These days, competing on analytics means gaining a competitive edge and streamlining costs in what often are uncertain markets. Many insurers have treasure-troves of data, and the right analytic applications, but these assets often aren’t used to their full potential. The ability to fully leverage data as a competitive asset requires an additional ingredient—effective management.
Christina Colby, principal with
“Insurance carriers are veterans at using data effectively for actuarial and underwriting,” she observes. “However, when it comes to monitoring and accessing data for other critical operations, carriers typically fall short.”
Colby provides some advice on how to avoid the data trap:
1. Don’t treat data quality as an IT issue: “Most organizations look to IT to sort out their data quality issues, but data quality management should be an ongoing program managed jointly by the business for ongoing upkeep at input, and IT for key transformations and clean-up,” Colby says.
2. Don’t trust everything you see: “Much operational and analytical data published in enterprise reporting solutions is far below a reliable level of quality to drive and support successful business decisions,” she cautions.
3. Don’t assume your data is insufficient to support predictive analytics: “Most carriers perceive their legacy systems and data quality issues to be inadequate at supporting predictive analytics and other advanced insights,” Colby remarks. However, she says she has been able to help companies deliver key results to clients with even the most creaky legacy systems. Data is data.
Colby says that to compete on analytics, insurance companies should take the following measures:
4. Underscore the value of information across the enterprise: “Solicit the support of senior leadership to enforce that key data can be managed as information and treated as a critical enterprise asset and differentiator,” she says.
5. Take critical enterprise information out of employees’ heads: “Deep knowledge of legacy insurance systems all too often is in the hands of a few key long-term employees,” Colby warns. She advises extracting this application knowledge from individuals’ heads and capturing it “in a sustainable metadata management solution, capable of tracking how data is managed between systems and also is available to complete impact analysis of potential future changes.”
6. Treat data governance and stewardship as a long-term program: “Most organizations view data quality clean-up as a one-time activity,” she says. “Instead, give the business the processes, tools and techniques to manage data in coordination with IT.”
7. Establish master data standards: Colby is an advocate of Master Data Management (MDM), but cautions that it has a “track record of failure when attempted as a ‘big bang’ enterprise solution.” Rather, look for opportunities to incrementally introduce MDM capabilities, she advises. “Consider using identity resolution technology to create an 'MDM-lite' version of a customer master to quickly provide initial business value and proof of concept.”
Joe McKendrick is an author, consultant, blogger and frequent INN contributor specializing in information technology.
Readers are encouraged to respond to Joe using the “Add Your Comments” box below. He can also be reached at
This blog was exclusively written for Insurance Networking News. It may not be reposted or reused without permission from Insurance Networking News.
The opinions of bloggers on
