Insurers continue to have the urge to merge. According to ananalysis by PricewaterhouseCoopers, the amount of merger activityin the industry has remained fairly constant since 2000, averagingabout 230 deals a year.

While mergers and acquisitions target a bevy of businessbenefits, they can exacerbate the multisystem, disparate datachallenge faced by insurers' IT departments. "A merger adds to theproblem simply because you have more [systems]. You have duplicatedomains and more people with different perceptions of data," saysLyn Robison, analyst at the Burton Group.

"The key to post-merger efficiency and effectiveness is toconsolidate or standardize platforms and data as quickly aspossible," says Enrico J. Treglia, senior vice president and chiefoperating officer of Wilton Re.

Connecticut-based Wilton Re has gone through several mergers inrecent years, including three in 2006. The company's guidingprinciple is to convert newly acquired companies to administrationsystems hosted and maintained by third-party administrators.Currently, Wilton Re uses CSC or Genesys TelecommunicationsLaboratories, depending upon which presents the better business fitand easier conversion.

"Keeping different systems running from all the companies we'veacquired, with different code to maintain and technology tointerface with, would drive our cost of ownership through theroof," Treglia says.

It's not just IT costs that are a concern, Robison adds, it'sthe impact on how business functions. "If you try to wave the magicwand and say we're one company now, but we have two different setsof information systems, you have an unstable platform. You end upwith broken processes," he explains. "Until you have datanormalized and cleaned up, you have separate companies."

Like other terms, normalized means different things in differentareas of IT. Technical database normalization and a discussion ofthe various normal forms are beyond the scope of this article.Instead, the insurers we spoke to are concerned with the practicalreality of how to deal with data stored differently in multiplesystems.

"When I use the word normalization, I mean taking data andputting it into a technically usable form that also makes sense forthe business. That might imply business normalization more so thanIT normalization," says John Lucker, principal at DeloitteConsulting, who leads the firm's advanced quantitative solutionsdata mining and predictive modeling practice.

There are different areas of data on which companies need tofocus post-acquisition, including operational, financial, andanalytical data, but one common thread runs throughout. "Themeaning is paramount. You have data in particular fields, and youneed to know the context in which that data exists," Robisonindicates.

Identifying that structure in systems with which a company isnot familiar, as often happens after an acquisition, is challengingbut critical. "Understanding what data actually means and howcomplete and accurate it is, is one of the dimensions of dataquality," Robison says. "Just like in a manufacturing process, ifyou have poor-quality raw materials, you end up with poor qualityin the process."

Susan Clarke, senior research analyst with Butler Group, alsostresses the compliance angle to data consolidation. "Storage isgetting cheaper, but the more information you store and the moreplaces you hold it in, the more difficult it is to achievecompliance," she says. "Oftentimes organizations have only a fewdays in which to produce requested information. And if you've gotit all over the place in different systems, it can be difficult tobring the information together in that time."

"There's not a lot of grace period [to compliance], yet you'vegot the data, and you have to understand what it is," Treglia says."[Unfortunately], in the early years before Sarbanes-Oxley andSection 404, [system] documentation wasn't something everybodycommonly did."

"We also see 'green IT' being on the agenda," Clarke adds."People want more efficient data centers. There's an awful lot ofduplicate information in data centers, and through datadeduplication, organizations can reduce their storage[requirements]."

Despite the advantages of consolidation and integration in IT,business issues take priority after an acquisition. "Very oftenwhat a company will do in the beginning is focus on mergingbusiness structures and cultures, trying to maintain the integrityand the performance of the business to assure agents and customersthe change will work out for all. In the meantime, behind thescenes, IT is learning what it needs to do to absorb this newcompany. So post-merger, you'll often see the continuation of[system] silos," Lucker says.

That strategy cannot sustain itself for long. "At some point, todeliver on the reduction in expense commitments, somerationalization of systems and processes needs to occur," addsLucker.

Insurers tend to follow a common hierarchy of activity in ITafter a merger. "Companies clean up applications first, then theirdata, then processes," Robison says. "There is a reason people takethe application-first approach, but a more careful approach wouldbe to look at processes holistically first and let the decisionsabout data and applications flow out from that."

Genworth Financial, formed in 2004 as a spin-off from GEFinancial Services, targets system and data consolidation from abusiness-priority perspective. The company is experienced inmeeting post-merger challenges.

"Over the years, the number of companies we've integrated toform our company as it exists today is in the teens," explains MikeShadler, CIO for the retirement and protection segment at GenworthFinancial, based in Richmond, Va. Genworth offers life,long-term-care, payment protection, retirement income, andinvestment products as well as mortgage insurance.

Shadler asserts there are three business phases to a typicalacquisition. "First is getting common operating processes,including financials and human resources, which have broaderimplications across the business. Then we move into sales andproduct teams, which is all about accelerating growth. Finally, wefocus on improving operations from a customer touchpointperspective," he says.

Data issues run through all these phases. "One issue is the needto consolidate business support systems, such as general ledgersand payroll. Then we need to rationalize our product systems andclose off less cost-effective platforms," Shadler notes.

Ideally, the company prefers to convert acquired business to itscore administration system, CSC's CyberLife, which contains data onmore than two million policies. "We take the opportunity from theconsolidation to reinvest the savings into other areas," Shadlersays.

That includes creating GENIUS, a new business processing andworkflow system the company built to integrate with imagingsystems, financial systems, and its policy administration systemsvia an IBM WebSphere MQ-based messaging hub. "We are able to investin projects such as GENIUS because of [savings from] consolidationefforts such as CyberLife," Shadler remarks.

There is no magic formula to the conversion process. "Withacquisitions, we have very old systems we could be bringingtogether," he continues. "A lot of times to get through theseconsolidations we have to do some forensics: what the data is, whatcalculations exist, what that data links to. That's the biggestchallenge of bringing together legacy technologies."

Although Genworth prefers to convert, there needs to be a solidbusiness case first. "Our decisions are based on whether we canenhance the customer's experience or improve productivity. If wecan't get at those two things, we don't do it," Shadlerexplains.

If not, Genworth will retain the acquired system and integrateit with the GENIUS system. "This creates common, standardizedprocesses that are not completely system specific," Shadler says."We have invested heavily in common workflows and common ways forus to work transactions outside the flow of individualsystems."

Cleaning up post-M&A data messes tends to be a biggerproblem in insurance than in other financial services sectors,Lucker claims. "Prior to insurance, I worked for a bank that was inthe business of acquiring little banks. It got to the point wherewe could gobble up a bank in a weekend. On Monday morning, Bank X'scustomers would go to the new bank and their data would be in thenew company systems," he says.

"The reason is a checking account is a checking account, asavings account is a savings account, and so on. But insurance ismore complex," Lucker contends. "A workers' compensation policy [atone company] might have only the bare minimum of information, justenough to write the policy, vs. one at another company that is moreculturally focused on gathering additional data to use indownstream processes."

That results in a typical divergence in data structure andquality between the acquired and acquiring company. Because of theimportance of data and the difficulty of dealing with data retainedin multiple systems, Lucker advises companies to analyze carefullydata quality when performing pre-acquisition due diligence."Companies should focus less on the functionality of systems beingacquired and more on the data being used by those systems," hesays.

And in the post-merger push to consolidate systems and gainefficiencies, insurers need to be careful not to discard or damagevaluable data assets. "A lot of times when companies look ateliminating a piece of software, they look at how much it costs andhow many users it supports," says Robison. "What often getsoverlooked is what data it provides."

Consolidation of different platforms invariably leads to achange in the way one company had performed business and in themanner by which users are presented data and information.

"That really becomes apparent in a merger, because you have thesame data meaning different things to different people–differentdefinitions of the important entities that are tracked in thesedifferent systems. Also, inside each enterprise, there are multipledomains. A customer will be [defined] differently in the marketingdepartment than it will be in the accounting department, so theentity gets used in different ways," Robison notes.

The fundamental place where insurers seem to stumble, Robisonreports, is forcing the issue. "A typical mistake I've seencompanies make is creating one [enterprise data] definition thatignores the reality there are different domains within theenterprise. Information should be defined differently within thosedomains and transformed across them," he says.

Therefore, insurers need to manage projects with people who notonly can bring both a business and IT perspective on system anddata integration but who are skilled enough to resolveorganizational quarrels over data definitions.

"What is important is who is the 'data czar'–the person orpeople paying attention to the architecture and business decisionsaround what it means to manage data effectively across anenterprise," Lucker says. "That includes things such as creating aholistic information inventory and understanding the type of datayou have externally and internally. Looking for people within yourorganization who have a passion for managing data, leveraging thedata's intrinsic value, and creating a culture that recognizes thatdata is a valuable off-balance-sheet asset are incrediblyimportant."

Having the right resources in place has been essential topost-M&A success at Wilton Re. "If you have just IT folks doingthe work, they can go down a path that isn't appropriate from abusiness perspective because they haven't been exposed to thebusiness," Treglia says. "We have a lot of people with good IT andbusiness knowledge who can raise issues with the appropriate peopleto get resolutions quickly."

In addition to identifying and promoting people with thoseskills internally, Wilton Re demands those capabilities from itsoutsourcers, such as CSC. "Its conversion staff isn't just ITpeople. CSC understands the business, has seasoned professionalswho point out problems that can occur, and has a methodology andunderstanding of the business that really allows us to streamlinethe [conversion] process," says Treglia.

ACORD data standards also are important to both internal dataintegration and external data interchange at Wilton Re,particularly given the number of different insurers with which thereinsurer transacts business. "Being in the traditional lifereinsurance business, one of our major challenges is we have asignificant amount of disparate info coming in from differentsources. We've adopted the ability to use the ACORD XML standardsto translate all the data into a common format, so it doesn'tmatter where the data comes from," reports Treglia.

For the task of conversion from external sources, Wilton Re usesa rules-based transformation solution from Rivers Wave Consulting."The use of ACORD XML itself has helped in conversions becausewe're not reliant on positions of data, and it gives us moreflexibility for moving data around, but there's no magic to it,"Treglia states. "Mapping is mapping."

"We've been able to streamline the [post-acquisition] process byusing a common methodology; the same type of understanding of thetarget, and mapping to that target," Treglia says. "I boil it downto simplicity. Post-merger or post-acquisition, if you have anestablished methodology, it makes it easier. Instead, somecompanies look at the separate platforms and then wonder whatthey're going to do with them all."

Particularly for a company whose business model depends heavilyon acquisition, having a repeatable process for system integrationand data consolidation is essential. Acquisitions are a keystrategy for Meadowbrook Insurance Group, whose most recentpurchase was U.S. Specialty Underwriters in April 2007. The companyoperates through five different insurance companies and also hasfour agencies, a wholesale brokerage, and a half dozen insurancesubsidiaries.

Meadowbrook's first choice is to convert new companies to itsINSTEC QuickSolver rating and administration system. The companyalso looks to convert new MGAs to its ConceptOne management systemfrom Epic-Premier Insurance Solutions. "We determine whether [MGAs]are generating enough data to justify the investment from an ISstandpoint," explains Jim Lee, IS manager at Meadowbrook.

Meadowbrook has an established data integration process thatrelies heavily on a framework created by integration and businessprocess management vendor Adeptia. "We have a basic [integration]shell that is reusable. What changes are business rules aroundthings such as how we handle data, who gets notified in errors, andwhat data or files are held. If we can reuse that code orframework, it makes our job much easier," Lee says.

Meadowbrook's biggest challenge in dealing with disparatesystems, Lee adds, is discovering the meaning of data in newlyacquired systems. "The pain part is do we understand our data, what[data] needs to pass, and what are the business and mapping rulesaround data," he says. "Some of those issues are standardchallenges, but we do have some unique rules around data in oursystems, and in acquired systems, you don't always have thenecessary source data to build a corresponding new record."

That's where Adeptia comes in. "You need to determineprogrammatically what to put into missing fields. Adeptia is goodat building that–it queries to find the business rules to fill thegaps between source and target data, so that data becomes usefulinformation," Lee says.

While leaving systems as is post-merger isn't usually a viableoption except for run-off business, there are technical risks tothe conversion and normalization process–from broken integrationpoints to performance issues. "There is a usability problem whendata is overly technically normalized," Lucker indicates."Historically, data architects have been focused on technicalnormalization, which doesn't necessarily make sense forbusiness."

Whether it's dealing with operational, financial, or analyticaldata post-merger, there also is the danger of paralysis byanalysis. Lucker advises companies simply to keep moving forward."Step one is creating an inventory of your information. Step two isgetting your data ready for use, spending more time focusing onmeaningful utilization of data and less attention on getting itperfect. You always can do continuous improvement. Step three isfinally using the data as a valuable asset and leveraging it as adifferentiator," he says.

He illustrates his point with an example from analytics. "If youcan start using what you've got and generate some value fromanalytics, the return can pay for continuous improvement," Luckerpoints out. "Striving for perfection is a treadmill that neverends. People get tired and frustrated, and it doesn't get done atall vs. it getting done 'good enough.'"