Enterprise Data Management Checklist
A comprehensive checklist for implementing and maintaining enterprise-grade data management
practices, focusing on data governance, quality, master data management, integration,
privacy, and analytics. This checklist covers essential data management principles while
embracing modern data architectures and practices.
Data Quality
Required
Quality Framework
ⓘ
Implementation Questions:
Have you defined comprehensive data quality dimensions including accuracy,
completeness, consistency, and timeliness?
Are quality metrics established for each critical data element with
measurable acceptance thresholds?
Is there a systematic approach to identify and prioritize data quality
issues based on business impact?
Are quality metrics automatically calculated and reported with trend
analysis and alerting capabilities?
Have you established data quality SLAs with clear accountability and
remediation processes?
Is there integration between quality monitoring and business process
monitoring to show impact?
Key Considerations:
Focus on business-relevant quality metrics rather than purely technical
measures
Establish baseline quality measurements before implementing improvement
initiatives
Design metrics to be actionable and tied to specific remediation processes
Balance automation with human judgment for complex quality assessment
scenarios
Red Flags:
Quality metrics are defined but not regularly monitored or acted upon when
thresholds are breached
Focus on technical quality measures without understanding business impact or
user requirements
Quality framework is overly complex and difficult to understand or implement
consistently
Metrics show declining quality trends without corresponding improvement
initiatives or root cause analysis
Data Validation
ⓘ
Implementation Questions:
Are validation rules implemented at multiple points including data
ingestion, transformation, and consumption?
Do validation checks cover data format, range, business logic, and
referential integrity constraints?
Are validation rules configurable and maintainable without requiring code
changes for business rule updates?
Is there automated handling of validation failures with appropriate error
logging and notification systems?
Are validation rules tested and versioned to ensure consistency across
environments and deployments?
Do validation processes support both batch and real-time data processing
scenarios?
Key Considerations:
Implement validation as early as possible in data pipelines to prevent
propagation of poor quality data
Design validation rules to be business-driven and understandable to
non-technical stakeholders
Balance strict validation with business flexibility to handle exceptional
cases and evolving requirements
Ensure validation performance doesn't significantly impact data processing
throughput and SLAs
Red Flags:
Validation rules are hardcoded and difficult to modify when business
requirements change
Validation failures are not properly handled, causing data pipeline failures
or data loss
Rules are too strict and cause frequent false positives that lead to
operational overhead
Validation is only performed at final consumption point, allowing poor
quality data to propagate through systems
Quality Monitoring
ⓘ
Implementation Questions:
Is continuous data quality monitoring implemented with real-time alerting
for critical quality degradation?
Are quality reports automatically generated and distributed to relevant
stakeholders and data owners?
Does monitoring cover all critical data assets including transactional,
analytical, and master data?
Are quality trends tracked over time to identify patterns and proactive
improvement opportunities?
Is there integration between quality monitoring and incident management
processes?
Are monitoring dashboards accessible to business users with appropriate
visualization and drill-down capabilities?
Key Considerations:
Design monitoring to provide actionable insights rather than just reporting
on quality issues
Ensure monitoring overhead doesn't significantly impact system performance
or data processing SLAs
Customize reporting frequency and content based on stakeholder needs and
data criticality
Implement automated anomaly detection to identify quality issues before they
impact business processes
Red Flags:
Quality reports are generated but not regularly reviewed or acted upon by
responsible parties
Monitoring focuses on lagging indicators without providing early warning of
emerging quality issues
Quality dashboards are too technical for business users or too simplified
for operational teams
Monitoring systems generate excessive false alarms that lead to alert
fatigue and ignored notifications
Data Cleansing
ⓘ
Implementation Questions:
Are standardized data cleansing processes implemented across all data
ingestion and integration points?
Have you identified and implemented appropriate enrichment data sources for
completeness and accuracy improvements?
Are cleansing rules documented, version-controlled, and testable across
different environments?
Is there a systematic approach to validate cleansing effectiveness and
measure improvement outcomes?
Are enrichment processes designed to handle data source availability and
quality variations?
Do cleansing processes maintain audit trails for compliance and
troubleshooting requirements?
Key Considerations:
Prioritize cleansing efforts based on business impact and data usage
patterns rather than technical convenience
Implement reusable cleansing components that can be applied consistently
across different data domains
Balance automated cleansing with human review for complex or high-value data
correction scenarios
Design processes to be transparent and explainable to support audit and
compliance requirements
Red Flags:
Cleansing processes are ad-hoc and inconsistent across different systems and
data sources
Heavy reliance on manual cleansing that doesn't scale with data volume
growth
Enrichment introduces new data quality issues or dependencies without proper
validation
Cleansing logic is complex and undocumented, making maintenance and
troubleshooting difficult
Master Data Management
Required
MDM Strategy
ⓘ
Implementation Questions:
Have you identified and prioritized critical master data domains based on
business impact and complexity?
Is there a clear MDM architecture strategy (centralized, federated, or
hybrid) aligned with enterprise architecture?
Are master data governance roles and processes defined including data
stewardship and ownership?
Have you established master data lifecycle management including creation,
maintenance, and retirement processes?
Is there a roadmap for MDM implementation that phases approach based on
business value and technical complexity?
Are integration patterns defined for how systems will consume and contribute
to master data?
Key Considerations:
Start with high-value, well-understood master data domains before expanding
to complex or contentious areas
Align MDM strategy with existing enterprise architecture and avoid
disrupting stable business processes
Consider both operational and analytical master data requirements in
architecture design
Plan for scalability and performance requirements based on transaction
volumes and user concurrency
Red Flags:
MDM strategy is purely technology-focused without sufficient business
engagement and ownership
Attempting to implement MDM across all domains simultaneously without phased
approach
Architecture decisions are made without considering existing system
dependencies and integration complexity
Strategy lacks clear success metrics and business value proposition for
investment justification
Data Models
ⓘ
Implementation Questions:
Are master data models designed to support both current business
requirements and anticipated future needs?
Do data models include comprehensive attribute definitions, relationships,
and business rules?
Are hierarchical relationships clearly defined with support for multiple
classification schemes?
Is there version control and change management for data model evolution and
backwards compatibility?
Do models support localization and globalization requirements for
multinational organizations?
Are models validated with business stakeholders and aligned with industry
standards where applicable?
Key Considerations:
Balance model completeness with implementation complexity and
maintainability requirements
Design models to be extensible and configurable rather than requiring code
changes for business evolution
Consider performance implications of complex hierarchies and relationships
for operational systems
Ensure models can accommodate exceptions and edge cases without compromising
data integrity
Red Flags:
Data models are overly complex and difficult for business users to
understand or maintain
Models are designed based on technical convenience rather than business
requirements and usage patterns
Hierarchical relationships are rigid and cannot accommodate organizational
or business model changes
Models lack proper documentation and business context, making maintenance
and evolution difficult
Data Matching
ⓘ
Implementation Questions:
Are matching algorithms implemented with configurable rules for different
entity types and business contexts?
Do matching processes handle fuzzy matching, phonetic similarities, and
business synonym recognition?
Is there automated conflict resolution with business rules for determining
authoritative data sources?
Are matching results reviewable by business users with workflow for
exception handling and manual resolution?
Do processes maintain lineage and audit trails for regulatory compliance and
troubleshooting?
Are matching thresholds tunable based on business tolerance for false
positives versus false negatives?
Key Considerations:
Design matching processes to balance automation with human oversight for
complex or high-value entities
Consider performance implications of matching algorithms on real-time
operations and batch processing
Implement machine learning approaches that improve matching accuracy over
time with feedback loops
Plan for matching rule maintenance as business requirements and data sources
evolve
Red Flags:
Matching processes produce high rates of false positives or false negatives
that require extensive manual correction
Rules are hardcoded and difficult to adjust when business requirements or
data patterns change
Conflict resolution logic is unclear or inconsistent, leading to
unpredictable data outcomes
Matching processes don't scale with data volume growth and become
performance bottlenecks
Change Management
ⓘ
Implementation Questions:
Are change management workflows implemented with appropriate approval
processes for different types of master data changes?
Do processes include impact analysis to understand downstream effects of
master data modifications?
Is there automated propagation of approved changes to all consuming systems
and applications?
Are change notifications sent to relevant stakeholders with sufficient lead
time for system adjustments?
Do processes support both bulk changes and individual entity modifications
with appropriate validation?
Is there rollback capability for changes that cause unintended business or
system impacts?
Key Considerations:
Design change processes to balance data quality improvement with operational
stability
Implement risk-based approval workflows that scale with change complexity
and business impact
Ensure change management integrates with existing ITIL or enterprise change
management processes
Plan for emergency change procedures that can address critical data issues
quickly
Red Flags:
Changes are made directly to master data without proper approval or impact
assessment
Change processes are so complex they discourage necessary data quality
improvements
Downstream systems receive changes without sufficient notification or
preparation time
Change management creates bottlenecks that prevent timely resolution of
critical data issues
Data Integration
Required
Integration Strategy
ⓘ
Implementation Questions:
Have you defined integration patterns and standards for different data
movement scenarios (batch, real-time, API-based)?
Is there a comprehensive enterprise integration architecture that addresses
scalability and performance requirements?
Are data transformation and mapping standards established with reusable
components and patterns?
Do integration strategies address both operational and analytical data
movement requirements?
Are security and compliance requirements integrated into all data movement
and transformation processes?
Is there a roadmap for integration modernization that addresses technical
debt and emerging requirements?
Key Considerations:
Design integration architecture to support both current needs and
anticipated future growth and complexity
Balance consistency with flexibility to accommodate diverse source systems
and data formats
Consider total cost of ownership including development, maintenance, and
operational costs
Plan for integration testing and validation across different environments
and deployment scenarios
Red Flags:
Integration approach is primarily point-to-point without standardized
patterns or reusable components
Architecture decisions are made in isolation without considering
enterprise-wide integration requirements
Strategy focuses on technical capabilities without sufficient consideration
of business requirements
Integration complexity grows exponentially with each new system without
architectural governance
ETL Processes
ⓘ
Implementation Questions:
Are ETL/ELT processes designed with proper error handling, logging, and
restart capabilities for operational resilience?
Do processes include data validation and quality checks at each
transformation stage?
Is there comprehensive monitoring of process performance, data volumes, and
processing times with alerting?
Are processes optimized for parallel processing and resource utilization to
meet SLA requirements?
Do pipelines support both full and incremental processing modes with
automatic mode selection?
Are transformation logic and business rules externalized and maintainable by
business users where appropriate?
Key Considerations:
Design processes to be idempotent and recoverable to handle infrastructure
failures and restarts
Implement transformation logic that can adapt to source data schema changes
without pipeline failures
Balance processing speed with resource consumption to optimize cost and
performance
Plan for process scalability as data volumes grow and additional sources are
integrated
Red Flags:
ETL processes frequently fail and require manual intervention for completion
Transformation logic is hardcoded and difficult to modify when business
requirements change
Processing times increase significantly with data volume growth without
corresponding optimization
Monitoring relies on manual checking rather than automated alerting and
anomaly detection
Data Pipeline
ⓘ
Implementation Questions:
Is pipeline architecture designed for scalability with auto-scaling
capabilities based on workload demands?
Are pipelines implemented with proper orchestration and dependency
management across complex workflows?
Do monitoring systems track end-to-end data lineage and processing latency
across pipeline stages?
Are pipelines containerized or virtualized for consistent deployment across
environments?
Is there automated testing and validation for pipeline code changes and
deployments?
Do pipelines support schema evolution and backward compatibility for source
and target systems?
Key Considerations:
Design pipelines using modern orchestration tools that support complex
dependencies and parallel execution
Implement comprehensive logging and observability to enable quick
troubleshooting and performance optimization
Plan for multi-environment deployment with consistent configuration
management and promotion processes
Consider both streaming and batch processing requirements in architecture
design decisions
Red Flags:
Pipeline architecture is monolithic and difficult to modify or extend for
new requirements
Monitoring provides limited visibility into pipeline performance and data
quality issues
Pipelines are tightly coupled and failures cascade across multiple
processing workflows
Deployment processes are manual and error-prone, leading to inconsistencies
across environments
Error Handling
ⓘ
Implementation Questions:
Are error handling procedures implemented at multiple levels including
validation, transformation, and loading stages?
Do recovery procedures include automatic retry mechanisms with exponential
backoff and circuit breaker patterns?
Is there quarantine and dead letter queue management for data that cannot be
processed successfully?
Are error notifications and escalation procedures defined with appropriate
stakeholder communication?
Do procedures include data reconciliation and integrity checking after error
recovery?
Are recovery processes tested regularly to ensure they work effectively
during actual incidents?
Key Considerations:
Design error handling to be granular enough to isolate issues without
affecting unrelated processing
Implement comprehensive logging and audit trails to support root cause
analysis and debugging
Balance automatic recovery with human intervention requirements for complex
or high-value data
Plan for different types of errors including transient, systematic, and data
quality issues
Red Flags:
Error handling is minimal and causes entire pipeline failures when
individual records have issues
Recovery procedures are manual and time-consuming, leading to extended data
processing delays
Error logging is insufficient to support effective troubleshooting and root
cause analysis
Recovery testing is infrequent and procedures fail during actual incident
scenarios
Suggested
Real-time Integration
ⓘ
Implementation Questions:
Are streaming platforms implemented with appropriate message queuing and
event processing capabilities?
Do real-time processes handle data transformation and enrichment with
low-latency requirements?
Is there monitoring of stream processing latency, throughput, and error
rates with alerting?
Are real-time integrations designed with backpressure handling and flow
control mechanisms?
Do streaming processes support exactly-once processing guarantees for
critical data flows?
Are real-time and batch processing architectures integrated to provide
consistent data views?
Key Considerations:
Choose streaming technologies that align with scalability, durability, and
performance requirements
Design stream processing to handle out-of-order data and late-arriving
events appropriately
Consider operational complexity and skills requirements for streaming
platform management
Plan for disaster recovery and failover scenarios in streaming architecture
design
Red Flags:
Real-time systems cannot handle peak data volumes and frequently experience
backpressure or failures
Stream processing introduces data inconsistencies or duplicate processing
without proper deduplication
Monitoring and debugging capabilities are insufficient for complex streaming
data flows
Real-time and batch systems produce different results for the same data
without reconciliation processes
API Management
ⓘ
Implementation Questions:
Are API management platforms implemented with proper security, throttling,
and version management?
Do APIs follow consistent design standards and provide comprehensive
documentation for consumers?
Is there monitoring of API performance, usage patterns, and error rates with
SLA tracking?
Are APIs designed with pagination, filtering, and sorting capabilities for
large dataset handling?
Do API integration patterns support both synchronous and asynchronous data
exchange scenarios?
Are APIs versioned and backward-compatible to support existing integrations
during evolution?
Key Considerations:
Design APIs to be self-service and discoverable through developer portals
and catalogs
Implement proper authentication and authorization mechanisms aligned with
enterprise security policies
Consider API rate limiting and cost management for both internal and
external consumers
Plan for API lifecycle management including deprecation and migration
strategies
Red Flags:
APIs are inconsistently designed and difficult for developers to understand
and integrate
API performance degrades significantly under load without proper scaling or
caching mechanisms
Security implementations are weak or inconsistent across different API
endpoints
API changes break existing integrations due to lack of versioning and
compatibility management
Data Privacy & Security
Required
Privacy Framework
ⓘ
Implementation Questions:
Have you established comprehensive privacy policies covering data
collection, processing, storage, and deletion?
Are privacy impact assessments mandatory for all new data processing
activities and system implementations?
Do policies address cross-border data transfers and international privacy
regulation compliance?
Are data subject rights processes implemented including access,
rectification, portability, and deletion?
Is there a privacy governance structure with designated privacy officers and
accountability frameworks?
Are privacy policies regularly reviewed and updated to address evolving
regulations and business requirements?
Key Considerations:
Align privacy framework with applicable regulations (GDPR, CCPA, PIPEDA) and
industry standards
Design policies to be practical and implementable rather than purely
compliance-focused documentation
Ensure privacy requirements are integrated into system design and
development processes from inception
Plan for regular privacy training and awareness programs for all staff
handling personal data
Red Flags:
Privacy policies exist but are not actively implemented or enforced in
day-to-day operations
Policies are generic and don't address specific business processes and data
handling scenarios
Privacy requirements are treated as afterthoughts rather than integral parts
of system design
Staff lack awareness of privacy requirements and their responsibilities for
data protection
Data Protection
ⓘ
Implementation Questions:
Is data encrypted both at rest and in transit using industry-standard
encryption algorithms and key lengths?
Are encryption key management systems implemented with proper key rotation
and access controls?
Do data protection measures include database-level encryption, field-level
encryption, and tokenization where appropriate?
Are backup and disaster recovery systems protected with the same encryption
standards as production data?
Is there monitoring and alerting for encryption failures and unauthorized
access attempts?
Are encryption implementations regularly tested and audited for compliance
and effectiveness?
Key Considerations:
Balance security requirements with system performance and operational
complexity considerations
Implement encryption key management that supports both automated operations
and compliance requirements
Consider different encryption approaches based on data sensitivity and usage
patterns
Plan for encryption key recovery and disaster scenarios without compromising
security
Red Flags:
Encryption is implemented inconsistently across different systems and data
stores
Key management practices are weak with shared keys or inadequate access
controls
Performance issues from encryption cause business process delays or system
timeouts
Encryption implementations use outdated algorithms or insufficient key
lengths for current security standards
Enhanced Access Controls
ⓘ
Implementation Questions:
Are role-based access controls implemented with attribute-based access
control (ABAC) for complex authorization scenarios?
Do access controls support dynamic policies based on data classification,
user context, and business rules?
Is comprehensive audit logging implemented for all data access,
modification, and administrative activities?
Are real-time monitoring systems implemented with anomaly detection for
unusual access patterns?
Do access controls integrate with enterprise identity and access management
systems?
Are audit logs protected from tampering and regularly reviewed for security
incidents?
Key Considerations:
Design access controls to be fine-grained without creating excessive
administrative overhead
Implement monitoring that provides actionable security insights without
overwhelming security teams
Ensure audit logging captures sufficient detail for forensic analysis and
compliance reporting
Plan for access control scalability as user base and data volume grow
Red Flags:
Access controls are coarse-grained and provide excessive privileges to users
and applications
Audit logs are incomplete, difficult to analyze, or not regularly reviewed
for security incidents
Monitoring systems generate excessive false positives that lead to alert
fatigue and ignored violations
Access control implementation significantly impacts system performance and
user experience
Privacy Impact Assessments
ⓘ
Implementation Questions:
Are privacy impact assessments (PIAs) mandatory for all new systems and
significant changes to existing data processing?
Do PIAs include comprehensive analysis of data flows, processing purposes,
and legal bases for processing?
Are assessments conducted by qualified personnel with appropriate privacy
and legal expertise?
Do PIAs result in actionable recommendations that are tracked and
implemented before system deployment?
Are assessments regularly updated when business processes or regulatory
requirements change?
Do PIAs include stakeholder consultation and review processes with
appropriate sign-offs?
Key Considerations:
Integrate PIA processes into project management and system development
lifecycles
Design assessments to be proportionate to privacy risks rather than
one-size-fits-all approaches
Ensure PIAs consider both current and potential future uses of personal data
Plan for regular review and updates of PIAs as systems and business
processes evolve
Red Flags:
PIAs are conducted as paper exercises without practical implementation of
recommendations
Assessments are rushed or superficial, missing significant privacy risks and
compliance issues
PIAs are conducted by personnel without sufficient privacy expertise or
independence
Assessment findings are not tracked or implemented, leaving privacy risks
unaddressed
Data Subject Rights Management
ⓘ
Implementation Questions:
Are automated processes implemented to handle data subject requests within
regulatory timeframes?
Do processes include identity verification and request validation to prevent
unauthorized access?
Is there comprehensive data discovery capability to locate all personal data
across systems and databases?
Are deletion processes implemented with proper verification and audit trails
for compliance demonstration?
Do processes handle complex scenarios including data in backups, logs, and
third-party systems?
Are request management systems integrated with customer service and case
management platforms?
Key Considerations:
Design processes to be user-friendly and accessible while maintaining
security and verification requirements
Implement automation where possible to reduce manual effort and ensure
consistent response times
Plan for edge cases and exceptions including data required for legal or
regulatory purposes
Ensure processes can handle high volumes of requests without impacting
system performance
Red Flags:
Processes rely heavily on manual effort and frequently miss regulatory
response timeframes
Data discovery is incomplete, missing personal data in some systems or data
stores
Deletion processes don't properly remove data from backups, logs, or
integrated systems
Request verification is weak, creating risks of unauthorized data access or
manipulation
Cross-Border Data Transfer Controls
ⓘ
Implementation Questions:
Are all cross-border data transfers documented with legal basis and transfer
mechanisms (adequacy decisions, SCCs, BCRs)?
Do transfer controls include automated monitoring and blocking of
unauthorized international data movement?
Are data processing agreements in place with all international vendors and
service providers?
Is there regular review and updates of transfer mechanisms when adequacy
decisions or regulations change?
Do controls include data localization requirements and geographic
restrictions where applicable?
Are transfer risk assessments conducted considering destination country
surveillance laws and data protection standards?
Key Considerations:
Stay current with evolving international privacy regulations and adequacy
decisions
Implement technical controls that align with legal requirements rather than
relying solely on contractual protections
Design transfer controls to be flexible and adaptable as business and
regulatory requirements change
Plan for scenarios where transfer mechanisms become invalid and alternative
approaches are needed
Red Flags:
International transfers occur without proper legal basis or transfer
mechanisms in place
Transfer documentation is outdated and doesn't reflect current business
practices or regulatory requirements
Technical controls don't prevent unauthorized data movement across
jurisdictions
Risk assessments for international transfers are superficial and don't
consider destination country risks
Compliance
ⓘ
Implementation Questions:
Have you conducted comprehensive gap analysis against applicable privacy
regulations in all operating jurisdictions?
Are compliance monitoring systems implemented with regular assessment and
reporting capabilities?
Do you have documented evidence of compliance including policies,
procedures, and technical controls?
Are staff trained on privacy compliance requirements relevant to their roles
and responsibilities?
Is there regular legal review and updates to address evolving regulatory
requirements and guidance?
Are compliance violation response procedures established with appropriate
escalation and remediation processes?
Key Considerations:
Implement compliance as operational practice rather than one-time project or
documentation exercise
Plan for ongoing compliance maintenance as regulations evolve and business
processes change
Ensure compliance programs address both technical and organizational
requirements
Design compliance monitoring to provide early warning of potential
violations
Red Flags:
Compliance efforts focus on documentation rather than practical
implementation of privacy protections
Gap analysis is outdated and doesn't reflect current business practices or
regulatory developments
Compliance monitoring is reactive rather than proactive, identifying
violations after they occur
Staff lack awareness of privacy compliance requirements and their personal
responsibilities
Data Analytics
Required
Analytics Strategy
ⓘ
Implementation Questions:
Have you defined a comprehensive analytics strategy aligned with business
objectives and data-driven decision-making goals?
Are analytics capabilities architected to support both self-service and
advanced analytics use cases?
Is there a clear roadmap for analytics maturity progression from descriptive
to predictive and prescriptive analytics?
Are analytics governance frameworks established including data access, usage
policies, and quality standards?
Do analytics strategies address both real-time and batch processing
requirements for different business scenarios?
Are analytics platforms designed for scalability to handle growing data
volumes and user concurrency?
Key Considerations:
Align analytics strategy with business strategy and ensure executive
sponsorship and support
Design analytics architecture to be flexible and adaptable to evolving
business requirements
Consider total cost of ownership including licensing, infrastructure, and
human resource costs
Plan for analytics skills development and change management across the
organization
Red Flags:
Analytics strategy is technology-focused without clear business value
proposition or user adoption plans
Multiple disconnected analytics tools and platforms exist without
integration or consistent user experience
Analytics initiatives consume significant resources without demonstrable
business impact or ROI
Strategy lacks governance framework leading to inconsistent data definitions
and conflicting insights
Data Warehouse
ⓘ
Implementation Questions:
Have you chosen appropriate architecture (data warehouse, data lake, or data
lakehouse) based on business requirements and use cases?
Are data models designed with proper dimensional modeling or denormalized
structures for analytical performance?
Is the architecture scalable with partitioning, indexing, and performance
optimization strategies implemented?
Are data retention and archiving policies implemented with automated
lifecycle management?
Do storage and compute resources scale independently to optimize cost and
performance?
Are security and access controls implemented at data, schema, and table
levels with row and column-level security?
Key Considerations:
Balance structured and unstructured data storage requirements in
architectural decisions
Design for both batch and streaming data ingestion patterns with consistent
data formats
Consider multi-cloud and hybrid deployment scenarios for flexibility and
vendor independence
Plan for schema evolution and backwards compatibility as data sources and
requirements change
Red Flags:
Architecture choice is driven by technology trends rather than specific
business and analytical requirements
Performance degrades significantly as data volumes grow without
corresponding optimization efforts
Data lake becomes a "data swamp" with poor organization and limited
discoverability
Storage costs grow exponentially without proper lifecycle management and
archiving strategies
BI Platform
ⓘ
Implementation Questions:
Are BI platforms selected and configured to support diverse user personas
from executives to analysts?
Do reporting capabilities include interactive dashboards, scheduled reports,
and ad-hoc query functionality?
Are semantic layers implemented to provide consistent business definitions
and metrics across reports?
Do platforms support embedded analytics and API-driven integration with
business applications?
Are mobile and responsive design capabilities implemented for access across
different devices and contexts?
Is there centralized content management with version control and deployment
pipelines for reports and dashboards?
Key Considerations:
Prioritize user adoption through intuitive interfaces and integration with
existing business workflows
Design semantic layers to abstract technical complexity while providing
business context
Implement performance optimization including caching, aggregations, and
query optimization
Plan for governance including content certification, access controls, and
usage monitoring
Red Flags:
BI platforms are complex and require extensive training for basic business
user tasks
Report performance is poor leading to user frustration and abandoned
analytics initiatives
Multiple reporting tools exist with inconsistent data and metrics causing
confusion and mistrust
Content proliferation occurs without governance leading to outdated and
inaccurate reports
Data Models
ⓘ
Implementation Questions:
Are analytical data models designed using dimensional modeling techniques
with proper fact and dimension table structures?
Do models support both detailed transactional analysis and aggregated
performance reporting requirements?
Are business metrics consistently defined with clear calculations and data
lineage documentation?
Do data models support slowly changing dimensions and historical trend
analysis capabilities?
Are models optimized for query performance with appropriate indexing,
partitioning, and materialized view strategies?
Is there version control and change management for data model evolution and
metric definitions?
Key Considerations:
Design models to be business-friendly and understandable while maintaining
technical efficiency
Balance model normalization with query performance requirements for
analytical workloads
Implement conformed dimensions to ensure consistency across different
subject areas
Plan for model scalability as data volumes and analytical complexity grow
Red Flags:
Data models are overly normalized causing complex joins and poor query
performance
Business metrics are inconsistently calculated across different reports and
applications
Models lack proper documentation making maintenance and enhancement
difficult
Historical data handling is inconsistent leading to incorrect trend analysis
and comparisons
Suggested
Advanced Analytics
ⓘ
Implementation Questions:
Are machine learning platforms implemented with support for model
development, training, and deployment lifecycles?
Do advanced analytics capabilities include statistical analysis, predictive
modeling, and optimization techniques?
Are MLOps practices implemented with model versioning, A/B testing, and
performance monitoring?
Do platforms support both batch and real-time model scoring for different
business use cases?
Are feature stores implemented to enable consistent feature engineering and
reuse across models?
Is there model governance including approval processes, bias testing, and
explainability requirements?
Key Considerations:
Start with high-value, low-complexity use cases to demonstrate business
value and build organizational capability
Ensure data quality and volume are sufficient to support reliable model
development and training
Plan for ongoing model maintenance and retraining as data patterns and
business conditions evolve
Consider ethical AI principles and bias detection throughout model
development and deployment
Red Flags:
ML models are developed but not deployed to production or used for actual
business decisions
Model performance degrades over time without monitoring or retraining
processes
Advanced analytics projects consume significant resources without measurable
business impact
Models are black boxes without explainability capabilities required for
business confidence
Self-Service Analytics
ⓘ
Implementation Questions:
Are self-service platforms implemented with intuitive interfaces that
require minimal technical training?
Do platforms provide guided analytics workflows and templates for common
business scenarios?
Are certified data assets and pre-built analytics components available for
business user consumption?
Do self-service capabilities include data discovery, visualization, and
basic statistical analysis functions?
Are governance controls implemented to prevent inappropriate data access
while enabling self-service flexibility?
Is there support and training available for business users to effectively
utilize self-service capabilities?
Key Considerations:
Balance self-service flexibility with governance and data quality controls
Provide curated data sets and analytics components to accelerate user
adoption and success
Implement user community and knowledge sharing to support peer-to-peer
learning
Monitor self-service usage patterns to identify training needs and platform
improvements
Red Flags:
Self-service platforms are too complex and require significant IT support
for basic tasks
Business users create inconsistent or incorrect analyses due to lack of
guidance and governance
Self-service capabilities are limited and don't address real business
analytical requirements
Platform adoption is low due to poor user experience or insufficient data
availability
Data Operations
Required
DataOps Framework
ⓘ
Implementation Questions:
Are DataOps practices implemented with continuous integration and deployment
for data pipelines and analytics code?
Do automation frameworks include data quality testing, schema validation,
and performance regression testing?
Are infrastructure and configuration management automated with version
control and reproducible deployments?
Do DataOps processes include collaboration workflows between data engineers,
analysts, and business stakeholders?
Are monitoring and observability practices implemented across data pipelines
with automated alerting and remediation?
Is there automated documentation generation and metadata management
integrated with development workflows?
Key Considerations:
Start with high-impact, low-complexity automation scenarios before expanding
to comprehensive DataOps implementation
Ensure DataOps practices integrate with existing DevOps and IT service
management processes
Design automation to reduce manual effort while maintaining appropriate
human oversight and control
Plan for cultural change management to support collaborative DataOps
practices across teams
Red Flags:
Data pipeline deployments are manual and error-prone, leading to frequent
production issues
Testing is limited or manual, allowing data quality and pipeline issues to
reach production
DataOps implementation focuses on tools without addressing process and
cultural requirements
Automation increases complexity without providing corresponding improvements
in reliability or efficiency
Monitoring
ⓘ
Implementation Questions:
Are comprehensive monitoring systems implemented covering data pipeline
performance, quality, and availability metrics?
Do alerting mechanisms provide intelligent notification with appropriate
escalation and routing based on severity and impact?
Are monitoring dashboards designed for different user personas including
operations, business, and executive stakeholders?
Do monitoring systems include anomaly detection and predictive alerting for
proactive issue identification?
Are monitoring data and metrics integrated with enterprise monitoring and
incident management platforms?
Is there end-to-end observability across data flows with distributed tracing
and lineage tracking?
Key Considerations:
Design monitoring to provide actionable insights rather than just alerting
on symptoms
Balance monitoring comprehensiveness with system performance impact and
operational overhead
Implement monitoring automation to reduce manual effort while maintaining
visibility and control
Plan for monitoring scalability as data systems and operational complexity
grow
Red Flags:
Monitoring systems generate excessive false positives leading to alert
fatigue and ignored notifications
Critical data issues are discovered by business users rather than proactive
monitoring systems
Monitoring dashboards are too technical for business stakeholders or too
simplified for operations teams
Response times to data incidents are slow due to poor alerting and
escalation procedures
SLA Management
ⓘ
Implementation Questions:
Are data SLAs defined with measurable metrics for availability, latency,
throughput, and quality based on business requirements?
Do SLAs include clear accountability and escalation procedures when
performance thresholds are not met?
Are performance metrics automatically monitored and reported with trend
analysis and forecasting capabilities?
Do SLAs differentiate between critical, important, and standard data
services based on business impact?
Are SLA achievements regularly reviewed with business stakeholders and used
for continuous improvement?
Do metrics include both technical performance and business value indicators?
Key Considerations:
Design SLAs to be achievable and meaningful rather than aspirational or
purely technical
Ensure SLA definitions align with actual business requirements and usage
patterns
Implement SLA monitoring that provides early warning before thresholds are
breached
Plan for SLA evolution as business requirements and system capabilities
mature
Red Flags:
SLAs are defined but not regularly monitored or enforced, making them
meaningless commitments
Performance metrics focus on technical measures without connection to
business impact
SLA thresholds are frequently breached without corresponding improvement
actions or accountability
SLAs are one-size-fits-all without consideration of different business
criticality and requirements
Incident Management
ⓘ
Implementation Questions:
Are incident management procedures defined with clear classification,
escalation, and resolution workflows?
Do procedures include automated incident detection and notification systems
with appropriate routing?
Are incident response teams identified with defined roles and
responsibilities including on-call rotations?
Do procedures include communication templates and stakeholder notification
processes for different incident types?
Are incident resolution procedures documented with runbooks and
troubleshooting guides for common issues?
Is there post-incident review process with root cause analysis and
prevention improvement actions?
Key Considerations:
Integrate data incident management with existing IT service management and
enterprise incident response processes
Design procedures to be scalable and handle multiple concurrent incidents
without overwhelming response teams
Ensure incident management addresses both technical issues and business
impact considerations
Plan for incident communication that balances transparency with appropriate
confidentiality requirements
Red Flags:
Incident response is ad-hoc and inconsistent, leading to prolonged
resolution times and business impact
Incident classification and escalation procedures are unclear causing delays
in appropriate response
Communication during incidents is poor, leaving business stakeholders
without status updates
Post-incident reviews are superficial and don't lead to meaningful
prevention improvements
Suggested
Automation
ⓘ
Implementation Questions:
Are orchestration platforms implemented with comprehensive workflow
management and dependency handling capabilities?
Do automated operations include job scheduling, resource allocation, and
dynamic scaling based on workload demands?
Are operations workflows designed with proper error handling, retry
mechanisms, and failure recovery procedures?
Do orchestration systems provide visibility and control through monitoring
dashboards and operational interfaces?
Are automated operations integrated with enterprise scheduling and resource
management systems?
Is there configuration management and version control for orchestration
workflows and operational procedures?
Key Considerations:
Select orchestration tools that balance functionality with operational
simplicity and maintainability
Design automation to handle edge cases and exceptions without requiring
constant manual intervention
Implement automation gradually, starting with stable, well-understood
processes before expanding scope
Plan for automation monitoring and alerting to ensure automated operations
perform as expected
Red Flags:
Automated operations frequently require manual intervention, reducing
automation benefits
Orchestration workflows are fragile and fail when encountering unexpected
conditions or data
Automation increases system complexity without providing corresponding
operational improvements
Operational procedures are overly dependent on specific individuals and not
properly documented or automated
Cost Management
ⓘ
Implementation Questions:
Are cost monitoring systems implemented with granular tracking of storage,
compute, and data transfer expenses by business unit and project?
Do cost optimization strategies include automated resource scaling,
rightsizing, and lifecycle management?
Are cost allocation and chargeback mechanisms implemented to promote
responsible data usage across the organization?
Do monitoring systems provide cost forecasting and budget alerts with
automated notifications and controls?
Are data retention and archiving policies optimized based on usage patterns
and cost-benefit analysis?
Is there regular cost review and optimization with actionable
recommendations for cost reduction?
Key Considerations:
Balance cost optimization with performance and availability requirements for
critical business processes
Implement cost monitoring that provides actionable insights rather than just
expense reporting
Design cost optimization to be automated where possible while maintaining
appropriate human oversight
Plan for cost optimization that considers both immediate savings and
long-term architectural benefits
Red Flags:
Data costs are growing significantly faster than business value or data
volume growth
Cost optimization efforts negatively impact system performance or data
availability
Cost monitoring is retrospective without proactive budget management or
forecasting capabilities
Cost allocation is unclear leading to lack of accountability and continued
cost growth