Enterprise Data Platform

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Platform Charter ​

The Enterprise Data Platform provides a shared foundation for collecting operational data from multiple systems, correlating it into trusted models, and publishing reusable data products for reporting, applications, automation, governance, and decision support.

Use Implementation Planning to turn this charter into concrete decisions about use cases, ownership, governance, environments, operations, and adoption.

Background ​

Organizations often rely on many disconnected operational systems: identity platforms, collaboration tools, endpoint management, service desks, infrastructure platforms, security tools, business applications, and departmental systems. Each system may include its own reporting, but those reports rarely provide complete cross-system visibility.

This creates recurring challenges:

  • Manual onboarding and offboarding validation
  • Fragmented identity, permission, license, asset, and ticket reporting
  • Inconsistent operational definitions across departments
  • Delayed troubleshooting and audit evidence collection
  • Duplicate reporting effort and spreadsheet-based shadow systems
  • Limited ability to automate lifecycle workflows across systems

Problem Statement ​

The platform addresses the lack of a centralized operational data and analytics layer that can correlate enterprise system data into a unified source for reporting, automation, operational intelligence, and governance.

Critical operational processes should not depend on manually joining exports from multiple tools. EDP creates governed, reusable datasets that can answer cross-system questions consistently.

Objectives ​

Short-term objectives:

  • Establish the foundational platform infrastructure
  • Build the raw data store and Operational Data Store
  • Integrate high-value source systems
  • Publish initial operational dashboards
  • Improve identity lifecycle, access, licensing, and membership visibility
  • Centralize common operational reporting

Long-term objectives:

  • Develop enterprise warehouse and Data Vault capabilities
  • Support governance, risk, compliance, and audit reporting
  • Enable operational automation and workflow orchestration
  • Build reusable enterprise integrations and data models
  • Support future CMDB, inventory, intelligence, and custom operational applications

Scope ​

In scope:

  • Data ingestion pipelines and API integrations
  • Workflow orchestration and scheduled processing
  • Raw data, Operational Data Store, Data Vault, and Data Mart layers
  • Data transformations, testing, lineage, and semantic models
  • Dashboards, reports, alerts, and operational applications
  • Cross-system analytics and identity lifecycle reporting
  • Documentation, runbooks, standards, and data dictionaries

Out of scope for an initial phase:

  • Replacing source systems
  • Replacing ERP or core business platforms
  • Large-scale AI or machine learning initiatives
  • Unsupported modification of production systems
  • Direct changes to source systems outside approved APIs and governance

Principles ​

  • Use API-first integrations where possible.
  • Keep workloads containerized and modular.
  • Separate operational, historical, analytical, and presentation concerns.
  • Manage infrastructure, pipelines, transformations, schemas, applications, and documentation in Git.
  • Prefer peer-reviewed, version-controlled production changes.
  • Use role-based access control and least privilege.
  • Design for observability, auditability, and future high availability.
  • Treat documentation as part of the platform, not an afterthought.

Success Measures ​

  • Core system integrations are established and refreshed reliably.
  • Shared dashboards replace recurring manual reports.
  • Identity lifecycle, access, membership, licensing, asset, or ticket views become operationally useful.
  • Manual reporting effort measurably decreases.
  • Cross-system data definitions are documented and reused.
  • Transformations and models are version controlled and peer reviewed.
  • Automation use cases can build on governed operational datasets.

Delivery Phases ​

Phase 1: infrastructure, raw ingestion, Operational Data Store, and core integrations.

Phase 2: operational reporting, dashboards, curated marts, and early workflow visibility.

Phase 3: automation, advanced analytics, and broader cross-system integrations.

Phase 4: governance, CMDB-style applications, intelligence applications, and mature historical processing.

Pilot Pattern ​

A strong first pilot is identity lifecycle operational visibility. This use case commonly spans onboarding, offboarding, group and channel memberships, license assignments, endpoint records, service tickets, training status, and application provisioning. It produces immediate operational value while proving the platform's integration, correlation, and reporting patterns.