In the era of data processing, data quality construction is the way for enterprises to survive

I once heard a very interesting sentence , Enterprises know that there are good and bad business quality 、 There are good and bad products 、 Information is of good or bad quality , Why do many enterprises just ignore the data , I think the data is of high quality , Can be used directly .

Digital age , Data is growing explosively in the digital society , According to IDC （IDC） forecast ,2025 The amount of data generated in China is expected to reach 48.6ZB, The proportion in the world is 27.8%. therefore , In the future digital society , The most valuable is data , The most important thing is data , What enterprises really need is to be able to be used , Generate high-value, high-quality data assets , This is the necessity of data quality construction .

What is data quality construction

Data quality refers to the adoption of certain rules 、 The standard evaluates the data , So that it can be used by users and enterprises , In data collection 、 Storage 、 transmission 、 Handle 、 Mining plays a role in the whole life cycle , Become a valuable data asset .

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Data quality construction can be subdivided into data quality management and data governance . Through data quality management , Enterprises can establish a complete set of business processes for data 、 collection 、 Storage 、 Handle 、 analysis 、 Mining and other data management mechanisms throughout the life cycle , Identify the data 、 classification 、 classification 、 monitor , Enable enterprises to promote business development through data , Improve the level of decision making .

Data governance puts more emphasis on remediation , Is a long-term means to improve data quality , This governance process will also work in every aspect of the data , Identify data problems and solve them , Compared with data quality management , The data governance team has greater authority , Can guide different departments , Assign data governance tasks , Jointly promote the improvement of data quality .

Data quality standards

Before data quality management , Enterprises need to determine a perfect data quality standard system , Evaluate the data quality against the standard , Then, according to the determined information, the task of data quality management is planned . Generally speaking , The standard of data quality has five dimensions .

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1、 integrity

Data integrity refers to whether the enterprise data is missing , There may be two reasons for missing data , First, the business process of the enterprise is not standardized , Missing data or missing records ; Second, the technicians did not improve the database settings , Business data cannot be completely stored in the database .

2、 accuracy

Data accuracy refers to whether there are exceptions or errors in enterprise data , Data exceptions or errors may be caused by the fact that enterprises often use manually recorded data , It is prone to record errors or misaligned data , It is also possible that the data is garbled , Or the data is too large 、 Too small, etc. do not conform to business common sense .

3、 timeliness

The timeliness of data refers to whether the enterprise data has a long utilization cycle , Long data utilization cycle will lead to data from generation to viewing 、 Handle 、 The analysis time is too long , For example, it takes two days to see the data in the daily business report , This reduces the value of the data , Even lose the effect .

4、 Uniformity

Data consistency refers to whether the enterprise data has a unified specification , The consistency of data may be the problem of data record specification and data logic , Data collection 、 Handle 、 The method of analysis is inconsistent with the standard , Cause the problem of inconsistent attributes .

5、 Uniqueness

Data uniqueness refers to the duplication of enterprise data , Data duplication usually refers to the data name 、 Repeated indicators , When querying business data , Duplicate data with multiple different data sizes , Not only redundancy , It also leads to uncertainty about which is the real data .

Key planning for data quality construction

Data quality construction needs to be a long-term system , It is a systematic project . Data quality construction requires enterprises to set up professional construction teams , From two aspects of data quality management and data governance , Cut in from different angles , Realize the efficient implementation of data quality construction .

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Before the construction of data quality , The first thing to do is to set up a data quality construction team with actual authority and sufficient experience , The team needs top management 、 Data quality talent 、 Data quality construction experts and business personnel with efficient execution . Only those with sufficient permissions 、 Experience 、 technology 、 management 、 The team that executes the elements , Enterprises can prepare for data quality construction .

After the establishment of the data quality construction team , Enterprises need to determine the strategic planning of the data quality construction system at the first time , The data quality construction team starts from the thought 、 Starting from culture, the enterprise will become a modern enterprise with data-driven business growth , And add data business indicators to the enterprise KPI Assessment index , utilize , Use the reward and punishment system to supervise employees , Establish data and business normalization , Establish data as the cornerstone of the enterprise .