Project management roles and responsibilities in Big Data

Description

What does the Project management roles and responsibilities in Big Data course cover?

Project management roles and responsibilities in Big Data is covered here in 9 modules: Defining the Big Data Project Lifecycle and Governance Framework, Stakeholder Alignment and Cross-Functional Coordination, Resource Planning and Team Role Definition and 6 more. The outline lists 72 specific topics, opening with selecting between waterfall and agile methodologies based on data source stability and stakeholder feedback cycles and closing.

How do you approach Project management roles and responsibilities in Big Data step by step?

The work is sequenced in 9 stages. It starts with Defining the Big Data Project Lifecycle and Governance Framework, moves through Stakeholder Alignment and Cross-Functional Coordination and Resource Planning and Team Role Definition, and ends at Scaling and Continuous Improvement in Data Operations. Each stage carries its own topic list, so the sequence is followed rather than summarised.

What is in Module 1 of the Project management roles and responsibilities in Big Data course?

Module 1 is Defining the Big Data Project Lifecycle and Governance Framework. It works through selecting between waterfall and agile methodologies based on data source stability and stakeholder feedback cycles, establishing data governance councils with representation from legal, IT, and business units to approve data usage policies, defining project phase gates for data readiness, including schema validation and source availability checks and.

How is the Project management roles and responsibilities in Big Data course delivered?

The Project management roles and responsibilities in Big Data course is fully self-paced with immediate online access after enrolment. Access does not expire and future updates are included at no cost. It can be taken on any device, and a certificate of completion is issued by The Art of Service when you finish.

How much does the Project management roles and responsibilities in Big Data course cost?

The Project management roles and responsibilities in Big Data course is $296 as a one time payment. There is no subscription, no per seat licence and no hidden fee. Enrolment carries a 30 day satisfied or refunded guarantee, so it can be assessed in full before you commit.

More answers: what you get with every course, refund policy, all help answers.

This curriculum spans the breadth of a multi-workshop program on enterprise data governance and operational execution, addressing the same scope of decisions and trade-offs encountered in cross-functional advisory engagements for large-scale data initiatives.

Module 1: Defining the Big Data Project Lifecycle and Governance Framework

Selecting between waterfall and agile methodologies based on data source stability and stakeholder feedback cycles
Establishing data governance councils with representation from legal, IT, and business units to approve data usage policies
Defining project phase gates for data readiness, including schema validation and source availability checks
Implementing metadata management protocols to track lineage from ingestion to reporting layers
Creating escalation paths for data quality disputes between analytics and source system owners
Documenting data retention and archival rules in alignment with regulatory requirements (e.g., GDPR, HIPAA)
Integrating compliance checkpoints into sprint planning for regulated data environments
Assigning stewardship roles for master data entities across departments

Module 2: Stakeholder Alignment and Cross-Functional Coordination

Mapping data consumers by role to prioritize deliverables in multi-department initiatives
Facilitating joint requirement sessions between data engineers and business analysts to clarify KPI definitions
Negotiating SLAs for data freshness between operations teams and reporting units
Resolving conflicts between real-time processing demands and batch ETL maintenance windows
Coordinating change control approvals when upstream system modifications impact data pipelines
Managing expectations on prototype timelines versus production-grade deployment
Translating technical constraints (e.g., latency, volume) into business impact statements for executive reviews
Establishing feedback loops with end users to validate dashboard accuracy and usability

Module 3: Resource Planning and Team Role Definition

Deciding between embedded data engineers and centralized platform teams based on project scale and reuse potential
Allocating shared resources (e.g., cloud administrators, security officers) across concurrent data initiatives
Defining escalation paths for data pipeline failures with on-call rotation schedules
Specifying skill thresholds for roles such as data modeler, pipeline developer, and analytics translator
Outlining handoff procedures between development, QA, and operations for data workflows
Creating RACI matrices for data product ownership, including updates and deprecation
Balancing contractor vs. full-time hires for specialized skills like stream processing or MLOps
Planning for knowledge transfer when team members rotate off long-running data programs

Module 4: Data Infrastructure and Platform Decision-Making

Selecting cloud vs. on-prem data lake architectures based on data residency and egress cost analysis
Evaluating managed services (e.g., BigQuery, Redshift) against self-managed clusters for control and cost trade-offs
Designing network topology to minimize latency between ingestion sources and processing engines
Implementing multi-zone deployment strategies for high-availability data pipelines
Choosing file formats (Parquet, Avro, ORC) based on query patterns and schema evolution needs
Configuring auto-scaling policies for batch processing frameworks under variable workloads
Integrating identity federation for cross-platform access without shared credentials
Planning for disaster recovery of metadata repositories and workflow schedulers

Module 5: Data Quality, Monitoring, and Operational Oversight

Defining measurable data quality thresholds (completeness, accuracy, timeliness) per critical data element
Implementing automated anomaly detection on ingestion volumes and schema drift
Setting up alerting hierarchies for pipeline failures with severity-based notification rules
Creating runbooks for common failure scenarios (e.g., source API downtime, schema mismatch)
Tracking technical debt in data pipelines, such as hard-coded values or undocumented dependencies
Conducting root cause analysis for recurring SLA breaches in data delivery schedules
Integrating data observability tools with existing IT service management (ITSM) systems
Validating recovery procedures for corrupted fact tables in distributed storage

Module 6: Security, Privacy, and Compliance Management

Implementing attribute-based access control (ABAC) for sensitive datasets in multi-tenant environments
Masking or tokenizing PII fields during development and testing data provisioning
Conducting data protection impact assessments (DPIAs) for new data collection initiatives
Enforcing encryption standards for data at rest and in motion across hybrid environments
Logging and auditing data access patterns for compliance reporting and forensic investigations
Managing consent flags and opt-out preferences in customer data platforms
Coordinating data deletion requests across replicated systems and backups
Validating third-party vendor compliance with organizational data handling policies

Module 7: Budgeting, Cost Control, and ROI Tracking

Forecasting cloud compute and storage costs using historical usage patterns and growth projections
Implementing tagging strategies to allocate data platform costs to business units and projects
Negotiating reserved instance commitments based on predictable workload baselines
Optimizing data retention policies to reduce long-term storage expenses
Tracking cost per query in shared analytics environments to enforce accountability
Conducting cost-benefit analysis for data replication across regions
Monitoring idle resources and scheduling shutdowns for non-production environments
Reporting on data project ROI using metrics such as time-to-insight and automation savings

Module 8: Change Management and Data Product Lifecycle

Planning deprecation timelines for legacy data sources with active downstream consumers
Versioning data models and APIs to support backward compatibility during migrations
Managing schema evolution in streaming pipelines using compatibility checks (e.g., Avro schema registry)
Documenting data product dependencies to assess impact of changes
Coordinating cutover events for data warehouse migrations with minimal business disruption
Establishing feedback mechanisms for user-reported data issues in production systems
Archiving historical data workflows and associated documentation for audit purposes
Conducting post-mortems after major data incidents to update operational procedures

Module 9: Scaling and Continuous Improvement in Data Operations

Standardizing CI/CD pipelines for data model and ETL code deployment across teams
Implementing infrastructure-as-code (IaC) for consistent provisioning of data environments
Creating shared libraries for common data transformation logic to reduce redundancy
Establishing centers of excellence to disseminate best practices and reusable assets
Measuring team velocity using cycle time and deployment frequency for data workflows
Introducing automated testing frameworks for data validation at scale
Optimizing query performance through materialized views and indexing strategies
Scaling data literacy programs to improve self-service adoption and reduce support burden