What are the key points of the data analysis report

1. Determine the audience and purpose of the report

No matter what type of data analysis you write.

Report, you must first figure out who the report is for, and different audiences have different expectations for a data analysis report.

2. Clear framework and thinking

As the most important part of the output of data analysis conclusions, an excellent data analysis report should be able to accurately reflect your analysis ideas and allow readers to fully receive your information, so when making a report, the framework and ideas should be clear.

For example, when we get an analysis problem, it is impossible to find the reason behind the problem at once, and we need to use various means to disassemble and analyze the problem until the final conclusion is reached, at which time we may use the analysis frameworks such as MECE, PEST, and AAARRR.

3. Ensure the accuracy of data

If the data is not accurate, the results of the analysis are meaningless, and the report will lose its value, so it is necessary to pay attention to whether the data is reliable when collecting and integrating data, and verify the caliber and scope of the data.

4. Make the chart more direct

How to explain the relationship between graphs and tables, between graphs and graphs, and how to express the problems reflected are all things that need to be understood when doing data analysis charts. Many attentive leaders and specialists will ask questions about your data analysis and conclusions, because the current situation and the future are their primary concerns. Therefore, the presentation of data charts should also reflect your analysis ideas, not just to show the data.

1.Data acquisition

Data acquisition seems simple, but it is necessary to grasp the business understanding of the problem and transform it into a data problem to solve, to put it bluntly, what data is needed, from which angles to analyze, define the problem, and then carry out data collection. This part requires data analysts to have structured logical thinking.

2.Data Processing

Data processing requires mastering efficient tools: Excel basics, common functions and formulas, pivot tables, VBA program development equations; followed by Oracle and SQL Sever, which are indispensable skills for enterprise big data analysis; There are also distributed databases such as Hadoop, which also need to be mastered.

3.Analyze the data

Analyzing data often requires various statistical analysis models, such as association rules, clustering, classification, and models. SPSS, SAS, PYTHON, R, etcThe more the merrier.

4.Data presentation

Visualization tools are available in open-source Tableau and some commercial BI software, which can be mastered according to the actual situation.

1. What data to analyze.

Generally speaking, after determining the purpose of analysis, we can obtain data objectively, and then form a corresponding decision-making support strategy in the corresponding data framework system according to the data, so as to pave the way for subsequent data analysis.

2. When to analyze the data.

Generally speaking, data analytics basically permeates every aspect of the business, and data analysis needs to track the direction of the entire business operation. Many business practices need to be supported by data analysis.

3. Get data from **.

Relatively speaking, data analysis requires two kinds of data from the enterprise, the first is external data, and the second is internal data. Internal data includes the data accumulated and stored by the enterprise itself, and external data includes data from customers, as well as data from market research and industry scale.

4. Which data analysis tool to use to process the data.

Anyone who has done data analysis knows that there are many tools for data analysis, and there are different data analysis tools for different data analysis environments, so we can choose the tools suitable for this kind of data analysis after we determine what data we analyze. Generally speaking, the choice of data processing and analysis tools is a more important thing, and choosing a good data analysis tool can save a lot of time.

5. How to conduct data analysis.

Many people know that data is mainly analyzed by business, and the purpose of data analysis is to transform business topics into data problems, and then transform the results of data analysis into various scenarios. How to perform data analysis will depend on the specific situation, but the process from which it is carried out is within the framework of AMAT, so that the data analysis can be better carried out.

Through the above content, you must already know the specific content of data analysis.

1. Simple trend

Get real-time access to trends to understand the timely delivery of your business. Such as product type, business area (traffic factor), purchase amount, and the proportion of purchase amount to **business.

2. Multi-dimensional decomposition

According to the analysis needs, the indicators are decomposed from multiple dimensions. For example, the amount of product purchase, the scale of the business (to be quantified), the complexity of the product and so on.

3. Conversion funnel

Analyze overall and step-by-step conversions with funnel models following known conversion paths. Common conversion scenarios include different trends, such as the trend of timely delivery rate of different merchants.

4. User grouping

In the refined analysis, it is often necessary to analyze and compare the ** quotient group with a specific behavior; Data analysis needs to take multi-dimensional and multi-index as the grouping conditions, optimize the ** chain in a targeted manner, and improve the stability of the ** chain.

5. Examine the path carefully

Data analysis can observe the behavior trajectory of the first business and explore the interaction process between the first business and the company; This can lead to identifying problems, inspiring ideas, or validating hypotheses.

6. Retention analysis

Retention analytics explores the correlation between user behavior and return visits. Generally speaking, the retention rate we talk about refers to the proportion of "repeated behaviors" of "new and new merchants" over a period of time. By analyzing the retention differences of different business groups and the retention differences of different functional business providers, the optimization point of the ** chain is found.

1. Market supply analysis and market supply**

Including the current asset industry market supply estimate and the future asset industry market supply capacity.

2. Market demand analysis and asset industry market demand**

Including the current market demand estimate of the asset industry and the future market capacity and product competitiveness of the asset industry. Survey analysis, statistical analysis, and correlation analysis are commonly used.

3. Analysis of market demand level and market demand in various regions

That is, according to the characteristics of each market, population distribution, economic income, consumption habits, administrative divisions, best-selling brands, productive consumption, etc., to determine the needs of different regions, different consumers and users, as well as transportation and sales costs.

4. Market competition pattern

It includes the analysis of the main competitors in the market, the position of each competitor in the market, and the main competitive means adopted by the industry.

5. Estimate the product life cycle and saleable time of the asset industry

That is, the time required by the market, so that the production and distribution activities and the market demand are most appropriately coordinated. Through market analysis, the future demand, variety and duration of the product can be determined; product sales and competitiveness; Product specifications, varieties change and update; regional distribution of product demand, etc.

1. Visual analysis

The users of big data analysis include big data analysis experts and ordinary users, but the most basic requirement for big data analysis is visual analysis, because visual analysis can intuitively present the characteristics of big data, and at the same time, it can be very easy to be accepted by readers, just like looking at pictures and speaking.

2. Data mining algorithms

The theoretical core of big data analysis is the data mining algorithm, and various data mining algorithms can present the characteristics of the data itself more scientifically based on different data types and formats, and it is precisely because of these various statistical methods (which can be called the truth) recognized by statisticians all over the world that they can go deep into the data and dig out the recognized value. On the other hand, because these data mining algorithms can process big data more quickly, if an algorithm takes years to come to a conclusion, then the value of big data is impossible to talk about.

3. Ability to analyze sexuality

One of the ultimate application fields of big data analysis is the best analysis, mining the characteristics from big data, through the scientific establishment of the model, and then you can bring in new data through the model, so as to improve the future data.

4. Semantic engine

Big data analysis is widely used in network data mining, which can analyze and judge user needs from users' search keywords, tag keywords, or other input semantics, so as to achieve better user experience and advertising matching.

5. Data quality and data management

Big data analysis is inseparable from data quality and data management, high-quality data and effective data management, whether in academic research or commercial applications, can ensure the authenticity and value of analysis results.

The role of the data analysis report is to display the analysis results and provide the basis for decision-making.

Data analysis report is the principle and method of data analysis, using data to reflect, study and analyze the current situation, problems, causes, essence and laws of a certain thing, and draw conclusions and propose solutions.

1. The role of data analysis is mainly to judge according to what data is analyzed, and its role is mainly to have a role in decision-making.

2. For example, if you are analyzing human resources data, it will have an effect on your recruitment and your human resource planning.

3. If you analyze financial data, it will have an effect on the company's operation, profit analysis, cost control, and so on.

4. For example, if you analyze the big data of customer browsing, at this time, it will have an effect on marketing decision-making and marketing product research and development, in short, you should determine its role according to the field of data objects studied.