“Know your customers” is the new data mantra for the 21st century. Clean, high-quality customer data gives insurers powerful marketing and service advantages and prevents expensive headaches. A well-conceived data warehouse is a good place to start, but as core insurance systems develop problems over time, data quality issues grow undetected in the data warehouse.
These problems usually only show up when reports are generated from the warehouse and business people question the validity of the data. By then it is too late, and correcting the problem will take much time and money.
So how do you avoid this problem? The answer lies in proactively finding small data problems before they get bigger. And once they’ve been found, fix them right away.
The same principles for running a great data warehouse apply to property/casualty, life and health insurers. All have complex challenges, but health insurers, which deal with patients, providers, employers and brokers, may face the biggest data challenges.
To avoid these data integrity issues, carriers should consider establishing a simple yet effective four-step program to good data health.
1. Control totals. The standard approach is to keep track of the number of records in the file and make sure that same number ends up in warehouse. A good start, but take this concept further and use it with individual fields that are important for the business. For example, while loading patient data, we can get the control counts for male/female and match it with the membership system. Another example would be to get the control count based on age bands and make sure they match the membership system.
2. Aggregate data and check for trends. Your system should aggregate certain data to make sure that the percentage is as expected and lies within a trend. For example, in a typical month, 18 percent of members have claims. If it’s suddenly showing up as 8 percent or 28 percent, you know you probably have a data problem.
To track the change, calculate the percentage that matched up front and store it in the aggregate table. Storing of the aggregated data helps identify problems with the data quickly if normal trends change.
3. Set up automatic alerts. Your system should automatically issue alerts whenever it detects a problem: controls totals that do not match or a percentage that’s outside the range of expected results.
4. Build and empower data teams. Build a team whose job is to proactively identify the data quality issues. This team has to be knowledgeable about the business and understand trends. It should use the techniques outlined above to find problems and report them. Data quality team members should include representatives of various business departments and IT.
When any problems arise, the data quality team will report them to the data steward/governance team. The latter team is empowered to take prompt corrective action.
The key to making any data warehouse successful is to continually build trust and credibility in the data. Checking for data anomalies is not a one-time thing. It needs to be done continuously as part of a healthy data program.
Having a set of strategies to automate the checking of data quality helps maintain the trust in data over time. Building a support team that is vigilant about finding data quality issues is a must for ongoing data quality.
Yunus Burhani is a senior architect with X by 2, a technology consulting firm in Farmington Hills, Mich.
Readers are encouraged to respond to Yunus using the “Add Your Comments” box below. He can also be reached at email@example.com.
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