Lack of quality data is a common challenge that companies face in today’s business world. Keeping inventory and electronic data updated is becoming increasingly complex as manufacturing and asset-intensive organizations grow and evolve. In addition to adding complexity to the already challenging master data management process, growth occurs naturally and through acquisitions and mergers. Degradation of a material master can also be attributed to changes in technology and outdated or unclear business processes. There are several factors that contribute to low data quality, including: 

• Access to the system without restriction 
• Turnover of employees 
• Compliance with data standards is limited or nonexistent. 
• Transactions involving mergers and acquisitions. 
• The use of multiple ERP/EAM/CMMS platforms 
• Multiple sites or plants (often operating independently). 
• Languages (from around the world) 

Consequently, master data becomes less reliable and usable for the company when making critical business decisions. The following are some of the major data quality issues that companies face: 

• Mistakes in spelling 
• There are multiple names and codes for the same manufacturer 
• Manufacturer names and part numbers are missing 
• Having duplicates 
• Uncompleted descriptions 
• Information about the part is inaccurate 
• The data format and naming conventions are inconsistent 
• Misclassified materials 

In the absence of reliable data, maintenance and procurement have a difficult time maintaining operations, managing inventory, and sourcing parts when needed. Low data quality has several negative consequences and inefficiencies, including: 

• Items that are unidentifiable
• Locating and searching for parts is difficult
• Stock-outs that are false
• Expedited orders for parts and increased spot purchases
• Inventory is accumulating too fast
• Visibility of spend is limited. 
• Reports that are unreliable 
• ERP/EAM/CMMS functionalities are compromised. 

In many asset-intensive manufacturing companies, this is unfortunately the case. Through a data cleansing initiative and the implementation of a corporate data governance strategy, it can all be resolved. Maintaining the quality of data across all datasets can be achieved and maintained by implementing consistent, automated, and repeatable data quality measures. Over the past decade, OptimizeMRO has provided data quality solutions to its clients. It offers a full range of end-to-end data quality management solutions, including data profiling, cleansing, matching, deduplication, and merge purging.