Monday, 30 January 2017

Warranty Claims: The Need to Reduce Poor Quality Claim Data

Data related to warranty claims should be reliable and of high quality regarding accuracy, consistency, completeness and timeliness. To be able to debate on the data after it has been worked upon, it is most important that the data is free from defects. The difference in each case is between the actual value that has been used and the correct value.

Warranty claim data has immense significance from the point of view of constituting corrective measures. Poor quality claim data can seriously harm the OEM’s future plans of improving accuracy of claims, but more importantly, of ensuring that there is an improvement in product quality. Poor quality claim data can therefore harm the future of the production company. In times to come such data can bring about lower customer satisfaction and increased running costs besides retarding the efficiency of the process of decision making and even lowering the performance level. There is no doubt that poor quality data will surely harm efficient warranty claim management.

Corrective Measures

There is a drive to ensure that data is cleaner and complete. Over a period of time practices have developed and there is a marked improvement in the quality of data today. It is also recognized as the responsibility of the enterprise as data is no longer limited to the data warehouse. However, it is still a problem to improve data quality since it is a costly venture mainly because of multiple loopholes that permit data quality to deteriorate. There are several sources from which data quality can suffer. Some of the sources include questions such as:
  • ·         Was the information correct at the point of origin?
  • ·         Did the information integrity remain the same while being processed within the system?
  • ·         Identifiably is there any difference between the two objects?
  • ·         Due to sufficient time having elapsed is the data no longer trustworthy?

In order to be able to tackle poor quality of data, it is important to identify the area where the quality problems have cropped up. This is not an easy option and the right method has to be evolved for detection. One approach is to firstly make an inventory of the system and thereafter document the details which affect the quality of data adversely. For addressing the problem of data quality one will need to have a combination of analysis and technology. Modern day warranty management solutions have integrated analytics packages that give intelligent insights into data. Dashboards that deliver visual representation and analytical reports of warranty data can be a great help to manufacturers struggling with huge warranty expenses and related problems.
Poor quality data can be due to isolated systems. With integration one may be able to improve data consistency. Ultimately, the chief executives will need to set the acceptable standard for data quality and ensure that all the right systems are in place. It needs the change in mindset necessary in a company that prides in excellence and quality to be able to bring about a qualitative transformation in the accuracy of the data.

While at the departmental level the cost due to poor data may be small. However, this value becomes far greater when viewed across the spectrum of the enterprise. Properly identifying the reasons for parts failures and suppliers or vendors who cause recurring quality issues will help eliminate much of quality issues. The right corrective actions can be initiated only with the right insights; right insights can be attained only with right analytics; right analytics is possible only if the base data is of consistent quality. Therefore, the cost incurred for assuring data quality then becomes viable because it improves quality and brings down costs due to inefficient warranty claim management.

Author Bio:

Preethi Vagadia is currently a Senior Business architect with the Service operations practice at a well-known IT Industry in Bangalore. She has worked in several process improvement projects involving multi-national teams for global customers. She has over 8 years of experience in mortgage technology and has successfully executed several projects in logistics management, logistics integration, reverse logistics, campaign management software solution, warranty software and programmatic solutions.

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