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Data Quality Assessment in PLM

For any organization, their digital assets (software, algorithms and information and more) is of utmost importance today. With the advancement of technology such as cloud computing, the Internet of Things, and social networking, the amount of data is increasing exponentially and accumulating at an exceptional speed.

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For any organization, their digital assets (software, algorithms and information and more) is of utmost importance today. With the advancement of technology such as cloud computing, the Internet of Things, and social networking, the amount of data is increasing exponentially and accumulating at an exceptional speed.

In the manufacturing, and the maintenance sector the quality of data reporting is closely linked to the maintenance process. Organizations are trying to do more with less to ensure profitability and customer satisfaction.

Inconsistent and inaccurate data can result in lower customer service, loss of revenue, increased service costs and at times loss of customers. Most importantly, vital business decisions made based upon incorrect data and information can have a negative impact on product cost, quality, and time to market.

What is needed is a practical approach to align disparate data quality activities with one another to addresses the challenges of ensuring and maintaining high-quality levels.

Why is Data Quality Assessment Required?

Data quality assessments help managers to understand if the data they are using to manage a program and basing their decisions can be trusted.

The Data Quality Standards

To assess the quality of data there are five key data quality standards. These are:

  • Validity – Do data clearly represent the real-world values that they are expected to offer? Invalid data can impact analytic and operational decision making.
  • Consistency – Does the data offer conflicting information about the same data set? Are data values consistent across data sets? Without reliable data, organizations are running their business blindly and at times taking wrong decisions.
  • Reliability – Authenticate the consistency and accuracy of data through the ongoing use of error checking and validation routines. Data should be consistent across collection areas so that it does not result in duplication and affect valuable analysis
  • Timeliness – Refers to the time taken to access data i.e. when it is readily available for use. Because organizations need to make real-time decisions, timeliness is an important dimension of data quality
  • Accessibility – Data must be easily accessible to users. If you have to search for data, it can lead to waste of time, frustration, and eventually providers to stop looking for information.

PROLIM – PLM Implementation Experts

At PROLIM, we have implemented PLM at hundreds of companies, and have extensive capabilities to interrogate data and establish a quality assessment of that data.

  • Faster integration into existing systems – The PLM software can integrate into all your system, including CAD, CAM, CAE, etc.
  • Link between planning and production – The data and tools can be accessed directly by the CNC machines
  • Shorter time-to-market – Easy access to all data, processes, and resources accelerates the process cycle that automatically expedites the time take the market also

By effectively assessing and implementing the right processes, we ensure that data is available, reliable, accurate, consistent, and secure, thereby enabling better organizational performance.

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