Data risks left uncovered
March 16, 2017

Trafalgar House, the pensions administration specialist, is warning schemes of the data risks being left uncovered by only focusing on traditional conditional data testing. It suggests that other, more significant data issues might be sitting below the surface which simply won't be picked up through a basic conditional data analysis.

Claire Montgomery, Senior Business Analyst at Trafalgar House, comments: "Common and conditional reporting goes some way to identifying missing data, but just focusing on this assessment can mean several more complex associated problems are missed as a result of historical administration processes. These might include administrators having created non-standard data fields which cause items to be held in abstract fields, instances where dependent records are not linked to the original deceased members, and where valuable data and benefit information are being stored in notes fields - to name but a few."

Montgomery continues: "We regularly encounter these bigger problems with data as part of our scheme transition projects, and they come as a surprise to many trustees as they were given a clean bill of health from a conditional data analysis. Since it is now more widely recognized that good quality data are key in improving service to members, the time is right to take data quality interrogation to the next level and stop misleading trustees into believing that all data is complete and accurate because of the limits of working from a conditional data analysis alone."





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Trafalgar House, the pensions administration specialist, is warning schemes of the data risks being left uncovered by only focusing on traditional conditional data testing. It suggests that other, more significant data issues might be sitting below the surface which simply won't be picked up through a basic conditional data analysis.

Claire Montgomery, Senior Business Analyst at Trafalgar House, comments: "Common and conditional reporting goes some way to identifying missing data, but just focusing on this assessment can mean several more complex associated problems are missed as a result of historical administration processes. These might include administrators having created non-standard data fields which cause items to be held in abstract fields, instances where dependent records are not linked to the original deceased members, and where valuable data and benefit information are being stored in notes fields - to name but a few."

Montgomery continues: "We regularly encounter these bigger problems with data as part of our scheme transition projects, and they come as a surprise to many trustees as they were given a clean bill of health from a conditional data analysis. Since it is now more widely recognized that good quality data are key in improving service to members, the time is right to take data quality interrogation to the next level and stop misleading trustees into believing that all data is complete and accurate because of the limits of working from a conditional data analysis alone."



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