Essential for operations: 5 steps for data analysis!

As a data operator , where should we start when we encounter specific problems? What steps does a complete data analysis process include? In this article, Zhuge Jun will share with you the five steps of data analysis on this issue.

Five steps to data analysis

When we encounter problems at work, we often need to use data to solve them and use data to guide decision-making. In the process of implementing a complete data analysis, it can be divided into the following five steps in order: determining goals, collecting data, organizing data, analyzing data , and visualizing it.

1. Determine your goals

Before conducting data analysis, we need to determine the goal of data analysis and the measurable indicators based on our own business, break down the indicators, and find the smallest unit for collecting data. This will enable targeted data analysis, improve data operation efficiency, and avoid excessive data collection , which will result in the waste of useless data.

2. Collect data

Once we have determined the goal, we need to collect data in a targeted manner. The data collected here includes both the user's full life cycle data collected through embedding points, the data collected online by ourselves, such as industry data reports , and the manually sorted data obtained through visits or telephone calls. As for which specific data to use, it depends on the goal of data analysis.

3. Organize the data

After we collect the data, we need to organize it, especially when the data comes from many sources , such as data collected at embedded points, data collected online, and manually organized data. Sometimes there will be duplications, confusion, etc., so we need to organize the data to maximize the accuracy of the data.

4. Analyze the data

Zhuge Jun has shared data analysis with you many times. The eight data analysis models during the National Day are used to analyze data. It should be noted that in the process of data analysis, we must combine our own products and choose appropriate data analysis models. If necessary, we need to define our own analysis model. In short, the idea is: the method is here, how to use it is up to you.

5. Visualization

As a data operator, the results of data analysis often need to be reported to the leader and the entire team. At this time, we need to present the data analysis results visually, generally in the form of charts. Find out the business problems through data analysis and propose your own solutions. You not only need to know why, but also what to do.

Summary: The above five steps constitute a complete data analysis process, from thinking about the goal to the final visualization, from discovering problems to proposing solutions. As data operators, we need to have a holistic mindset and be able to grasp the data analysis methods from the entire process. We also need to pursue details and optimize each step. For example, how to collect data faster and more accurately can be used as an optimization goal. When we are able to perform data analysis with ease from both the overall and detailed perspectives, you are a qualified or even excellent data operator.

Source: Zhuge io Data Coach