What are the current mainstream data governance platforms?

Ruizhi data governance platform is a groundbreaking, one-stop comprehensive data governance overall solution completely independently developed by Yixin Huachen. Ruizhi is the only breakthrough product in China that has achieved full coverage of data governance scenarios, with nine core modules: metadata, data standards, data quality, master data, data assets, data security, data exchange, data processing, data life cycle, etc., which ensure the integrity, accuracy, consistency and timeliness of enterprise business data in the whole process of collection, aggregation, conversion, storage and application in an innovative way, and comprehensively tailor a data governance system that meets its own characteristics for customers.

Ruizhi has always stood in the top echelon in China, and has widely applied the latest technologies such as MQ, distributed computing, and Zookeeper. At the same time, it leads the development trend of the domestic industry

1. Automatic exploration of data quality, built-in conventional mathematical statistical algorithms support binding machine learning algorithms;

2. Intelligent construction of data relationships, based on stored procedures, SQL, and database definitions, automatically understand the relationship between data;

3. Advanced technologies such as active perception of asset catalog, activation and update to ensure that it becomes a well-deserved leader.

1.The essence of data governance.

Usability means that the data is available, credible, and quality-assured, and will not be biased by the accuracy of the analysis results, so that practitioners can make business decisions based on the data results with confidence; Integrity is divided into two aspects, on the one hand, it refers to the need for data to cover all kinds of data applications, and on the other hand, it means that there will be no loss of data assets due to data governance not in place, that is, it will affect the accumulation of data assets, which is also the reason why Sensors Data has carried out privatization deployment at the beginning of its business; Security means that the governance and sharing process needs to be secure and controllable, not infringe on user privacy, and not leave security risks to the organization.

2.The importance of data governance.

Data governance is the foundation of all data applications, and the quality of data governance directly affects the value of all data applications.

Whether it's data-based reports, interactive multi-dimensional analysis, or more complex personalized recommendations, all data applications need to have a good data governance outcome. Through the practice of this product, we found that its implementation cycle is generally longer than that of several other products, which is also because personalized recommendations have relatively higher requirements for data quality and accuracy.

Data governance is the foundation of the precipitation of an organization's data assets, and the quality of data governance directly determines whether the organization's data assets can be precipitated and whether they can give full play to their value.

Data governance is essentially the process of evaluating, directing and supervising (EDM) the data of an organization (enterprise or department) from collection and integration to analysis and management and utilization, and creating value for enterprises by providing continuous innovative data services.

The Data Governance Institute (DGI) believes that enterprises not only need a system for managing data, but also a complete system of rules and regulations. Data governance basically covers all data-related content in the enterprise, so the entire enterprise, including workflows, people involved, and technology used, needs to be carefully considered to ensure data availability, consistency, integrity, compliance, and security, and ensure high data quality throughout the data lifecycle.

Overall, the goal of data governance is to improve the quality and maximize the value of data. Specifically, the tasks of data governance include the following:

Build a flexible, standardized, and modular multi-source heterogeneous data resource access system;

Build a standardized, process-oriented and intelligent data processing system;

Build a refined data governance system and an integrated classification system for data resources of the organization;

Build a unified scheduling, accurate service, safe and available information sharing service system.

The domestic data governance tool platform includes the Ruizhi data governance platform.

Function description: Ruizhi intelligent data governance platform is independently developed by Yixin Huachen, integrating metadata management, data standard management, data quality management, data integration management, master data management, data exchange management, data asset management, data security management, data life cycle management nine product modules, each product module can be used independently or in any combination, open up all aspects of data governance, and can quickly meet the needs of enterprise users in various different data governance scenarios.

Foreign data governance tool platforms include:

alation

Platform: ASG Technologies

Description: Altitude provides a platform for a wide range of data intelligence solutions, including data search and discovery, data governance, data management, analytics, and digital transformation. The product has a behavioral analytics engine, built-in collaboration features, and an open interface.

Altitude can also analyze data and monitor usage to ensure that users have an accurate understanding of data accuracy. The platform also provides insight into how users create and share information from raw data.

ASG Technology.

Platform: ASG Enterprise Data Intelligence.

Description: ASG Technologies provides a data intelligence platform that discovers data from more than 220 traditional and big data sources. The tool is accompanied by the function of automatic data labeling through pattern matching, reference data integration and rich indicators.

Automated business pipelines give users a better understanding of their data, and governance capabilities include the ability to track data in data lakes and traditional**. ASG's EDI products offer an impressive mix of features, with reference customers praising the vendor's support for a variety of business use cases.

Data governance and governing data are two different concepts.

Data governance is the process of systematically and continuously managing data within an organization. It involves the management of data quality, data security, data usage rules, data ownership, and data architecture. Data governance aims to ensure data integrity, reliability, and compliance to enhance the value of an organization's data and support the decision-making process.

Governing data refers to the act of managing data. It involves operations such as data acquisition, storage, processing, cleaning, and analysis. In practice, data governance often relies on the means and tools to govern data, such as data cleaning tools, data warehouses, and data analysis platforms.

Therefore, the relationship between data governance and data governance is complementary and interdependent. Data governance requires the support and guarantee of governance data, and governance data also needs to comply with the norms and standards of data governance.

Which data governance management platform in China has the best and fastest performance? I hope you recommend it, thanks.

Today, we will analyze this problem in detail, please read on.

1. Stream processing.

2. Parallelization.

3. Abstract index.

4. Data visualization.

Personally, I think that the domestic data governance is better than Yixin's Ruizhi, the maturity of the technology is very high, and it can solve various data quality problems, and at the same time can reduce the cost of data management.

Data governance is the process of moving from using fragmented data to using unified master data, from having little or no organizational and process governance to enterprise-wide comprehensive data governance, and from trying to deal with master data chaos to being organized.

The whole process of data governance.

The purpose of this system is to integrate the knowledge and opinions of IT and business departments, and to supervise the information construction of enterprises in an all-round way through a virtual organization similar to a supervisory committee or project team, which is based on the delegation of authority from the top management of the enterprise and the constructive cooperation between the business department and the IT department. In terms of scope, data governance covers data analysis from the front-end transaction processing system, back-end business database to the terminal, and forms a closed-loop negative feedback and group system (a stable system in control theory) from the source to the terminal and back to the source. In terms of purpose, data governance is to supervise the acquisition, processing, and use of data (supervision is our negative feedback to the information system at the implementation level), and the function of supervision is mainly ensured by the execution of the following five aspects - discovery, supervision, control, communication, and integration.

Data governance is the process of managing and controlling an organization's data to ensure data quality, consistency, confidentiality, and compliance. It includes developing data management policies, rules, and standards, ensuring that data is stored and used in compliance with regulatory and transaction commission requirements, identifying and mitigating data risks, and ensuring that data resources generate value and benefits to the organization.

Data governance is not a substitute for data management, but an important aspect of data management. It's not just about technical issues, it's about working across departments and integrating different data endpoints. Data governance is not yet a static process, but a dynamic practice that requires regular updates and reassessments.

Why are business leaders so keen on digital governance? It is undeniable that data governance is very popular, and in the DAMA data management knowledge system guide, data governance is located at the positive end of the "wheel diagram" of data management, and is the general outline of 10 big data dismantling and flushing management fields such as data architecture, data modeling, data storage, data security, data quality, metadata management, and master data management, providing overall guidance strategies for various data management activities.

When it comes to data governance, many enterprises often say that it is a complex involving enterprise strategy, organizational structure, data standards, management norms, data culture, and technical tools. Those who have no practical experience in data governance will definitely think that data governance is "high"! It's strategy, it's standard, it's culture.

However, only if you have really done data governance will you know that data governance is not only dirty and tiring, but also a thankless job that is often blamed and the leader does not see the value. In the process of data governance, sometimes it is not understood.

Data governance is a foundational project, people always see the "high-rise building" of data application, the data governance team is busy every day, and the leaders don't know what "this gang" is doing. But whenever there's a problem with data, the first person to be held accountable is the data governance team.

It is said that data is an asset, and data governance is very important. Everyone also says that data governance is very important, and leaders attach great importance to it, but in the process of real implementation in many enterprises, they will always encounter problems such as insufficient support from senior leaders, insufficient cooperation of business department personnel, and data governance always has to make way for business. Here's why:

When the leader says that he attaches importance to data slag, does he really attach importance to it, or does he pay lip service? Has it been incorporated into the company's strategic action plan?

Data governance requires strategy, system, and organization, which is a top-level strategy, each of which affects the whole body, and requires strong support and promotion from senior leaders, and close coordination between business departments and technical departments.

Data governance requires the establishment of standards, processes, and data clearance, and it is necessary to sort out and standardize each data domain, data entity, data entry, and data item, and sometimes even manually define data standards and verify data quality one by one and field by field. Data governance personnel must not only have good data thinking, but also have enough care, patience and physical strength to continuously improve the quality of enterprise data and polish the data standards suitable for enterprises.

Data governance is a collection of processes, roles, policies, standards, and metrics that ensure the effective and efficient use of information to enable businesses to achieve their goals. It establishes processes and responsibilities to ensure the quality and security of data used throughout the enterprise or enterprise. Data governance defines who can take what action on what data, under what circumstances, and with what methodology.