In the era of data processing, data analysis has become the basic construction

2020 year , With information technology 、 Digitalization has become the symbol of the times , Digital economy has replaced the traditional economy and become a new pillar of the national economy with rapid growth , Data in this case , Its own value has also been recognized by all sectors of society , Became the successor land 、 Labour 、 The fifth largest factor of production after capital and technology .

According to the IDC Statistical prediction of ,2025 The total amount of data generated in China is expected to reach 48.6ZB, Occupy the whole world 27.8%. In the era of data processing , Data is information , That's the value . But to successfully realize data value , Use it on the ground , The most direct way is data analysis , Through data analysis, data can become value information , Provide information support for individuals and enterprises , therefore , In the visible future , Data analysis will become the infrastructure , Everywhere .

What is data analysis

The emergence of data analysis is because it is difficult for human beings to understand the information presented by massive data , We can't find the corresponding laws to correspond to the real things , We all know that data is of great value , But the value that cannot be used , It doesn't make any sense .

To solve this problem , Data analysis is constantly improved in the process of long-term data utilization , Simply speaking , Data analysis is to analyze a large amount of data collected and stored through statistical analysis , Summarize it 、 inductive 、 Understand and digest , To realize the utilization value of data , Play the role of data .

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at present , Data analysis continues to grow , And through big data 、 Data visualization and other forms are continuously extended , It has strong vitality . Take data visualization for example , The data analysis composed of pure data text has a high reading threshold , So in the process of development, data analysts began to take graphical means as the basis , The complex 、 Abstract and incomprehensible data are represented by charts , Convey information clearly and effectively .

Data visualization is an extension of data analysis , Analysts use statistical analysis methods , Convert data into information , Then carry out visual display . Let the finance in the enterprise 、 production 、 operating 、 Employees with different positions in different departments such as sales , Can get information through data visualization , Help enterprises develop better through data analysis .

What is the key to data analysis

seeing the name of a thing one thinks of its function , The key to data analysis is absolutely inseparable from “ data ” and “ analysis ” These two words , Data is the premise of analysis , Analysis is the means to realize the value of data , The two complement each other , Be short of one cannot .

1、 Data cultivation

Data cultivation is the foundation for effective data analysis , Not all data can be used for data analysis , Enterprises pay attention to the accumulation of data while , We should also pay attention to the quality of data accumulation , Combine the awareness of data cultivation with task requirements , Implement the mechanism of data cultivation from top to bottom .

for instance , Many enterprises are aware of informatization 、 The importance of digital construction , Business intelligence will be deployed BI Information construction has been put on the agenda . But in business intelligence BI When planning the project , It is easy to find that enterprises have not deployed business intelligence at all BI Conditions for data analysis visualization , The reason is the lack of data 、 Mistakes happen frequently , Relevant business department system databases have not been built , Lack of business data , This is the consequence of failing to cultivate data .

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Want to cultivate high-quality data , The data training plan must be made in advance , Mobilize all employees of the enterprise to jointly complete the data management mechanism . This is not something that can be done in a short time , Instead, it requires employees in their daily business activities , Follow a unified process 、 Standard to produce 、 Management data , Hold on for a long time , Precipitate data in business activities , To standardize 、 Process 、 Standardization gradually fills the key database of the enterprise .

Of course , Letting employees perform data training tasks cannot be enforced only by regulations , We should establish a perfect reward and punishment system , Take the data as the daily assessment index . meanwhile , Enterprises should also deploy business information systems , Let the enterprise's Finance 、 sales 、 production 、 There are data training tools for employees in different departments such as operation , Automatically transfer data after completing business activities , Daily business processes 、 The data in the process is deposited in the system background database .

2、 Analysis method

The method of analysis is to use data effectively 、 An important means to realize the value of data . If there is no talent in data analysis and skilled analysis methods , Even with the best data , Nor can it be transformed into valuable information . Before data analysis , Data analysts must master the mainstream analysis methods , For example, comparative analysis 、 Quadrant analysis 、 Trend analysis 、 Descriptive analysis 、 Forecast analysis, etc .

A simple example , Humans are naturally sensitive to the size of numbers , Take a set of data without any identification to show , People will analyze their size differences at a glance , If these data are related to each other , Then this is an effective comparative analysis .

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Generally, comparative analysis is used , Usually within the selected area of time , Compare the differences of business in different situations , Analyze whether the business has increased or decreased .

for example , Above picture 2021 year 9 Compared with the sales volume in January 8 Sales decreased in the month , At this time, it is necessary to deeply analyze why the month on month sales volume will decrease , You can consider transferring this year 3 Month and last year 3 Monthly production quantity , See if production is down month on month , Resulting in less sales . Empathy , You can also put the supply chain 、 Distributor 、 The flow of people and so on are compared and analyzed , Identify what is affecting sales .

All in all , 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 .

The basic steps of data analysis

The basic steps of data analysis include clarifying requirements 、 data collection 、 Data processing 、 Data analysis and data presentation .

1、 Clear requirements

Data analysis is the process of combining abstract data with actual business , In the actual data analysis process, you need to understand the business situation , Clear industry knowledge , Highly integrated with business .

So the first step of data analysis is based on data analysis requirements , Analyze business requirements , Split it into different levels 、 Tasks with different themes , According to the data indicators of the business 、 Labels etc. , Prioritize , Prepare for the next data retrieval .

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One to one correspondence between business indicators and data is the key to demand confirmation , The data analyst can confirm the indicators according to the data dictionary of the database or data warehouse 、 Labels etc. , Investigate the data quality , Conduct pilot evaluation , Maximize the accuracy of data analysis .

2、 data collection

Analysts before data analysis , The data needed for the task should be collected in advance , Make preparations before analysis . At this stage , Analysts can work with technicians , The indicators needed for subsequent data analysis 、 label 、 Dimension and other data are retrieved from the data warehouse , Prepare for data analysis .

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In the process of preparing data , Analysts can further confirm the business data , Communicate and cooperate with front-line business personnel , Confirm that the data and business indicators fit each other , The data is also consistent with business changes . Then you can think about the relationship between the data , Organize and mark key data .

3、 Data processing

Data processing refers to processing the collected data 、 Arrangement , In order to carry out data analysis , It's an essential stage before data analysis . This process occupies the most time in the whole process of data analysis , To some extent, it also depends on the construction of data warehouse and the guarantee of data quality .

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At this stage , The analyst can also use the indicators confirmed at the time of collection 、 label , Summarize the data into different topics , Data grouping operation , Facilitate the use of appropriate data in data analysis . Effectively extract valuable data from massive and chaotic data for analysis .

4、 Data analysis

After the preparatory work is finished , Data analysts can choose the appropriate method from the mainstream statistical analysis methods , Conduct actual analysis of the processed data , Extract the value information behind the data , Support the information decision-making of enterprise business and managers .

Data analysis - Parker data business intelligence BI Visual analysis platform

When analysts analyze data , Combine actual analysis with business activities , Produce data reports that are really relevant to enterprise development , Instead of mindless stacking of data and information . Besides , Analysts can also use charts , Conduct visual analysis , Avoid mistakes and omissions in the face of massive data .

5、 Data presentation

Analysts put business intelligence BI The complete page in the data visualization designer is divided into different sections 、 level , Display the data in layers and blocks , As much information as possible . At the same time, designers should also pay attention to the division of data 、 Priority of information , In the overall visual design , Put the core data indicators in the most important position , Occupy a large area , The remaining indicators are spread around the core indicators in order of priority .

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Of course , In the actual visual analysis process , The data requirements given by the management personnel may be more , It is required to show as much information as possible on the same page . At this time, designers need to consider the key information when meeting the requirements 、 Divide the data into more levels on the basis of balanced layout space and simplicity and intuition .

Visualization screen - Parker data business intelligence BI Visual analysis platform

Last , Back to data analysis itself , Analysts can choose to attach the information they think from the business logic to the completed visual chart , Help users better distinguish the meaning of the chart . thus , The data analyst can start to make the data analysis report , Complete the data to information conversion process , Realize the effective transmission of data value .