According to a survey by Gartner, more than 90% of data governance projects in my country have failed. The same is true of everyone's feelings: data governance projects are not easy to implement, and there is a huge gap between theory and practice in the implementation of data governance projects, which is difficult to bridge.
There are various reasons for failure, and they can be summed up into four categories:
The first category: passive data governance. Data governance only focuses on business processes, not the quality of actual data, and passively manages for the sake of governance.
The second category: partial data governance. Data governance is regarded as a project, a project, and it is a one-time activity that ends immediately after completion, without continuous improvement and operation.
The third category: isolated data governance. Enterprises have many norms and standards for data management, but the implementation effect is relatively poor. The business department has done a lot of data application, but has not communicated effectively with the information department, and the data quality is worrying.
The fourth category: instrumental data governance. Simply adopt data governance tools and technologies, while ignoring the data organization culture of the entire enterprise. At present, many companies in China do not have a data governance culture, and everyone is not mentally prepared for data applications, resulting in a high failure rate of data governance.
01
Solutions
Aiming at the core contradiction of data governance, in summary, it can be solved through "scenarios, engineering, and intelligence".
The first is that in the past few years, the industry has been accustomed to raising data governance to a particularly high level, emphasizing global data governance and full-caliber data governance of the entire industry. Today is the era of data explosion, and managing and applying each bit of data is currently a job with a relatively poor return on investment. A more pragmatic approach is to put data governance in the scenario, and combine the characteristics of various industries to carry out industry practices. For example, the financial, manufacturing, and government affairs industries have a lot of personalization.
The second is that in the current data governance implementation process, the current common phenomenon is manualization, which is completed by a large number of on-site manual services and personalized consultation. In the future, only when almost 80% of the work is streamlined in all walks of life can it be promoted on a large scale. Statistics show that there are less than 100,000 employees in the data management industry across the country, and the actual industry demand is more than 1 million. Faced with such a situation, it will be unsustainable to rely solely on the manual labor of data talents. Only by replacing people with technology, replacing people with technology, and engineering and standardizing data governance to improve implementation efficiency can this outstanding contradiction be resolved.
The third is that many excellent products and tools have emerged in the entire data governance operation process, but the homogeneity is very serious. Through automation, manpower is released from simple and low-level repetitive work to truly solve practical problems. This is an industry trend that will be seen in the data governance market in the next 1-2 years.
02
our solution
After having an idea, how to combine the concept with the actual business to promote the practice of enterprise data governance?
Relying on the successful experience of tens of thousands of data projects, Yixin Huachen integrates the core concepts and ideas of data governance into the solution, and provides customers with a three-in-one data governance service plan of "consultation + product + implementation", which runs through the enterprise data governance work The whole process from planning to implementation.
Based on the DCMM data management capability maturity assessment model, Yixin Huachen helps customers establish a data architecture and data governance system that conforms to their own characteristics, including data standard management, metadata management, data quality management, asset service management, data security management, Data lifecycle management empowers business application scenarios and helps enterprises build a solid data foundation and realize digital operations.
The content of the plan mainly includes three aspects: data governance top-level design, data governance system construction, data service and data insight, forming a governance closed loop consisting of overall planning and planning-construction and operation-operation and evaluation-improvement and optimization.
03
Ruizhi Data Governance Platform
In the entire solution, consulting planning and implementation services are carried out on the basis of the data governance platform, so it is very important to choose a good data governance product, let us know about the Yixin Huachen Ruizhi Data Governance Platform!
Ruizhi Data Governance Platform is a set of solutions composed of multiple products, and it is an intelligent and agile data life cycle management application platform for implementers. The platform is based on metadata, and all modules are not connected in series, but each module can be used alone or in combination with other modules, and supports local or cloud use.
Platform-based, highly integrated product modules
Ruizhi data governance platform covers 10 major data governance fields, adopts micro-service architecture, can be highly integrated with the existing systems of enterprises, and can be infinitely extended with the development of future informatization. The internal functional modules of the platform have also achieved a high degree of integration of functions, including:
1. The metadata of the business system has been standardized. When establishing a standard, it can be directly picked up from the metadata and mapped to become a standard.
2. The data standard has been formulated and passed, and the data quality inspection rules can be automatically generated according to the standard and the model subject table can be created according to the standard.
3. When the data needs to be shared and exchanged and the quality of the exchanged data needs to be checked, the quality of the data can be checked by invoking the quality inspection plan of the data quality, and the corresponding performance evaluation score can be formulated for quality evaluation.
4. For landing mapping, quality rules, data archiving, and data destruction based on metadata, you can view the corresponding matching rules in the metadata.
Intelligent, reduce implementation cost
Ruizhi continues to integrate AI technology. After the platform is intelligent, it can reduce implementation costs and guide users to quickly implement some implementations.
1. Intelligently recommend mapping data standards , reduce the manual matching of landing mapping, and at the same time check the quality of metadata mapping, and supplement and improve on the current basis through intelligent recommendation.
2. Data standard intelligent batch mapping , by optimizing the mapping algorithm, complete matching and fuzzy matching can be performed according to the metadata code and metadata name, making the work of metadata drop-off more intelligent and convenient, reducing a lot of manual redundant operations, and at the same time can Accurately and quickly check metadata drop-offs.
3. Intelligent repair of data quality , which can repair the rules of null value, value range, and specification (ID card, date, full half-width), and the system will automatically repair after setting the corresponding repair strategy, reducing the duplication of simple work by business personnel Execute and reduce the error rate of manual repair, and improve the efficiency of problem data repair.
4. Data security intelligent scanning , based on sensitive data labels, can intelligently scan data assets to identify sensitive data, automatically mark sensitive data, and set sensitive data labels and sensitivity levels for data assets, which is convenient for batch encryption and desensitization. Greatly simplify the user's operation.
Visualization, low operating threshold
Ruizhi provides visualization of the whole life cycle of data from creation to extinction for different roles such as managers, technicians, and business personnel, and also realizes the visualization of all roles. It can visually display the data governance process and results; at the same time, the visual operation interface has low user operation threshold and is more user-friendly.
Since its release, the Ruizhi Data Governance Platform has been applied in many industries such as government affairs, finance, manufacturing, energy, education, and medical care, supporting the data governance work and middle-end construction of many enterprises, providing empowerment for the digital transformation of enterprises, and gaining Obvious effect.