WHAT DOES A DATA MANAGEMENT ANALYST DO?
Published: October 1, 2024 - The Data Management Analyst workflow development entails creating systems to efficiently manage metadata, including a business glossary, data catalog, classification, tagging, and lineage. Evaluation of internal systems, establishment of robust documentation processes, and definition of data quality requirements are implemented to ensure data integrity and accessibility. This position's partnerships with technology and business teams enhance data management practices across multiple systems, translating business needs into precise data deliverables and improving data availability through strategic extractions and reports.
A Review of Professional Skills and Functions for Data Management Analyst
1. Data Management Analyst Duties
- Data Quality Improvement: Measure and improve data quality by running data quality metrics, publishing results to a data quality dashboard, and completing issue resolution analysis on any anomalies found with minimal direction.
- Metric Definition: Participate in the definition and creation of new data quality metrics.
- Data Validation: Validate critical production analytic data feeds, implement new validation criteria, and communicate results with stakeholders, both internal and external.
- Business Relationship Development: Develop relationships with business and analytic partners to understand business processes and serve as a resource for data and business rule questions.
- Agile Support: Support the DHP agile development process by partnering with business and analytic team members to gather data requirements.
- Business Requirement Partnership: Collaborate with EDW/Analytics agile teams to ensure a full understanding of business requirements for optimal solution design.
- User Acceptance Testing: Prepare and implement user acceptance test plans for assigned features, including production validation.
- Reporting Contribution: Contribute to improving MIS and Risk reporting.
- Stock Reconciliation: Perform daily stock position reconciliation to ensure company records align with the custodians used.
- Cross-Team Collaboration: Work closely with other teams within operations and beyond, including the Asset Lifecycle Team, Trade Management Team, and Investment Performance.
- Data Systems Development: Participate in the development and implementation of new data systems.
2. Data Management Analyst Details
- HR Data Governance: Manage the content of the HR Data Dictionary, ensuring metadata (Data Families, Data Domains, Data Elements, and Data Owners) is governed and fit for purpose.
- Data Documentation: Work with Data Domain Owners, Sub Domain Owners, and Critical Data Element Owners to document data challenges in domains.
- Stakeholder Coordination: Coordinate stakeholder collaboration in the design of data quality rules, thresholds, and reporting.
- Data Quality Solutions: Identify solutions to data quality issues, including process adoption, technical enhancements, or data quality measurement.
- Business Requirements Documentation: Document business requirements for data management initiatives, undertake benefits case analysis and support implementation activities.
- Data Flow Documentation: Document data flows and transformations from authoritative data sources to strategic reports.
- Quality Control Implementation: Document data quality controls, identify gaps, and implement remediation plans for data quality control gaps.
- Data Quality Rule Definition: Define and build data quality rules and measures using Ab Initio to meet end-user requirements.
- Data Lineage Attestation: Undertake the data lineage attestation process.
- Technical Leadership: Act as the technical lead for the Data Management tool implementation and rollout by facilitating the ingestion of both business/technical metadata and data lineage using APIs and native connections.
3. Data Management Analyst Responsibilities
- Workflow Development: Develop workflows to facilitate the management of metadata.
- System Evaluation: Evaluate internal systems for efficiency, problems, and inaccuracies, developing and maintaining protocols for performing metadata management such as business glossary, data catalog, classification/tagging, and data lineage.
- Process Documentation: Establish, implement, and document management processes to promote consistency, quality, integrity, and accessibility.
- Data Management Partnership: Partner with technology and business teams to drive data management activities and serve as a subject-matter expert for critical data assets across multiple systems.
- Data Policy Implementation: Define and implement data management processes within the business line following the data policy requirements while considering business relevance.
- Business Needs Translation: Translate business needs into data deliverables, including data management activities.
- Data Analysis: Analyze the data by querying and creating data extractions as needed.
- Data Quality Definition: Define data quality requirements with the business, set up related monitoring, and contribute to defining action plans for improvement.
- Stakeholder Coaching: Coach all stakeholders on data-related activities, including data management.
- Data Availability Improvement: Improve data availability within the organization through extractions, reports, and DataMarts.
4. Data Management Analyst Job Summary
- Data Lifecycle Management: Works closely under the Data & Analytics Manager to complete data requirements and plans for the full data life cycle from project conception to long-term data storage.
- Data Extraction and Quality Assurance: Responsible for the extraction, aggregation, and quality assurance of data from multiple studies in support of project reporting, operational reporting, and quantitative analyses of utilization.
- Clinical Data Management: Works with clinical research teams to implement CRFs in REDCap and manage data transfers from survey engines such as Qualtrics and other existing systems.
- EDC Design Compliance: Conforms EDC design to public registries and federal repositories by using existing Common Data Elements and forms, or defining new Unique Data Elements and forms to promote open collaborative science.
- Research Protocol Design: Assists the MIRROR Regulatory Team in the design of research protocols, including data collection plans, data management plans, and privacy and confidentiality plans.
- Data Documentation Development: Develops internal Data Management Plans and Data Dictionaries and maintains SOPs and other MIRROR documentation.
- Data Entry Support and Training: Supports and trains data entry personnel when studies initiate, and as data entry forms are added or modified.
- Process Improvement Recommendations: Recommends operating methods to improve processing, distribution, data flow, collection, and database editing procedures.
- Data Quality Optimization: Optimizes data quality and automates data collection and management processes.
- Quality Assurance Oversight: Oversees quality assurance for all study databases, tests data to identify data integrity issues, makes recommendations, and plans solutions based on research goals.
- Ad-Hoc Reporting: Provides research and management ad-hoc reports as requested, maintains real-time dashboards.
5. Data Management Analyst Accountabilities
- Data Quality Management: Manage data quality control and certification projects associated with customer-facing data using internal systems.
- Process Integration: Integrate data quality best practices into business processes and product functionality, including certifying data for intended use cases.
- Strategic Coordination: Globally coordinate and communicate recommended product data strategies to gain buy-in from senior management and key internal stakeholders.
- Subject Matter Expertise: Act as the SME for product development lifecycle processes related to data quality to ensure accurate deliverables for requirements and deployment.
- Process Expertise: Serve as the key process expert who understands data quality issues and requirements for data and BvD’s business processes.
- Risk Management: Follow proper risk disciplines and proactively escalate issues.
- Standards Adherence: Adhere to Enterprise Data Management Standards and Risk Data Aggregation requirements.
- Data Reconciliation: Use advanced data management and reconciliation tools to maintain data integrity within the system.
- Reporting and Metrics: Compile and create metrics and reports.
- Documentation Maintenance: Update and maintain document records and release databases.