem from both macro and micro levels. At the macro level, we have to consider the impact of macro factors such as market factors, policy factors, force majeure factors on the company's turnover. At the micro level, we have to consider the internal factors of the company, we can split the turnover and look at it, for example, turnover = customer unit price * number of products, and then divide it into two parts for in-depth analysis, which can be analyzed from the perspective of the performance of the sales team, product stratification structure, customer satisfaction, etc.

Interviewer.
What indicators do you refer to when making recommendations for your company?
GUEST.
When making recommendations, I would find the Polaris indicators in conjunction with the industry the company is in, and then refer to other multi-dimensional indicators such as people, goods, and field. For example, if it is in SaaS industry, I will consider the overall operation of the company, first look at the important operation indicators every year, such as: sales, order volume, renewal rate, etc., and find the part that declines abnormally for in-depth analysis, specifically which type of product sales of the company declines or abnormally, whether it is related to the market situation, customer demand and other objective factors in that year, whether there is a cycle, and also study the competitors, etc. I will conduct a detailed analysis of these indicators and make recommendations based on the results of the analysis.

Interviewer.
What difficulties do you often encounter in your work? And how do you overcome them?
GUEST.
In my work, I often encounter problems such as large amount of data, unorganized data, mixed information, and complex models. To overcome these difficulties, I will use tools, such as Python, SQL, SPSS, R, etc., to handle the data better. I will also strengthen my thinking skills and improve my analytical skills through continuous learning, thinking and practice. At the same time, I will also learn from experience and learn from the experience of my predecessors to avoid repeating mistakes. For those who are new to analytics, you can read The Pyramid Principle. Structured view and dismantling of problems is the key to data business analysis.

Interviewer.

What difficulties and challenges have you encountered in your data analytics work in the accounting industry? How did you overcome these difficulties?
We need to understand the meaning of the data fields, the relationship between tables, etc. We need to understand as much as possible. The customer's data is huge and mixed, and it needs a lot of data cleaning to get the analysis data we want. Small TIPS, before doing the analysis, must be sure to figure out the company data logic, field meaning, business model, operation mode.

Interviewer.
What do you think are the research trends in the accounting industry? What changes do you think will happen in the research of accounting industry in the future?
GUEST.
As the degree of information technology increases, the work that the B-party can do for the A-party company gradually increases, such as data analysis, data quality inspection, IT audit and other work around the information system. In the future, there may be independent data processing platform, that is, the data into which we directly choose how to deal with data, and the data for modular analysis and presentation of visual charts, we will reduce the time of data processing, and strive to put more time on the analysis.

Interviewer.
What are the tools and methods that you use most often in your work? What do you see as the role of these tools and methods in research in the accounting industry?
GUEST.
The tools I use most often in my work are Python, SQL, R, and excel. these tools can be used for data analysis in most industries. These tools can help us save time in data processing when we encounter large volumes of data. The methods commonly used in data analysis start with the end in mind, trend method, comparison method, outlier analysis method, cluster analysis, etc.

Interviewer.
What interesting or meaningful experiences have you encountered in your work? What have you learned from them? How do you think these experiences have influenced your professional development?

Guest: 

A particularly interesting thing I encountered in data analysis work is to visit companies in the field, to understand the various aspects of production and operation, and to follow the front-line sales staff to visit customers, so that I can really understand the company's data, and it is also easier to find the problems in the company's operation and give analysis suggestions. In this process, I can see the different ways of operation and business model of the industry, which is a knowledge sink for me.