GHT: Glass House Technologies
GHT service / Data center

Data-center infrastructure management

A model of the floor you can trust

Sites, rooms, racks, assets, ports, circuits, and sensors get normalized, supported data sources connect, and physical changes gain a defined path into the model.

What you should have at closeoutRack, asset, power, connectivity, and sensor records checked against the data-center floor, with import exceptions and update procedures assigned.
References4 reviewed
Last updated2026-08-20
Overview

What Data-center infrastructure management includes

A DCIM platform is only useful when rack, asset, port, circuit, and sensor identifiers match the floor and every physical change updates the record.

A staged rollout usually starts with one use case, such as rack inventory, power capacity, or environmental alarms, then expands after data quality and support ownership are proven.

Question to answer before designWhich source controls each record, what decision will the data support, and who updates it after a physical change?
Common situations

This service may fit when:

  • Rack, asset and port records disagree across systems
  • Power or space capacity is estimated manually
  • Environmental alarms lack location or escalation context
  • A DCIM platform exists but operational adoption is low
System components

What the system includes

A complete scope covers each part below and the connections between them.

01

Sources

Assets, ports, power, sensors, network and facility systems

02

Normalization

Identifiers, relationships, units and data-quality rules

03

DCIM model

Physical hierarchy, capacity, connectivity and telemetry

04

Operations

Alarms, reports, planning, work orders and change reconciliation

Project record

How site information becomes a tested project

A complete project record connects the conditions found on site, the design decisions made from them, and the tests and closeout documents delivered afterward.

Site information

What we confirm before design

  • Priority operational use cases and success measures
  • Current systems, exports, APIs and data owners
  • Physical hierarchy and identifier conventions
Design decisions

What those findings determine

  • Starting scope: Begin with the use case that has an owner and reliable source data.
  • Collection: Automate only after identifier matching and error handling are defined.
  • Accuracy: Assign who closes the loop after every physical change.
Closeout records

What you should receive

  • DCIM data and integration architecture
  • Naming/identifier and field dictionary
  • Normalized import with exception report

The exact inputs, decisions, and acceptance records depend on the site and signed scope.

Project stagesSurvey through closeoutView details
Project stages

How the work moves from survey to closeout

Each stage should produce the records and test results needed before the next stage begins.

  1. 01

    Use case and source

    Choose the decisions to support and identify system owners and sources of record.

    EvidenceUse-case charter, field dictionary and ownership matrix
  2. 02

    Model and normalize

    Define site, room, row, rack, asset, port, circuit, sensor and relationship identifiers.

    EvidenceData model, naming rules and import/reconciliation report
  3. 03

    Integrate and validate

    Connect supported APIs, telemetry and imports; compare samples against physical state.

    EvidenceIntegration map, validation sample and error/exception log
  4. 04

    Operationalize

    Build roles, alarms, reports and change procedures around maintained data.

    EvidenceRunbook, role matrix, dashboard acceptance and update audit
Design choicesCompare the available approachesView details
Options

How to choose the right approach

The right choice depends on the site, application, operating risk, and acceptance requirements. More equipment does not automatically improve the system.

FactorTypical approachMore demanding conditionsWhat determines the choice
Starting scopeTypical approach: Asset and rack inventoryMore demanding conditions: Connectivity, power, environment and workflowsWhat determines the choice: Begin with the use case that has an owner and reliable source data.
CollectionTypical approach: Controlled bulk import and manual updatesMore demanding conditions: API and telemetry integrationsWhat determines the choice: Automate only after identifier matching and error handling are defined.
AccuracyTypical approach: Scheduled physical reconciliationMore demanding conditions: Change-driven updates with audit historyWhat determines the choice: Assign who closes the loop after every physical change.
OutputTypical approach: Inventory and capacity reportsMore demanding conditions: Threshold alarms and operational workflowsWhat determines the choice: Each output needs a named decision, audience and response.
Before design

What we need to know

  • Priority operational use cases and success measures
  • Current systems, exports, APIs and data owners
  • Physical hierarchy and identifier conventions
  • Telemetry protocols, units, intervals and retention
  • Roles, change process, alarm routing and security boundary
At closeout

What you should receive

  • DCIM data and integration architecture
  • Naming/identifier and field dictionary
  • Normalized import with exception report
  • Validation and reconciliation results
  • Operations runbook, dashboards and ownership matrix
Best fit

When this service makes sense

  • New DCIM deployment or platform migration
  • Inventory and naming normalization
  • Power/environmental telemetry integration
  • Operational workflow and dashboard enablement
Before we commit

What we verify first

  • Incomplete or conflicting source records
  • Vendor/API coverage and licensing
  • Credentials, network segmentation and least-privilege access
  • Ongoing ownership after adds, moves and removals
Site contextSee where this work is usedView details
Where it is used

How site conditions change the design

Occupancy, operating hours, user activity, regulation, weather, construction, and access can change the design.

Common questions

What people usually ask

Is DCIM the same as network monitoring?

No. Network monitoring observes device and service behavior. DCIM models physical infrastructure, capacity, connectivity, power and environmental context; the systems may exchange data.

Should every integration be automated at launch?

Usually not. First prove identifiers, source authority, data quality, and exception handling with one limited use case, then automate stable flows.

What keeps a DCIM model accurate?

A change process that assigns record updates, validates critical fields against physical state and audits unresolved exceptions. Software alone cannot maintain site truth.

Standards and referencesReview the source materialView details