Transforming Asset-Heavy Industries: How Centralized Data Governance and AI Automation Drive Operational Excellence

Implementing a centralized data governance solution requires substantial effort and change management. An organization will need commitment from its leadership and buy-in from the teams that manage and use the data. Success depends on pairing technology with the right processes and governance culture internally.

At Info-Tech LIVE (June 2025), Dr. Imad Syed (CEO of PiLog) presented how robust data governance and intelligent automation can address critical issues in modern asset and supply chain management. For technology leaders in asset-heavy sectors, his talk highlighted strategic ways to enhance operational efficiency and achieve a competitive advantage through better data management. In an interview, Dr. Syed discussed data governance challenges in asset-heavy industries and outlined his approach to addressing these issues.

Navigating Data Complexity and Operational Inefficiency

Asset-heavy industries like oil and gas, manufacturing, and utilities face persistent challenges in managing assets and supply chains. Fragmented data sources, inconsistent records, and siloed systems hinder efficiency. As organizations scale, these issues intensify, underscoring the need for solutions that harmonize data, streamline processes, and unlock value.

Core challenges include:

• Disparate & inconsistent data
• Manual, resource-intensive processes
• Limited visibility and reactive maintenance
• Inefficient supply chains
• Barriers to digital transformation

Before leveraging advanced analytics, IoT, or AI, organizations must ensure their data is consistent and trustworthy. Dr. Syed’s presentation addressed these pain points and introduced PiLog’s strategy to resolve them.

PiLog’s Data-Centric Approach

1. Data Harmonization & Centralized Repository

PiLog consolidates data from ERP, EAM, CMMS, IoT sensors, GIS, and other systems into a unified repository. Using machine learning, it merges duplicates, reconciles inconsistencies, and standardizes formats.

The platform supports 21 languages and multiple classification standards (e.g. OTD, eClass, UNSPSC, HS codes, NATO codification), enabling global organizations to aggregate and compare data uniformly. This interoperability eliminates silos and ensures all stakeholders – from maintenance to procurement – access the same accurate data.

2. AI-Powered Automation & Content Enrichment

PiLog’s AI agents automate data management tasks using industry knowledge bases (OEM catalogs, engineering standards, maintenance manuals). They enrich master data by filling in missing attributes and validating existing ones.

For example, missing part numbers or specifications can be inferred or retrieved automatically. The system adheres to standards like ISO 14224, ensuring consistent naming conventions and units of measurement. This reduces manual workload and allows technical staff to focus on strategic tasks like failure analysis or maintenance optimization.

AI also improves accuracy by catching errors and auto-populating fields, with performance improving over time as the system learns.

3. Integrated Insights for Predictive Maintenance

With harmonized and enriched data, PiLog enables predictive maintenance by integrating real-time sensor data, historical logs, and asset profiles. Dashboards and analytics (e.g. SAP APM integration) provide a comprehensive view of asset health.

This supports methodologies like risk-based maintenance (RBM) and risk-based inspection (RBI), allowing organizations to prioritize maintenance based on risk and impact. Predictive insights help prevent breakdowns, reduce downtime, and optimize resource allocation.

4. Strategic Data Migration & Governance

During system migrations or digital transformations, PiLog emphasizes cleansing and standardizing data to avoid transferring legacy errors. Its methodology aligns data with best practices before integration into new systems.

Post-migration, PiLog enforces governance through automated workflows, approval processes, and validation rules. For example, it can prevent duplicate entries or flag noncompliant data for review. This prevents data decay and ensures long-term quality, enabling digital systems to perform effectively.

Source: SoftwareReviews Master Data Management, Report Published April 2025.

Our Take

There are several positives to highlight in PiLog’s approach.

First, the emphasis on creating a single source of truth and cleaning data during migrations ensures that companies are not building on faulty foundations. This strategy is well-founded, as numerous digital initiatives do not succeed due to inadequate underlying data.

Second, the use of AI to reduce manual data work is a forward-looking move that can save time and reduce errors. It allows skilled staff to concentrate on higher-value activities, which is a sensible allocation of resources.

Third, enabling predictive maintenance through integrated data is likely to yield tangible benefits, as leveraging IoT and maintenance data for predictive insights can reduce downtime and maximize asset life. Similarly, adopting analytics-driven approaches like RBM and RBI can optimize asset performance and safety by focusing efforts where they matter most.

However, technology leaders must carefully evaluate whether PiLog’s solution aligns with their organization’s specific operational needs, data maturity, and transformation goals. Fit-for-purpose assessment is essential to ensure successful implementation and long-term value.