WHAT DOES A DATA QUALITY ANALYST DO?
Published: October 3, 2024 - The Data Quality Analyst collaborates with business and technical stakeholders to map data flow and maintain documentation on data origin, relationships, and quality. This role involves building data queries and visualizations to monitor accuracy and consistency critical for marketing program reporting. The analyst will drive data best practices and implement improvements with data stewards to enhance overall data quality through technology and analytics.
A Review of Professional Skills and Functions for Data Quality Analyst
1. Data Quality Analyst Duties
- Data Automation: Responsible for automating data collection for various top-priority global projects
- Cloud Data Management: Responsible for maintaining and building cloud-based data flows
- Cloud Dataset Management: Building and maintaining cloud-based datasets and quality dashboards
- Data QA: QA data ready for use by a wide range of business users
- Data Reconciliation: Reconcile data sources and sign off ready for operational usage by large teams of analysts
- Data Integration: Use a mixture of third-party tools and self-built processes to land data for consumption
- Data Management: Manages, analyzes, and disseminates vital records data from various sources
- Quality Improvement: Reviews, analyzes, and disseminates data for quality improvement efforts
- Data Quality Enhancement: Works to improve the quality, accuracy, and timeliness of data submitted to Vital Records
- Epidemiological Data Reporting: Conducts data analysis of vital records data and assists in preparing reports and communications on epidemiology of vital records
2. Data Quality Analyst Details
- Data Procedure Development: Develop procedures to enhance the accuracy and integrity of CCS data
- Data Health Monitoring: Monitor overall health of the data and execute cleansing efforts
- Data Quality Resolution: Resolve data quality problems
- Data Cleansing Support: Support data cleansing and data migrations between applications and platforms
- SQL Query Writing: Write SQL queries to extract data from the data warehouse
- ETL Tools Usage: Use ETL and Data Integration tools (SSIS, Jitterbit, DemandTools, etc.) to extract, transform/clean, and upload data to different systems
- Collaboration: Work closely with DAS, Digital, IT, BTO, and business leaders on various projects to identify, recommend, plan, and implement data quality solutions, standards, and optimization processes to improve data integration, re-use, and accuracy
- Data Query Execution: Run data queries to identify coding issues and data exceptions, as well as clean data
- Process Documentation: Document processes and maintain data records
- Policy Adherence: Ensure data management policies and procedures are adhered to and best practices are utilized
- Root Cause Analysis: Determine root cause for data quality errors and make recommendations for long-term solutions
3. Data Quality Analyst Responsibilities
- Goal Understanding: Understanding team’s goals and requirements, breaking them down into clear instructions and briefs for the research team
- Feedback Communication: Communicate feedback with the research team
- Deliverable Assurance: Ensuring consistent, high-quality deliverables are produced in a timely manner by the research team
- Quality Checks: Carrying out regular quality checks, assessing and analyzing large amounts of data
- Process Improvement: Work closely with the rest of the sales support team to improve processes
- Issue Analysis: Research and determine the scope and complexity of issues to identify steps to fix them
- Trend Identification: Gathering data from different data sources to identify and interpret trends
- Data Analysis: Analyze and provide data solutions to solve business problems/opportunities that may include process improvement, systems enhancement, user training, and/or software procurement
- Project Support: Support data projects and initiatives by assessing proposals and designs for architectural compliance (data and solutions) and promote best practices during the testing, build, and implementation phases
4. Data Quality Analyst Job Summary
- Data Monitoring: Monitoring and reviewing data that is generated, checking for completion and accuracy
- Data Quality Resolution: Identifying and resolving any data quality issues
- Data Dictionary Maintenance: Create and maintain common data dictionaries and tools or methods that support data standards
- Best Practice Development: Developing data quality best practice guidelines and providing user training
- Ad-Hoc Support: Providing ad-hoc support to ensure that best practices are followed
- Root Cause Analysis: Addressing the root cause of data inconsistencies and recommending improvements
- Relationship Building: Develop good relationships with users across the company and interact with them to identify data domains and data owners
- Data Access Assurance: Ensuring that the business has access to complete and accurate data to support decision-making
- Progress Reporting: Measuring and reporting to management on the progress of data quality improvement
5. Data Quality Visual Analyst Accountabilities
- Data Quality Development: Working hands-on on developing Data Quality Scorecards for clients
- Stakeholder Collaboration: Collaborating closely with stakeholders to understand their business needs and concepts
- Dashboard Design: Designing complex Tableau dashboard solutions that take advantage of all Tableau functionalities, including data blending, actions, parameters, and more
- Issue Resolution: Addressing any issues with the appropriate level of urgency and performing the troubleshooting effort
- Functionality Enhancement: Developing new or enhancing existing functionalities to improve processes
- Solution Accuracy: Following business needs and technical requirements to deliver accurate solutions
- Task Documentation: Documenting all task work in DQ tickets
- Help Desk Resolution: Resolving Data Quality Help Desk tickets raised for production issues in a timely manner
- Scrum Participation: Participating in Daily Scrum meetings on behalf of self and the Platform Team to report progress, roadblocks, and daily plans
- Mentorship: Providing guidance and mentorship to other junior team members during more challenging projects and assignments
6. Data Quality Analyst Functions
- Quality Control: Perform quality control on data by analyzing patterns and data discrepancies and resolve related issues
- Data Inquiry Support: Support and resolve data inquiries and issues with accuracy and in a timely manner
- Process Improvement: Identify ways to improve data quality processes either by automation or other means
- Data Analysis: Perform data analysis using a variety of methods, including statistics and relational databases
- Report Production: Produce reports using Business Intelligence tools that describe data characteristics and highlight trends and issues
- Documentation Contribution: Contribute towards documentation of the processes/projects as they near completion
- Quality Measurement Implementation: Implement data quality measurements on critical data
- Root Cause Analysis: Perform root cause analysis to discover the cause of data issues
- Data Error Monitoring: Monitor, investigate, and correct data errors
- Data Governance Collaboration: Work with data owners and data stewards to promote sound data governance principles
- Issue Escalation: Escalate urgent issues to the team leader for help in resolution
7. Data Quality Analyst Job Description
- Stakeholder Partnership: Partner with business and technical stakeholders to map end-to-end data flow and maintain documentation on data origin, relationships, classification, quality, downstream usage, and read/write access
- Data Query Development: Build sophisticated data queries, statistical techniques, and visualizations to monitor the accuracy, consistency, and quality of the data that is integral to reporting on the success of marketing programs
- Change Impact Investigation: Investigate and document impacts of proposed changes in data fields, relationships, processes, and new proposed integrations
- Data Best Practices Advocacy: Act as an agent of change to help drive data best practices by partnering with data stewards on data quality improvements, error detection, and process design strategies
- Audit Process Improvement: Plan and implement audit process improvement opportunities through the use of technology, analytics, and best practices to strengthen overall data quality
- Documentation Maintenance: Maintain documentation on data quality auditing techniques and processes
- Marketing Analytics Collaboration: Partner with Marketing Analytics to maintain documentation on data points used in various program health metrics, including attribution models
- Data Strategy Alignment: Drive alignment of data strategy and learnings with long-term product vision for Sales Experience
- Requirements Determination: Collaborate with the business, Master Data COE, IT, and other Data Transformation Analysts to determine the relevant data requirements for operational systems as well as MDM and data governance tools
- System Support: Support the build-out of these systems and tools to manage data, including building export files and performing data validations
8. Data Quality Analyst Overview
- Report Management: Manage a large number of reports to interpret the data and communicate findings to the rest of the team
- Quality Solutions Exploration: Explore alternative solutions to ensure quality results
- Data Investigation: Responsible for conducting data investigations to explain and highlight changes in trends
- Issue Escalation: Escalate data/tool issues and support the proper team in the resolution
- Backend Task Management: Responsible for most backend tasks of mapping and classification
- Operations Optimization: Proactively work with Team Leads, Senior Analysts, and TAMs to optimize/improve operations
- Automation Project Involvement: Actively work on automation projects, providing support and inputs on how to automate currently manual processes
- Tool Maintenance: Responsible for the maintenance of all automation tools used by the tagging team
- Database Updates: Perform daily updates in the team database
- Documentation Support: Support tools and process documentation
9. Data Quality Analyst Details and Accountabilities
- Issue Escalation: Understand when to escalate issues to the manager and/or send them back to the client
- Data Accuracy Assurance: Ensure that all data is correct with the highest degree of accuracy and confidence
- Tax Knowledge Establishment: Establish and maintain a working knowledge of tax returns, which will be part of onboarding
- Workflow Learning: Learn the various workflows and perform them with minimal supervision
- Defect Logging: Log and track any software defects found along the way and be able to explain them to the BeSmartee team
- Data Assurance Support: Support and execute data assurance processes
- Data Profiling: Conduct data profiling, produce data quality reports, and scorecards
- Data Testing: Design and run data tests aimed at improving data quality and integrity
- Data Cleansing: Proactively perform data cleansing and enable data quality remediation
- Root Cause Analysis: Interrogate data and apply root-cause analysis to determine the cause of errors and resolve these
- Training Material Creation: Create training materials for business teams on data entry and quality
- Data Provisioning: Provide accurate data to external stakeholders upon request
10. Data Quality Analyst Tasks
- Data Flow Analysis: Analyze information flows and identify key areas of data quality improvement to provide support to all business processes
- Deep Dive Analysis: Perform comprehensive deep dive analysis to identify key areas of improvement in the client data landscape and provide data-driven solutions to improve overall quality of data
- Data Ownership Resolution: Resolve inconsistent definitions, metrics, and assumptions by enabling unambiguous data ownership and the possibility to monitor data quality
- Process Development: Develop and implement data-driven processes to analyze data to ensure data quality standards are met
- Business Engagement: Engage with business and functional areas and engineers to ensure that data-related business requirements for the protection of critical data are implemented, leveraging data governance tools and best practices
- Change Advocacy: Act as an agent of change to help drive data best practices and promote the concept of “data as an asset”
- Governance Framework Support: Support the rollout of an enterprise-wide data governance framework, focusing on improving data quality and protecting critical data through modifications to organizational behavior, policies and standards, principles, governance metrics, processes, and implementation of related tools and data architecture
- Data Review: Review changes to client data to ensure accuracy and completeness
- Client Liaison: Liaise with Client Management Teams to resolve any missing or inconsistent client data
- IT Collaboration: Work with the IT Development Teams to gather requirements, review, and test any changes to systems that collect or change client data
- Data Quality Reporting: Prepare regular data quality-related management information for the Director of Operations