1, data culture
Data cultivation is the basis of effective data analysis, and not all data can be used for data analysis. Enterprises should pay attention to the quality of data accumulation while paying attention to the amount of data, combine the awareness of data cultivation with the task requirements, and implement a top-down data cultivation mechanism.
For example, many enterprises realize the importance of informatization and digitalization, and put the deployment of business intelligence BI on the agenda. However, in the planning of BI project, it is easy to find that enterprises simply do not have the conditions to deploy BI for data analysis and visualization. The reason is that data is missing, errors are frequent, the system database of relevant business departments is not built, and business data is lacking, which is the consequence of not cultivating data.
Data Warehouse-Pike Data Business Intelligence BI
In order to cultivate high-quality data, we must make a data training plan in advance and mobilize all employees of the enterprise to complete the data management mechanism. This is not something that can be completed in a short time, but requires employees to produce and manage data in accordance with unified processes and norms in their daily business activities, persist for a long time, precipitate data in business activities, and gradually fill the key databases of enterprises in accordance with standardization, process and standardization.
Of course, it is not only mandatory for employees to perform data training tasks according to regulations, but also to establish a perfect reward and punishment system, taking data as daily assessment indicators. At the same time, enterprises should also deploy business information systems, so that employees in different departments of enterprises such as finance, sales, production and operation have tools for data training, automatically transmit data after completing business activities, and precipitate data in daily business processes and processes into the background database of the system.
2. Analytical methods
Analysis method is an important means to effectively use data and realize data value. Without data analysts and skilled analytical methods, even the best data can't be transformed into valuable information. Before data analysis, data analysts must master mainstream analysis methods, such as comparative analysis, quadrant analysis, trend analysis, descriptive analysis, predictive analysis and so on.
For a simple example, human beings are naturally sensitive to the size of numbers. Take a set of data without any logo and show it at a glance, and people will analyze their size differences. If these data are interrelated, then this is an effective comparative analysis.
Analysis Method-Peco Data Business Intelligence BI
Generally, comparative analysis method is adopted, which is usually used to compare the differences of business under different conditions in a selected time area and analyze whether the business has increased or decreased.
For example, the sales volume of 202 1 in September in the above picture decreased compared with that in August. At this time, it is necessary to analyze in depth why chain sales will decrease. We can consider collecting the production quantity of products in March this year and March last year to see if the production chain is reduced, resulting in a decrease in sales. In the same way, we can also compare and analyze the supply chain, dealers and people flow. , and confirm what affects sales.
In a word, the advantage of comparative analysis is that it can clearly analyze the differences between different values, so as to get the reasons behind these differences.