Data quality management for data quality management

Data quality management is a circular management process, and its ultimate goal is to enhance the value of data in use through reliable data, and ultimately win economic benefits for the enterprise.

Information factors: The main reasons for this part of the data quality problems are: incorrect description and understanding of metadata, various properties of data measurement (such as inconsistent data source specifications) are not guaranteed, and the frequency of change is inappropriate.

Technical factors: mainly refers to data quality problems caused by abnormalities in various technical aspects of specific data processing. Data quality problems mainly include data creation, data acquisition, data transmission, data loading, data use, and data maintenance.

Process factors: refers to the data quality problems caused by improper settings of the system operation process and manual operation process, mainly in the system data creation process, delivery process, loading process, use process, maintenance process and audit process.

Management factors: refers to data quality problems caused by personnel quality and management mechanism. Such as personnel training, personnel management, training, or improper reward and punishment measures resulting in management deficiencies or management deficiencies.

1. Business factors.

Front-line business personnel have always been the first to contact business data in the enterprise, and they are the producers and stores of business data. These characteristics determine that business personnel play a key role in data quality, and it is an important part that cannot be ignored.

The data indicator system is not closely integrated with the business, resulting in the acquisition of data detached from the actual business needs.

The business needs are not clear enough, and the enterprise does not form a set of fixed business processes;

Business personnel are prone to errors when manually entering data, and the quality of business data cannot be guaranteed;

Enterprises do not have standardized data storage rules, and business personnel have no data awareness when performing front-line business;

The sample size of enterprise business data is small, and IT personnel cannot find the problem data entered by business personnel.

2. Technical factors.

If the entire data process of the enterprise is divided into regions, the business personnel perform production and input, and the IT technicians are responsible for storage and output. It is also an important part of the enterprise data system, and technology will also have a profound impact on data quality.

The design of the data storage model is problematic, resulting in a large amount of duplicate data in the database;

Failure to process the data and eliminate problematic data, resulting in insufficient data accuracy;

If there is a problem with the data interface configuration, the database cannot obtain the latest business data.

There is a problem with the background design of the system, and the system crashes during peak hours, resulting in data loss and mismatching.

3. Management factors.

In the entire data quality management system, business and technical personnel are responsible for specific implementation, and managers are responsible for "top-level design". If there is a problem with the top-level design at the beginning, then no matter how hard the business personnel and technical personnel try, they will have little effect.

Managers have no data awareness, do not pay attention to the cultivation of data quality, and can only rely on experience to do things;

There is no unified planning of the business system of various departments of the enterprise, resulting in different data indicators and business data cannot be used;

Failure to handle the relationship between business and technical personnel leads to a lack of communication between the two and the data cannot be closed;

The lack of KPI indicators for data quality leads to the lack of division of labor and responsibility system for data quality processes.

Paco Data Business Intelligence BI Visual Analysis Platform.

1. Data quality analysis.

Data quality analysis is the most important part of data preparation in data mining, the premise of data preprocessing, and the basis for the validity and accuracy of data mining analysis conclusions.

The main task of data quality analysis is to check whether there is dirty data in the original data, which is the data that generally does not meet the requirements and cannot be directly analyzed. In common data mining, common dirty data includes: missing values; outliers; inconsistent values; Duplicate data and values with special symbols.

A: Reason for missing values:

1.Some information is not available, or it is too expensive to obtain.

2.Omission of Information.

3.The property value does not exist.

b: Effect of missing values:

1.Data mining modeling will lose a lot of useful information.

2.The uncertainty of the data mining model is more significant, and the rules contained in the model are more difficult to grasp.

3.Data with null values can throw the modeling process into a mess, resulting in unreliable output.

c: Handling of missing values:

1.Delete records with missing values, 2Possible values are imputed and not processed.

Box plot analysis for outlier analysis:

Summary. Hello, dear to your question (what is the difference between data management and data quality management) to provide you with the answer is as follows: the two are completely different.

The basic purpose of data processing is to extract and derive data that is valuable and meaningful to some specific people from large amounts of data that may be disorganized and difficult to understand.

Data processing is a fundamental part of system engineering and automatic control. Data processing runs through all fields of social production and social life. The development of data processing technology and the breadth and depth of its application have greatly affected the development process of human society.

Data management is the process of using computer hardware and software technology to effectively collect, store, process and apply data. The aim is to get the most out of data. The key to effective data management is data organization.

With the development of computer technology, data management has gone through three stages of development: manual management, file system and database system. The data structure established in the database system more fully describes the intrinsic relationship between the data.

It is convenient for data modification, update and expansion, and at the same time ensures the independence, reliability, security and integrity of data, reduces data redundancy, and improves the degree of data sharing and data management efficiency.

What is the difference between data-based management and data-based quality management?

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Hello, dear to your question (what is the difference between data management and data quality management) to provide you with the answer is as follows: the two are completely different. The basic purpose of data processing is to extract and derive data that is valuable and meaningful to some specific people from large amounts of data that may be disorganized and difficult to understand.

Data processing is a fundamental part of system engineering and automatic control. Data processing runs through all fields of social production and social life. The development of data processing technology and the breadth and depth of its application have greatly affected the development of human society.

Data management is the process of effectively collecting, storing, processing and applying data using computer hardware and software technology. The aim is to get the most out of data. The key to effective data management is data organization.

With the development of computer technology, data management has gone through three stages of development: manual management, file system and database system. The data structure established in the database system more fully describes the intrinsic relationship between the data. It is convenient for data modification, update and expansion, and at the same time ensures the independence, reliability, security and integrity of data, reduces data redundancy, and improves the degree of data sharing and data management efficiency.

Hopefully, the answers provided will help you<>

Data quality includes: accuracy, i.e., the proximity of the band plexus between a recorded value and its true value; precision, i.e., the level of detail with which the phenomenon is described; spatial resolution, i.e. the smallest discernible difference between two measurable values; Scale, i.e. a ratio between the distance recorded on the map and the real distance it represents; Error, i.e., the difference between a recorded measurement and its facts; Uncertainty, including spatial location uncertainty, attribute uncertainty, and data incompleteness.

Article 20 of the Regulations on the Quality Management of Construction Projects The geological, surveying, hydrological and other survey results provided by the survey unit must be true and accurate.

Data quality includes: accuracy, which is the proximity between a recorded value and its true value; precision, i.e., the level of detail with which the phenomenon is described; spatial resolution, i.e. the smallest discernible difference between two measurable values; Scale, i.e. a ratio between the distance recorded on the map and the real distance it represents; Error, i.e., the difference between a recorded measurement and its facts; Uncertainty, including spatial location uncertainty, attribute uncertainty, and data incompleteness. Article 9 of the Regulations on the Quality Management of Construction Projects shall provide the original information related to the construction project to the relevant survey, design, construction, engineering supervision and other units.

The original materials must be true, accurate and complete.

Legal Analysis: Data sock slip quality includes: accuracy, i.e., the proximity between a recorded value and its true value; precision, i.e., the level of detail with which the phenomenon is described; spatial resolution, i.e. the smallest discernible difference between two measurable values; Scale, i.e. a ratio between the distance recorded on the map and the real distance it represents; Error, i.e., the difference between a recorded measurement and its facts; Unbridging determinism, including spatial location uncertainty, attribute uncertainty, and data incompleteness.

Legal basis: "Regulations on the Quality Management of Construction Projects" Article 20 The geological, surveying, hydrological and other survey results provided by the survey unit must be true and accurate.