taobao招聘要求

基础扎实，做事认真，善于总结，有想法，能承受压力，有一技之长就行。
总结很精辟。：）
有兴趣的看链接~~
http://rdc.taobao.com/blog/dba/html/203_taobao_jobs_2009.html

Data Quailty: A survival guide for marketing

http://www.businessobjects.com/product/im/data_quality.asp

Typical Data Problems in a Marketing Campaign

DUPLICATE ACCOUNT RECORDS

• 8:1 duplication ratio between customer records.
• Potential reasons: Mergers and acquisitions (M&A) ;
• Account managers;
  
• Poor visibility and linkage across systems.

INCOMPLETE DATA (blank data fields)

• Deliver the message is impacted.(Crucial Information: address, email, or phone number field) [Y: Report related]
  
• Segment prospects into the correct categories or demographics is impacted.(Title, salutation, job code, or ethnicity) [Y: Dimention depends]
  
• Identify similar or related records across systems (Social security, account number, log-in ID, or account name ) [Y: PK related identifier]

THE WRONG DATA

• NO-ontime update
• System migrations
  
• Fraud

HOW DO I FIND MY DATA QUALITY PROBLEMS?

• The scope and depth
• The cause of the defects
  
• Many data quality problems are processed-based.
• reporting of the findings.
• The best place for direct marketers to cleanse their data is as close to the point of creation as possible.

5 Places to cleansing data

• Transactional updates–often at the point of creation.e.g. Web Site. Use real-time processing,respond in milliseconds, and also be able to service multiple transactional applications.
  
• Operational feeds–upstream and before the data enters your system. Use batch-oriented data quality functions;utilizes a predefined cleansing job;scheduled to run automatically on a specific data flow
• Purchased data–if you’re buying it, demand that it is clean.Validating purchased data;matching the purchased data against your current data set.
• Legacy migration–data is in the enterprise, but not in your system yet.
• Regular maintenance–as your data ages, you need to cleanse it.

A typical lead-generation process is used to map each of the five cleansing opportunities and highlight how they relate to each other.



DQ 9 functions


Data Quality Architecture

• Data Quality Repository
• Configuration rules: transforms, blueprints, substitution files etc.
• Data examples: sample data used with blueprints
• Runtime metadata: log files etc.
• Data Quality Server: host Data Quality engine.
  
• Metadata Repository: a relational database used by the Data Quality Server and Project Portal. This database contains statistics and samples generated by running projects.
  

Client Tools

• Data Quality Project Architect: a graphical user interface used to create projects.
• Data Quality Command Line: a command line utility used to run projects.
• Integration SDK: an Integration API written in C++ and Java that allows you to create socket connections for direct interaction with the Data Quality Server, without need for the web services.
• Data Quality Documentations
• Web Tools
• Data Quality Web Service: the mechanism used to send and receive data between your application and the Data Quality Server.
• Data Quality Web Service Samples
• Data Quality Project Portal: a web-based tool that is designed to provide you with one environment for manageable administration of your processes.

Data Quality Integration

• Web services
• Integration SDK
• JAVA API
• C++ API
• Socket