Big Data_ Maturity Assessment Model for Data Center Construction

        Data application capability maturity can be summarized into four stages: statistical analysis, decision support, data-driven, and operation optimization. For different stages, reference and judgment are made from multiple aspects such as enterprise strategic positioning, enterprise data form, data application scenarios, data application tools, enterprise organizational structure, and different characteristic dimensions, which constitutes the data application maturity model evaluation model. According to the preparation of this stage, enterprises can conduct self-assessment of data application maturity. The higher the maturity of data application capability, the stronger the support ability of data to business; The lower the degree of dependence.

        Self-Assessment Model Comparison Table 

maturity stage	Enterprise strategic positioning	Enterprise data form				Data Application Scenarios	Data Application Tool	Organizational Structure
		Data accumulation	data dimension	data organization	data quality
Statistical Analysis Phase	no data strategy, purely business driven	A small amount of business data accumulation	Single data dimension	Unorganized data, decentralized storage and management of business data	No data quality control	Simple business statistical reports	A system report module and Excel- based	No data-related departments and positions, mainly related to IT and business departments
decision support stage	Start supporting business decisions with data	Pay attention to the accumulation and collection of data in the business process	The data dimension is gradually enriched	Data organization in the form of an indicator system for business entities	Start to implement data quality control and clean and process relevant data	Provide decision support for enterprise management	Focus on data warehouse, data development and professional BI reporting tools	Start related data analysts, may set up special data departments and data value mining and other related positions
data-driven stage	Start to regard data as an important asset of enterprises, and provide data services for enterprises through cross-border data applications	The initial scale of each business data accumulation, and the amount of data is increasing	Global data fusion, richer data dimensions	Aggregation, connection, and global data organization of related data involved in starting business	Started data standardization construction and stricter data quality control	Realize the deep integration of data and business, and drive business development through data	Big data processing technologies such as batch computing, stream computing, ad-hoc analysis, online query, etc. represented by the Hadoop ecosystem, as well as machine learning and deep learning algorithms for data aggregation development	Started to set up an independent big data department and big data engineers, algorithm engineers, data visualization engineers, data scientists and other related positions
Operational Optimization Phase	Enterprises start to build data centers, and the data center strategy continues to optimize operations	With the construction of data closed loop, the volume of enterprise data is growing rapidly	The data dimension is more perfect	Establish a data application closed loop	Form a complete set of data quality management specifications and management processes	Resume a unified data service system to provide data service support for business optimization and business innovation	Establish a set of systematic data aggregation, processing, management, service and application systems, and gradually realize big data capabilities as tools, tool platforms, and platform intelligence	Set up a data management committee at the management level and set up a dedicated data asset operation department