How to do data reporting in operations? Here are 6 tips for you!

As the saying goes, a picture is worth a thousand words. To a certain extent, charts can show data patterns more intuitively and are easier to understand than text.

Today, I will analyze in detail the six methods of data reporting, so that all optimizers can show their talents in front of their leaders and reach the peak of their lives.

Data aggregation analysis is usually used to calculate the final result of a certain data. That is: the key core indicators that leaders are most concerned about.

For example: annual operating income, annual consumption costs, annual net profit, etc., which are corporate data indicators directly linked to corporate profits.

When presenting statistics, it should be clear at a glance and the core indicators of the enterprise that decision makers are most concerned about should be directly presented.

As shown in the figure below, the dashboard component is used to calculate the company's three core indicators: annual total revenue, annual consumption cost, and net profit. And through the data aggregation and analysis function of the dashboard, the "numbers" are clearly displayed, making the annual revenue status clear at a glance.

Development trend analysis is usually used to understand the financial status of a company over a period of time, and can intuitively show the trend of operating data or financial ratios within a continuous range.

On the one hand, it can analyze whether there are any abnormalities in the increase or decrease of data and discover possible problems of the enterprise; on the other hand, it can also help the enterprise predict its future financial status and judge its development prospects.

This type of analysis can directly present the development trends of the enterprise. Usually, a line chart can be used for analysis and statistics, with the horizontal axis representing time (year, month, day) and the vertical axis representing indicators such as operating income, cost expenditure, and profit margin.

As shown in the figure below, the left axis shows the trends of annual operating income and cost expenditure, and the right axis shows the trends of annual profit margin. Through this, the company's development trends and indicators such as profits are also clearly visible.

As the saying goes, only through comparison can we make judgments. Each data in the promotion data indicator system is compared with the indicator evaluation standard of the same nature through a certain indicator to reveal the promotion revenue status, promotion status of each channel and traffic status.

Generally speaking, the reference standards for comparative analysis include the following four aspects:

1. Time comparison: Compare with the actual data of the previous period and the same period last year
2. Entity comparison: Compare with the data of advanced enterprises or other enterprises in the same industry
3. Comparison with the planned or budgeted data line
4. Structural comparison: Compare the composition of two data on the basis of composition analysis and analyze the changes in financial indicators

When performing data comparison and analysis, it is recommended to use column charts and bar charts to compare the size of data; when performing data structure comparison, it is recommended to use cumulative column charts and cumulative bar charts for comparative analysis; this way, data comparison is more intuitive

In addition, other charts can be used depending on the specific circumstances of the analysis.

For example, you can use the high and low lines of a line graph to show the comparison of the highest and lowest conversion rates of several promotion channels . You can also use a radar chart (suitable for quickly comparing and locating weak indicators) to compare the classification statistics of the data indicators of the formula. (The following figures and text are based on the financial indicators of a certain clothing company)

The compositional structure analysis method can usually be used to conduct data analysis on the constituent elements of each item in an object.

For example, analysis of the consumption composition of each promotion channel and analysis of the management cost composition of each department.

Usually, to represent the composition of data structures, pie charts, donut charts, percentage stacked bar charts, and percentage stacked column charts can be used for element composition analysis. To represent the numerical value of element composition, stacked bar charts, stacked column charts, etc. can be used.

In addition, when you need to analyze both the composition structure and the hierarchical structure of data, a multi-layer pie chart is undoubtedly the best choice.

As shown in the figure below, the sales revenue distribution of each quarter can be easily counted through multi-layer pie charts. At the same time, the monthly sales revenue corresponding to each quarter is also counted at the quarterly level. On the right, the stacked bar chart shows the regional performance corresponding to each sales product.

In financial analysis, it is often necessary to show the progress of achieving a certain indicator or a certain task. For example, the department’s performance completion status, the progress of expense reporting, etc.

In order to more intuitively display the progress of various indicators and tasks, data progress can generally be displayed through stacked column charts, stacked bar charts, and Gantt charts.

When analyzing financial data, the factors that influence the data have two main different meanings:

1. Factor analysis, such as price and volume differences that affect sales revenue;
2. Various cumulative impacts from one indicator to another;

When analyzing data on multi-channel influencing factors, the waterfall chart is undoubtedly the best choice, which can quickly perform differentiated statistics on the data and accumulate statistics on the data at the same time.

The above are the six data analysis ideas we commonly use when doing data analysis and the corresponding data charts, which can be used as a reference for everyone's data analysis and statistics.

In addition, statistical methods of different levels for the same chart type will also affect the business meaning expressed by the data chart.

As shown in the following figures, both of them are statistical comparisons of the number of payment types and contract types, but the chart on the left focuses on expressing the number comparison of contract types for the same payment type, while the chart on the right focuses on expressing the number comparison of payment types for the same contract type.

The above are the six major methods for making chart analysis.

The author of this article is @Fanruan Data Application Research Institute. It is compiled and published by (Qinggua Media). Please indicate the author information and source when reprinting!

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