Modern businesses live and die by the quality of the data they collect and use. Retail companies—both e-commerce and brick-and-mortar—as well as manufacturing, transportation, and services, have come to think of themselves as “data companies.” Perhaps nowhere is this truer than in the insurance industry, though.
– In life insurance, actuaries rely on data from many sources to discover and define ever more granular health and lifestyle attributes to determine the overall risk level of each applicant.
– Automobile insurers are encouraging customers to install tracking devices on their cars to provide precise data about driving habits and mileage.
– Property insurers are using troves of geographical, geological, climate, and other data to assess all kinds of hazard risks.
– All types of insurance companies are leveraging data analytics to detect and prosecute fraud.
Insurance Metadata Management
Although more data than ever is available to the insurance industry, it comes with a downside. Keeping track of it all is an extraordinary challenge, not to mention evaluating its accuracy, storing it securely, and using it wisely.
In particular, stakeholders in the insurance industry need to carefully design and validate their reports. Even small reporting errors by an insurance BI team can cause insurers to make poor (read: costly) decisions about whom to insure, what premiums to charge, and whether and how much to pay on claims.
Data Governance and Metadata Management for the Insurance Industry
Both of these two keys deal with metadata. Metadata, of course, is the information about the data—not just technical details such as table and field names, data types, and relationships with data in other tables, but information about where the data came from, the transformations applied to it along the way, and all the places it’s used, such as reports and dashboards.
Data governance is the set of controls (such as policies, procedures, and training) that prescribe the entire data lifecycle: how data is acquired, stored, secured, accessed, used, tracked, monitored, and disposed of. In insurance, effective data governance is particularly important, in no small part because of the need to comply with a multitude of state and federal insurance regulations.
None of this is possible without robust metadata management. In insurance, as in most other industries, metadata management is the process of discovering and cataloging the metadata for all data sources and assets.
What does metadata have to do with data governance? Plenty. Metadata is the very foundation of a good data governance program. Without metadata management, you have no clue where your data came from, where it’s stored, whether it’s properly secured, who has access to it, and for what purpose.
Traditionally, metadata management, and thus data governance, could only be done manually. No more! With the immense volume and wide variety of data in the insurance industry, manual approaches would take an army of data analysts, BI experts, and IT resources, and it would still be slow and error-prone.
These days, the best approach to metadata management is automation.
More data doesn’t necessarily mean more insights unless you KNOW what data you have.
Learn how to navigate efficiently throughout the data journey.
Check out our white paper, “Metadata Management as a Strategic Imperative“
Insurance Metadata Management Automation Tools
A metadata management system for insurance can give those responsible for the data peace of mind that it’s under control:
– Automated tools can discover and catalog metadata from all your data assets, ensuring that nothing falls through the cracks.
– By automatically matching up similar data (names, addresses, and so on) from different sources, metadata management systems enable BI teams to build complete and correct reports.
– Any reporting errors that do occur are more easily tracked down and corrected, because the automated tools tell the BI team exactly where to look.
Insurers succeed by minimizing risk. By adopting automated metadata management tools, they can minimize the risk of out-of-control data, bad reports, and bad business decisions—the biggest risk of all.