The Rise of Managed Data Services in Asset-Intensive Industries
In asset-intensive industries, data grows faster than teams can control. Duplicate records pile up, equipment histories become unreliable, and critical decisions depend on outdated or incomplete information. This is exactly why Managed Data Services have become the preferred approach for organizations seeking accuracy, consistency, and real-time visibility across their operations. By shifting data ownership to specialized experts, enterprises eliminate the chaos of manual data handling and gain a scalable, always-optimized foundation for analytics, governance, and digital transformation.
What Are Managed Data Services?
Managed Data Services refers to an outsourced, end-to-end model where a specialized provider handles the creation, cleansing, governance, enrichment, and ongoing maintenance of your enterprise master data.
Unlike one-time MDM cleanup projects, Managed Data Services work on a continuous, subscription-based model, ensuring your data stays consistent, compliant, and high-quality over time.
They typically include:
- Data discovery and health assessment
- Data cleansing and deduplication
- Enrichment and standardization using AI/ML models
- Governance workflows and lifecycle controls
- Domain expertise for asset, equipment, material, vendor, and service masters
- Real-time validation before data enters ERP/EAM systems
- Ongoing monitoring and KPI reporting
For industries with thousands of assets, hundreds of vendors, and complex maintenance operations, this model removes the burden on internal teams and ensures data never decays again.
Why Asset-Intensive Industries Are Adopting Managed Data Services
Industries such as Oil & Gas, Utilities, Manufacturing, EPC, Metals, Ports, Aviation, and Real Estate rely on data-heavy operational systems. A single refinery or power plant alone may have:
- 100,000+ spare parts
- 50,000+ equipment records
- 10,000+ service items
- Thousands of suppliers and maintenance histories
In such environments, poor data is not just inefficient; it’s costly.
1. High cost of downtime
Unreliable asset data delays work orders, increases diagnostic time, and leads to unplanned maintenance. A mismatched or incorrect material record can turn a routine repair into a costly shutdown.
2. Duplicate and inconsistent spare parts
Asset-heavy sectors regularly discover that 20–40% of their spare parts are duplicated under different names or codes. This inflates inventory holding costs and procurement budgets.
3. Compliance pressure
Regulated industries need traceable, accurate asset data for audits, safety reporting, and operational certifications.
4. Multiple ERP and EAM systems
Enterprises with SAP + Maximo + Oracle + legacy systems struggle with fragmented master data across plants and regions.
5. Skill shortages
Internal IT teams cannot manage both operations and continuously evolving data governance needs. Managed Data Services fills this gap instantly.
Business Impact & ROI of Managed Data Services
For decision-makers evaluating this approach, the ROI is the strongest reason to act. Managed Data Services consistently deliver value in four measurable ways:
1. Reduced Inventory Costs
When spare parts are deduplicated and standardized, carrying costs drop significantly.
- Typical savings: 5–15% reduction in inventory value
- Primary drivers: fewer duplicates, correct stock levels, catalog accuracy
2. Higher Asset Uptime
Clean equipment and material masters improve maintenance planning, reduce search times, and enable predictive maintenance models.
Result: fewer delays, faster work completion, reduced emergency procurement
3. Lower Procurement Spending
With standardized descriptions and manufacturer details:
- Sourcing becomes faster
- Suppliers can be consolidated
- purchasing errors decline
4. Stronger Compliance & Risk Control
Governance ensures every material, asset, and vendor record meets industry and regulatory standards. This protects the organization during audits, inspections, and reporting cycles.
How Managed Data Services Work (Step-by-Step)
1. Data Discovery & Baseline Assessment
Every engagement begins with a complete diagnostic of existing data across ERPs, EAMs, CMMS, spreadsheets, and legacy systems.
This includes:
- Full data profiling (completeness, accuracy, consistency, uniqueness, validity)
- Identifying duplicates, obsolete items, and conflicting records
- Mapping metadata gaps such as missing OEMs, part numbers, equipment classes, maintenance attributes
- Assessing taxonomy alignment with standards (UNSPSC, ETIM, eClass, ISO 14224, ISO 8000)
Outcome: A quantified picture of data issues, including an ROI forecast showing potential inventory savings, procurement improvements, and maintenance efficiency gains.
2. Data Cleansing, Standardization & Deduplication (Powered by AI/ML)
Once the baseline is clear, large volumes of data are processed using a mix of AI-driven classifiers and human domain validation.
This step includes:
- Standardizing naming conventions (e.g., noun–modifier structure for materials)
- Applying OEM catalog rules for accuracy
- Removing redundant, duplicate, incomplete, or conflicting records
- Normalizing units of measure across plants or regions
- Enriching missing fields (manufacturer, model, specifications, attributes, class codes)
Why this matters:
Standardization eliminates ambiguity across plants, improves searchability in ERP/EAM systems, and reduces time wasted by technicians and procurement teams.
3. Classification & Taxonomy Alignment
For asset-heavy industries, poor classification is a common root cause of bad maintenance and inventory outcomes.
This step ensures every record is aligned to:
- Industry standards (ISO 14224 for equipment, UNSPSC/eClass/ETIM for materials)
- Internal business rules
- Multi-plant taxonomies and cross-functional usage patterns
Impact:
Improves analytics, enables accurate spend categorization, supports digital twin development, and enhances maintenance planning.
4. Governance Framework Setup
Governance prevents data decay, something traditional one-time MDM cleanups cannot achieve.
This phase includes:
- Designing data quality rules (mandatory fields, controlled vocabularies, attribute thresholds)
- Establishing maker-checker approval workflows
- Setting SLA-driven service models for new record creation or updates
- Implementing automated validation pipelines
- Building dashboards for data quality KPIs (DQI, DQI trend lines, SLA compliance, error rates)
Outcome:
A sustainable system where data cannot enter ERPs unless it passes quality and compliance checks.
5. Technology Integration with ERP/EAM/CMMS
Managed Data Services plug directly into operational systems such as:
- SAP ECC / S/4HANA
- Oracle EBS / Oracle Cloud
- IBM Maximo
- Infor
- Microsoft Dynamics
- Proprietary or home-grown systems
Impact:
Eliminates siloed data, supports plant standardization, and reduces ERP/EAM errors.
See how seamless integration can simplify your entire data landscape.
6. Continuous Stewardship & Data Maintenance
This is the heart of Managed Data Services, where ongoing value is created.
Activities include:
- Real-time validation of all new item/asset/vendor requests
- Periodic audits to catch classification drift or attribute decay
- Lifecycle updates (commissioning, warranty, maintenance history, decommissioning)
- Vendor normalization and consolidation
- Attribute enrichment for predictive maintenance models
- Uploading new OEM data packs as equipment changes
Outcome:
Your data improves each month instead of degrading, ensuring reliable operations, accurate planning, and compliance.
7. Reporting, Insights & ROI Measurement
To keep leadership aligned, monthly and quarterly reporting covers:
- Data quality improvement scorecards
- Duplicate elimination trends
- Inventory value reduction opportunities
- Change request cycle-time improvements
- Classification accuracy
- Plant-wise or region-wise performance
Impact for decision-makers:
Clear, visible, measurable ROI, not just clean data.
Where Managed Data Services Deliver Maximum Value (Industry Use Cases)
Oil & Gas: Reliability & Shutdown Planning
Oil & Gas companies depend on accurate equipment and spare parts records for safe and efficient operations. Managed Data Services help eliminate duplicates, enrich maintenance histories, and standardize asset taxonomies.
Example:
A refinery preparing for a major turnaround found 18% duplicate spare parts in SAP. By standardizing descriptions and aligning them with ISO 14224, procurement planning accuracy improved, and shutdown duration was shortened by three days.
Utilities: Compliance & Predictive Maintenance
Utilities must maintain audit-ready data for assets spread across large grids or plants. Managed Data Services ensure every asset record is complete, validated, and governance-controlled.
Example:
A power distribution utility consolidated asset data from six plants. Once data was standardized and aligned with regulatory codes, inspection reports became 40% faster to prepare.
Manufacturing: Production Continuity
Manufacturers depend on correct material records to avoid delays in repairs and production interruptions. Managed Data Services help technicians find the right parts quickly and reduce machine downtime.
Example:
A global manufacturing plant discovered multiple versions of the same motor spare. After deduplication and attribute enrichment, mean time to repair (MTTR) dropped by 12%.
Marine & Ports: Equipment Lifecycle Management
Ports operate fleets of cranes, conveyors, vehicles, and critical lifting equipment. Managed Data Services build accurate asset hierarchies and ensure reliable maintenance documentation.
Example:
A port authority standardized master data for 8,500 assets across three terminals. This resulted in a 25% improvement in maintenance planning accuracy.
Government & Defense: Audit-Ready Data
Government entities face strict compliance requirements. Managed Data Services unify data from siloed systems and eliminate inconsistencies.
Example:
A defense organization reduced vendor duplication by 30% after implementing governance-led record creation, improving contract management and procurement transparency.
Why Managed Data Services Are More Effective Than Traditional In-House Models
| Feature | Traditional MDM Project | Managed Data Services |
|---|---|---|
| Duration | One-time | Continous |
| Value Retention | Data decays fast | Sustained quality |
| Cost Structure | High upfront cost | OPEX-friendly subscription |
| Governance | Often missing | Built-in workflows |
| Expertise | Internal teams only | Domain + data experts |
| ERP Integration | Limited | Continuous synchronization |
| ROI | Slow, inconsistent | Fast and measurable |
Get a quick, accurate picture of your asset, material, and equipment master data
Frequently Asked Questions
1. What makes Managed Data Services different from traditional MDM?
MDM is usually a one-time clean-up. Managed Data Services provide continuous governance, validation, and stewardship.
2. Can Managed Data Services connect to SAP, Oracle, or Maximo?
Yes. Leading providers integrate seamlessly with ERP/EAM systems to maintain consistent data across platforms.
3. How soon can we expect ROI?
Most enterprises see measurable improvements in 3–6 months, depending on data volume and complexity.
4. Is this model secure?
Yes. Managed Data Services use encryption, audit logs, access controls, and compliance frameworks to keep your data safe.
5. Do we need an internal data team?
Not necessarily. Internal teams oversee strategy, while the service provider handles operations and governance.
Ready to Transform Your Enterprise Data into a Reliable Strategic Asset?
