The ability of insurers to transform their raw data into knowledge is critical to their survival yet, despite this imperative, many data warehousing efforts have been postponed; others still have failed, according to a recent Conning and Company study. Though some insurers have enjoyed notable data warehousing successes, many more have found success elusive due to organizational issues, poor data quality and poor project planning.
In a recent study, “Data Warehousing and Data Mining in the Insurance Industry: Floods of Information, Fountains of Knowledge, “Conning has found that organizational issues tend to discourage data warehouse development because many employees view the widespread sharing of horizontal data it allows as a threat to their job status.
“The goal of a data warehouse should be to provide every member of the organization access to the wide range of information that will enable them to be more effective,” said Jack Gohsler, senior vice president at Conning and Company. “However, the reality is that many areas are unwilling to give up control of ‘their’ data and so access is limited to the traditional ‘data elite’.”
Data warehouse initiatives are strategic, not tactical, and usually do not directly address an immediate need, according to Conning. As a result, they are often considered to be of low priority. At a time when many insurers are struggling to increase revenues and reduce expenses, business leaders are wary about giving the green light to any project that does not immediately affect the bottom line.
The study also found that, after people and organizational issues, data quality has proven to be the biggest challenge in warehousing. Poor data quality is responsible for many of the time and cost overruns insurers have encountered in building a warehouse. Particularly with policy and claim transactions that were processed years ago, insurers may not have used appropriate edits and validations to insure data quality. As a result, as insurers scrutinize this data more rigorously, either prior to loading it into the data warehouse or after loading it, they found that a great deal of analysis and “cleansing” is required.
Conning emphasizes that data warehouse implementation requires a close partnership between a company’s business and IT leaders. It is a complex effort that requires many complementary skill sets (i.e., the data modeling and programming skills of the IT professional and a business leader with thorough knowledge of insurance data). “A data warehouse that puts the responsibility for implementation solely on the shoulders of the IT department is doomed to fail. The business leaders must define the data needs of the organization,” added Gohsler.
“The fact that IT and business leaders have very different skill sets and points of view has made such partnerships very challenging for many companies.”
Was this article valuable?
Here are more articles you may enjoy.