The Importance of Data Quality in Minimizing Your RiskBy Virginia R. Prevosto | July 16, 2013
In most industries, companies make risk decisions based on the information available. That means an organization must be diligent in maintaining data quality to reap the desired competitive advantage. Through advancements in technology, companies no longer have to rely on a “one size fits all” approach to data quality — nor should they. That said, I’ve outlined a few tips most any company should follow to minimize risk.
- Adhere to best practices at all organizational levels. While much data aggregation and analysis is accessible at the desktop level, companies don’t typically train the average employee in higher-level best practices. To obtain the promised rewards of data quality, an organization must apply controls throughout the organization.
- Employ sophisticated tools. As another colleague noted before, with the growth of detailed and granular data comes an increasing need for advanced analytic technologies. The right tools of the trade, including metadata repositories, data dictionaries, and data and text mining, can engineer data integrity and infuse clarity into every phase of the data analytic process.
- Work with trusted third-party data resources. If your organization uses third-party data, you must hold it to the same standards as your internal data. By knowing the data source, accessing data quality characteristics — especially freshness, completeness, and accuracy — and requiring complete and thorough data element documentation, you’ll help ensure uniform quality.
Without quality data, insurers can’t achieve their short-term and long-term goals, and their credibility and capabilities are compromised as well. Insurers that don’t embrace changes in maintaining and acquiring quality data are destined to fall behind their competitors and fail in achieving their desired business results.
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