Analysing the Cost of Bad Data

There is no doubt that data is crucial to specialty insurance’s future success. Its use in providing insight and analysis to drive business decisions and its importance as the foundation for AI are clearly acknowledged. We know that data is important, but has the real cost of bad data been captured? 

Specialty insurance is a market with high margins, and the cost of data errors is often absorbed into operating costs and seen as part of the price of doing business. 

Does this mean that specialty insurers are sleeping on capturing the cost of bad data?  

We think so.   

The Cost of Confidence 

Many specialty insurers still rely on outdated or incomplete methods to manage data quality, and it’s costing them more than they think. It is not just the cost of fines or remediation costs quietly swallowing up BAU budgets behind the scenes. 

It’s the old adage, rubbish in, rubbish out. 

Errors in data, incomplete data, and misaligned data can lead to processing delays, disputes over coverage and claims, and the submission of incomplete or unreliable documentation or reports. All this will impact your bottom line. 

Everyone must have confidence in the data used to make business decisions. You need your organisation to have trust in the data used in their work, but also to trust in your data governance processes and the checks. People aren’t going to believe that the data is “good” on faith; you need to demonstrate how the data is captured, processed, checked, amended and used. 

Without confidence in the data, your organisation will lack trust in the results of data analysis or implementation of programmes based on that data. Lack of trust is an impediment to success. 

Capturing Cost 

There are several ways you can assign value to data, and it can be quite subjective. 

You can show how good data benefits the business – demonstrating how the data is used to meet the business’s goals and to drive success. 

Or you can show the cost of failing to act – number of issues that occur, potential fines, resources needed to fix issues, remediation costs, etc, that come from “bad” data. 

Demonstrate the benefits to your business by providing real examples of how data is used across the business. Capture use cases and success stories to present back to the business. By giving concrete examples they can relate to, you can show the cost and the value of having good data quality. 

Another way to capture the cost of bad data is to monitor the checks that were run, how many issues were detected and how they were resolved. Highlight the issues which have a measurable impact on operating costs, efficiency or compliance and assign a business cost to each issue based on the time and resources needed to resolve it. 

Track this information. This can help you demonstrate the impact data has on the business and the potential costs that inaccurate or incomplete data can have on the business. 

Share and Discuss 

Capturing information on how your data impacts your business is important, but so is sharing that information. Don’t be reluctant to share where there have been issues and be transparent about where you can improve. Confidence is built on trust and understanding. 

The goal is to create a data quality approach that not only identifies data quality issues but also shows the potential business impact and cost to the business. 

The cost of bad data can be viewed in two ways 1) the impact of bad data on the organisation and 2) the avoidance cost. Whilst, good data can drive success, conversely, bad or incomplete data used downstream can have a negative, costly impact. By addressing both sides, you then begin to establish the value of data and the impact on your business. 

As we move into 2026, we think the question insurers need to be asking is, do you know the cost of bad data? 

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