Shulou(Shulou.com)06/03 Report--

At present, enterprise reform has become the only way for enterprises to adapt to the drastic changes in the market environment and achieve long-term development. However, the chimney-type isolated island business system that brought work efficiency improvement to the organization in the past has become the resistance to organizational reform and reorganization, which is also the most fundamental source of demand for opening up each organizational unit from the data level and realizing the rapid reorganization of business units.

In the era of data, the data of any enterprise is very important, and all aspects of the enterprise need corresponding data support. Through the collection, analysis, processing and prediction of relevant data, the enterprise can accurately understand and master the business situation, management work and other aspects, so as to make reasonable decisions. Without the ability to manage data, the company is slowly dying.

Is Data Governance Really Important?

Intelligence is based on data, and data is based on a lot of artificial and engineering effort, so artificial intelligence is also quite a part of "artificial". Data collection requires manual determination of data sources or manual writing of crawlers; data processing requires observation of data and manual writing of the entire cleaning process; data labeling requires specific business to see how to label data.

These processes can take a lot of effort and sometimes lead to duplication or redundancy of human effort if the processing path is unclear. Therefore, determining a specific processing flow in advance, making clear how data should be managed, how computing power should be allocated, and how models should be deployed, then the entire development process can reduce a lot of labor costs and engineering burdens.

Data governance has three benefits:

Data governance saves money. Simply put, data governance can reduce errors in the database and provide reliable data resources for the enterprise, thus saving valuable time for the enterprise and improving the efficiency of the enterprise.

Wrong data poses risks to the business, and data governance can mitigate those risks. If there is wrong data in the enterprise database, then the enterprise cannot make correct judgments based on this data, and some risks may arise.

Good data governance provides enterprises with clear and standardized data. Effective data governance is generally clear, accurate and provides the quality of enterprise data.

If we want to reduce the cost of data governance and optimally deploy data, models, and computing power, we need a mature data governance platform.

The data governance platform integrates nine products, namely metadata, data standards, data quality, data integration, master data, data assets, data exchange, life cycle and data security. Each module function can be called to each other, and the whole process can be visually operated to get through each link of data governance. At the same time, it provides any combination of each product module to quickly solve different data governance scenarios of enterprises.

Metadata: Collect and summarize information about enterprise system data attributes to help users from all walks of life gain better data insights and mine the value hidden in resources through the relationship and influence between metadata.

Data standards: provide a set of uniform definition standards for data naming, data definition, data type, assignment rules, etc. for data scattered in various systems, and ensure the consistency and standardization of data in complex data environment through standard evaluation, ensure the correctness and quality of data from the source, and improve the consistency and efficiency of development and data management.

Data quality: Effectively identify various data quality problems, establish data supervision, form a data quality management system, monitor and reveal data quality problems, provide detailed query of problems and suggestions for quality improvement, comprehensively improve the integrity, accuracy, timeliness, consistency and legitimacy of data, reduce data management costs, and reduce decision-making deviations and losses caused by unreliable data.

Data integration: data cleaning, conversion, integration, model management and other processing work. It can be used to correct problem data and provide reliable data models for data applications.

Master Data: Helps organizations create and maintain a single view of shared data internally, improving data quality, unifying business entity definitions, simplifying business processes, and improving business responsiveness.

Data assets: Collect all data resources that can generate value in the enterprise, provide users with asset views, quickly understand enterprise assets, discover non-performing assets, provide decision-making basis for administrators, and improve the value of data assets.

Data exchange: It is used to realize the transmission and sharing of data or files between different systems of different institutions, improve the utilization rate of information resources, ensure the interconnection of information distributed among heterogeneous systems, complete the collection, centralization, processing, distribution, loading and transmission of data, and construct unified data and file transmission and exchange.

Life cycle: manage data birth, death and death, establish automatic data archiving and destruction, and comprehensively monitor and display the life process of data.

Data security: Provide various data security policies such as data encryption, desensitization, obfuscation, account monitoring, etc. to ensure proper authentication, authorization, access and audit measures during data use.

For enterprises, effective data management can help provide work efficiency in advance, save labor costs, and good data governance can make enterprise data more clear, standard, accurate, and allow enterprises to make accurate planning through data.

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