The necessity of popularizing self-service analysis

• ● Bank data is the most intensive: every 1TB of data can bring 5 million RMB in revenue
• ● The data element is the leverage: Promote the digitalization and intelligence of the banking industry
• ● Improve business revenue: BI penetration rate must be increased (see the picture on the right)

  Problems Faced by Banks in Self-analysis

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  Business users: difficult to choose analysis tools

  Don't know where the data to be analyzed is

  Analysis tools are not easy to use; Accustomed to using Excel analysis

  Don’t know how to get help when having problems

• 

  Data operation: It is difficult to create a data operation culture

  Unable to cultivate the habit of self-service analysis of users

  Analysis results cannot be deposited into knowledge assets

  The analysis results lack convenient channels for sharing

• 

  IT administrators: how to ensure healthy operation

  User permissions cannot be finely controlled

  Prone to downtime when there are too many users

  Stuttering easily occurs when the amount of data is large

  Key requirements for banks in self-service analysis

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  Various functions

  There must be "private cars"

  and "bicycles" as well as "taxi" and "buses"

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  Shared power

  Precipitating experience in the analysis

  store, and new participants do not need to start from scratch

• 

  Timely service

  Deal with technical requirements in time

  and solve data quality problems in time

  User-centric construction ideas

  Solutions for banks in self-service analysis

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  Business: Provide analysis data that can be found quickly and easily understood, and provide a variety of practical analysis tools.

  Semantic modeling: data assetization
  Data navigation: easily find data
  Data Q&A: Get support at any time
  Analysis tools: provide various analysis tools such as ad hoc query, perspective analysis, self-service dashboard, Excel fusion analysis, data mining, etc.

• 

  Operation: Provide a set of operation mechanism to fully mobilize the enthusiasm of business personnel for data analysis.

  Analysis store: results display and sharing, community interaction
  Statistics: formulate and promote operation strategies

• 

  Technology: Provide guarantees in security, stability, performance, etc.

  Data security: data access control, data desensitization
  System stability: cluster dynamic distribution, FailOver failover
  High performance: Smartbi high-speed cache

  Scalability and Compatibility of the solutions

  Value of popularizing self-service analysis

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  Improve efficiency

• 

  Accumulate knowledge

• 

  Share experience

• 

  Build culture

  A successful example of popularizing self-service analysis

Aladdin's strategic goals of Minsheng Bank:

• 1.The data product group contributes more than 10% of the bank's profit
• 2.Carry out the "Thousand Talents Plan" to train big data analysis talents
• 3.Improve business decision-making capabilities through self-service analysis and mining