Inventory claimed that the spare item was “available,” but in reality, there were two different requests for it. As employees worked feverishly and costs increased, executives questioned why their multimillion-dollar ERP was unable to handle the chaos.
It had nothing to do with technology. The information was not correct.
This tale is not brand-new. Every company has a different division, such as oil and gas, utilities, and manufacturing. The recurring feature in almost all of these cases is that inaccurate data results in incorrect conclusions.
The Unspoken Price of Misinformation
Consider how frequently your company uses documents such as purchase orders, supplier lists, asset registries, and customer information. Now consider:
There are three examples of a merchant, each spelled slightly differently.
Every plant records measurements using a separate set of units.
Important information is hidden in free-text descriptions.
Important characteristics or classification are absent from half of the records.
These appear to be minor mistakes on their own. Together, they lead to data pandemonium, which includes issues with compliance, outages, high procurement costs, and never-ending firefighting.
According to industry research, poor data quality results in an annual loss of 15–25% of revenue. Instead of a single major failure, this is the product of thousands of little mistakes that accumulate daily.
The Arguments for Leaders to Take Note
CEOs are starting to view data quality as both an IT hygiene requirement and a competitive lever at this crucial moment.
According to finance professionals, clean data has a clear correlation with cost savings.
Because uptime depends on accuracy, operations teams desire it.
Data integrity is essential to compliance officers’ reporting and auditing processes.
Leaders in digital transformation are aware that without clean inputs, automation and artificial intelligence fall short.
In other words, data quality presents both opportunities and challenges for everyone.
Data Quality ≠ Only Cleaning
It’s a common misconception that the only way to improve data quality is to “clean up spreadsheets” or perform one-time deduplication procedures. The truth is more profound:
confirming that each asset, vendor, and material has the appropriate classification and characteristics.
Standardization of the format will prevent “Motor, Electric, 5 HP” from appearing as “5HP Elec Motor.”
adding missing data, such as manufacturer codes, model numbers, or part numbers, to records.
regulating the procedure to ensure that the data remains clean as soon as new entries are made.
This is what separates a short-term solution from a long-term data base.
How PiLog Alters the Game
PiLog developed its Data Quality Suite as a framework for long-term accuracy and control rather than as a technology. PiLog tackles the problem in a unique way by utilizing global taxonomies, more than 25 years of industry experience, and SAP-approved solutions:
Automated Intelligence: Auto Structured Algorithms (ASA) are used to quickly and accurately classify and improve data.
Flexible Standardization Structured, harmonized records are created from unstructured, free-text descriptions.
Bulk Quality Control: Teams may swiftly examine and verify thousands of records by using QC tools and dashboards.
Part numbers, models, vendors, and UOMs are extracted and merged from dispersed descriptions via reference enrichment.
First, governance: rules and procedures make sure that data doesn’t deteriorate after cleaning.
The Real Benefits for Purchasers
Investing in data quality is not justified by “better records” as seen by the buyer. It is related to quantifiable business results:
Cut Costs: Minimize back on unnecessary inventory and unnecessary purchases.
Improved 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: The information is accurate, traceable, and system-compliant.
Future-Readiness: Industry 4.0, AI, and IoT projects are powered by structured data.
In summary, better data leads to better business outcomes.
An Overview of the Field
PiLog analyzed more than two million material master data for a global energy company. Purchase delays were significantly decreased, duplicates were cut by 22%, and maintenance planners were able to get the precise goods they required in a matter of months.
The value that is hidden? Executives now only use their ERP dashboards, which were once regarded with suspicion. Previously uncertain decisions are now known.
Looking Ahead: The Future of Data Quality
The following elements will affect the quality of data in the future:
automation driven by AI that enhances and verifies documents at the point of entry.
cross-platform governance, in which supply chain, CRM, and ERP platforms all adhere to the same rules.
frameworks that require verifiable, clear data for compliance and sustainability.
connection with prediction algorithms that make use of high-quality inputs and digital Systems.
Businesses that put quality first in this environment will be strong and flexible leaders in the future.
Last Word
Data quality is the cornerstone of all business initiatives, from enabling AI to reducing procurement costs, therefore it is not an afterthought.
Businesses utilize PiLog’s Data Quality Suite to create a strong basis for future expansion rather than just correcting data from the present.
Are you prepared to pay off your data debt and switch to reliable intelligence? Let’s get it done.
The Hidden Cost of Bad Data: Why Quality Determines Success or Failure for Modern Enterprises