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1.3 Data center is a set of solutions
The data center is a sustainable mechanism for "making enterprise data useable", a set of solutions, not just a platform. Let the data support the front-end business more flexibly, and form a complete set of data usage mechanisms from data collection, governance, development to data services by continuously accumulating enterprise data reuse capabilities.
"Consulting + Platform + Implementation" Trinity Middle-Taiwan Construction Plan
The trinity of "consulting + software platform + implementation" ensures the successful construction of the data middle platform:
Consulting services: top-level design, determining data strategy and data planning, drawing a blueprint, and guiding the construction and implementation of the middle platform.
Software platform: the carrier of the China-Taiwan strategy and the support tool for the implementation.
Implementation service: the key guarantee for the landing of the data center.
Data Center Consulting Planning
The core content of data center planning is to determine the data strategy of the enterprise, as well as the organizational guarantee (organization, process, system) and data guarantee (data architecture, data security, data standard, data warehouse, data quality, data service) and technology platform guarantee.
Data Strategy: Aligning with Business Strategy
data management organization
Responsible for the development of data management work, the promotion and implementation of policies as a whole, and as the final decision-making organization for data management issues, resolve disputes, monitor and supervise the performance of data management work, and ensure budget support for data governance work.
According to the strategic goals of the data management leadership group, establish data management processes, stage goals, plans, formulate and maintain data management methods, general principles, tools, and frameworks, and resolve and make decisions on data issues and disputes across departments and fields.
Complete data standard management, data architecture management, data security management, data quality management, metadata management, master data management, data warehouse management, data service management and other activities according to the plan.
data structure
Taking the enterprise business architecture and application architecture as input, plan and design the enterprise data architecture. The main content includes data subject domain and subject division, data entity identification and enterprise-level data model design, data flow sorting and data distribution map drawing.
data quality
Identify quality responsibilities and process specifications
Identification of rights and responsibilities: Confirm the rights and responsibilities department for each type of data assets sorted out in the early stage, such as: the human resources department takes the lead in handling the quality problems of personnel data
. Process specifications related to quality work assessment
Define Data Quality Judgment Rules
Technical rules: Define technical rules from the physical dimension of database storage, such as data type, data encoding, primary key, etc.
Business rules: Define business rules from the business dimension, such as: value range, data format, business association logic, calculation logic, etc.
Evaluation strategy: determine the frequency and scope of various data quality inspections
Design data quality analysis indicators
Quality analysis indicators: clarify statistical rules for data quality issues, design quality analysis dimensions and analysis indicators, and comprehensively and intuitively display data quality issues.
Quality early warning mechanism: Design data quality early warning mechanism, including early warning methods, early warning indicators, early warning values, early warning information rules, etc.
Establish a data quality assessment mechanism
Design assessment indicators: Design a data quality assessment indicator system to quantitatively evaluate the enterprise data quality governance work Establish an assessment
mechanism: regularly assess the enterprise data quality governance work according to the assessment indicators, and promote the continuous and healthy development of enterprise data quality governance work.
Data Security
Based on data security management organizations, processes and systems, analyze the security needs of enterprises at different stages of the data life cycle, set corresponding management and control strategies, and ensure that the goals of enterprise data security management are achieved.
Data Warehouse Hierarchical Design
Data Warehouse ODS Layer Overall Planning
Data Warehouse DW Floor Overall Planning
Data warehouse DW layer data model
Data Warehouse DM Layer Overall Planning
Data warehouse DM layer data model
Characteristics of the data model:
It is established entirely based on requirements, and its subject domain and topic division are different from those of the DW layer;
there are two types of subject division: comprehensive analysis topics serving the enterprise supervisor level; professional services serving the enterprise business supervisor level Analysis topics;
data are divided into two categories: one is statistical analysis based on detailed data or lightly summarized data in the data warehouse, and the other is data that is further analyzed and mined based on statistical analysis; data mart models usually use
star shape model modeling.
Indicator system design
Example of indicator system
Data service system
The sources of demand for data services include business collaboration and transfer needs, and data analysis application (business analysis and optimization) needs. Based on service requirements, formulate corresponding service specifications and service management systems.
Consulting and Planning Services
Data center software platform framework planning
Governance Implementation Module - Metadata Management
Governance Implementation Module - Data Quality Management
Governance Implementation Module - Data Security Management
Data Warehouse Implementation Module--Indicator Processing Process Based on Data Warehouse
Data warehouse implementation module--data collection and processing
The web-side drag-and-drop, visualized data development tool can get rid of complex, cumbersome, technically difficult, and difficult-to-maintain data processing methods such as database SQL scripts, ETL tools, and EXCEL formula functions. Business personnel can also easily play with data and activate the free exploration channel of data value.
Data Service Module--Data Asset Catalog
The data open and shared window, users can perform global data retrieval access, data subscription and API service interface application based on the asset catalog. Change the status quo that enterprise data deposited on the bottom layer of the database is invisible, difficult to manage, difficult to obtain, difficult to understand, and difficult to use, and stimulate users' enthusiasm and efficiency in discovering data value.
Data Service Module--Data Exchange Service
Data Analysis Application Module--Data Mining Analysis
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