Many companies tend to pay a high price to ask a consulting company to analyze the overall operation of the company and generate a report. However, for most enterprises that already have data management, targeted data analysis can be carried out for a specific enterprise and a specific problem, and the problem can be solved from point to point. Nowadays, enterprises have more data sources and means to obtain data, and an effective enterprise data analysis report can obviously bring great value to the enterprise.
Enterprise data analysis reports can not only make judgments on the overall market environment and macroeconomic trends, but also go deep into every link of production and operation and every customer who consumes services to understand the real situation. For example, marketing data analysis can reduce costs and improve sales conversion by formulating refined advertising strategies. Ultimately, we solidify the results of big data analysis in the form of big data tools, so that our big data effectiveness can continue.
That is to say, truly valuable big data analysis reports can bring value to enterprises in all aspects such as medium and macro planning, micro/segment market analysis, program execution and strategy deployment.
The following will tell you the overall idea, framework and value of big data reporting for data analysis.
1. How to make a big data analysis report? What is the process of big data analysis?
A complete big data analysis process includes six steps: understanding business problems, understanding data, preparing data, analyzing data, producing analysis reports, and proposing solutions, and is a closed-loop and continuously optimized process. To simplify, it is to analyze what problems, what documents are required, what data processing is to be done, and conclusions are drawn and implemented. For enterprises, it may not be necessary to master difficult analysis and processing capabilities, but it is very important to master data analysis ideas and cultivate data management methods.
2. What do we need to study? What is the idea of big data analysis?
According to the function, it can be divided into the following 4 types:
1. Business/market analysis: analysis of the status quo and problem judgment of a certain business; market status analysis and development trend forecast of subdivided fields;
2. User portrait: understand the demographic characteristics of various users and the differences in user behavior of different groups of different products;
3. Competitive product monitoring: Do a comparative study on the functions, user scale and market conditions of similar products on the market;
4. Operation management analysis: analysis of major decisions in the operation process.
For the above types, we will discuss in detail the four typical analysis reports of our team, which indicators and dimensions need to be analyzed (the indicators are only listed and cannot be fully covered), and attach the corresponding data analysis report demo (the analysis tool is FanRuan Big Data). BI - FineBI).
Market Environment Analysis
User portrait
Competitive monitoring
Marketing Analysis
3. Data sources
Data sources can be divided into internal data and external data. Internal data can be the data of various systems of the enterprise, or the data obtained by burying, collecting, and integrating web pages/products. In general, there are also some external channels for obtaining data:
1. Web crawler data: collect relevant information on web pages through programs;
2. SDK data: The data automatically packaged and returned by the SDK in games and other applications, such as Umeng and talkingdata, are mainly integrated and processed and analyzed based on the SDK data;
3. Operator data: a large number of customer attributes and online behavior data in the three major areas of operation, business and management of the three major operators;
4. Third-party company data: statistical data generated by a large number of research activities of consulting companies;
5. Customized data: put forward requirements to data owners/collectors, and carry out data collection work according to specific conditions.
In addition, the value of the report and the accuracy of the analysis results are greatly affected by the quality of the data source. Therefore, when collecting and integrating data, it is necessary to pay attention to whether the data is reliable, and to verify the data caliber and data range.