The way of data governance to help enterprises complete the digital transformation

The so-called "no rules, no circle", due to historical reasons, enterprises in the development process has formed a system of stand-alone situation, the collection of data to the data platform of the data are unique, the lack of standards, norms, governance of the data has lost the value of use. In order to standardize the data processing process, highlighting the business value of data, data platform data need to be managed comprehensively, build standardized, process-oriented, automated, integrated data governance system to ensure that the data architecture planning is reasonable, data processing is clear, data processing can be managed and controlled, data knowledge can be passed on. Effective data governance can ensure that enterprise data is comprehensive, consistent and credible, thus fully releasing the value of data assets.

In the traditional data platform stage, the goal of data governance is mainly to do control, for the data department to establish a governance work environment, including standards, quality and so on. In the data platform stage, the user's demand for data continues to grow, the scope of users from the data department to expand to the whole enterprise, data governance can no longer just for the data department, the need to become a working environment for the whole enterprise users, the need to take the whole enterprise user as the center, from the point of view of the services provided to the user, manage the data at the same time for the user to provide self-service access to the ability to obtain big data, to help enterprises to complete the digital transformation.

By analyzing the data, we are able to provide a more efficient and effective way of managing the data.

By analyzing some of the problems that have arisen in the actual process of data governance, we have summarized several key elements of data governance:

1) Data governance requires system construction: in order to leverage the value of the data center, it is necessary to satisfy the three elements: a reasonable platform architecture, perfect governance services, and systematic means of operation.

According to the size of the enterprise, the industry, the amount of data and other circumstances to choose the appropriate platform architecture; governance services need to run through the entire life cycle of the data, to ensure that the data in the collection, processing, *** enjoyment, storage, application of the entire process of the completeness of the process, accuracy, consistency and effectiveness; operational means should include the optimization of the norms, organizational optimization, platform optimization and process optimization and so on. The company's business model is based on the idea that the company's business model should be a good one.

2) Data governance needs a solid foundation: data governance needs to be progressive, but in the early stages of the construction of the data center at least need to pay attention to three aspects: data specification, data quality, data security.

Standardized model management is a prerequisite to ensure that data can be governed, high-quality data is a prerequisite for data availability, and data security control is a prerequisite for data can be ****enjoyed exchange.

3) Data governance needs IT empowerment: data governance is not a pile of standardized documents, but the need to governance process generated by the norms, processes, standards landed on the IT platform, in the data production process through the forward approach to data governance, to avoid the increase in operation and maintenance costs brought about by the aftermath of the audit.

4) Data governance needs to focus on data: the essence of data governance is to manage data, so it is necessary to strengthen the management of metadata, make up for the relevant attributes and information of data, such as: metadata, quality, security, business logic, blood, etc.; should be through the metadata-driven way to manage data production.

5) Data governance requires the integration of building and management: the consistency of the data model lineage and task scheduling of the data middle office is the key to the integration of building and management, which helps to solve the problem of inconsistency in the caliber of data management and data production, and avoids the emergence of the inefficient management mode of the two skins.

Data governance software: the work to do a good job, it must be the first tool

Currently the industry's popular data governance software, generally also known as data asset management products, data governance products, the main functional components include metadata management tools, data standards management tools, data model management tools, data quality management tools, master data management tools, data security management tools, and so on. Here we must recommend Yixin Huachen one-stop data governance management platform - Ruiji, through all aspects of data governance.

Enterprises use Yixin Huachen's data governance platform to solve the problems encountered in the process of integrating data from different sources of the enterprise, and it can provide unified metadata integration, data standard management, data model design, data quality auditing, data asset catalog, data analysis services and other capabilities.

The process of data governance needs to identify a short-term goal, but does not necessarily mean that the solution to this scenario should stop the data governance, the start of the data governance project should be considered a train that can not be stopped, and only all the way forward, in order to guide the business of the enterprise to continue to optimize and achieve the optimal state of the internal departments to work together to ultimately form the departments of the company or functional departments of the consistency of the business objectives. or functional departments of the business objectives of a consistent understanding, so that after the formation of their respective sub-work objectives recognized and effectively implemented.