Best practices for maintaining data to deliver accurate, trusted insights

In our day-to-day lives, there’s no shortage of data flow in the insurance value chain. In many ways, obtaining data on just about anything is the easy part. The hard part? Maintaining, organizing, and governing data so that any analysis gives confident results and is as accurate as possible. If the information captured is incorrect, inconsistent, or incomplete, the data is of no value for any use cases.

Insurance organizations are increasingly turning to AI to boost efficiency and productivity, putting a greater priority on the need for good data maintenance practices. No matter how great an AI solution is, the technology has little value if it works with substandard data.

From our experience of seeing data usage and collection, these five tips can help insurers have better control over the data in their systems:

1. Create safeguards when capturing data: It starts with correctly capturing and entering data into the systems. Any missing, incorrect, incomplete, duplicate or non-standard values yield erroneous results and risk inaccurate decision-making. One of the most common reasons for incorrect data is human error or manual intervention. But if an AI-powered tool is used to help with data capture, accompanied by human oversight to verify the data when interpretation is needed, the output could be more accurate. By utilizing data processing solutions, insurers can implement standardization in capturing data. Also, some technologies can review data, identify outliers, determine if there is missing or incomplete information, and detect if there is outlying data that an expert should review.
2. Maintain data integrity by limiting touch: Removing data silos reduces data duplication, makes processes more efficient, and can improve the customer experience by eliminating the need to provide the same information over again. The guiding principle is to collect and structure all data available at once, reasonably, without any elimination, and use as needed. But sharing data doesn’t mean that everyone should also have unfettered access. Most team members at an insurance organization only need access to specific data sets, which can be managed with user access rules. This gives the data management team better control over who accesses and uses the different information.
3. Conduct routine data cleaning: Staying on top of data cleaning can prevent issues before they arise and eliminate the need for the team to clean whole datasets. Plan to do data audits at least once a quarter. Develop a cleaning plan, which can include standardizing data, removing outliers, and having a process to correct incomplete information. This step is very important as wrong cleansing, standardization, and context could change the meaning of data. Hence, a standard, transparent process for routine data cleaning is required.
4. Develop a robust data governance plan: After data is cleaned, half the journey to decision-making is won. However, what becomes extremely important is data quality and data governance. Good data management begins with a comprehensive plan. Protecting customer privacy and following data regulations is a top priority for insurers. Still, the landscape has gotten more challenging as AI grips hold of the industry with minimal regulatory oversight. Insurance organizations should create governance policies that go beyond existing data regulations like GDPR and the California Data Privacy Law and include the principles of ethical data use. Provide transparency into what types of information are being collected and how it will be used. Have strong privacy safeguards to ensure that customer information is protected. This includes examining partnerships with third-party solutions to ensure their privacy standards are at the same level as the insurers. Be aware of unintentional bias and discrimination, especially when using AI-powered tools. Have procedures in place to monitor the output from these solutions to ensure they are not delivering unintentionally biased results.
5. Plan data migration with meaningful use cases: New solutions enter the market daily to help insurance organizations enhance their performance and customer service. From Policy Administration Systems to customer relationship management systems to claims systems, insurers will upgrade their platforms or adopt off-the-shelf solutions. Data will have to be migrated into these news systems efficiently and accurately. Develop a plan that outlines the need for migration and what information will have to be migrated. Backup data. Classify and map what data needs to be moved where. Ensure that the data being migrated is clean and complete. Before doing the entire migration, start with a test sample to identify any areas of the migration strategy that need to be corrected.

No matter how advanced an AI technology is, if the data it is working from is incorrect, incomplete, or has errors, the insights it delivers will be of little value. In the age of digitally transformed insurance workflows, data management should be of utmost importance. By having comprehensive data maintenance procedures articulated and supported by all stakeholders across the value chain, insurers can have confidence in the outputs of their technology solutions and improve the insurance experience both internally and for policyholders.

Monalisa Samal is Head of Data & Risk Analytics at Xceedance, the global provider of strategic operations, support, technology and data services for insurance organizations. She has more than 17 years of corporate experience in analytics, consulting and leadership with expertise in risk management, portfolio strategies, consulting, and analytics.

Opinions expressed here are the author’s own.

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