The spare item was “available,” according to inventory, but there were really two requests for it. Executives wondered why their multimillion-dollar ERP couldn’t manage the pandemonium as workers worked nonstop and expenses rose.
Technology had nothing to do with it. The data was inaccurate.
This is not a new story. Each business has a distinct section, such as manufacturing, utilities, or oil and gas. Almost all of these situations have one thing in common: erroneous data leads to erroneous conclusions.
The Price of Misinformation That Is Unspoken
Think about how often your business uses asset registries, supplier lists, purchase orders, and customer data. Now think about:
Each of the three merchant instances has 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 important classifications or attributes.
On their own, these seem like small errors. When combined, they cause data chaos, which includes outages, compliance problems, expensive procurement, and never-ending firefighting.
Industry research indicates that a loss of 15–25% of revenue occurs annually as a result of poor data quality. This is the result of thousands of small errors that mount up every day rather than a single significant failure.
The Reasons Leaders Should Pay Attention
At this critical juncture, CEOs are beginning to see data quality as a competitive lever as much as an IT hygiene need.
Finance experts say there is a direct link between cost savings and 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 understand that automation and artificial intelligence are insufficient without clean inputs.
To put it another way, everyone faces both opportunities and challenges when it comes to data quality.
Quality of Data ≠ Just Cleaning
There is a widespread misperception that doing one-time deduplication procedures or “cleaning up spreadsheets” is the sole option to enhance data quality. The reality is deeper:
Verifying that every material, vendor, and asset has the proper classification and attributes.
The format’s 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 what distinguishes a long-term database from a temporary fix.
The Way PiLog Modifies the Game
Rather than creating a system, PiLog created its Data Quality Suite as a foundation for long-term accuracy and management. PiLog uses worldwide taxonomies, over 25 years of industry knowledge, and SAP-approved solutions to address the issue in a novel way:
solutions to address the issue in a novel way:
The hidden value? ERP dashboards were originally viewed with distrust, but today executives exclusively use and consult them. Decisions that were previously unclear are now known.
Looking Ahead: Data Quality’s Future
Future data quality will be impacted by the following factors:
AI-powered automation that improves and validates documents at the point of entry.
cross-platform governance, wherein ERP, CRM, and supply chain platforms all follow the same guidelines.
frameworks that demand transparent, verifiable data for sustainability and compliance.
relationship with prediction algorithms that utilize digital twins and high-quality data.
Companies that prioritize quality in this
setting will be resilient and powerful leaders in the future.
Final Word
Data quality is not an afterthought because it is the foundation of all business activities, from enabling AI to lowering procurement costs.
Instead of only fixing data from the present, businesses use PiLog’s Data Quality Suite to build a solid foundation for future growth.
Are you ready to settle your debt to data and move to trustworthy intelligence? Let’s finish it.
Why Only Companies with Reliable Data Will Thrive in the Age of AI and Digital Transformation