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Data Science AnalyticsTop 10 Best Information Management Services of 2026
Compare top Information Management Services providers with clear ranking criteria, technical strengths, and tradeoffs for enterprise buyers.
How we ranked these tools
Core product claims cross-referenced against official documentation, changelogs, and independent technical reviews.
Analyzed video reviews and hundreds of written evaluations to capture real-world user experiences with each tool.
AI persona simulations modeled how different user types would experience each tool across common use cases and workflows.
Final rankings reviewed and approved by our editorial team with authority to override AI-generated scores based on domain expertise.
Score: Features 40% · Ease 30% · Value 30%
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Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
Deloitte Consulting
Governance-led data model and RBAC role design tied to controlled provisioning and audit log practices.
Built for fits when enterprises need governed integration across systems with RBAC, approvals, and audit log controls..
Accenture
Editor pickGoverned data model with schema evolution and audit-ready operations.
Built for fits when enterprise programs need governed integration, automation, and admin controls across domains..
IBM Consulting
Editor pickGovernance-led data integration planning that aligns RBAC, audit logs, and schema standards.
Built for fits when multi-system programs need governed data models, controlled provisioning, and audit-ready automation..
Related reading
Comparison Table
The comparison table benchmarks information management service providers by integration depth, data model schema control, and the automation and API surface used for provisioning, synchronization, and extensibility. It also maps admin and governance controls, including RBAC, audit log coverage, and configuration options that affect throughput and operational guardrails. Providers such as Deloitte Consulting, Accenture, IBM Consulting, Capgemini, and PwC are used as reference points to highlight tradeoffs across these dimensions.
Deloitte Consulting
enterprise_vendorDelivers enterprise information management programs for data governance, data architecture, and analytics-ready data foundations across large organizations.
Governance-led data model and RBAC role design tied to controlled provisioning and audit log practices.
Deloitte Consulting approaches information management as an end-to-end integration and control exercise that spans data model design, schema mapping, and provisioning for target platforms. Work artifacts commonly include data models, canonical schema standards, lineage and metadata definitions, and runbooks for operational throughput. Automation and API surface are addressed through integration patterns that coordinate ingestion, transformation, and data quality checks, with configuration managed across development, staging, and production environments.
A tradeoff appears in longer program cycles because governance artifacts such as RBAC roles, approval workflows, and audit log conventions are treated as deliverables rather than configuration after the fact. The service is a strong fit when multiple systems must be integrated under consistent schema rules and change controls, such as regulated reporting pipelines or cross-domain master data alignment.
- +Governed data model design with explicit schema mapping deliverables
- +Integration programs that coordinate ingestion, transformation, and orchestration
- +Admin controls that cover RBAC, approvals, and audit log expectations
- +Environment provisioning support for controlled change and traceability
- –Program governance artifacts can extend timelines on complex rollouts
- –Automation depth depends on chosen target platform integration patterns
Best for: Fits when enterprises need governed integration across systems with RBAC, approvals, and audit log controls.
More related reading
Accenture
enterprise_vendorBuilds governed data platforms and information management operating models that support data science and analytics with controlled data access and lineage.
Governed data model with schema evolution and audit-ready operations.
Integration depth shows up in how Accenture sequences data access, identity mapping, and pipeline orchestration across platforms and vendors. Data model work targets shared schema conventions, lineage capture, and controlled schema evolution to reduce breakage during ingestion. Automation and API surface appear through pipeline orchestration hooks, event-driven workflows, and integration patterns that connect upstream systems to downstream consumption layers. Extensibility is handled through configuration and engineering extensions rather than manual one-off mapping.
A concrete tradeoff is that outcomes depend on implementation scope and operating model design, so internal data platform teams must align on target RBAC, audit log retention, and governance workflows. A common usage situation is a multi-domain program where new sources need onboarding through repeatable provisioning steps and where schema changes must roll out with validation gates across dev, test, and production. Another frequent fit is when integration breadth spans CRM, ERP, and streaming feeds while admin controls must stay consistent across domains.
- +Governed data model work with schema evolution controls
- +Integration delivery coordinated across multiple enterprise platforms
- +RBAC and audit logging integrated into operational governance
- +Automation and API-driven provisioning patterns reduce manual onboarding
- +Lineage and change management support safer pipeline updates
- –Requires strong internal alignment on governance and target operating model
- –Complex integrations can increase lead time for environment readiness
Best for: Fits when enterprise programs need governed integration, automation, and admin controls across domains.
IBM Consulting
enterprise_vendorProvides information management consulting for data governance, reference data, master data, and analytics enablement through end-to-end delivery teams.
Governance-led data integration planning that aligns RBAC, audit logs, and schema standards.
IBM Consulting delivers information management services by connecting data model decisions to integration breadth across platforms and environments. Engagements commonly cover schema governance, metadata capture, data quality enforcement points, and migration planning that keeps provisioning repeatable. The delivery model emphasizes extensibility through documented APIs, integration patterns, and configuration managed across stages.
A concrete tradeoff is that deep governance and API-driven automation increase upfront design and stakeholder time for schema and controls. This fit works best when the program needs controlled provisioning, audit log coverage, and RBAC policies across multiple teams or systems. It can be heavier for small scopes that only require point integrations without a durable schema and governance operating model.
- +Integration delivery grounded in an explicit data model and schema governance
- +Automation patterns that support provisioning, configuration, and repeatable rollout
- +Admin controls with RBAC and audit log expectations for regulated environments
- +API-first extensibility for connecting new pipelines and systems
- –Governance and design work can slow early iteration for small initiatives
- –More coordination needed across stakeholders for schema and control alignment
- –API-driven integrations often require disciplined platform configuration
Best for: Fits when multi-system programs need governed data models, controlled provisioning, and audit-ready automation.
Capgemini
enterprise_vendorDesigns and implements information management and data governance solutions that standardize data models and support analytics at scale.
Enterprise governance integration with RBAC and audit log aligned to the data model.
Category context matters because information management depends on integration depth across data sources, schemas, and operational workflows. Capgemini delivers control-focused integration programs that align enterprise data models to governance, including RBAC and audit logging across environments.
Service delivery typically centers on automation through APIs and workflow provisioning, with extensibility for event-driven pipelines and operational controls. Governance and administration are designed to support repeatable onboarding, configuration management, and traceable changes across systems.
- +Strong integration delivery across heterogeneous data sources and target schemas
- +Governance support with RBAC and audit log oriented controls
- +Automation and provisioning via documented APIs and workflow integration
- +Extensible approach for event-driven pipelines and operational configuration
- –Automation surface depends on chosen architecture and vendor tooling stack
- –Administrative depth varies by engagement design and deployment model
- –Hands-on tuning of throughput and schedules often requires specialist delivery
Best for: Fits when enterprises need managed integration with governance controls and controlled automation.
PwC
enterprise_vendorRuns data governance and information management transformations that align data quality, metadata, and lineage to analytics and reporting needs.
Governance-to-integration delivery using RBAC, audit log requirements, and enforced data contracts.
PwC delivers information management services that include governance design, data model definition, and integration planning across enterprise sources. Engagements typically cover schema mapping, master and reference data strategy, and provisioning workflows that connect systems through documented APIs and middleware.
Delivery emphasizes admin and governance controls such as RBAC alignment, audit log requirements, and data lineage to keep automated integrations controllable at scale. Automation and API surface are addressed through repeatable ingestion, validation, and enrichment pipelines tied to agreed data contracts.
- +Governance and data model work aligned to enterprise schema and stewardship roles
- +Integration planning covers source to target mappings and data contract enforcement
- +Automation pipelines support repeatable ingestion, validation, and enrichment steps
- +Admin controls emphasize RBAC alignment and audit log requirements for traceability
- +Extensibility planning covers additional domains and schema evolution paths
- –Service delivery depends on PwC-led workshops and architecture decisions
- –API surface depth can be constrained by client platform choices and integration patterns
- –Automation throughput depends on agreed data contracts and staging architecture
- –Complex environments may require more coordination across stakeholders and tool owners
Best for: Fits when large enterprises need governed integrations and a controlled data model.
KPMG
enterprise_vendorSupports information management programs that improve data quality, stewardship workflows, and governed data access for advanced analytics.
Governance operating model design with audit log and RBAC mapping across data platforms.
KPMG fits organizations that need integration depth with enterprise data management programs and controlled delivery across teams. Delivery typically combines data governance and operating model design with implementation for data platforms and migration planning.
Integration breadth is supported through structured data model work, schema alignment, and repeatable provisioning patterns. Automation and extensibility depend on the chosen platform and tooling, with API and workflow execution often delivered via connectors and orchestrated provisioning.
- +Integration governance work aligns data model, schema, and lineage expectations early
- +Admin controls and RBAC designs support controlled access across environments
- +Migration planning and data quality rules are documented for auditability
- +Automation delivery emphasizes repeatable provisioning and workflow orchestration
- –API surface depth varies by selected platform and integration tooling
- –Extensibility can be limited when source systems lack stable connectors
- –Sandbox and throughput testing depend on engagement scope and environment readiness
Best for: Fits when large enterprises need governed integration and data model alignment with controlled rollout.
EY
enterprise_vendorDelivers data governance, data architecture, and information management roadmaps that enable reliable data science and analytics delivery.
Governance-focused RBAC and audit-log operating model for managed data access and schema changes.
EY delivers information management through service-led governance, integration planning, and controls for enterprise data domains. Delivery often centers on defining a target data model, mapping schemas across sources, and setting RBAC aligned to operating roles.
Automation and integration depth typically show up as engineered workflows, API-enabled connectivity, and repeatable provisioning patterns across environments. Admin oversight is expressed through audit log practices, policy controls, and documented change management for schema and access changes.
- +Strong governance patterns that define roles, policies, and audit expectations
- +Integration mapping work that focuses on schema alignment across systems
- +API and automation surfaces used to standardize data flows and provisioning
- +Change control practices for data model and access adjustments across environments
- –Service-led delivery can require heavy client participation for requirements
- –Automation depth depends on engagement scope and the chosen reference architecture
- –Data model customization can slow down initial schema stabilization work
- –Extensibility outcomes vary based on how integration endpoints are standardized
Best for: Fits when enterprises need controlled schema integration with documented RBAC, audit, and change governance.
Tata Consultancy Services
enterprise_vendorOffers information management and data governance services that industrialize data pipelines, metadata management, and analytics data platforms delivery.
RBAC plus audit log trails tied to data provisioning and change workflows
Tata Consultancy Services delivers information management services with deep integration work across data platforms and enterprise systems. Delivery emphasizes an explicit data model approach, schema design, and controlled data movement for governed analytics and operational reporting.
Automation is supported through repeatable provisioning patterns, workflow orchestration, and an API surface designed for integration and extensibility. Admin and governance controls are structured around RBAC and audit log trails to support compliance-grade monitoring and change control.
- +Integration delivery across enterprise systems and multiple data platforms
- +Schema-led data modeling with defined contracts for downstream consumers
- +Automation via provisioning patterns and repeatable workflow orchestration
- +Governance controls using RBAC and audit logs for traceability
- +Extensibility through documented APIs and integration touchpoints
- –Complex governance needs may require significant setup and standards alignment
- –API breadth depends on the selected integration pattern per engagement
- –Extensive customization can slow initial throughput during stabilization
- –Data model changes require disciplined schema versioning practices
Best for: Fits when enterprises need governed data integration with automation and audit-ready controls across teams.
CGI
enterprise_vendorProvides data governance, information architecture, and governed analytics data services for enterprises with regulated or complex data landscapes.
RBAC plus audit log coverage for administrative and provisioning actions.
CGI performs information management services that integrate enterprise data operations with controlled governance workflows. Its delivery model focuses on data model design, schema alignment, and provisioning steps that reduce integration drift across systems.
Automation and API surface work is structured around repeatable job execution, interface contracts, and extensibility for downstream pipelines. Admin and governance controls emphasize RBAC, audit logging, and operational configuration needed to manage throughput and change management safely.
- +Integration work includes schema mapping across heterogeneous enterprise systems
- +Automation can be expressed via API-driven workflows and scheduled job execution
- +Governance supports RBAC controls and audit logging for administrative actions
- +Extensibility options support custom integrations and pipeline additions
- –Deeper governance configuration can add setup effort for complex environments
- –Integration depth depends on defined data contracts and interface ownership
- –Automation outcomes rely on consistent schema change discipline across teams
- –Extensibility may require engineering time to match internal standards
Best for: Fits when enterprises need governed data integration with an API-first automation surface.
Slalom
enterprise_vendorExecutes information management and data architecture engagements that connect governed data foundations to analytics and decision platforms.
Managed integration delivery that couples data model governance with API and automation runbooks.
Slalom fits organizations needing managed information management delivery with integration depth across data, workflow, and governance surfaces. Its engagements typically combine data model design, schema mapping, and API-first integrations with automation for provisioning and operational handoffs.
Strong governance coverage includes RBAC patterns, audit log practices, and configuration controls that support reviewable change management. Extensibility is driven through integration breadth and documented interfaces that support controlled throughput and repeated deployments.
- +Integration delivery across data platforms and enterprise workflows
- +Data model and schema mapping for cross-system consistency
- +Automation for provisioning and repeatable operational handoffs
- +Governance patterns using RBAC and audit-log aligned practices
- –Automation scope depends on engagement design and reference architecture
- –API surface quality varies by chosen system integrations
- –Complex governance setups require deliberate change-management planning
- –Throughput tuning needs explicit workload definitions
Best for: Fits when enterprises require governed integration and automation for information management programs.
How to Choose the Right Information Management Services
This buyer's guide covers how to select Information Management Services providers across Deloitte Consulting, Accenture, IBM Consulting, Capgemini, PwC, KPMG, EY, Tata Consultancy Services, CGI, and Slalom.
The focus is on integration depth, data model clarity, automation and API surface, and admin and governance controls so evaluation stays grounded in concrete delivery mechanisms across enterprise environments.
The guide also maps common rollout risks to observed provider constraints so buyers can plan governance artifacts, platform configuration effort, and throughput testing time before work starts.
Each section names specific providers and the exact mechanisms they use, including RBAC, audit log expectations, schema evolution controls, and provisioning workflows.
Information management delivery that ties governed data models to controlled integration and operations
Information Management Services brings a governed data model into practice by connecting source systems to target platforms with ingestion, mapping, and orchestration workflows that run under admin controls. The work typically includes schema standards, lineage expectations, provisioning workflows, and environment change management so data moves with traceability.
Deloitte Consulting executes this as governance-led data model design plus RBAC role design tied to controlled provisioning and audit log practices. Accenture extends the same governed model approach into schema evolution and audit-ready operations across teams and platforms.
Evaluation points that reflect real integration depth and controlled operations
Integration depth must be evaluated as an end-to-end mechanism that spans mapping, orchestration, and environment provisioning, not only as data modeling workshops. Deloitte Consulting pairs governed data model design with integration programs that coordinate ingestion, transformation, and orchestration while maintaining RBAC approvals and audit log traceability.
Automation and API surface must be evaluated for how repeatable onboarding and provisioning are under change control. Providers like PwC and Tata Consultancy Services explicitly tie documented APIs and middleware to repeatable ingestion, validation, enrichment, and provisioning steps under agreed data contracts.
Governed data model with explicit schema mapping deliverables
Deloitte Consulting and IBM Consulting lead with an explicit data model and schema mapping work that grounds integration decisions in named contracts. PwC adds contract enforcement through schema mapping tied to lineage and metadata expectations so downstream integrations remain controlled.
Schema evolution control and change governance tied to auditability
Accenture emphasizes schema evolution controls paired with audit-ready operational governance so pipeline updates remain reviewable. EY and KPMG pair RBAC and audit-log practices with documented change management so schema and access changes follow controlled workflows.
Automation and provisioning workflows with a documented API touchpoint
Slalom couples data model governance with API-first integrations and automation runbooks for provisioning and operational handoffs. CGI and Capgemini express automation through API-driven workflows and workflow provisioning so administrative actions and provisioning changes remain traceable.
Admin and governance controls across environments using RBAC and audit logs
Across Deloitte Consulting, Accenture, and IBM Consulting, admin controls include RBAC role design plus audit log expectations across environments for traceability. CGI adds RBAC plus audit logging for administrative and provisioning actions so throughput and change management remain managed.
Integration breadth across heterogeneous systems with interface contracts
Capgemini and KPMG focus on aligning enterprise data models to governance across heterogeneous data sources and target schemas. CGI structures automation around repeatable job execution and interface contracts to reduce integration drift across systems.
Extensibility that fits new pipeline additions and event-driven integration patterns
Capgemini supports extensibility for event-driven pipelines and operational configuration while keeping governance aligned to the data model. IBM Consulting and Tata Consultancy Services support API-first extensibility so new pipelines and systems can be connected through disciplined platform configuration and documented integration touchpoints.
A provider selection framework for governed integration, automation, and control depth
Start evaluation by testing whether the provider can translate a governed data model into controlled integration that includes ingestion, mapping, and orchestration under RBAC and audit log practices. Deloitte Consulting is a strong match when RBAC approvals and audit log expectations must be tied to controlled provisioning across environments.
Then validate automation and API surface as a delivery mechanism that reduces manual onboarding while staying compatible with your target platform configuration. Accenture and Slalom both emphasize API-enabled connectivity and repeatable provisioning patterns, while PwC focuses on documented APIs and enforced data contracts for controllable pipelines.
Score integration as an end-to-end pipeline that includes orchestration and provisioning
Ask how Deloitte Consulting builds ingestion, transformation, and orchestration inside integration programs while supporting environment provisioning for traceability. For Accenture and IBM Consulting, require a walkthrough of how automated ingestion and pipelines connect across platforms under operational governance and controlled change management.
Validate the data model approach using concrete schema mapping and contract artifacts
Request deliverable examples from Deloitte Consulting or PwC that show explicit schema mapping and data contract enforcement. Confirm whether IBM Consulting or Tata Consultancy Services uses schema-led modeling with defined contracts that downstream consumers can rely on for governed analytics and operational reporting.
Inspect automation depth by measuring how provisioning and onboarding work stays repeatable
Direct the evaluation toward automation and workflow provisioning mechanisms that reduce manual onboarding. Slalom and CGI focus on automation expressed through API and repeatable job execution, and that emphasis should be tied to workload definitions that affect throughput tuning.
Check admin and governance controls across environments using RBAC and audit logs
Define required controls in RBAC role design, workflow approvals, and audit log expectations for administrative and provisioning actions. Deloitte Consulting, EY, and KPMG align RBAC and audit-log operating practices with documented change management so access and schema adjustments follow governed workflows.
Stress-test schema evolution and change management discipline for pipeline updates
Use Accenture and PwC as references for schema evolution controls and enforced data contracts that keep lineage and change safer. Confirm whether the provider’s automation can handle schema change without breaking provisioning workflows and audit-ready traceability.
Evaluate extensibility through documented interfaces and integration endpoint standards
Ask Capgemini and IBM Consulting how extensibility is delivered through documented APIs, event-driven pipeline support, and operational configuration. Confirm Tata Consultancy Services and CGI can add new pipelines through interface contracts and platform configuration without losing governance alignment.
Which organizations benefit most from governed integration and controlled automation
Different providers map to different governance intensity and integration breadth needs. The best fit depends on whether the program needs RBAC approvals and audit log traceability tied to provisioning, or whether it primarily needs schema evolution controls and operational governance coordination.
The segments below use each provider’s best_for positioning based on what their engagements typically cover.
Enterprises that require governed integration with RBAC approvals and audit log traceability
Deloitte Consulting is best when governed integration must include RBAC role design plus workflow approvals and audit log expectations tied to controlled provisioning. CGI is a fit when administrative and provisioning actions must remain covered by RBAC and audit logging under API-first automation.
Multi-domain programs that must coordinate automation, schema evolution, and lineage-ready operations
Accenture fits enterprise programs that need a governed data model plus schema evolution controls integrated with audit-ready operations and automated ingestion pipelines. IBM Consulting fits multi-system programs that need governed data models with controlled provisioning and audit-ready automation patterns.
Large enterprises that need a controlled data model with enforced data contracts across integrations
PwC fits large enterprises that need governed integrations tied to enforced data contracts, RBAC alignment, audit log traceability, and lineage and metadata alignment. KPMG fits large enterprises that need governed integration with data model alignment and controlled rollout backed by audit log and RBAC mapping across data platforms.
Enterprises building controlled schema integration with documented RBAC and change governance
EY fits enterprises that want documented RBAC, audit-log operating models, and change governance practices for schema and access adjustments. Slalom fits programs that require API-first integrations plus automation runbooks that couple governed data model work with operational handoffs.
Organizations industrializing pipeline provisioning with audit-ready monitoring across teams and platforms
Tata Consultancy Services fits teams that need schema-led data modeling, repeatable provisioning patterns, and RBAC plus audit log trails tied to data provisioning and change workflows. Capgemini fits when governance integration must align enterprise data models to RBAC and audit log controls while supporting extensibility for event-driven pipelines.
Common selection and delivery pitfalls tied to governance, integration depth, and automation scope
A frequent failure mode is underestimating governance artifacts that extend timelines in complex rollouts. Deloitte Consulting explicitly notes that program governance artifacts can extend timelines on complex rollouts, and that same planning gap can appear when RBAC and audit expectations must be designed end-to-end.
Another frequent failure mode is treating automation as a generic workflow feature instead of a repeatable provisioning and API touchpoint. KPMG, EY, and Slalom all tie automation depth to the chosen platform and engagement architecture, so mismatch can surface as limited API surface depth or throughput tuning delays.
Choosing a provider without a clear governed data model schema mapping workflow
If schema mapping deliverables are not explicit, downstream provisioning and enrichment steps become harder to keep audit-ready. Deloitte Consulting, IBM Consulting, and PwC emphasize governance-led data model and explicit schema mapping so integration remains grounded in enforceable contracts.
Assuming automation depth will match expectations without disciplined target platform configuration
API-driven integrations often require disciplined platform configuration to achieve repeatable provisioning patterns. Accenture and IBM Consulting highlight that complex integrations can increase lead time for environment readiness, so evaluation should include the provisioning workflow mechanics, not only tool familiarity.
Skipping environment governance details like RBAC approvals and audit log coverage
Controlled data movement fails when admin oversight is not mapped to environments. Deloitte Consulting, KPMG, and EY tie RBAC and audit log practices to controlled change management, so the onboarding plan must include approvals and audit expectations.
Under-scoping API surface evaluation for extensibility and pipeline additions
Extensibility can stall when source systems lack stable connectors or when integration endpoints are not standardized. KPMG calls out that extensibility can be limited when source systems lack stable connectors, and Capgemini shows how extensibility depends on chosen architecture and documented APIs.
Ignoring schema evolution discipline and change control workload during pipeline updates
Schema changes can break orchestration if change governance is not built into the automation runbooks. Accenture’s schema evolution and audit-ready operations focus is a direct counter to this risk, and EY’s documented change governance for schema and access adjustments provides a parallel control model.
How We Selected and Ranked These Providers
We evaluated Deloitte Consulting, Accenture, IBM Consulting, Capgemini, PwC, KPMG, EY, Tata Consultancy Services, CGI, and Slalom on three criteria that match real information management delivery work. Capability coverage received the most weight because integration depth, governed data model work, automation mechanics, and admin controls determine whether provisioning stays audit-ready.
Ease of use and value each carried less weight than capability coverage because delivery can still succeed when operations are complex, but the right governance and integration mechanisms must be present. Deloitte Consulting set itself apart with governance-led data model design plus explicit RBAC role design tied to controlled provisioning and audit log practices, and that combination lifted its capability coverage through deep admin and governance control integration.
Frequently Asked Questions About Information Management Services
How do integration and API touchpoints differ across Deloitte, Accenture, and Capgemini?
Which providers focus most on RBAC, audit logs, and access change governance during information management delivery?
What does data migration look like when governance and schema standards must remain enforced?
How do service providers handle schema evolution and schema change control across multiple teams?
Which providers are better suited for onboarding new domains without integration drift?
When extensibility requires event-driven or downstream pipeline growth, how do the approaches differ?
How do these services support admin controls for configuration and operational handoffs across environments?
What technical delivery model signals stronger emphasis on automation over one-time integration work?
How do providers reduce governance gaps between data lineage needs and the actual integration mechanics?
Conclusion
After evaluating 10 data science analytics, Deloitte Consulting stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
Tools reviewed
Primary sources checked during evaluation.
Referenced in the comparison table and product reviews above.
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