Enterprise Data Quality Solutions for Clean, Reliable Master Data
PiLog’s AI‑driven Data Quality solutions deliver measurable business outcomes that translate directly into cost savings, productivity gains, and risk reduction.
- Cost Efficiency
- Operational Agility
- Risk Mitigation
Deduplication
Duplicate‑record reduction of 30‑45% on average, eliminating redundant master‑data entries across ERP, CRM and cloud systems.
Low Effort
Manual data‑cleansing effort lowered by 40‑60%, freeing staff to focus on higher‑value activities such as analysis and innovation.
Shorter Procurement Cycle
Procurement cycle time shortened by 15‑25% after standardizing vendor and material master data, accelerating order fulfilment.
Audit Readiness
Compliance‑related audit findings reduced by 50‑70% through automated validation, ISO 8000 alignment and full audit trails.
The Problem Statement
Reliable Data Begins with Quality
Despite investing in SAP and sophisticated ERP landscapes, many organizations still wrestle with fragmented, inconsistent, and incomplete master data across more than 25 core objects (materials, assets, services, suppliers, etc.). The lack of a unified, AI enhanced data quality layer leads to duplicate golden records, missed alerts, and manual rework that drains the time of data stewards and frontline operators.
Production downtime and safety risk
Erroneous asset master data leads to incorrect maintenance schedules,
resulting in unplanned outages and heightened safety exposure.
Regulatory non‑compliance
Incomplete audit trails and non-standardized data quality checks expose companies to fines and reputational damage.
Cost Leakage
Duplicate and outdated records inflate procurement spend, cause over stocking, and generate unnecessary purchase orders amounting to millions of dollars annually.
Analytics paralysis
Inconsistent master data skews analytics, undermining strategic decision making and delaying digital transformation initiatives
Manual data‑cleansing effort
Data stewards spend up to 60% of their time resolving duplicates, leaving less capacity for value adding activities.
Integration friction
Misaligned taxonomies across your SAP touchpoints can cause synchronization errors and equire costly custom interfaces.
User frustration
Inconsistent master data leads to repeated error messages in downstream applications, reducing employee productivity and morale.
Scalability bottleneck
As the volume of records grows, legacy rule-based validation struggles to keep pace, slowing onboarding of new assets.
Core Capabilities That Address the Challenges
What Makes PiLog Data Quality Different
Despite investing in SAP and sophisticated ERP landscapes, many organizations still wrestle with fragmented, inconsistent, and incomplete master data across more than 25 core objects (materials, assets, services, suppliers, etc.). The lack of a unified, AI enhanced data quality layer leads to duplicate golden records, missed alerts, and manual rework that drains the time of data stewards and frontline operators.
Industry Focused
Governance Engine
Domain driven, ISO aligned rules validate every master data object at the moment of entry, stopping errors before they propagate downstream.
iMirAIÂ Assisted Stewardship
& Golden Record Creation
Governance Engine
iMirAI continuously deduplicates, enriches and validates incoming records, maintaining a single source of truth for analytics and predictive maintenance.
iContent Foundry
30 K+ pre‑built taxonomy templates and 25 M+ validated items enable instant, consistent classification of parts, equipment hierarchies and service codes.​
Deep SAP Integration & Certified Connectivity
300+ SAP certified connectors provide bi‑directional sync with S/4HANA, SAP MDG, SAP EAM and SAP Business Network Asset Collaboration, keeping data audit ready and ISO compliant.
Real‑Time Governance & Auditable Controls
Role‑based access, immutable audit trails and continuous quality scoring give instant visibility and proactive alerts.
In asset-intensive sectors, incorrect data can lead to catastrophic outcomes, including unplanned downtime, safety incidents, and regulatory fines. High-quality data ensures that maintenance plans are accurate, spare parts are available when needed, and asset performance is optimized. Poor data quality leads to dirty inputs, resulting in flawed analytics and risky operational decisions.Â
PiLog uses a combination of AI-driven automation (iMirAI) and ISO-compliant standards to govern asset data. The solution cleanses, standardizes, and enriches master data by matching it against global taxonomies (such as ISO 81346 and ISO 14224). This ensures that every asset from a simple pump to a complex refinery unit—is uniquely identified and accurately described.Â
The iContent Foundry provides a pre-validated library of 25+ million records and 30,000+ templates for assets, spares, and services. For asset-intensive industries, this means enterprises can leverage industry-specific data structures (such as failure codes, task lists, and Bill of Materials (BOMs)) rather than building them from scratch. This accelerates data onboarding and ensures immediate compliance with international standards.Â
Asset-intensive enterprises often operate in hybrid environments with legacy ERP systems and modern cloud platforms (such as SAP S/4HANA). PiLog’s Data Quality Management solution offers robust ETL (Extract, Transform, Load) capabilities and real-time integration APIs. It ensures that data remains consistent and synchronized across all systems, eliminating silos and ensuring a Single Source of Truth.Â
High-quality data directly improves MRO efficiency. By ensuring that equipment IDs, spare parts numbers, and maintenance plans are accurate and standardized, PiLog helps reduce MRO costs by up to 20%. It prevents issues such as incorrect part procurement, redundant maintenance tasks, and delays in repair orders caused by missing or incorrect asset information.Â
Yes, PiLog is built on a foundation of ISO compliance. It specifically supports ISO 14224 (Reliability-Centered Asset Data) for collecting and managing reliability records, and ISO 81346 (Reference Designation System) for structuring asset identification. Adhering to these standards ensures that data is globally interoperable and meets rigorous industry requirements for reliability and maintenance.Â
iMirAI uses machine learning to automatically detect anomalies, duplicates, and inconsistencies in asset data. It can extract data from unstructured sources (such as PDF manuals or emails) and validate it against established rules. This reduces manual effort by up to 85% and ensures that new assets are onboarded with high accuracy, maintaining the integrity of the master data pool.Â
Asset managers benefit from:Â
- Increased Asset Uptime: Accurate data leads to better predictive maintenance. Â
- Cost Reduction: Lower inventory holding costs and reduced procurement errors. Â
- Regulatory Compliance: Audit-ready data that meets ISO and industry standards. Â
- Operational Efficiency:Â Streamlined workflows for asset onboarding and lifecycle managementÂ
Frequently Asked Questions
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