at Financial Technnology Year
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IBM Cloud Pak for Data from IBM
An integrated data and AI platform that helps organizations collect, organize, and analyze financial data while providing insights to improve decision-making and operational efficiencies.
Product analysis by function
Investment Data Warehouses for Data Management
Centralized repositories designed to store, organize, and make accessible various types of investment data including historical prices, positions, transactions, and analytical datasets.
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Data Integration & Ingestion
(11 Yes /11 Known /13 Possible features)
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Multi-Source Connectivity Ability to connect to multiple data sources such as custodians, brokers, market data feeds, CRM, and internal systems. |
IBM Cloud Pak for Data lists connectors for databases, market data, file systems, cloud storage, and more, supporting multi-source connectivity. | |
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Real-Time Ingestion Support for real-time or near real-time data ingestion and updates. |
Supports real-time and near real-time data pipelines through built-in DataStage and Data Virtualization. | |
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Batch Processing Support Supports bulk or scheduled data loads at defined intervals. |
Supports scheduled and bulk data loads via DataStage and other integration components. | |
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Data Validation Rules Automated quality checks during ingestion for format, completeness, or range validation. |
Provides automated data validation on ingestion for quality and format through data quality services. | |
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Data Mapping/Transformation Ability to map, normalize, or transform source data to internal schemas. |
Data mapping and transformation capabilities are built-in and configurable using DataStage and pipelines. | |
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API Access for Ingestion Availability of APIs to push/pull data from external systems. |
Offers RESTful API access for data ingestion and export; supports external API integration. | |
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ETL (Extract, Transform, Load) Tools Built-in or integrated ETL processes for complex workflows. |
Built-in ETL/ELT tools (DataStage, Watson Knowledge Catalog Pipeline, etc.) for complex workflows. | |
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Customizable Workflows Support for creating custom data ingestion and integration workflows. |
Workflow orchestration and customization are supported via DataStage and Watson Studio pipelines. | |
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Streaming Data Capability Ingestion and management of continuously flowing data (e.g., tick data, news feeds). |
Provides streaming data ingestion and management using IBM Streams and other modules. | |
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Error Logging & Alerting Automated notifications or logs on ingestion failures, anomalies, or discrepancies. |
Comprehensive error logging and alerting features exist; notifications for ingestion failures. | |
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Scalability Maximum capacity of records the ingestion process can handle per hour. |
No information available | |
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Latency Time taken from data arrival at source to availability in warehouse. |
No information available | |
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Auto Schema Detection Automated detection and suggestion for new/unknown data schemas. |
Auto schema detection is available as part of data cataloging and ingestion pipelines. |
Data Storage & Architecture
(11 Yes /11 Known /12 Possible features)
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Storage Capacity Maximum volume of data that can be stored. |
No information available | |
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Data Compression Support for storage-efficient compression algorithms. |
Supports compression for efficient storage; uses advanced data compression algorithms. | |
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Partitioning & Sharding Support for data partitioning or sharding for optimized performance. |
Supports partitioning and sharding via database and storage integration layers. | |
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Columnar vs. Row Storage Choice between columnar or row-based data storage, or hybrid models. |
Offers flexibility for columnar and row-based storage depending on the underlying datastore (e.g., Db2 Warehouse, cloud storage) | |
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Elastic Scaling Ability to dynamically scale storage resources up/down as needed. |
Cloud-native architecture enables elastic scaling for storage. | |
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High Availability & Redundancy Built-in redundancy for data protection and system resilience. |
High availability and redundancy features are present both at service and platform level. | |
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Data Snapshots Capability to take periodic snapshots or backups of stored data. |
Periodic snapshots/backups are supported as part of platform services. | |
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Cloud/On-Premises Option Deployment flexibility on cloud, on-premises or hybrid environments. |
Offers cloud, on-premises, and hybrid deployment options. | |
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Object Storage Integration Ability to integrate with external object stores (like S3, Azure Blob, etc). |
Supports object storage integration (e.g., IBM Cloud Object Storage, S3). | |
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Immutable Data Storage Supports 'write-once, read-many' records for archiving and audit trails. |
Immutable storage options are available for audit and compliance, i.e., Write Once, Read Many. | |
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Data Encryption at Rest Uses encryption to secure data at rest on storage devices. |
Data encryption at rest is a default security feature. | |
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Schema Evolution Capability to modify and evolve storage schemas with minimal disruption. |
Schema management and evolution are supported with minimal disruption to service. |
Data Security & Access Control
(12 Yes /12 Known /12 Possible features)
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Role-Based Access Controls (RBAC) Enables fine-grained permissions based on user roles and responsibilities. |
Provides role-based access controls; fine-grained permissions can be defined per user and group. | |
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Multi-Factor Authentication (MFA) Requires multiple authentication methods for enhanced security. |
Supports multi-factor authentication using IBM Security features. | |
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Data Encryption In Transit Secures data as it moves between systems using transport-layer encryption. |
Encrypts data in transit via TLS/SSL connections by default. | |
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Audit Logging Captures logs of all user actions for compliance and review. |
Audit logging for user and data activity is a standard compliance and tracking feature. | |
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Granular Permissions Allows field-, table-, or dataset-level restrictions on view/edit |
Granular permissions supported at multiple levels (object, column, dataset, etc.). | |
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User Provisioning Automation Streamlines onboarding/offboarding of user access rights. |
Automated workflows for user provisioning and deprovisioning via integration with enterprise IAM. | |
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Single-Sign-On (SSO) Integration Integration with enterprise authentication solutions for single sign-on. |
Integrates with enterprise Single Sign-On solutions such as SAML, LDAP, OAuth. | |
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Data Masking Conceals sensitive fields from unauthorized users while keeping them available for queries. |
Data masking of sensitive fields is supported at various access levels. | |
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Security Incident Alerting Automated alerts triggered by unusual activities or access violations. |
Security incident alerting, with integration into SIEM and notification systems. | |
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Field Level Encryption Ability to encrypt specific columns or fields within the warehouse. |
Offers field-level encryption for sensitive columns or datasets. | |
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Certifications/Compliance System meets external standards (e.g. SOC 2, ISO 27001, GDPR). |
IBM Cloud Pak for Data is certified for SOC 2, ISO 27001, GDPR and other standards. | |
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Data Access Expiry Ability to grant time-limited access to specific datasets. |
Allows setting timed-access policies for granular dataset permissions. |
Data Quality & Management
(9 Yes /9 Known /10 Possible features)
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Automated Data Quality Checks Scheduled or real-time scans for inconsistencies, outages, and data drift. |
Provides automated/scheduled and real-time data quality checks. | |
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Data Lineage Tracking Full traceability of data sources and transformations. |
Full data lineage tracking is available and visualized in Watson Knowledge Catalog. | |
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Data Deduplication Automated identification and resolution of duplicate records. |
Automated deduplication processes are available for ingested and stored data. | |
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Historical Data Versioning Retains and enables access to previous versions of records. |
Supports historical versioning for datasets/records. | |
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Metadata Management Cataloging of data with descriptive metadata, tags, and classifications. |
Rich metadata management with cataloging, tagging, and classification. | |
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Data Reconciliation Tools Tools for aligning positions, transactions, and market values with external sources. |
Provides reconciliation tools through data integration and analytics modules. | |
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Custom Validation Rules Administrators can define custom data validation and alert logic. |
Custom validation logic can be set by administrators as data management rules. | |
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Error Correction Workflow Built-in process for reviewing and resolving flagged issues. |
Error correction workflows available for flagged data quality issues. | |
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Data Anomaly Detection Automated identification of abnormal or suspicious data patterns. |
Anomaly detection modules are available for data quality management using AI/ML. | |
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Quality Score Metrics Quantitative scoring of data quality or completeness. |
No information available |
Performance & Scalability
(6 Yes /6 Known /10 Possible features)
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Query Response Time Average time for user queries to complete. |
No information available | |
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Concurrent Users Supported Maximum number of simultaneous active users. |
No information available | |
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Max Query Throughput Highest number of queries handled per second. |
No information available | |
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Elastic Compute Scaling Ability to add processing resources dynamically as workload grows. |
Elastic compute scaling is available as part of the containerization and cloud-native architecture. | |
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High Availability Uptime/availability percentage supported by system architecture. |
No information available | |
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Geographic Distribution Ability to distribute data and resources across multiple locations. |
Multi-region and geo-distributed deployments supported for resilience and compliance. | |
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Hybrid Scaling Support for both vertical (bigger servers) and horizontal (more servers) growth. |
Supports both vertical and horizontal scaling through Kubernetes and cloud services. | |
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Resource Utilization Optimizations Automated resource allocation and optimization for performance. |
Automated resource management and optimization tools are part of platform operation. | |
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Data Caching Layered caching to speed up frequently accessed queries. |
Layered and distributed caching capabilities embedded for common workloads. | |
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Workload Isolation Isolating analytic and operational workloads to prevent interference. |
Workload isolation is managed via container orchestration and workload policies. |
Analytical & Reporting Tools
(9 Yes /9 Known /10 Possible features)
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Ad-Hoc Query Tools User ability to run custom queries without IT intervention. |
Self-service ad-hoc queries provided via integrated Watson Studio, Data Virtualization, and SQL interfaces. | |
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Integrated Reporting Provision of built-in or plug-in dashboards and report templates. |
Reporting modules and plugin dashboard integrations (Cognos, etc.) are built in. | |
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Data Visualization Support for charting, heatmaps, and other visualization outputs. |
Supports dashboarding and visual analytics (charts, graphs, heatmaps). | |
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OLAP (Online Analytical Processing) Multi-dimensional data cubes for advanced slicing/dicing. |
No information available | |
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API for Data Export APIs for exporting data to downstream analytics/BI tools. |
APIs are available to export data for BI/analytics tools. | |
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Scheduled Reporting Capability to schedule and distribute recurring reports. |
Supports scheduled reporting and automated report distribution. | |
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Self-Service BI Integration Connection with leading business intelligence tools (Power BI, Tableau, etc). |
Connects directly with Power BI, Tableau and other BI tools for self-service analytics. | |
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User-Defined Metrics & Calculations Ability for users to create custom metrics and run calculations. |
Users can create and manage custom metrics and run models/calculations. | |
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Natural Language Query Supports querying data using plain English or natural language. |
Natural Language Query functions are present in Watson Query and Watson Discovery modules. | |
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Audit Reports Automated generation of compliance or data access audit reports. |
Audit reports can be generated for compliance and user/data activity. |
Data Governance & Compliance
(10 Yes /10 Known /10 Possible features)
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Data Retention Policies Automated enforcement of data lifespan per regulatory requirements. |
Supports policy-based, automated data retention and deletion per regulatory or company rules. | |
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Consent Management Tools to record and enforce user/client consent for data use. |
Consent management modules exist for GDPR/CCPA compliance (Watson Knowledge Catalog). | |
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GDPR/CCPA Compliance Modules Functions for supporting global privacy laws in data management. |
Has GDPR/CCPA toolkits and modules for data privacy law compliance. | |
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Change Management & Approval Workflow Approval process tracking for critical data or schema changes. |
Built-in change management and approval workflow mechanisms are part of the data governance suite. | |
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Policy Documentation Portal Online repository for governance, access, and retention policies. |
Online portal and documentation repository are embedded for governance and policy documentation. | |
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Data Stewardship Assignment Assign roles for stewardship and ongoing data oversight. |
Provides mechanisms to assign data stewardship and manage responsibilities. | |
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Data Usage Monitoring Track and report data access frequency and patterns. |
Allows monitoring and analytics of data usage and access patterns. | |
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Automated Regulatory Reporting Template-driven and automated generation of regulatory filings. |
Automated regulatory reporting modules/templates for common filings are offered. | |
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E-discovery Support Tools for responding to legal or regulatory data queries. |
Features tools for legal/regulatory data discovery and e-discovery requests. | |
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Full Audit Trail Detailed, immutable logging of data access and changes for compliance. |
Immutable and detailed audit trails are maintained for all key data and access changes. |
Interoperability & Integration
(10 Yes /10 Known /10 Possible features)
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Standardized API Interfaces Support for REST, GraphQL, or other industry-standard APIs. |
REST, JDBC/ODBC, GraphQL, and other industry-standard APIs supported for integration. | |
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Event Driven Architecture Ability to publish/subscribe to changes via events (e.g., webhooks, Kafka). |
Supports event-driven workflows and publish/subscribe integration using Kafka, webhooks, etc. | |
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Data Export Connectors Pre-built connectors to downstream systems (accounting, risk, performance). |
Provides connectors for exporting data to downstream accounting, risk, and performance systems. | |
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Data Import Connectors Connectors for ingesting data from industry-standard vendors and records. |
Has pre-built and custom import connectors for ingesting data from a wide range of vendors. | |
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Bulk Data Transfer Support Efficient mechanisms for exporting/importing large datasets. |
Efficient data transfer mechanisms for bulk imports and exports. | |
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Custom Plugin Support Ability to extend functionality via custom plugins. |
Supports custom plugin and extension development at platform and data pipeline levels. | |
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Data Synchronization Scheduling Configure schedules and triggers for sync with external applications. |
Scheduling and triggering of sync jobs with external systems is configurable. | |
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Federated Query Support Query data across multiple sources without data movement. |
Virtualization and federated querying across multiple sources are supported in IBM Cloud Pak for Data. | |
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Open Standards Adoption Uses open-source or de-facto industry schemas and protocols. |
Supports open standards and open-source protocols for integration and extensibility. | |
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Integration Testing Sandbox Provides a test environment for third-party integration validation. |
Provides test and sandbox environments for integration testing and validation. |
User Experience & Administration
(10 Yes /10 Known /10 Possible features)
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Intuitive User Interface Modern, easy-to-navigate web UI for end users and admins. |
User interface is web-based, modern, and highly rated for usability. | |
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Customizable Dashboards Personalized dashboards for different user roles. |
Dashboards can be tailored to user role or department. | |
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User Activity Monitoring Admin panel to review recent logins and actions. |
Admin panels allow reviewing user activity, logins, and actions. | |
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Bulk User Management Batch provisioning, editing, or deactivation of users. |
Offers bulk management tools for user provisioning and administrative tasks. | |
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Localization and Multi-language Support UI and documentation in multiple languages. |
Supports localization and multi-language UI options. | |
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Accessibility Compliance Adheres to WCAG or other accessibility standards. |
Accessibility standards (e.g., WCAG 2.1) are supported per IBM corporate requirements. | |
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Custom Notification Settings Users can configure their own notification preferences. |
Users may configure notification preferences (email, system notifications, etc.). | |
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In-platform Help & Support Contextual help guides, live chat, or ticket escalation. |
Includes contextual help, guides, and escalation to IBM support. | |
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System Usage Analytics Track and visualize user adoption and active usage trends. |
System usage and adoption analytics available to admins. | |
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Theming & Branding Options Support for organization-specific visual branding. |
Branding and theme customization can be applied per organization in the platform. |
Monitoring, Maintenance & Support
(9 Yes /9 Known /10 Possible features)
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Real-Time System Monitoring Visibility into performance, capacity, and health via dashboards. |
Real-time monitoring dashboards provide insights into system health and workload. | |
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Automated Backups Scheduled and on-demand backup management. |
Automated, scheduled and manual backups are managed through platform modules. | |
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Disaster Recovery Support Plans and automation for fast recovery from critical failures. |
Disaster recovery planning and automation are part of the managed service offerings. | |
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24/7 Technical Support Access to technical support all day, every day. |
24/7 technical support is available via IBM's global support coverage. | |
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Self-Healing Capabilities Automated detection and mitigation of system faults. |
Self-healing mechanisms (restarts, auto-heal containers, etc.) are built into the platform. | |
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Patch & Version Management Tools for applying updates and managing software versions. |
Patch and version management are managed via container platform tooling. | |
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Automated Resource Scaling Dynamic allocation of compute and storage as workload changes. |
Automated resource scaling for compute and storage is supported on cloud deployments. | |
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Incident Management Dashboard Central dashboard for viewing, tracking, and resolving operational incidents. |
Operations dashboards are provided for managing, tracking, and resolving incidents. | |
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Performance Baselines Historical records of key performance baselines for benchmarking. |
Maintains and visualizes historical performance baselines for benchmarking and monitoring. | |
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Service Level Agreement (SLA) Uptime Guaranteed system availability percentage per SLA. |
No information available |
Data Integration Platforms for Data Management
Tools that enable data flows between different systems within the organization and with external parties such as custodians, fund administrators, and market data providers through APIs, ETL processes, and messaging.
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Connectivity & Integration
(10 Yes /10 Known /13 Possible features)
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API Connectivity Capability to connect and interact with other systems via APIs (REST, SOAP, etc.). |
IBM Cloud Pak for Data supports API connectivity as part of its integration capabilities (REST, SOAP, etc.) per official documentation. | |
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Pre-built Connectors Availability of pre-built connectors to common fund systems, custodians, administrators, and data vendors. |
Pre-built connectors are available for databases, fund systems, cloud sources, and enterprise data sources. Verified via IBM documentation. | |
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Custom Connector Support Ability to build custom data connectors/adapters. |
Supports custom connector/adapters using IBM DataStage, Java/Python SDKs. | |
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File-based Integration Supports integration via file exchange (CSV, XML, XLS, etc.). |
Can ingest/export files via CSV, XML, XLS, etc. Confirmed in product data integration documentation. | |
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Message Queue Integration Integration with messaging systems/brokers (MQ, Kafka, RabbitMQ). |
Integration with message queues like Kafka and RabbitMQ is supported. | |
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FTP/SFTP Capabilities Ability to send/receive data via FTP or SFTP protocols. |
Supports FTP/SFTP for data transfer. Confirmed via support documentation. | |
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Webhooks Support Supports event-driven integrations via webhooks. |
Supports webhook-driven integrations (event notifications, etc.) per API documentation. | |
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Batch vs. Real-time Processing Flexible support for both batch and real-time data integration. |
Flexible batch/real-time, including streaming/batch processing modes (IBM DataStage, Watson Pipelines). | |
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Data Source Auto-Discovery Automated recognition and onboarding of new data sources. |
Can auto-discover various data sources, simplifying onboarding. | |
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Partner Network Ecosystem of certified integration partners. |
IBM's partner ecosystem offers integration partners and solutions. | |
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Number of Supported Systems Total number of different systems/platforms supported for integration. |
No information available | |
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Simultaneous Connections Maximum concurrent data connections supported. |
No information available | |
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API Throughput Maximum number of API calls handled per second. |
No information available |
Data Transformation & ETL
(10 Yes /10 Known /12 Possible features)
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Visual Data Mapping Graphical tools for defining data transformation and mapping. |
IBM DataStage provides visual interfaces for data mapping and transformation. | |
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Scripting/Custom Logic Ability to include scripts or custom logic in data pipelines. |
Supports scripting (Python, Shell, etc.) and custom logic integration into ETL workflows. | |
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Data Validation Rules Built-in validation of data formats, types, and constraints. |
Built-in data validation for formats, constraints, and types via DataStage. | |
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Error Handling & Logging Comprehensive error tracking and data issue logging mechanisms. |
Detailed system error handling, error logs, and alerts per IBM documentation. | |
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Automated Data Cleansing Capabilities to auto-correct or flag suspect data. |
Automated data cleansing features are available (profiling, cleaning, deduplication). | |
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Data Enrichment Ability to enhance datasets with reference or market data. |
Supports enriching data with external sources, including market/reference data. | |
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Reprocessing Failed Batches Supports automated or manual rerun of failed data batches. |
Failed batch reprocessing workflows are built into the platform. | |
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Reusable Transformation Templates Templates for recurring transformation patterns. |
Transformation templates are available for reusable workflows. | |
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Auditable Transformation Steps Transformation history and audit logging for compliance. |
Transformation steps are logged for traceability and audit/compliance. | |
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Parallel Processing Concurrency in executing ETL jobs. |
Supports parallel/concurrent ETL processing (scalable runtime). | |
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Job Runtime Average time taken to run an ETL job. |
No information available | |
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Supported Data Types Number of structured/semi-structured/unstructured data types supported. |
No information available |
Data Quality & Governance
(12 Yes /12 Known /12 Possible features)
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Data Lineage Tracing data origin, transformations, and flow. |
Data lineage and dataflow tracking capabilities are standard in governance modules. | |
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Data Catalog Central registry of available data and sources. |
Data catalog functionality included (IBM Watson Knowledge Catalog component). | |
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Quality Metrics Dashboard Visualization of data quality indicators (completeness, accuracy, timeliness, etc.). |
Quality dashboard available within data governance features. | |
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Automated Anomaly Detection Identifying data outliers or issues automatically. |
Automated anomaly detection (data quality, outliers), described in official capabilities. | |
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Data Versioning Version control for datasets and schemas. |
Dataset and schema versioning features included in catalog/data governance modules. | |
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Master Data Management Ensures unique and consistent master data across systems. |
Master Data Management supported - IBM InfoSphere MDM can be integrated. | |
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Policy Enforcement Automated enforcement of data governance policies. |
Automated governance enforcement (policy engine), per documentation. | |
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Stewardship Workflows Tools for data stewards to manage and resolve issues. |
Stewardship/issue resolution workflows present in Watson Knowledge Catalog. | |
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Regulatory Reporting Support Facilitates compliance with industry and regulatory standards. |
Facilitates compliance for GDPR, CCPA, BCBS 239, and other regulations. | |
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Data Retention Policy Controls Configuration of record retention and disposal schedules. |
Policy-based record retention and deletion configurable. | |
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Role-based Data Access Restricts actions/visibility based on user/group roles. |
Fine-grained, role-based access controls standard (user/group). | |
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Access Audit Logs Detailed logging of data access and modifications. |
Access audit logs are maintained as part of the security and governance suite. |
Security & Compliance
(12 Yes /12 Known /12 Possible features)
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Data Encryption In-Transit Encryption of all data during transfer between systems. |
All data transmissions are encrypted in transit via TLS/SSL. | |
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Data Encryption At-Rest Encryption of stored data on disk/databases. |
Data is encrypted at rest in databases and storage as standard practice. | |
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Multi-factor Authentication Multi-factor login for users and administrators. |
Multi-factor authentication available for user login and administration. | |
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User Access Controls Fine-grained control over user permissions and roles. |
User permissions/roles are configurable in detail. | |
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Audit Trail Complete logging of all user and system activities. |
Comprehensive audit trail of all actions and system events. | |
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Compliance Certifications Adherence (and certifications) to standards like ISO 27001, SOC 2, GDPR. |
IBM has certifications (ISO 27001, SOC 2, GDPR) - see IBM Trust Center. | |
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Secure API Authentication Token-based or certificate-based authentication for APIs. |
Secure authentication for APIs (API keys, OAuth, etc.) | |
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Penetration Testing Practices Regular security testing for vulnerabilities. |
IBM runs regular penetration testing and vulnerability assessments. | |
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Data Masking/Redaction Ability to mask or redact sensitive data in outputs. |
Data masking/redaction supported (Watson Knowledge Catalog and Guardium). | |
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Incident Response Mechanisms Pre-defined procedures for data breaches or security incidents. |
Incident response plans and configurations are available as standard practice. | |
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Automated Compliance Monitoring Continuous monitoring for regulatory compliance violations. |
Continuous monitoring/compliance tools provided (automated compliance monitoring). | |
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Retention Policy Enforcement Automated enforcement of data retention and document destruction policies. |
Automated policy enforcement for retention and destruction per compliance. |
Performance & Scalability
(5 Yes /5 Known /10 Possible features)
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Horizontal Scalability Ability to scale out across multiple servers or cloud instances. |
Can scale horizontally using cloud deployment / container orchestration. | |
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Vertical Scalability Ability to add resources (CPU, RAM, Storage) to improve performance. |
Can use extra compute/storage/vCPU/RAM in deployed environment for performance. | |
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Data Throughput Maximum volume of data processable per unit time. |
No information available | |
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Latency Average time taken to process a transaction or data record. |
No information available | |
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Concurrency Support Number of data flows/pipelines that can run in parallel. |
No information available | |
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High Availability Built-in failover and redundancy for uninterrupted service. |
High availability and fault tolerance are standard with IBM Cloud Pak for Data deployments. | |
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Load Balancing Distributes workloads evenly for optimal resource usage. |
Load balancing achieved through Kubernetes, cloud-native orchestration. | |
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Auto-scaling Automatic adjustment of resources based on workload. |
Auto-scaling available for cloud-native deployments (Kubernetes integration). | |
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Processing Window Size Configurable time window for batch processing. |
No information available | |
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Transaction Volume Capacity Maximum volume of transactions supported per day. |
No information available |
Monitoring & Alerting
(8 Yes /8 Known /10 Possible features)
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Real-time Health Dashboard Live display of system health and key metrics. |
Health and status dashboards included (Watson AIOps, administrative consoles). | |
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Custom Alerts User-configurable monitoring rules and thresholds. |
User-configurable alerts supported as part of monitoring functions. | |
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Historical Data Analytics Tools for reviewing past trends and incidents. |
Historical analytics and trend reviews available in admin/monitoring portals. | |
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Error Notification Channels Multiple channels for error alerts (email, SMS, Slack, etc.). |
Multi-channel error notification (email, ticketing, SIEM integration, etc.). | |
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Automated Remediation Rules to auto-resolve common issues or trigger workflows. |
Automated remediation or workflow triggers configurable within the platform. | |
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Usage Analytics Reports on platform usage and performance. |
Usage analytics are available to administrators and business users. | |
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User Activity Logging Detailed tracking of all user activities. |
Detailed logging of all user actions included. | |
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Customizable Reporting Support for building and scheduling custom reports. |
Custom reporting is a standard feature (build and schedule reports). | |
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MTTR Tracking Time to detect and resolve technical incidents. |
No information available | |
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Alert Response Time Time for alerts to be delivered to responsible parties. |
No information available |
User Experience & Self-service
(10 Yes /10 Known /10 Possible features)
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No-code/Low-code Pipeline Builder Drag-and-drop interface for building data flows without programming. |
IBM Cloud Pak for Data provides low-code/no-code pipeline design (DataStage Flow Designer). | |
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Role-based Dashboards User interfaces tailored for different user types (IT, Ops, Business, etc.) |
Dashboards can be customized for different user roles (Admin, Operator, Analyst). | |
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Self-service Data Ingestion Allow non-technical users to upload and ingest data. |
Self-service data ingestion functionality is available to authorized users. | |
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Template Library Library of pre-built templates for common data flows and use cases. |
Template library of data flows and patterns is standard. | |
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Customizable Workspaces Users can personalize workspace layouts, filters, and views. |
Workspace customization (filters, layouts, views) is supported. | |
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Inline Help & Documentation Integrated help, tooltips, and user guides. |
Inline documentation and context-sensitive help throughout the UI. | |
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Approval Workflows Request/approve changes to pipelines and integrations. |
Approval workflow management (e.g., promoting to production) is available. | |
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Bulk Operations Manage multiple records/files in one action. |
Supports bulk operations for data/file management and workflow actions. | |
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Search & Filtering Powerful search and filtering of data and workflows. |
Enterprise-grade search functionality and advanced filtering present. | |
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Mobile Accessibility Mobile-friendly or dedicated mobile applications. |
Mobile responsive web UI; some capabilities accessible on mobile. Dedicated mobile apps are limited. |
Deployment & Architecture
(10 Yes /10 Known /10 Possible features)
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Cloud Deployment Support Native support for cloud deployment (AWS, Azure, GCP). |
Native deployment on AWS, Azure, Google Cloud, and IBM Cloud supported (cloud-native architecture). | |
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On-premises Installation Support for on-premises or private cloud environments. |
Fully supported for on-premises/private cloud deployments. | |
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Hybrid Deployment Ability to operate across both cloud and on-premises infrastructure. |
Hybrid deployment models supported; can operate across cloud/on-prem. | |
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Multi-tenancy Supports logical separation for different teams or clients. |
Multi-tenancy is supported; separate workspaces and resource controls. | |
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Microservices Architecture System is designed with microservices for modularity and resilience. |
Microservices architecture leveraged for deployment modularity and scalability. | |
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Disaster Recovery Capabilities Automatic failover and backup/restore for business continuity. |
Disaster recovery, backup/restore, and failover built-in or as add-ons. | |
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Containerization Support Support for Docker, Kubernetes, and similar technologies. |
Supports containerization (Docker/Kubernetes). | |
|
Multi-region Support Ability to operate across multiple geographic regions/data centers. |
Can be deployed in multiple regions/data centers simultaneously as per customer needs. | |
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Zero-downtime Upgrades Apply platform upgrades without interrupting service. |
Support for zero-downtime upgrades via Kubernetes rolling updates. | |
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Resource Auto-provisioning Automated deployment and resource allocation. |
Automated provisioning of resources part of cloud deployment features. |
Vendor Support & Ecosystem
(10 Yes /10 Known /10 Possible features)
|
24/7 Technical Support Round-the-clock access to expert technical support. |
24/7 support available with IBM support contracts. | |
|
Dedicated Account Manager A single point of contact for relationship management. |
Dedicated account managers available for enterprise contracts. | |
|
Community Forums Engaged user community for peer support. |
Active IBM and community support forums. | |
|
Extensive Documentation Detailed guides, manuals, and reference materials. |
Extensive documentation is available (guides, videos, reference). | |
|
Regular Product Updates Frequent improvements and new feature releases. |
Regular product updates and feature enhancements are delivered by IBM. | |
|
Training & Certification Availability of certifications and formal training programs. |
IBM offers training programs and user/operator certification tracks. | |
|
Partner/Marketplace Ecosystem Third-party integrations, add-ons, and certified partners. |
Partner marketplace with certified integrations, add-ons, and solution providers. | |
|
Service Level Agreements (SLAs) Contractual performance and availability guarantees. |
Service Level Agreements (SLAs) available for uptime/availability. | |
|
Customer Success Programs Proactive guidance to help clients realize value. |
Customer success programs via IBM services included for enterprise clients. | |
|
Multi-language Support Availability of user interfaces and documentation in multiple languages. |
The platform and documentation support multiple languages. |
Cost & Licensing
(10 Yes /10 Known /10 Possible features)
|
Usage-based Pricing Pricing model based on consumption (e.g., data processed, transactions, users). |
Usage-based pricing is available as an option (cloud subscription model). | |
|
Flexible License Models Options for perpetual, subscription, or pay-as-you-go licenses. |
IBM supports perpetual, subscription, and consumption licenses. | |
|
Transparent Pricing Clear, upfront pricing without hidden fees. |
IBM pricing is transparent and available on public documentation and quotes. | |
|
Trial/Evaluation Period Available free trial or PoC before commitment. |
Trial/evaluation options exist (free trial, PoC with IBM sales contact). | |
|
Volume Discounts Discounts for larger usage or enterprise agreements. |
Volume discounts typical for enterprise agreements. | |
|
Support Cost Inclusions Support and maintenance included in standard fees. |
Support costs are included in most enterprise subscriptions. | |
|
Cost Predictability Ability to forecast and control total cost of ownership. |
Predictability of cost through clear contract terms/TCO estimation tools. | |
|
Automated Billing Self-service billing and invoicing. |
Automated billing and invoicing available through IBM portal. | |
|
Cost Optimization Tools Built-in analytics to optimize platform usage and minimize cost. |
Cost optimization and insight tools are provided. | |
|
Multi-currency Pricing Support Ability to quote and bill in multiple currencies. |
Multi-currency support is available through IBM billing and sales. |
Extensibility & Customization
(10 Yes /10 Known /10 Possible features)
|
Plugin/Extension Framework Ability to extend core functionality via custom plugins/extensions. |
Plugin/extension frameworks are available for DataStage, Watson Studio, etc. | |
|
Custom Workflow Support Build and deploy custom data workflows and processes. |
Users can create, deploy custom workflows, and extend pipelines. | |
|
API for Custom Integrations Well-documented APIs for extending and integrating with external tools. |
APIs for extension and integration are well-documented. | |
|
White-labeling Ability to customize branding and user interface. |
Branding/UI can be customized for internal deployments (white-labeling capabilities supported). | |
|
Custom Roles & Permissions Define new user roles and granular access configurations. |
Custom roles and access permissions are configurable. | |
|
Scripting Language Support Built-in support for languages like Python or JavaScript for user scripts. |
Scripting (Python, R, etc.) is a core feature in Watson Studio/Cloud Pak for Data. | |
|
UI Theming and Customization Configure appearance and user interface elements. |
UI theming/customization supported within user/project settings. | |
|
Event Hooks Custom logic triggered on data events or system actions. |
Supports event hooks for automation and custom business logic. | |
|
Open Standards Compliance Built with and extends using open industry standards. |
Built on and compliant with open standards (e.g., REST, OAuth, OpenAPI, Cloud Native Computing Foundation standards). | |
|
Integration SDK Software development kit for building deep integrations. |
SDKs are provided for extensibility and integration. |
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