--- title: Integration & Data Architecture — Matter + Energy Services description: Data pipeline design, source system connections, and the integration architecture that keeps ITFM and AI platforms running on accurate, governed data. image: https://www.matternenergy.com/hubfs/matter-energy/og-default.jpg --- [![Matter + Energy home](https://www.matternenergy.com/hs-fs/hubfs/matter-energy/logo/me-logo-horizontal.png?width=273&height=32&name=me-logo-horizontal.png)](https://www.matternenergy.com/) - [Home ▾](https://www.matternenergy.com/) [Who we are](https://www.matternenergy.com/company) [Technology Ecosystem](https://www.matternenergy.com/technology-ecosystem) [Teaming & Partnerships](https://www.matternenergy.com/teaming) - Solutions ▾ [Overview](https://www.matternenergy.com/solutions) [Technology Value Management](https://www.matternenergy.com/it-financial-management) [AI Adoption & Productivity](https://www.matternenergy.com/solutions/ai-adoption-productivity) - Services ▾ [Our Approach](https://www.matternenergy.com/services) [Maturity Assessment](https://www.matternenergy.com/services/maturity-assessment) [Proof of Value](https://www.matternenergy.com/services/proof-of-value) [Integration & Data Architecture](https://www.matternenergy.com/services/integration-data-architecture) [Retained Operations](https://www.matternenergy.com/services/retained-operations) - Government ▾ [Federal Capabilities](https://www.matternenergy.com/government) [Commercial Solutions Openings (CSO)](https://www.matternenergy.com/government/commercial-solutions-offerings) [OTA & Rapid Prototyping](https://www.matternenergy.com/government/rapid-prototyping) [Solutioning as a Service™](https://www.matternenergy.com/solutioning-as-a-service) [Book a Lab Session](https://www.matternenergy.com/schedule-lab-sessions) - [Insights](https://www.matternenergy.com/insights) - [Events](https://www.matternenergy.com/events) [Schedule a Call](https://www.matternenergy.com/discovery) [![Matter + Energy home](https://www.matternenergy.com/hs-fs/hubfs/matter-energy/logo/me-logo-horizontal.png?width=205&height=24&name=me-logo-horizontal.png)](https://www.matternenergy.com/) Home ▾ [Home](https://www.matternenergy.com/) [Who we are](https://www.matternenergy.com/company) [Technology Ecosystem](https://www.matternenergy.com/technology-ecosystem) [Teaming & Partnerships](https://www.matternenergy.com/teaming) Solutions ▾ [Overview](https://www.matternenergy.com/solutions) [Technology Value Management](https://www.matternenergy.com/it-financial-management) [AI Adoption & Productivity](https://www.matternenergy.com/solutions/ai-adoption-productivity) Services ▾ [Our Approach](https://www.matternenergy.com/services) [Maturity Assessment](https://www.matternenergy.com/services/maturity-assessment) [Proof of Value](https://www.matternenergy.com/services/proof-of-value) [Integration & Data Architecture](https://www.matternenergy.com/services/integration-data-architecture) [Retained Operations](https://www.matternenergy.com/services/retained-operations) Government ▾ [Federal Capabilities](https://www.matternenergy.com/government) [Commercial Solutions Openings (CSO)](https://www.matternenergy.com/government/commercial-solutions-offerings) [OTA & Rapid Prototyping](https://www.matternenergy.com/government/rapid-prototyping) [Solutioning as a Service™](https://www.matternenergy.com/solutioning-as-a-service) [Book a Lab Session](https://www.matternenergy.com/schedule-lab-sessions) [Insights](https://www.matternenergy.com/insights) [Events](https://www.matternenergy.com/events) [Schedule a Call](https://www.matternenergy.com/discovery) [01 — Maturity Assessment](https://www.matternenergy.com/services/maturity-assessment) [03 — Proof of Value](https://www.matternenergy.com/services/proof-of-value) [04 — Integration & Data Architecture](https://www.matternenergy.com/services/integration-data-architecture) [06 — Retained Operations](https://www.matternenergy.com/services/retained-operations) [Home](https://www.matternenergy.com/)/[Services](https://www.matternenergy.com/services)/Integration & Data Architecture Service 04 # The technical layer that makes *every other service work.* Data pipeline design, source system connections, API configuration, and the integration architecture that keeps platforms running with accurate, timely, governed data. For both Technology Value Management and AI Adoption deployments. TVM · TBM · FinOps AI Adoption Enterprise Data Integration Data Architecture — Hover to explore Sources GL / ERP CMDB Cloud Billing HR Systems ServiceNow Contracts Pipelines & APIs Platform ITFM Platform AI Platform Governance Layer Validated Outputs Consumers Cost Reports Planning Models AI Agents Dashboards Illustrative architecture — actual design is environment-specific Why This Service Exists ## What breaks when the *integration layer is wrong.* Most platform deployments that underperform have the same root cause — not the platform, but what is or is not flowing into it. These are the patterns we see repeatedly. 01 Incomplete cost coverage Cost models that connect to three of eight source systems produce cost visibility that stakeholders cannot trust — because everyone knows the number is wrong, they just do not know by how much. Stale data feeding live decisions Pipelines that refresh weekly or manually cannot support real-time planning decisions. When the data in the platform is 10 days old, the reports it produces are 10 days old — regardless of how well the platform is configured. 02 Undocumented integrations nobody owns When the person who built the integration leaves, it becomes a black box. Pipelines fail silently. Reconciliation breaks. Nobody knows how to fix it because nobody documented it — and the platform is blamed for data problems that are actually pipeline problems. 03 AI models trained on ungoverned data Agentic AI systems that consume data without quality validation, lineage documentation, or access controls produce outputs that cannot be audited — and in regulated environments, cannot be used. 04 Reconciliation consuming analyst time When platform data does not match source data, someone has to manually reconcile every cycle. This is the most reliable signal that the integration layer was not built with validation controls in mind. 05 No architecture to hand off Integrations built by external teams without documentation create perpetual dependency. When the platform is handed to internal teams, they inherit a system they cannot maintain, extend, or troubleshoot independently. 06 Architecture Model ## Five layers. *Each one designed for maintainability.* We build integration architecture in discrete, documented layers — so your team can understand, operate, and extend each component independently. Click a layer to explore. 01 ■ Source Layer System connections and data extraction We catalog every source system that should feed your platform, document the connection method, authentication requirements, and data availability. API connections, file-based extracts, database queries, and vendor-provided connectors are all inventoried and documented before a single pipeline is built. APIsDatabase connectorsFile extractsVendor feeds 02 ▶ Pipeline Layer Transformation, scheduling, and monitoring Data pipelines are built with transformation logic, refresh cadence, error handling, and alerting configured from day one. We do not build pipelines that fail silently — every pipeline has monitoring, failure notification, and a documented runbook for remediation. ETL/ELT logicSchedulingError handlingAlerting 03 ● Validation Layer Data quality controls and reconciliation checkpoints Before data reaches the platform, it passes through validation rules that check completeness, consistency, and expected ranges. Reconciliation checkpoints compare pipeline outputs against source system totals — so discrepancies are caught at the pipeline level, not discovered by analysts during reporting cycles. Validation rulesReconciliationAnomaly detectionQuality scoring 04 ◆ Governance Layer Access controls, lineage, and audit trail For AI deployments in particular, data governance is not optional. We document data lineage from source to model, configure access controls that enforce need-to-know at the pipeline level, and produce the audit documentation that compliance and oversight require. Data lineageAccess controlsAudit trailCompliance docs 05 ★ Handoff Layer Documentation and operational runbooks Every integration we build is documented in a format your team can actually use — architecture diagrams, data dictionaries, pipeline runbooks, troubleshooting guides, and a connector inventory with owner assignment. The goal is a team that can operate and extend the architecture without calling us. Architecture docsRunbooksData dictionaryConnector inventory Practice Areas ## Same principles. *Different architectures.* The integration approach for a Technology Value Management deployment is structurally different from an AI Adoption deployment — different sources, different validation requirements, different governance needs. Technology Value Management AI Adoption & Productivity ### A cost model is only as good as the *data feeding it.* TVM integration connects financial and operational source systems into a coherent [cost model architecture](https://www.matternenergy.com/insights/allocation-model-problem). The challenge is not just connecting systems — it is aligning them around a common cost taxonomy, enforcing allocation logic at the pipeline level, and building reconciliation controls that Finance will trust. - General ledger and sub-ledger connection with period alignment - CMDB and asset register integration for technology tower mapping - Cloud billing feeds (AWS, Azure, GCP) with tag normalization - HR and headcount data for labor cost allocation - Contract and license data for software spend coverage - Reconciliation against Finance-owned actuals each cycle Key Integration Considerations Taxonomy alignment Source systems often use different cost classification schemes. Mapping these to a common taxonomy at the pipeline level — before data reaches the platform — prevents downstream reclassification that undermines allocation accuracy. Period alignment Financial systems, cloud billing, and operational systems often use different period definitions and cut-off dates. Aligning these at the pipeline level is essential for accurate period-over-period reporting. ### AI without governed data is a *liability, not an asset.* AI integration connects operational data sources to the model and workflow infrastructure — with governance controls that ensure the data feeding your AI systems is accurate, traceable, and access-controlled. In regulated environments, this is not optional. - Data source inventory and access classification - Ingestion pipelines with quality validation and anomaly detection - Lineage documentation from source to model input - Access governance aligned to need-to-know principles - Agent workflow data connections with refresh cadence - Audit trail for model input data and decision outputs Key Integration Considerations Data quality before model training AI models amplify data quality issues — they do not correct them. Validation controls must be enforced at the pipeline level before data reaches any model, not treated as a post-deployment remediation task. Compliance-ready lineage Regulators increasingly require organizations to demonstrate what data trained a model, who approved its use, and how decisions were produced. Building lineage documentation from day one is far less expensive than reconstructing it under audit. Systems We Connect ## Over 20 systems. *Filter by category.* All Finance & ERP Cloud & Infrastructure ITSM & Operations Data & Analytics HR & Workforce SAP S/4HANA Oracle ERP Microsoft Dynamics 365 Coupa Workday Financials AWS Cost & Usage Reports Azure Cost Management Google Cloud Billing VMware vCenter ServiceNow CMDB ServiceNow ITAM Jira Jira Align Archer GRC Splunk Power BI Tableau SharePoint GitHub / GitLab Workday HCM Microsoft Entra / Active Directory Salesforce What You Leave With ## Five deliverables. *Built to outlast the engagement.* 01 Integration Architecture Document Full documentation of data flows, source systems, transformation logic, and pipeline design — in a format your team and any future vendor can read and operate against. 02 Configured Data Pipelines Production-ready pipelines with monitoring, alerting, validation rules, and error-handling built in from day one. Not prototype pipelines hardened post-deployment — built right the first time. 03 API & Connector Inventory A documented catalog of all system connections — authentication methods, refresh cadence, data fields, owner assignment, and support contacts for each integration. 04 Data Quality Framework Validation rules, reconciliation checkpoints, and anomaly detection configured at the pipeline level — so quality issues surface before they reach the platform or your stakeholders. 05 Handoff & Operational Runbook Step-by-step operational documentation for your team — covering routine maintenance, pipeline failure response, data refresh procedures, and how to extend the architecture as your environment changes. "The goal is a team that can operate and extend the architecture without calling us — that is how we define a successful handoff." Matter + Energy — Delivery Philosophy Next Service Retained Operations — 06 [View service →](https://www.matternenergy.com/services/retained-operations) ## Built right the first time. *Documented to last.* Schedule a [discovery call](https://www.matternenergy.com/discovery) to discuss your integration environment, the systems you need to connect, and what a well-designed architecture would change for your team. 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