Management

Turn Fragmented Asset Data into Trusted Operational Intelligence

Create the structured asset data foundation required for integrity, reliability, inspection, and digital transformation success - replacing fragmented records and legacy data with a governed, high-quality information architecture.
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The Challenge

Many integrity and maintenance programs are fundamentally limited by the quality of their underlying data. Incomplete equipment hierarchies, inconsistent master data, disconnected inspection records, and legacy data trapped across decommissioned systems all combine to undermine engineering decisions, delay digital implementations, and reduce confidence in RBI and reliability outputs. Without a trusted data foundation, even the best engineers and the most advanced platforms cannot deliver their full value.

Service Components

What We Offer

A comprehensive suite of asset data and information management services - from hierarchy development and master data governance to legacy migration and inspection history digitization - designed to build a trusted, structured data foundation for your integrity and reliability programs.

01

Asset Hierarchy & Master Data Development

  • Asset hierarchy design and build (functional location, equipment, component)
  • Equipment master data standardization and enrichment
  • Asset taxonomy and naming convention development
  • Asset register validation against P&IDs and physical inventory

02

Inspection History Digitization

  • Paper and legacy inspection record digitization
  • Thickness measurement history capture and structuring
  • Inspection finding and anomaly record migration
  • Historical corrosion data extraction and validation

03

Data Migration & Integration

  • ETL migration from legacy systems to AsInt applications
  • Data migration to SAP, Maximo, and other EAM platforms
  • Integration design between IDMS, ERP, and APM systems
  • Data cleansing, deduplication, and enrichment workflows

04

Data Governance Frameworks

  • Asset data governance policy and standards development
  • Data ownership and stewardship framework design
  • Data quality KPI definition and monitoring
  • Ongoing data quality management processes and tools

Our Methodology

How We Deliver Asset Data Programs

A structured, engineering-led data delivery model - from data discovery and quality assessment through migration, governance, and sustainment - designed to produce a trusted, maintained asset data foundation.

Why Methodology Matters

Data programs fail when they are treated as IT extract-and-load exercises. Asset data requires engineering judgment - knowing which equipment records are correct, what inspection history is valid, and how data relationships support integrity decisions. Our team combines data engineering capability with deep asset integrity expertise.

100%

Engineering-Led Data Methodology

4-12 Weeks

Foundation to Live

Ongoing

Data Governance Sustainment

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Phase 1

Data Discovery & Quality Assessment

We conduct a structured review of all existing data sources - CMMS records, inspection databases, spreadsheets, paper archives, and P&IDs - assessing quality, completeness, and migration readiness against the target data model.

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Phase 2

Asset Hierarchy & Taxonomy Design

The target asset hierarchy and data taxonomy are defined - establishing the equipment structure, naming conventions, and attribute standards that all downstream data will conform to. Design is validated against P&IDs and physical inventory.

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Phase 3

Data Cleansing & Enrichment

Source data is systematically cleansed, enriched, and restructured to meet target data quality standards. Engineering review is applied to records that require judgment rather than just reformatting - a step that pure IT data migration services cannot provide.

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Phase 4

Migration & Integration Execution

Cleansed data is migrated to target platforms - AsInt applications, SAP APM, Maximo, or other EAM and IDMS systems. Integration connectors between systems are built and validated to ensure data flows remain current after migration.

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Phase 5

Data Governance & Quality Sustainment

A data governance framework is established - defining ownershipquality standardschange management processesand monitoring KPIs. Sustainment workflows are documented to ensure data quality is maintained as the asset base evolves.

Business Value

What You Gain

Measurable data quality improvements that unlock the full value of your engineering programs, digital platforms, and integrity investments.
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Improved Data Quality

A cleansed, validated, and enriched asset register provides the accurate, complete data foundation that RBI, reliability, and digital programs require to function correctly.

Data Quality
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Faster Digital Implementation

High-quality asset data significantly reduces APM and IDMS implementation timelines - eliminating the data remediation work that stalls most digital platform deployments.

Implementation Speed
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Better Engineering Decisions

Engineers working with trusted, complete, and correctly structured asset data make faster, more accurate integrity and maintenance decisions - reducing rework and decision errors.

Decision Quality
Icon forHigher Confidence in RBI & Reliability Outputs
Higher Confidence in RBI & Reliability Outputs

RBI models and reliability analytics are only as good as the data they run on. A trusted data foundation directly improves the accuracy and credibility of every output your integrity program produces.

Program Credibility

Is poor asset data limiting your integrity program or stalling your digital implementation?

Our team can assess your current data quality, identify the highest-impact gaps, and build a practical remediation roadmap.

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Ready to Build a Results-Driven Asset Data & Information Management Program?

Whether you're preparing for a digital transformation, remediating data quality problems, or building an asset data foundation from scratch, AsInt brings the engineering expertise, data management methodology, and structured delivery model to create asset data you can trust.

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