Market Data Management Platforms features

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Systems that acquire, process, validate, and distribute market data from various sources, including pricing information, reference data, corporate actions, and benchmarks across the organization.

We ask vendors to mark their product against our feature list. It is not available for all products and in some cases we have derived data from publicly available sources. The list of features follows. Vendors can add a further 10 key features of their own.

Administration & Usability

Functions that support operational oversight, usability, and manageability of the platform.

Change tracking: Logs and highlights system setting changes.
Configuration management: User-friendly tools for configuring data sources, rules, and outputs.
Custom reporting: Users can create ad hoc or scheduled reports.
Customizable dashboards: Interactive dashboards for monitoring data flows and system health.
Help and documentation: Comprehensive user guides, APIs, and support documentation.
Multi-language support: User interface available in multiple languages.
Onboarding tools: Guides and tools for fast setup of new data sources or users.
Self-service data access: Allows users to query and retrieve data without IT intervention.
User management dashboard: Admin interface for adding, removing, and managing users.
User roles supported: Number of customizable user roles/groups.
Workflow automation: Tools for automating operational processes.

Data Acquisition

Features that enable seamless collection of market data from diverse sources such as exchanges, data vendors, OTC contributors, and internal systems.

Automated feed scheduling: Automatic scheduling and orchestration of data imports.
Concurrent sources supported: Maximum number of data sources that can be ingested simultaneously.
Custom feed handler: Support for custom or user-defined data feed integration.
Data coverage breadth: Number of distinct data asset classes supported.
File and message protocol support: Handles protocols such as FIX, SFTP, API, and others.
Historical data import: Capability to load and process historical datasets.
Intraday updates: Capability to acquire and process intraday or tick-level data.
Latency: Average time between data publication and ingestion.
Multi-source connectivity: Ability to connect to multiple data sources (exchanges, vendors, etc.).
Real-time data ingestion: Supports real-time streaming ingestion of data feeds.
Retry mechanisms: Automated retry and failover during feed interruptions.
Vendor API compatibility: Supports major market data vendor APIs (Bloomberg, Refinitiv, ICE, etc.).

Data Distribution & Integration

Mechanisms to disseminate validated and enriched data to downstream consumers, applications, and users.

API access: Availability of RESTful or other programmatic interfaces.
Bulk download support: Ability to download large datasets on demand.
Consumer permissions management: Controls which users/applications receive which data.
Downstream system integration: Supports integration with trade platforms, analytics, risk, and reporting systems.
File export: Exports to standard file formats (CSV, Excel, XML, JSON).
Latency to delivery: Time taken from data validation to downstream delivery.
Message queue integration: Delivers data via messaging systems (Kafka, MQ, etc.).
Notifications on data delivery: Alerts stakeholders when data is delivered or when delivery fails.
Output data volume: Maximum data that can be distributed within a given period.
Real-time data publishing: Sends updates to consumers as soon as new data arrives.
Scheduled batch data delivery: Supports timed or event-triggered distribution.
User-defined delivery channels: Support for custom or client-specified distribution mechanisms.

Data Processing & Enrichment

Capabilities that enable normalization, enrichment, validation, and transformation of raw market data.

Batch processing support: Handles scheduled or on-demand batch jobs for data transformation.
Benchmark enrichment: Integration of benchmark indices for comparison or analytics.
Business rule engine: Configurable rules for custom transformations and enrichment.
Corporate actions adjustment: Processes events like dividends, splits, and applies them to security data.
Cross-referencing: Cross-linking between identifiers (RIC, ISIN, SEDOL, CUSIP, etc.).
Data enrichment: Augmenting records with additional metadata from internal or third-party sources.
Data lineage tracking: Tracks the origins and transformation steps for each data element.
Data mapping: Maps vendor or source fields to internal data models.
Data normalization: Automated standardization of source data into a consistent format.
Derived values calculation: Calculation of derived metrics such as VWAP, moving averages.
Identifier management: Tracks and manages multiple security identifiers.
Processing speed: Rate at which data records can be processed.

Data Validation & Quality Assurance

Ensures that acquired and processed data meets quality standards and business validation rules.

Alerts and notifications: Automatic notifications on validation failures or data anomalies.
Audit logs of validations: Maintain a full history of validation actions and outcomes.
Completeness checks: Verification that all required data fields are populated.
Data accuracy scoring: Assigns data quality scores based on predefined rules.
Data consistency checks: Ensures that data is internally consistent across time and sources.
Duplicate detection and resolution: Identifies and merges or removes duplicate entries.
Exception management workflow: Manages data issues and exceptions with audit trails.
Outlier detection: Automated checks to identify out-of-range or anomalous values.
Range validation: Validates that data falls within expected parameters.
Repair tools: Manual and automated tools for correcting data errors.
Validation rule configurability: Ability to define and maintain custom validation rules.
Vendor data comparison: Cross-checks data from multiple vendors/sources for discrepancies.

Monitoring & Alerting

Capabilities to track operational health, detect problems, and alert stakeholders for timely intervention.

Alert notification channels: Supports email, SMS, dashboard pop-ups, etc.
Application performance monitoring: Monitors resource usage, latency, and system responsiveness.
Custom alert configuration: Allows users to set specific alerting conditions.
Dashboard visualization of alerts: Consolidated view of all operational metrics and alerts.
Downtime tracking: Measures and logs system or feed outages.
Escalation workflow for critical alerts: Automated escalation process for unresolved incidents.
Event log retention: Length of time logs and metrics are stored.
Feed health monitoring: Continuous monitoring of inbound/outbound data feeds for up/down states.
Incident tracking integration: Integrates with ticketing/incident systems (ServiceNow, Jira, etc.).
Proactive anomaly detection: AI/ML-driven detection of unexpected system behaviors.
SLA monitoring: Checks if platform performance meets service-level agreements.
User activity monitoring: Tracks user-level activity for security and compliance.

Reference Data & Corporate Actions

Core capabilities to manage reference data, identifiers, and process corporate actions accurately.

Audit history of changes: Maintains a trail of changes to reference data and corporate action processing.
Automated data refresh frequency: Frequency at which reference data is automatically refreshed.
Corporate action event validation: Validates and confirms authenticity of announced events.
Corporate actions feed integration: Integrates with standardized action feeds from vendors and custodians.
Cross-asset class reference data: Supports all asset types: equities, fixed income, derivatives, etc.
Entity relationship mapping: Captures relationships between entities (issuer, parent, subsidiary, etc.).
Event impact calculation: Determines the effect of actions (splits, mergers, etc.) on security data.
Exception workflow for action events: Routes problematic events for manual review or escalation.
Golden copy management: Maintains a single validated version of key reference data.
Multi-identifier support: Manages various instrument and counterparty identifiers (ISIN, LEI, SEDOL, etc.).
Orphaned record detection: Identifies reference data without supporting records (for deprecation/removal).
Source reconciliation: Cross-verifies reference data from multiple sources.

Reporting & Analytics

Analytical tools and capabilities for generating insights, usage reports, and supporting regulatory or management reporting.

Ad hoc query support: On-demand querying and analysis of market or reference data.
Audit and compliance reporting: Dedicated reports for compliance review and audits.
Custom report builder: Create, schedule, and distribute custom reports.
Custom visualization tools: Built-in tools for data charting and visualization.
Data lineage analysis: Visualize the data’s journey from source through transformation to consumer.
Drill-down analytics: Enables detailed exploration of underlying datasets.
Export to BI tools: Integration with popular business intelligence platforms.
Large dataset reporting performance: Maximum data volume supported for interactive reporting.
Scheduled report delivery: Automated report generation and emailing.
Standard report library: Pre-built reports for common market data, quality, and usage metrics.
Time series analytics: Analyzes trends and patterns in price or reference data over time.
Usage statistics dashboard: Interactive dashboard for visualizing consumption, errors, and bottlenecks.

Scalability & Performance

Ensures the platform can meet current and future demands cost-effectively, scaling with data volume and user needs.

Cloud-native deployment: Supports cloud and hybrid deployments.
Data partitioning: Splits data for scalable storage and processing.
Disaster recovery: Integrated disaster recovery and rapid restore features.
Edge deployment support: Deployable close to data sources for ultra-low-latency.
Elastic resource allocation: Resources scale automatically with demand.
High availability: Built-in redundancy and failover for continuous operation.
Horizontal scalability: Supports scaling by adding more servers or nodes.
Load balancing: Automatic distribution of workload across resources.
Maximum throughput: Peak volume of data that can be processed per unit time.
Minimum supported response time: Fastest guaranteed response for a standard query.
Rolling upgrades: Enables upgrades without downtime.
Supported concurrent users: Maximum number of simultaneous users.

Security & Compliance

Measures and features that ensure the platform is secure and meets relevant regulatory and compliance requirements.

Audit trail: Comprehensive logging of all user and system actions for compliance.
Compliance reporting: Automated generation of reports for regulatory purposes.
Data masking: Redacts sensitive fields from unauthorized users.
Data retention management: Configurable policies for data storage and deletion.
Encryption at rest: Stores data in encrypted format on disk.
Encryption in transit: Secures data as it moves across networks.
GDPR/CCPA compliance features: Functions to support privacy obligations and subject requests.
Penetration tests frequency: Number of penetration/security tests performed per year.
Role-based access control (RBAC): Granular user and system permissions.
Secure API gateway: Protects and monitors all API accesses.
Third-party security certifications: Holds certifications such as ISO 27001, SOC 2, etc.
Vendor access controls: Restricts and audits external vendor access to systems and data.

Vendor & Data License Management

Features to ensure compliance with data usage agreements and optimize vendor relationships.

Audit of vendor usage: Auditable trails of all vendor data accesses.
Automated entitlements provisioning: Automates granting and revoking of data rights.
Contract management: Stores, tracks, and alerts on market data contracts.
Cost allocation tools: Allocates and reports data costs by user, team, or business unit.
Data usage dashboard: Visualizes consumption to optimize vendor spend.
Data vendor entitlement management: Controls access to data assets as per vendor contracts.
Excess usage alerts: Notifications when data access exceeds contractual entitlements.
Flexible entitlement models: Supports complex licensing structures (by asset class, geography, user type, etc.).
Inbound/outbound data usage limits: Maximum data volumes allowed by licensing contracts.
License enforcement: Monitors and enforces contractual data usage limits.
License exception handling: Processes permissions for exceptional or urgent access needs.
Usage analytics: Tracks how and by whom data is consumed.

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