The Data Quality Analyst (DQA) primarily serves as liaison between the end users and data project team to define data definition and data quality business rules, as well as conduct technical data quality profiling of datasets. The DQA is an expert in understanding data quality objectives, questions, and problems, and at obtaining data, profiling it, assessing its quality and presenting data quality metrics and findings back to data stewards and other information stakeholders. This personal should also be able to advise on approaches and strategies to address data quality deficiencies retrospectively as well as proactively. The analyst understands the tools and techniques used to profile and assess data quality that range from using traditional basic queries tools, as well as more sophisticated data quality assessment tools.
 
Extensive insight into data systems and data structures are essential.
 

DUTIES AND RESPONSIBILITIES:

The DQA has a variety of responsibilities throughout the Data Management Lifecycle:
 
1. Requirements definition phase:

• Interview end users to determine requirements for data, reports, analyses, metadata, and data quality.
• Help identify and assess potential data sources
• Validate that proposed business data quality rules meets requirements and service level agreements.
• Assess data structures and recommend more technical and standardised data quality rules, over and above business defined rules.
• Document requirements in relevant formats:
  • Data Dictionaries
  • Data Quality Rules
  • Data Stewardship workflows

2. Data Profiling:

• Profile data and test technical as well as data quality assumptions and rules.
• Generate ad hoc data quality metrics and report on findings.
• Formulate technical specifications for ongoing data quality monitoring processes.

3. Design Phase:

• Work with architects to translate requirements into technical DQ Systems specifications
• Advise on retrospective data improvement initiatives planning
• Advise on proactive data quality monitoring and improvement system improvements or new DQ system designs.

4. Development Phase:

• Support development of DQ improvements to current systems, or development of standalone central DQ systems.
• Support unit testing and validation of DQ rules as implemented in systems.
• Support Data Steward workflow capability developments.

5. Testing Phase:

• Conduct technical functional acceptance testing and support systems integration testing
• Support or facilitate User Acceptance Testing.

6. Change Management and Deployment:

• Educate Data Stewards and other information stakeholders on the functionality and utilisation of new capabilities.
• Define and get agreement with business users on service-level agreements
• Assist with definition of support plan
• Interface with process teams regarding business process reengineering

 

REQUIRED SKILLS:

• General Analysis competencies:
  • Requirements analysis planning and monitoring
  • Requirements elicitation
  • Requirements management and communication
  • Data profiling and DQ rule prototyping using traditional querying tools or advanced DQ tools.
• Tools and techniques:

Data Dictionary and Glossary

• Data Flow Diagrams
• Data Modeling
• Focus Groups
• Interviews
• DQ Metrics and Key Performance Indicators
• Non-functional Requirements Analysis
• Problem Tracking
• Process Modeling
• Prototyping
• Risk Analysis
• Scenarios and Use Cases
• Structured Walkthrough
• PL-SQL or T-SQL querying skills
• Any specialised data quality tool such as:
  • Datanomic dn:Director
  • Oracle – Enterprise Data Quality (EDQ)
  • Microsoft – Master Data Quality (MDQ)
  • Informatica Data Quality (IDQ)
  • IBM – Infosphere Information Server for Data Quality
  • SAP – Data Quality Management, Information Stewards and Data Stewards
  • SAS – Data Quality, Data Management, and Data Quality Desktop
  • Talend – Talend Open Studio for Data Quality and Talend Data Management Platform
• Personal skills:
  • Strong communication skills.
  • Must be able to pay attention to detail.
  • Work independently.
  • Must be a team player.
  • Work on more than one project at a time.

• Project related skills:
  • Good general understanding of the relevant industry.
  • Substantial data analysis experience in a technical environment.
  • A good understanding of business systems and processes.
  • Be able to work within the framework of Data Quality methodologies and work independently on support and maintenance tasks.

REQUIRED QUALIFICATIONS / TRAINING:

• Ideally candidates should have an IT or business-related qualification.
• Advantage: DAMA Data Management Professional Certification with Data Quality elective.
• Any other Data Quality Management training or certification.