SPACE

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Getting started ​

What is SPACE? ​

The Succeedium Planning Analytics Cloud Extension (SPACE) is an AI-powered platform for IBM Planning Analytics that brings analysis, development, automation, administration, and lifecycle management into a single environment.

SPACE can be used directly from IBM Planning Analytics Workspace (PAW) or through the standalone SPACE web application. Business users can explore models and data with natural language, while developers and administrators can work with model objects, Python, automation, logs, Git, releases, security, and environment operations.

SPACE overview

Accessing SPACE ​

SPACE provides two ways to access the same platform capabilities.

From Planning Analytics Workspace ​

SPACE can be opened alongside IBM Planning Analytics Workspace through the SPACE browser integration. This keeps SPACE close to the user's existing Planning Analytics workflow while providing access to SPACE AI, model tools, Python, logs, lifecycle management, and other configured capabilities.

Standalone web application ​

SPACE can also be opened directly in a web browser without Planning Analytics Workspace or the SPACE browser extension.

The standalone application provides the same SPACE functionality and is useful when users want to work with SPACE independently of PAW, access multiple configured Planning Analytics environments, or work with other supported data connections.

The Planning Analytics access available to each user still depends on the configured connection, authentication method, and applicable permissions.

Configure SPACE ​

Before users begin working with models, data, AI, or automation, administrators can configure the connections and platform settings required for their environment.

The Configuration guide covers:

  • connections to IBM Planning Analytics and supported external data sources;
  • SPACE users, roles, and access;
  • AI providers and model profiles;
  • content and synchronization settings;
  • scheduled jobs and the journal;
  • server and system settings.

Once the required connections and access are configured, users can work with the SPACE capabilities available to their role.

AI across SPACE ​

SPACE AI provides a common, task-oriented interface across the platform. Instead of first deciding which module or tool to use, start with a question, request, or investigation.

Business users can use natural language to understand Planning Analytics models, explore and analyze data, and investigate variances. Developers and administrators can use the same interface to understand model logic, investigate issues and logs, work with supported connected data, and perform supported development and operational tasks.

Where supported, SPACE AI can use SPACE-controlled tools and configured connections to carry out the required steps while respecting SPACE access controls and IBM Planning Analytics permissions.

Review the assistant's assumptions and results before relying on them for business decisions. See the AI Assistant guide for SPACE AI capabilities, model-aware assistance, connected data, and governed access.

SPACE platform ​

SPACE brings together model understanding, data analysis, development, administration, and lifecycle management in one platform. Its modules share common connections, security, and permissions, while teams can use the capabilities relevant to their work.

SPACE platform

Where to start ​

If you are new to SPACE, start with the area that matches what you want to do:

  • Configure SPACE - Set up connections, access, AI configuration, content, jobs, journal, and server settings.
  • Use SPACE AI - Ask questions about Planning Analytics models and data, understand model logic, analyze connected data, or investigate an issue.
  • Understand a Planning Analytics model - Visualize cubes, dimensions, rules, processes, and dependencies with Model documentation. Use Model search to find model objects and logic.
  • Work with Python - Create Python scripts, use Jupyter notebooks, install custom packages, securely access SPACE-managed secrets and connections, use TM1py, and build integrations and automation.
  • Investigate TM1 access - Understand and troubleshoot why a user can or cannot access specific TM1 objects or data.
  • Investigate TM1 logs - Search IBM Planning Analytics audit, transaction, and server logs to investigate activity and issues.
  • Manage releases and deployments - Compare environments, organize changes into releases, and promote Planning Analytics objects between environments.
  • Use TM1 Git - Link TM1 models to Git repositories for version control, collaboration, and change history.

For source control of Python project files and Jupyter notebooks, see Git.

Connections and security ​

A single SPACE instance can connect to multiple IBM Planning Analytics environments, including IBM PA Local, IBM PA Cloud, and IBM PA as a Service, as well as supported external data sources such as Google BigQuery.

SPACE supports SSO for IBM Planning Analytics and Google BigQuery, allowing users to authenticate with their own identity and access data according to their existing source-system permissions without providing their source-system credentials to SPACE.

SPACE roles and connection access determine which platform capabilities and connections users can use. Native IBM Planning Analytics authentication and permissions continue to apply when accessing TM1; SPACE does not replace or bypass TM1 security.

Service-account credentials can be configured separately for supported non-interactive scenarios such as scheduled jobs.

See the Configuration guide for connection and SPACE access settings and Users and access for analyzing and troubleshooting TM1 user access.

Deployment ​

SPACE is containerized and can be deployed on AWS, Microsoft Azure, Google Cloud Platform, IBM Cloud, or on-premises.

Succeedium provides an environment-specific welcome kit covering prerequisites, deployment, authentication, and onboarding.