BI Implementation in Big Data

This curriculum spans the full lifecycle of a multi-workshop BI implementation program, covering the technical, governance, and operational disciplines required to deploy and sustain analytics at scale in a regulated enterprise environment.

Module 1: Strategic Alignment and Business Requirements Gathering

• Conduct stakeholder interviews across finance, operations, and analytics to map critical KPIs and reporting dependencies.
• Define data latency requirements per business unit (e.g., real-time for logistics, batch for financial close).
• Negotiate scope boundaries between self-service analytics and governed reporting to prevent shadow IT proliferation.
• Document data ownership and stewardship roles for each core business entity (customer, product, transaction).
• Assess existing BI tooling and integration points to determine migration complexity and reuse potential.
• Establish a prioritization framework for use cases based on business impact and data readiness.
• Validate regulatory constraints (e.g., GDPR, SOX) that influence data access and retention policies.
• Develop a business glossary with formally approved definitions to ensure semantic consistency.

Module 2: Data Architecture and Platform Selection

• Evaluate data lakehouse vs. data warehouse trade-offs in query performance, schema enforcement, and cost.
• Select ingestion patterns (CDC, batch, streaming) based on source system capabilities and SLA requirements.
• Design a medallion architecture with bronze, silver, and gold layers to enforce data quality progressively.
• Implement partitioning and clustering strategies in cloud storage to optimize query cost and speed.
• Choose between managed and self-hosted compute engines (e.g., Databricks, Snowflake, BigQuery) based on team skillset and operational overhead tolerance.
• Define data retention and archival policies for each layer considering compliance and cost.
• Integrate metadata management tools to automate lineage tracking from source to dashboard.
• Plan for cross-region replication and disaster recovery in distributed data environments.

Module 3: Data Integration and ETL/ELT Engineering

• Develop idempotent data pipelines to support safe reprocessing without duplication.
• Implement change data capture for transactional databases using Debezium or native log shipping.
• Handle late-arriving dimensions in dimensional models using SCD Type 2 with effective dating.
• Orchestrate pipeline dependencies using Airflow or Prefect with failure alerts and retry logic.
• Apply data masking or tokenization during ingestion for PII fields based on role-based access rules.
• Optimize wide table joins by pre-aggregating or denormalizing in gold-layer datasets.
• Monitor pipeline latency and data freshness with automated SLA dashboards.
• Version control data transformation logic using Git and integrate with CI/CD for deployment.

Module 4: Data Modeling for Analytics

• Design conformed dimensions to ensure consistency across multiple fact tables and business areas.
• Choose between star and snowflake schemas based on query patterns and maintenance overhead.
• Implement slowly changing dimension strategies aligned with business update frequency and audit needs.
• Model time-based facts using snapshot tables for balance and inventory reporting.
• Define grain for each fact table explicitly (e.g., daily sales per SKU per store).
• Denormalize lookup attributes into fact tables when query performance outweighs storage cost.
• Handle hierarchical dimensions (e.g., organizational units) with bridge tables or path encoding.
• Validate model assumptions with sample queries to prevent unusable aggregations.

Module 5: Semantic Layer and Metrics Standardization

• Define metric logic in a centralized semantic layer (e.g., using dbt metrics or Cube.js) to prevent definition drift.
• Implement metric versioning to track changes in calculation logic over time.
• Expose calculated KPIs (e.g., CAC, LTV) with documented inputs and business rules.
• Map semantic layer entities to business glossary terms for auditability.
• Configure row-level security policies in the semantic layer based on user attributes.
• Cache frequently accessed metrics using materialized views or in-memory engines.
• Integrate semantic layer with BI tools via standard APIs (ODBC/JDBC) to ensure broad compatibility.
• Monitor metric usage patterns to identify underutilized or redundant calculations.

Module 6: BI Tooling and Dashboard Development

• Select dashboard tools (e.g., Power BI, Tableau, Looker) based on embedded analytics and governance needs.
• Implement parameterized reports to support dynamic filtering without custom coding.
• Design responsive layouts that function effectively on desktop and tablet devices.
• Apply role-based dashboard access to restrict visibility of sensitive data.
• Use incremental refresh in dashboards to reduce load times for large datasets.
• Embed data validation alerts within dashboards to signal data quality issues.
• Standardize visual design (colors, labels, units) to reduce cognitive load and misinterpretation.
• Document data sources and update frequency directly in dashboard footers.

Module 7: Data Governance and Security

• Implement column-level encryption for sensitive fields using cloud KMS or envelope encryption.
• Enforce attribute-based access control (ABAC) for datasets based on user roles and data classification.
• Conduct quarterly access reviews to revoke unnecessary permissions on datasets and dashboards.
• Integrate data classification tools to auto-tag sensitive data (PII, financial) at rest.
• Log all data access and query activity for audit and anomaly detection.
• Establish data quality scorecards with thresholds for completeness, accuracy, and timeliness.
• Define data retention policies in alignment with legal holds and compliance requirements.
• Implement data lineage tracking from source to report to support impact analysis.

Module 8: Performance Optimization and Scalability

• Profile slow-running queries to identify missing indexes, inefficient joins, or data skew.
• Implement result set caching at multiple levels (BI tool, query engine, application).
• Precompute aggregations for high-frequency queries using materialized views or summary tables.
• Scale compute resources dynamically based on workload patterns (e.g., end-of-month reporting).
• Optimize file formats and compression (e.g., Parquet with ZSTD) for faster I/O.
• Partition large fact tables by date and cluster by high-cardinality dimensions.
• Monitor storage growth trends and implement lifecycle policies to manage costs.
• Test query performance under peak concurrency to identify bottlenecks in resource allocation.

Module 9: Change Management and Operational Support

• Establish a change request process for modifying data models or metrics with impact assessment.
• Develop runbooks for common operational issues (pipeline failures, data drift, access requests).
• Implement monitoring and alerting for data pipeline breaks and SLA violations.
• Conduct onboarding sessions for new data consumers to promote self-service best practices.
• Rotate credentials and refresh tokens on a scheduled basis to maintain security hygiene.
• Document data incident response procedures for data corruption or unauthorized access.
• Schedule regular review cycles for deprecated reports and unused datasets.
• Integrate user feedback loops to prioritize enhancements and deprecate low-value artifacts.