Importance of Data Quality and Data Consistency in Master Data and how to achieve it

Data Consistency In Master Data

In today’s business environment, information architecture and storage are essential for smooth operations due to the growing volumes of data. Master data management (MDM) helps create a single reference for crucial company information such as inventory, plant operations, maintenance, and supply chain.

The goal of MDM is to simplify data access and reduce errors, as well as to make it available through a cloud-based data system. MDM efficiency is ultimately determined by the quality of the data in your database, regardless of how well organized it may be.

Adverse affects of low-quality data

Lack of accuracy, validity, completeness, and consistency in data collection harms customer service and the company’s reputation.

  • A great deal of time is wasted on false work orders or purchase orders for false stock shortages.
  • Incorrect inventory data leads to poor procurement strategies and higher costs
  • Incomplete or incorrect supplier data results in longer sourcing cycles and incorrect materials.

Key elements of monitor Data Quality

  • Data Governance Mechanism: Implementing a data governance structure and managing data quality operations. This framework should provide data standards, data policies, and the necessary roles.
  • Data Quality KPIs: Data uniqueness, data completeness, and data consistency are just a few examples of the data quality dimensions that can be associated with data quality KPIs, also known as Data Quality Indicators (DQIs).
  • Data Stakeholders and Ownership: Maintaining a log of data quality issues with entries for each issue. These entries include details of the assigned data owner, any participating data stewards, the impact of the issue, how it was resolved, and when the required actions were taken.
  • Root Cause Analysis: Start with a root cause analysis for each problem with data quality that is mentioned. Problems with data quality won’t be solved unless the underlying reason is dealt with. Rather than relying on downstream data purification, develop procedures and technologies that prevent problems from arising as soon as possible at the point of data input.

How to achieve data accuracy?

  • Implement an industry specific Data Taxonomy: Data entry errors can be reduced by using a standardized industry specific data taxonomy for equipment, inventory, and procurement data.
  • Establish data validation checks: By implementing data validation checks, you can ensure that all data entered is accurate, complete, and in the correct format. As a result, incorrect data will not be entered, and the integrity of the data will be maintained.
  • Perform regular data audits: Maintaining accurate and up-to-date data in the MRO system requires regular data audits. As a result, you will be able to identify and correct any inaccuracies as soon as possible.
  • Real-time integration of data: By integrating data from different sources, such as procurement and inventory management systems, all data can be kept accurate and current.
  • Data governance platform: By providing a framework for data governance policies, processes, and standards, the platform will ensure that data is managed consistently, accurately, and securely. An organization can increase the accuracy of MRO data by implementing a data governance platform that ensures data is captured, stored, and maintained in a centralized, controlled manner.
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