Once more, the production line had halted
Despite inventory stating that the spare item was “available,” it was really the subject of two separate requests. Executives worried why their multimillion-dollar ERP couldn’t keep up with the pandemonium as workers worked frantically and expenses rose.
The technology wasn’t to blame. The data was inaccurate.
This story is not new. Every business has a unique split, including manufacturing, utilities, and oil & gas. In practically all of these situations, the constant theme is that faulty data leads to erroneous conclusions.
The Hidden Cost of False Information
Think about how often your business uses records like asset registers, supplier lists, purchase orders, and customer data. Now imagine:
Three instances of a merchant are made, each with a slightly different spelling.
Each plant uses a different set of units to record measurements.
Free-text descriptions conceal important information.
Half of the records lack crucial features or classification.
On their own, these seem like small errors. When combined, they result in data chaos, which includes endless firefighting, outages, excessive procurement costs, and compliance problems.
Industry studies show that 15–25% of income is lost annually due to poor data quality. This is the result of thousands of small errors that mount up every day rather than a single significant failure.
The Justifications for Leaders to Pay Attention
At this critical juncture, CEOs are beginning to see data quality as a competitive lever in addition to an IT hygiene imperative.
Finance executives say cost savings are directly related to clean data.
Operations teams want accuracy since uptime depends on it.
The reporting and auditing procedures used by compliance officers depend on data integrity.
Digital transformation leaders know that automation and artificial intelligence fail without clean inputs.
In other words, everyone now faces both opportunities and challenges related to data quality.
Quality of Data ≠ Just Cleaning
The idea that “cleaning up spreadsheets” or doing one-time deduplication procedures is the only method to enhance data quality is a common misconception. The reality is deeper:
The essential elements of genuine data quality are governance, trust, and structure.
This suggests:
Verifying that every material, vendor, and asset has the proper classification and attributes.
Format standardization will stop “Motor, Electric, 5 HP” from showing up as “5HP Elec Motor.”
adding missing information to records, including part numbers, model numbers, or manufacturer codes.
controlling the process to guarantee that, as soon as fresh entries are made, the data stays clean.
This is the difference between a temporary fix and a long-term data basis.
The Way PiLog Modifies the Game
Rather from being a technology, PiLog created its Data Quality Suite as a framework for long-term accuracy and control. PiLog uses worldwide taxonomies, over 25 years of industry knowledge, and SAP-endorsed solutions to address the issue in a novel way:
Automated Intelligence: Data is swiftly and precisely categorized and enhanced using Auto Structured Algorithms (ASA).
Adaptable Standardization Unstructured, free-text descriptions are transformed into structured, harmonized records.
Bulk Quality Control: With the use of QC tools and dashboards, teams may quickly review and validate thousands of records.
Reference enrichment is used to extract and combine part numbers, models, vendors, and UOMs from scattered descriptions.
First, governance: protocols and regulations ensure that after being cleaned, the data doesn’t decay.
The Actual Advantages for Buyers
“Better records” from the buyer’s point of view are not the reason to invest in data quality. It has to do with measurable business outcomes:
Reduce Costs: Reduce redundant procurement and needless inventories.
Operational Efficiency: Find the right material or source fast rather than after searching for hours.
Increased Uptime: Accurate spare parts data results in faster and less delayed repairs.
Reliable Analytics: Dashboards assist individuals in making better decisions by reflecting reality.
Audit Confidence: The data is clean, traceable, and compliant with the system.
Future-Readiness: Structured data powers Industry 4.0, AI, and IoT initiatives.
In short, better business results from better data.
A Synopsis of the Domain
For a multinational energy corporation, PiLog examined over two million material master records. In just a few months, maintenance planners were able to locate the exact items they needed, purchase delays were greatly reduced, and duplicates were reduced by 22%.
The hidden value? Previously viewed with distrust, executives now only use their ERP dashboards. Decisions that were once unsure are now certain.
Looking Ahead: Data Quality’s Future
Future data quality will be influenced by the following factors:
AI-powered automation that improves and authenticates papers at the point of entry.
cross-platform governance, where ERP, CRM, and supply chain platforms all follow the same guidelines.
frameworks that demand clean, traceable data for sustainability and compliance.
integration with prediction algorithms that use digital twins and high-quality inputs.
In this setting, companies that prioritize quality will be resilient and adaptable leaders in the future.
Final Word
Data quality is not an afterthought; it is the foundation of all corporate endeavors, from enabling AI to lowering procurement costs.
Instead of only fixing data from today, businesses use PiLog’s Data Quality Suite to build a solid foundation for future growth.
Are you ready to settle your data debt and make the transition to trusted intelligence? Let’s finish it.
Poor Data Quality Is Costing You More Than You Think — Here’s How to Turn It into a Business Asset