Insufficient data quality may not necessarily be immediately noticeable, but it can have serious consequences for a company. High data quality is the basis for many digitization projects and the development of new business models. This should be incentive enough for insurers to address the issue.
Small cause, big effect: The "Mars Climate Orbiter" was supposed to provide NASA with new insights into the atmosphere of Mars. But unfortunately, a unit error caused the orbiter to take a wrong course and burn up in space. An admittedly spectacular example of what insufficient data quality can lead to. However, when it comes to AI, insufficient data quality can have similarly serious consequences for insurers.
Al is not a marvel
The expectations placed in AI methods are high. Information from the IoT forms the basis for telematics tariffs or parametric insurance. Using artificial intelligence methods such as machine learning and predictive analytics, insurers want to find out more about policyholder behavior. AI analyses should provide important sources of inspiration for developing new products and calculating their profitability and risks.
For many decision-makers in companies, however, the term AI remains abstract. It seems to be a marvel of state-of-the-art IT, but it is easy to forget that AI is developed by humans. And they can continue to make mistakes, or they can incorporate their own views and values into the development of algorithms.
AI relies on data. It must be available in large quantities and provide correct information. And its not always as correct as it should be.
Bad data leads to bad assumptions
It is precisely because people make mistakes that information is repeatedly collected incorrectly. Transposed digits in an input mask or an incorrect selection in a form leads, for example, to incorrect addresses or salutations in the customer master data. An incorrect salutation in a mailing simply looks unprofessional and can be quickly rectified.
However, incorrect information is more serious in subsequent processes, such as when business-critical processes are based on insufficient data (such as risk analysis and risk management) or when it is incorporated into AI analyses.
In these cases, AI is no different from standard software, such as Photoshop. Even the best algorithm can't create a masterpiece from a bad photo. And the best AI analytics technology will lead the company down the wrong path if the data quality isn't right.
And threats to data quality also lurk deep in the IT engine room. If headquarters and subsidiaries are not working on the same version level of their core solutions, changes to the data models can lead to discrepancies and contradictions in the data.
If the sources of error mentioned above as examples affect the entire data warehouse, the errors increase. And the overall quality of the data declines.
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