Data is like water, water is everything, and it can destroy everything. How can we use big data and make the data operation more perfect?
The first trick: find the data.
In order to achieve business goals, many companies have spared no expense to invite data analysts, and aim to expand the territory through data operations. But not to ask the data analysts to sit back and relax, many people can only call it a data engineer, because they have not yet reached the level of analysts, empty theory, but no business sense, do not know what data to use to analyze I fell into the fog of the clouds.
They poured the untidy data that had not been sorted out into the CEO, and did not explain the meaning behind the data, what the customer's behavior was, what the meaning of the curves was, and so on. When the CEO got these huge amounts of fragmented data, they were in a state of collapse.
The CEO needs to know at a glance that the data reflects the ambiguity, what is the market trend? Instead of spending extra effort on the data to interpret the data. For commercial data, there should be an antenna that is as sensitive as an octopus. For example, when the number of elderly people in the past two years is rising, it can be predicted that the sales of products related to the elderly will increase.
Data analysts need to get in touch with the business department, even turn to the business department, understand the business department and develop business sense. What the CEO needs to look at is the analysis of the data, which can accurately grasp the direction of the market.
The second measure: communication data.
Many industries are hoping to make changes through big data. E-commerce has a natural advantage in data acquisition, but it is rarely perfected in data analysis. Today, physical business is paying more and more attention to data analysis. As a result, the company actively collected data, and later found that the data was very confusing and scattered, and I did not know what to use. The data could not be correlated, and the hidden content could not be analyzed. Gradually, the data died in the report.
At the time of data collection, the accuracy of the data needs to be ensured, and the standard and detailed classification are required. This requires the data analysts and the business departments to interconnect and avoid data collation. The data analyst must objectively analyze the data in the hands of the salesman to give the CEO a true analysis result.
Communication data is the communication between departments, the communication between departments and data, and the communication between data and data. In the business scenario, most of the time, the data between different dimensions such as passenger flow, transaction rate, collocation rate, customer unit price, efficiency, and experience rate are interconnected and analyzed to draw conclusions. For example, the passenger traffic doubled and the transaction rate decreased. It was found that the employees were labor-intensive and the reception capacity was insufficient.
In this move, the standard of statistical data of different departments is unified to ensure the smooth exchange of data between different departments, and the integration of data from different dimensions is a top priority.
The third measure: data operation and sharing.
Second, look for references. The data needs to be compared horizontally and vertically, and the results are different. For example, when a company conducts a promotion, it often needs to compare with the promotion range, passenger flow, transaction rate, and customer evaluation of the activity. Rather than comparing it to the usual sales day, data analysis is meaningless if you choose the wrong object.
Third, after data collection, of course, it is used for analysis and use. In this age of looking at the value, inappropriate forms of presentation can lead to understanding obstacles and misunderstandings in the analysis of data, such as the most primitive EXECL table, which can be regarded as presbyopia. A good form of data presentation helps decision makers understand the meaning of the data and make sound decisions.
The ultimate goal of data analysis is to find and solve problems, effectively acquire, use, share, coordinate, connect, and simplify data so that everyone can analyze and reasonably judge the data. This is the most ideal state when employees are Actively investing in such data analysis work, data operations have entered a virtuous circle.