Data Governance and Data Quality Management

Data is, as we all know, the essential component of Bentley Microstation corporate digital transformation, and the integration of organizational data, growth of organizational competitiveness, and achievement of business innovation and industrial upgrading are the objectives of big data construction. Additionally, the quality of the data will determine how much of a role it can play. Big data will lead to poor decision-making and possibly hazardous outcomes without high-quality data. High-quality data is the cornerstone of the enterprise's business capabilities since improving data quality is to consolidate the outcomes of big data development.

What is management of data quality?

1. Data caliber

Data quality in the business environment must satisfy the particular requirements of CDE Solution provider the business situation while also serving the demands of data consumers. The two components of data quality are the data's inherent quality and the process's inherent quality.

The quality of the data itself is well understood; for instance, the data must honestly and accurately reflect the actual course of business, the data of any business operation has not been missed, there are numerous constraints on the data, and such constraints cannot be mutually exclusive, and so forth.

The process of data quality is the usage of data in line with industry standards, such as data storage, which involves determining whether the data is properly and safely stored on the suitable media to prevent harm from outside forces. Of course, data storage is only one step in the data usage process; there are also capture, transmission, application, and deletion steps, which together make up the many phases of the data life cycle.

2. Quality Data Management

In order to ensure the improvement of data quality, the management level of the organization must be improved and elevated. These management activities include identifying, measuring, monitoring, and CDE solution alerting of various data quality problems that may be triggered by the data in each stage.

In other words, data quality management is not a one-time data governance approach but rather a continuous cycle of management process that integrates methodology, management, technology, and business. Given that data governance is a lengthy process, it illustrates on the one hand that it is challenging for enterprise data to meet the requirements for use all at once. On the other hand, it highlights the significance of data quality as well as the fragmentation and triviality of data quality work.

Root-cause analysis of issues with data quality

Partners who have worked on BI or data warehouse initiatives are undoubtedly aware of the fact that business and technology frequently argue over and place blame for the reasons of data quality issues. In many circumstances, the organization will refer the technical department's attention to data quality issues so that it may identify and address them. However, as technology is ultimately to blame for the enterprise's data quality issues, the tech department staff will undoubtedly respond, "I do not take this pot!"

In reality, there are three key variables that impact data quality: technical, business, and management. In the following, we'll examine these three areas to determine what causes data quality issues.

technological elements

Data model design quality difficulties include: database table structure, database restrictions, data validation rules of the design and development that are irrational, preventing data entry from being validated or resulting in inappropriate verification, producing duplicate, incomplete, and wrong data.

Some data are collected from the production system, where there are issues with duplication, incompleteness, accuracy, etc., and where there is no cleansing process for these issues in the collection process, which is also fairly common. These are examples of data quality issues that exist in the data source.

Data collection failure, data loss, data mapping failure, and conversion failure result from issues with the data collection process' quality, including incorrectly set data collection points, frequency, content, and mapping relationships. Another issue is the data collection interface's low efficiency.

Data quality issues can arise during the data transmission process for a variety of reasons, including issues with the data interface itself, incorrect setting of data interface parameters, unstable networks, etc.

issues with the data loading process, such as issues with the setup of the data cleaning, data conversion, and data loading rules.

Problems with data storage quality include: irrational data storage design; restricted data storage capacity; artificial background adjusting of data; and data loss, invalidity, distortion, and record duplication.

Because the business systems are disjointed, there is a significant issue with data inconsistency between systems.

Operational facets

Uncertain business needs, such as unclear business rules and data definitions, prevent technology from creating an accurate and appropriate data model.

Data model design, data input, data collecting, data transmission, data loading, data storage, and other aspects will be impacted by changes in business needs, and even a little negligence will result in issues with data quality.

Business data input is not standardized, therefore if you're not cautious, basic data entry issues like case, full and half-corners, special characters, etc., might be recorded incorrectly. The accuracy of manually entered data is directly tied to the business workers who record the data. When these individuals work diligently and seriously, the data quality is generally high, and when they do not, it is bad.

Data forgery—you read that correctly—is data forgery. Operators may manipulate data in order to increase or decrease the assessment indications, which makes it impossible to verify the data's validity.

Management

cognitive difficulties. Poor data quality is acceptable in the enterprise because of enterprise management's lack of data thinking, high reliance on systems and light data, and belief in the omnipotence of the system.

There is no distinct department or position for data management, there is no process for data recognition, and when problems with data quality arise, it is impossible to identify the responsible party.

Data quality-related rules and mechanisms are lacking, as well as data planning and goals that are crystal clear.

Even when working with the same firm, separate business units may have different data entry specifications, which can lead to data inconsistencies or contradictions.

Lack of an efficient method for resolving data quality issues, inability to end a loop on data quality issues, and lack of a unified process and system for detection, assignment, management, and optimization.

Data quality issues cannot be identified since there is a lack of clear and effective control procedures for historical data quality verification and fresh data quality calibration.

CDE solution

10