The Confidence Score: Identifying the quality of data
Published on 13 de March de 2024

In the insurance and banking industry, the accuracy and quality of data is crucial for making commercial or strategic decisions, because bad quality information can result in wasted resources and poorer customer service. But how can we be sure of the quality of the information we receive?

Confidence Score is the key

The Confidence Score is the parameter we use at Wenalyze to provide our clients with a scale of data quality. For an insurer or a bank, it is essential that the data they obtain about their customers is accurate, because it will determine the sales or communication strategies they design. Therefore, at Wenalyze we analyse the data and deliver it with maximum transparency and quality. However, it must be taken into account that there are some business data that, due to their lack of precision, cannot be enriched.

This is why we created the Confidence Score, thanks to this metric we can classify data into three levels according to its reliability, so that our customers know on which data they can base their decisions. There are three factors that determine the quality of the information: how up-to-date the data sources are, how accurate the matching process is, and how many sources have the same data, because the more sources match, the more valuable the data is.

What are the levels?

We have defined three different levels according to the level of accuracy of the information we obtain:

Level 1: Data in this level allows our clients to automate their decisions because there is complete confidence that it is 99.9% accurate. We deliver an average of 95% of enriched merchants in this category.

Level 2: At this level, decisions must be made with a prior manual review, as some data sources have shown discrepancies between data. An average of 4% of the analysed merchants are delivered at this level of confidence.

Level 3: Finally, at this level are those data that have shown many discrepancies of information in the analysis across sources. Therefore, decisions must be made with a thorough review. Only 1% of the analysed businesses are at this level.

Evaluating data integrity is essential

Classifying data is fundamental to delivering the best results and identifying the least accurate data, as it allows us to establish a demanding quality control, ensuring that the information we deliver is transparent and of great use to our client. Without data control all information would be delivered without differentiation, which would not allow our clients to make the decisions they need to make based on the quality of the data on which they are basing their strategies, which could directly affect the results of their sales strategies or deteriorate their customer service.

If you want to enrich your data by taking advantage of the potential of the latest technology, contact us or ask directly for a demo.