Insurers continue to have the urge to merge. According to an analysis by PricewaterhouseCoopers, the amount of merger activity in the industry has remained fairly constant since 2000, averaging about 230 deals a year.
While mergers and acquisitions target a bevy of business benefits, they can exacerbate the multisystem, disparate data challenge faced by insurers’ IT departments. “A merger adds to the problem simply because you have more [systems]. You have duplicate domains and more people with different perceptions of data,” says Lyn Robison, analyst at the Burton Group.
“The key to post-merger efficiency and effectiveness is to consolidate or standardize platforms and data as quickly as possible,” says Enrico J. Treglia, senior vice president and chief operating officer of Wilton Re.
Connecticut-based Wilton Re has gone through several mergers in recent years, including three in 2006. The company’s guiding principle is to convert newly acquired companies to administration systems hosted and maintained by third-party administrators. Currently, Wilton Re uses CSC or Genesys Telecommunications Laboratories, depending upon which presents the better business fit and easier conversion.
“Keeping different systems running from all the companies we’ve acquired, with different code to maintain and technology to interface with, would drive our cost of ownership through the roof,” Treglia says.
It’s not just IT costs that are a concern, Robison adds, it’s the impact on how business functions. “If you try to wave the magic wand and say we’re one company now, but we have two different sets of information systems, you have an unstable platform. You end up with broken processes,” he explains. “Until you have data normalized and cleaned up, you have separate companies.”
Like other terms, normalized means different things in different areas of IT. Technical database normalization and a discussion of the various normal forms are beyond the scope of this article. Instead, the insurers we spoke to are concerned with the practical reality of how to deal with data stored differently in multiple systems.
“When I use the word normalization, I mean taking data and putting it into a technically usable form that also makes sense for the business. That might imply business normalization more so than IT normalization,” says John Lucker, principal at Deloitte Consulting, who leads the firm’s advanced quantitative solutions data mining and predictive modeling practice.
There are different areas of data on which companies need to focus post-acquisition, including operational, financial, and analytical data, but one common thread runs throughout. “The meaning is paramount. You have data in particular fields, and you need to know the context in which that data exists,” Robison indicates.