The production line had stopped again.
The same spare part, which inventory said was “available,” was actually the subject of two different orders. As employees worked frantically and costs increased, executives wondered why their multimillion-dollar ERP was unable to control the chaos.
It was not the fault of technology. The information was faulty.
This is not a unique tale. Every company, including manufacturing, utilities, and oil and gas, has a different breakdown. Unreliable data leads to unreliable conclusions, and this is the clear recurring theme in almost all of these cases.
The Unspoken Price of Inaccurate Data
Consider how frequently your company uses records, such as purchase orders, supplier lists, asset registers, and customer information. Imagine now:
A vendor makes three appearances, each with a slightly different spelling.
The units used to record measurements vary from plant to plant.
Important details are hidden in free-text descriptions.
Classification or important attributes are absent from half of the records.
These appear to be minor mistakes on their own. When combined, they lead to data chaos, including exorbitant procurement costs, outages, compliance issues, and never-ending firefighting.
Studies conducted by the industry reveal that poor data quality costs 15–25% of revenue every year. This isn’t due to a single major failure, but rather to thousands of tiny mistakes that accumulate daily.
The Reasons Leaders Need to Take Interest
This is the pivotal moment: executives are starting to view data quality as more than just an IT hygiene consideration, but also as a competitive lever.
Clean data is directly linked to cost savings, according to finance chiefs.
Because uptime depends on accuracy, operations teams demand it.
Data integrity is essential to compliance officers’ reporting and auditing processes.
Leaders in digital transformation are aware that without clean inputs, automation and AI fail.
To put it another way, data quality has become a problem and an opportunity for everyone.
Data Quality ≠ Only Cleaning
A frequent misunderstanding is that performing one-time deduplication tasks or “cleaning up spreadsheets” is the only way to improve data quality. The truth is more profound:
Structure, trust, and governance are key components of true data quality.
This implies:
confirming that each asset, vendor, and material has the appropriate classification and characteristics.
standardizing formats to prevent “Motor, Electric, 5 HP” from appearing as “5HP Elec Motor.”
adding missing values, such as manufacturer codes, model numbers, or part numbers, to records.
regulating the procedure to ensure that data remains clean as soon as new entries are made.
This is what distinguishes a long-term data foundation from a band-aid solution.
How PiLog Changes the Game
PiLog developed its Data Quality Suite as a framework for long-term accuracy and governance rather than as a tool. PiLog tackles the problem in a unique way by utilizing global taxonomies, more than 25 years of industry experience, and SAP-endorsed solutions:
Automated Intelligence: Auto Structured Algorithms (ASA) quickly and accurately classify and enrich data.
Scalable Standardization Structured, harmonized records are created from unstructured, free-text descriptions.
Bulk Quality Control: Teams can swiftly examine and verify thousands of records with the help of QC tools and dashboards.
Part numbers, models, vendors, and UOMs are extracted and integrated from dispersed descriptions through reference enrichment.
Governance First: Procedures and rules make sure that data doesn’t deteriorate after being cleaned.
What’s the difference? Maintaining data reliability on a daily basis is more important than making one-time corrections.
The Real Benefits for Purchasers
Investing in data quality is not about “better records” from the perspective of the buyer. It concerns observable business results:
Cut Costs: Cut down on unnecessary inventory and duplicate procurement.
Operational Efficiency: Locate the appropriate supplier or material quickly rather than after hours of looking.
Increased Uptime: Repairs are completed more quickly and with fewer delays when spare parts data is accurate.
Reliable Analytics: By reflecting reality, dashboards help people make better decisions.
Audit Confidence: Data is traceable, clean, and system-compliant.
Future-Readiness: AI, IoT, and Industry 4.0 projects are powered by structured data.
To put it briefly, better data leads to better business.
An Overview of the Field
PiLog reviewed more than two million material master records for a global energy company. Purchasing delays decreased significantly, duplicates were cut by 22%, and maintenance planners were able to finally find the precise parts they required in a matter of months.
The value that is hidden? Executives now rely solely on their ERP dashboards, which were previously regarded with suspicion. Previously uncertain decisions are now confident ones.
Looking Ahead: The Future of Data Quality
The following factors will influence data quality in the future:
automation powered by AI that enhances and verifies documents at the point of entry.
cross-platform governance, in which supply chain, CRM, and ERP systems all adhere to the same standard.
frameworks for compliance and sustainability that require clean, traceable data.
integration with predictive models that rely on high-quality inputs and digital twins.
Organizations that put quality first in this environment will be resilient and agile leaders in the future.
Last Word
Data quality is the cornerstone of every enterprise endeavor, from enabling AI to reducing procurement costs, so it is not an afterthought.
Businesses use PiLog’s Data Quality Suite to create a reliable foundation for future expansion rather than merely correcting today’s data.
Are you prepared to move toward trusted intelligence and pay off your data debt? Let’s get it done.
Why Data Quality Decides Business Winners and Losers: Build a Competitive Edge with PiLog