BDMP
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BDMP
The ICPP Business Data Management Professional (BDMP) certification equips analysts and data managers to translate business needs into robust data solutions, covering requirements, governance, master data, analytics, and operational data management.
90
Minutes
Who Should Take This
It is intended for business analysts, data stewards, and data‑centric professionals with two to ten years of experience who regularly bridge business objectives with data‑management initiatives. These learners seek to validate their expertise, enhance cross‑functional communication, and lead data‑driven decision making across the enterprise.
What's Covered
1
Domain 1: Business Data Requirements and Analysis
2
Domain 2: Business Data Governance and Ownership
3
Domain 3: Master Data and Reference Data for Business
4
Domain 4: Business Intelligence and Analytics Consumption
5
Domain 5: Operational Data Management
6
Domain 6: Business Data Strategy and Value
What's Included in AccelaStudy® AI
Adaptive Knowledge Graph
Practice Questions
Lesson Modules
Console Simulator Labs
Exam Tips & Strategy
20 Activity Formats
Course Outline
57 learning goals
1
Domain 1: Business Data Requirements and Analysis
3 topics
Business data requirements elicitation
- Implement data requirements gathering processes using interviews, workshops, surveys, and document analysis to capture business data needs from diverse stakeholder groups.
- Apply business process modeling techniques including BPMN notation to map data flows, identify data touchpoints, and document data dependencies within business workflows.
- Analyze business data requirements to identify conflicting needs, implicit assumptions, data gaps, and prioritization criteria for data management investments.
- Design a data requirements management framework that tracks requirement evolution, manages stakeholder expectations, and ensures traceability from business needs to data solutions.
Business rules and data specifications
- Implement business rule documentation including data validation rules, derivation rules, transformation rules, and constraint rules that govern business data quality and consistency.
- Apply data specification techniques to create functional data specifications that bridge business requirements and technical implementation with clear acceptance criteria.
- Analyze business rule conflicts and inconsistencies across departments to reconcile competing definitions and establish authoritative business rules for critical data elements.
- Design a business rules management strategy that centralizes rule definitions, enables version control, and provides transparency into how business data rules are applied across systems.
Stakeholder management and communication
- Implement stakeholder communication plans for data management initiatives including executive briefings, user newsletters, training schedules, and feedback collection mechanisms.
- Analyze stakeholder satisfaction with data management services by conducting surveys, tracking support requests, and measuring data service consumption patterns.
- Design a stakeholder engagement strategy that identifies data champions, builds advocacy networks, and establishes communication channels for ongoing data management collaboration.
2
Domain 2: Business Data Governance and Ownership
3 topics
Business data ownership and stewardship
- Implement business data ownership structures that assign accountability for data domains to business leaders with defined responsibilities for quality, access, and lifecycle management.
- Apply business glossary management practices to define, standardize, and maintain business terms, metrics, and KPI definitions across organizational departments.
- Analyze business data governance effectiveness by measuring ownership coverage, glossary adoption, business rule compliance, and cross-departmental data consistency.
- Design a business data governance strategy that embeds data accountability into business management structures, performance reviews, and organizational incentive systems.
Data quality from the business perspective
- Implement business-oriented data quality assessments using profiling, stakeholder surveys, and process audits to identify quality issues from the business user perspective.
- Apply data quality improvement techniques at the business process level including source data entry controls, workflow validation, and business rule enforcement at point of capture.
- Analyze the business impact of data quality issues by quantifying lost revenue, customer churn, regulatory penalties, and operational inefficiencies attributable to poor data quality.
Data governance for business operations
- Implement operational data governance controls including data entry validation, workflow-embedded quality checks, and automated business rule enforcement in transactional systems.
- Apply change management practices for data governance initiatives including impact assessment, user communication, training delivery, and adoption tracking for governance policies.
- Analyze operational data governance effectiveness by measuring data entry error rates, business rule violation frequencies, and process compliance across business systems.
3
Domain 3: Master Data and Reference Data for Business
3 topics
Customer and product master data
- Implement customer master data management including golden record creation, duplicate detection, data enrichment, and 360-degree customer view construction for CRM systems.
- Implement product master data management including product hierarchy design, attribute standardization, cross-catalog harmonization, and product information management for commerce systems.
- Analyze master data quality across business systems by evaluating duplicate rates, completeness gaps, cross-system consistency, and impact on customer experience and operational efficiency.
- Design a master data management strategy that establishes governance processes, quality standards, and integration patterns for critical business entities across the enterprise.
- Implement supplier master data management including vendor onboarding data quality, duplicate vendor detection, and vendor data harmonization across procurement and accounts payable systems.
Business reference data management
- Implement reference data governance including code table management, hierarchy maintenance, cross-reference mapping, and version control for enterprise reference datasets.
- Analyze reference data consistency across business applications to identify conflicting codes, missing mappings, and stale reference values that impair cross-system reporting.
Data matching and deduplication for business
- Implement data matching and deduplication processes for business entities including customer, vendor, and product records using business-defined matching rules and survivorship logic.
- Apply data enrichment techniques to augment business records with external data sources including firmographic data, demographic data, and industry classification codes.
- Analyze data matching effectiveness by evaluating precision, recall, false positive rates, and business impact of duplicate elimination on operational and analytical processes.
4
Domain 4: Business Intelligence and Analytics Consumption
3 topics
Business intelligence for decision making
- Implement business KPI frameworks that define measurable outcomes, calculation methodologies, data sources, reporting frequencies, and accountability for performance metrics.
- Apply data visualization best practices to design dashboards and reports that communicate business performance clearly, highlight actionable insights, and support executive decision-making.
- Analyze business intelligence effectiveness by evaluating report adoption rates, decision quality improvements, time-to-insight metrics, and user satisfaction with analytics tools.
- Design a self-service analytics governance strategy that enables business users to create their own analyses while maintaining data quality, security, and metric consistency.
Data literacy and organizational analytics culture
- Implement data literacy programs that build foundational data skills across the organization including data interpretation, critical thinking about data, and data-driven decision frameworks.
- Analyze organizational data culture maturity by assessing data literacy levels, analytics adoption patterns, and barriers to data-driven decision making across business units.
- Design a data culture transformation strategy that builds analytics capabilities incrementally, establishes data champions, and creates incentives for data-driven behavior.
- Design a data democracy strategy that enables secure self-service data access while maintaining data governance guardrails, quality standards, and metric consistency across the organization.
Advanced analytics for business users
- Implement predictive analytics consumption for business users including model output interpretation, confidence level understanding, and action recommendation follow-through.
- Apply business experimentation using analytics including A/B test design, control group management, and statistical significance interpretation for marketing and product decisions.
- Analyze the effectiveness of analytics-driven business decisions by tracking decision outcomes, measuring prediction accuracy, and quantifying business value from analytical insights.
5
Domain 5: Operational Data Management
3 topics
CRM and ERP data management
- Implement CRM data management practices including lead data quality, opportunity data standardization, account hierarchy management, and marketing data integration.
- Apply ERP data management practices including chart of accounts governance, vendor master data, material master data, and cross-module data consistency for financial and operational reporting.
- Analyze cross-system data integration challenges between CRM, ERP, and specialized business applications to identify reconciliation gaps and recommend harmonization approaches.
Data management for business compliance
- Implement business data retention and disposal processes that comply with legal requirements, contractual obligations, and regulatory mandates while supporting operational needs.
- Apply data privacy compliance from the business perspective including consent tracking, customer data access requests, data portability, and right-to-erasure processes in business systems.
- Design a business data compliance strategy that integrates data governance, privacy controls, and regulatory reporting into everyday business operations without impeding productivity.
Supply chain and financial data management
- Implement supply chain data management including supplier master data, procurement data quality, inventory data accuracy, and logistics tracking data integration.
- Apply financial data management practices including chart of accounts governance, journal entry validation, reconciliation processes, and audit trail maintenance for financial reporting.
- Analyze cross-functional data consistency between finance, operations, and sales systems to identify reconciliation gaps affecting management reporting accuracy.
6
Domain 6: Business Data Strategy and Value
2 topics
Data as a business asset
- Apply data valuation techniques to quantify the business value of data assets including customer databases, product catalogs, and intellectual property datasets.
- Analyze data monetization opportunities by identifying data products, partnerships, and analytics services that could generate revenue or reduce costs for the organization.
- Design a business data strategy that positions data as a competitive asset, defining investment priorities, capability building, and measurable business outcomes from data management initiatives.
Data-driven digital transformation
- Implement data management practices for digital transformation initiatives including customer journey data integration, digital experience analytics, and omnichannel data unification.
- Analyze digital transformation data maturity by assessing real-time data availability, customer data completeness, and analytics capability alignment with digital business models.
- Design a data-driven digital transformation roadmap that sequences data capability building, technology adoption, and organizational change to enable new digital business models.
Scope
Included Topics
- Business data management practices as tested on the ICCP Business Data Management Professional exam, bridging business strategy with data management execution.
- Business data analysis and requirements including business process modeling, data requirements elicitation, stakeholder management, and translating business needs into data management solutions.
- Data management for business operations including CRM data management, ERP data integration, supply chain data, financial data management, and operational reporting.
- Business-oriented data governance including data ownership from a business perspective, business glossary management, business rules definition, and data quality from the business user viewpoint.
- Data-driven decision making including business intelligence consumption, analytics literacy, KPI definition, dashboard design from a business perspective, and self-service analytics governance.
- Master data management from a business perspective including customer data management, product data management, supplier data management, and cross-functional data harmonization.
Not Covered
- Deep technical data engineering, database administration, and infrastructure management covered by technical data professional certifications.
- Advanced data science, machine learning, and statistical modeling covered by the CDS certification.
- Enterprise-level data governance program design and stewardship operations covered by the DGSP certification.
- Big data platform architecture and distributed systems engineering covered by the CBDP certification.
Official Exam Page
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