Data quality can help prevent catastrophic losses
Despite the struggles of our current economic crisis, some businesses are continuing to grow and expand into larger corporations. With this growth comes increased profits, a larger and more capable workforce and possible presence in international markets. However, there are some inherent dangers with growth and expansion.
As companies become bigger, they ultimately have much more at stake, which could possibly result in catastrophic losses. To prevent such occurrences, it is increasingly important for businesses of any size to invest in data quality management solutions, according to Insurance Networking.
ECityRisk reports that data quality was recently identified as the No. 1 concern for businesses based on a recent Ernst and Young survey of underwriting offices, as it is becoming highly analyzed from rating agencies in an attempt to minimize loss.
High-quality exposure data is essential for effective catastrophe risk management. By investing in obtaining, evaluating and storing up-to-date and accurate data through stringent verification, cleansing and deduplication methods, companies can substantially limit potential losses associated with poor quality data, according to ISO Review.
By Paul Newton
As companies become bigger, they ultimately have much more at stake, which could possibly result in catastrophic losses. To prevent such occurrences, it is increasingly important for businesses of any size to invest in data quality management solutions, according to Insurance Networking.
ECityRisk reports that data quality was recently identified as the No. 1 concern for businesses based on a recent Ernst and Young survey of underwriting offices, as it is becoming highly analyzed from rating agencies in an attempt to minimize loss.
High-quality exposure data is essential for effective catastrophe risk management. By investing in obtaining, evaluating and storing up-to-date and accurate data through stringent verification, cleansing and deduplication methods, companies can substantially limit potential losses associated with poor quality data, according to ISO Review.
By Paul Newton
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