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What are the four levels of big data analysis system? in view of this problem, this article introduces the corresponding analysis and solutions in detail, hoping to help more partners who want to solve this problem to find a more simple and feasible method.

The construction of big data system of enterprises can be divided into four levels, each level can be a progressive relationship, although the business leadership is different, but the construction ideas are the same.

I. data basic platform

Basic data platform construction work, including the construction of basic data platform, data standardization, the establishment of data warehouse, data quality, unified business caliber and so on.

The data of many companies can not be used effectively, on the one hand, the data is scattered on the servers of the products of various departments, and the data of each business system is not connected; the other is the lack of unified data specifications, and the business system data is reported according to their own caliber and understanding habits, and without standardized SDK and reporting protocols, it is difficult to build a high-quality data warehouse.

The construction of big data platform architecture is not a high-end technical activity, the embodiment of the value of the entire platform, in fact, requires the cooperation of all departments of the company, is an interdependent relationship. For example, the establishment of the key data index system needs to be refined from the business indicators of various departments and recognized by the business departments. Common key indicators, such as new users in marketing business, effective new users, active conversion rate, cumulative retention, channel effect and so on. For example, the sales department, daily sales, monthly sales, rebate percentage and so on.

Data report and Visualization

In the first level, by standardizing the data index system, uniformly defining and unifying the dimensions, we can easily design standardized configurable data reports and intuitive visual output design. including finance, sales, supply chain and other data categories.

Basic query report: from the grass-roots business and daily work, the function plays a role in a specific work, such as sales performance query, commodity inventory query, on-the-way inventory query, purchase order query and so on.

Management analysis: not limited to a specific work, covering a certain work module of the relevant personnel. For example, manager performance management Kanban, inventory management, abnormal store management and so on. This kind of report is based on the daily management work, by viewing this kind of data report to monitor the current status of the responsible business, to find problems, mainly for decision-making assistance.

Theme analysis: different from daily management reports, this kind of reports are more targeted and proactive, need to analyze a certain module and theme, through the analysis of report data to find and think about problems.

Each category is aimed at different levels and different purposes. The basic report is used for business personnel to query, and the management report is used for management analysis to make decisions and report to superiors. Topic analysis is used to analyze problems and develop business.

Third, fine business analysis

Some businesses need fine management, such as the operation of Internet e-commerce, so the concept of "growth hacker" is put forward. Based on the establishment of data platform and visualization, the existing sales user behavior and income data are analyzed, and daily, weekly, monthly and various thematic analysis reports are output. Take the Internet as an example, the common data analysis work is as follows:

1. Product analysis and optimization through Ahand B test

two。 Funnel model is used for user touch analysis, such as the transformation of advertisements from exposure to activity.

3. Real-time feedback of marketing promotion activities

4. Business long-term health analysis, such as product growth and health analysis from user flow model and product life cycle

IV. Strategic Analysis and decision

Strategic analysis and decision-making are more based on the analysis of business level and the analysis of major decision changes, these decisions often need the support of a large number of data and indicators, but rely on reports and experience in the past.

If enterprises want to implement the big data system, the suggestion is to use machines to monitor business operations, and on this basis, let people do experience analysis and strategic judgment that human beings are better at.

The answers to the questions about what are the four levels of big data's analysis system are shared here. I hope the above content can be of some help to you. If you still have a lot of doubts to be solved, you can follow the industry information channel for more related knowledge.

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