Data strategies are not universal throughout the insuranceindustry. The number is sometimes seen as being equal to the linesof business offered in the industry, according to Karen Pauli,research director in TowerGroup's insurance practice.

"It depends on the product line," says Pauli. "If you take alook at the personal lines, I think most carriers have come up withsome level of strategy to handle getting at the data."

Many personal lines insurers have replaced or are in the processof replacing core administration systems or developed a strategy towrap their core systems with business intelligence or analyticstools so they can use their data.

"If you are not getting at your data and don't have some levelof confidence that it is accurate data, you are way behind theeight ball," says Pauli. "Personal lines insurers also have beenable to use integrated third-party data, so it's more accurate.Core data standards have helped there."

Pauli believes commercial lines carriers are in a more difficultposition because many of them still labor under legacy systems.That could be about to change, though.

"There are a lot of good core admin systems out there," shesays. "We are seeing pretty aggressive adoption in the mid-market,so once you get the core admin systems onboard, and you have yourdata in order, you can ramp it up to use for other purposes.Everyone started with the basic personal auto and moved it toproperty. You'll find the same thing in commercial lines."

Clients of Capgemini Financial Services are speaking outincreasingly on data governance, according to Christina Colby, vicepresident of business information management for insurance withCapgemini.

The complication comes when companies don't necessarilyunderstand how to operationalize their data and get business and ITto work together, according to Colby, who adds that data quality isa massive component of this problem.

One thing Capgemini has done is to work with carriers toimplement a meta data management solution to capture informationabout the data being stored. One such solutions involved a qualitymeasurement that had been pulled together from a number ofdifferent rules.

Using Informatica as the ETL tool, Capgemini conducted dataanalysis with that platform, using (Data Explorer and Data Quality)components, according to Colby.

"We set up a series of rules so that as the information wasbeing transformed in the TL engine, we could score that piece ofinformation," she says. "We then loaded that into the meta datamanagement solution, and for that client we used Adaptive. Forevery piece of information whether a source piece of information orcoming into the reporting repository, we had a score on the dataquality.

Capgemini had an indicative quality score on each of thereports.

"Most people when they see a published report assume all thedata is correct," says Colby. "We had a few different thresholds—ared/amber/green status. If it was less than 60 percent confidencein the data quality, it would show up as red. Lower than 60 percentis less than ideal. And most of the reports were red."

This gave the insurer the impetus to implement a data governanceprogram in earnest.

"If you see a published report, you assume it's correct," saysColby.

Another case study Colby shared involved a financial servicesfirm where a team under the leadership of the CFO went through andquantified the cost of bad data so employees understood there is acost to the organization if the inherent quality of the data is lowor incorrect.

"As you can imagine, the data quality started to improvesignificantly," says Colby. "It's a way to give heightenedvisibility to how poor the quality of the data is."

LEGACY SYSTEMS

Most carriers have their own definition of the problems withtheir data, explains Pauli. Certainly one issue is siloedinformation.

"The enterprise data that allows you to make your contact centerso efficient and allows for outreach to customers is the hitch inthat plan," says Pauli. "That's a big order."

When studying carriers with multiple administration systems,Pauli believes they would love to be able to retire all but one ofthe core systems, but consolidation to the primary two or threesystems usually is the best they can do.

Carriers find it difficult to go through the conversion process,explains Pauli.

"They need a strategy that allows them to get at the data andthat's where BI or integration layers are the answer," says Pauli."Some are quietly doing a good job with that. Farmers [Insurance]stood up at ACORD and told everybody how they've used BI andintegration layers on their legacy systems to create somestate-of-the-art content center functionality. Not everyone canreplace all their legacy systems—it's not possible. That's where BIand integration strategies become critical."

Craig Loughrige, senior consultant for Robert E. Nolan Co.,believes insurers are trying to return to an expanded toolset withsome fundamental building blocks as a data strategy. One of thosebuilding blocks involves a clear delineation of transactional datavs. decision-support data.

"[Insurers] are coming at it in a backwards way through BItools," says Loughrige. "That's a great way to look at it, but ifyou don't have your components in place [BI is] harder to useeffectively."

Among the components insurers are using are a data dictionary,an enterprise data model, and the appropriate datastores—operational data stores and data marts. Many data marts comefrom data warehouses, explains Loughrige, but others come fromoperational data stores and bypass the data warehouse alltogether.

The final piece of the component is the data case managementsystem.

"Having a right mix of tools, having meta data—structure andcontent—are important," says Loughrige. "BI tools and the need forinformation are the catalyst. I just left a client who is on thepath to expand their toolset—both reporting tools and adding insome different database structure in order to build a foundation.That's when, your other tools make a little more sense."

DATA GOVERNANCE

Most insurers acknowledge they are in a relatively immatureposition in terms of data governance.

"It's simply because the nature of their data landscape ischanging so much," says Colby. "[Insurers] have gone from the pathof consolidation into major data warehouses to more siloed versionsof information. It's perfectly acceptable—it's the directioncarriers need to move to have repositories that are virtuallycombined vs. physically combined. But it really starts tocomplicate how you govern the data."

Most insurers recognize the need to do more,but don't understand how to effectively engage their businessstakeholders.

"[Business leaders] have to be the data stewards," says Colby."IT can help support them but fundamentally the business has tounderstand they're the ones who steward the information on behalfof the rest of the enterprise. Organizations have varying degreesof understanding. Meta data management is something we see ascritical because it creates an area where you can have a commonlanguage and understanding between the business and ITcommunities."

When you look at any insurance company, their most significantassets are their data. Colby believes that in the actuarial spaceit is understood that data is the company's most significant asset,but when you try to extrapolate that across the organizationeveryone has an opportunity to improve.

"There's such an interest about predictive analytics, but ifyou're putting garbage data into the model, you'll get garbage dataout of it," says Colby. "There are certain things you can do to tryto clean the data among the models themselves, but the reality isyou should be fixing it at the source so you have confidence behindthe data."

TYPES OF DATA

Data comes in two forms for insurers: transaction anddecision-support and Loughrige maintains it is important forcarriers to set the two apart.

"The bread and butter for insurers is transaction data," hesays. "You are generally working on a row at a time, but you haveto structure it in order to make the inserts and changes work foryou. Then you want to keep everything else—query tools andsuch—differently."

Decision-support data involves multiple rows at a time withinthe data warehouse, explains Loughrige.

"While you start with a data model that is structured, typicallyyou design the normal form, which is a scheme to minimize damage todata and reduce timing issues," he says. "You have to denormalizethe data a bit in the processing world and you do that differentlyin transaction and decision support. Transaction is one row at atime; decisions support is multiple rows at a time with queriesthat can take much longer. You don't want to bog down thetransaction system with queries about something else."

There are structural problems that carriers face with legacysystems and it takes time to put a better data structure in place,explains Loughrige. Another problem involves the practical use ofthe data.

"People look at the data in different ways," he says. "It's hardto take a global view because it means different things in the wayyou count things or the timing of things."

Quality of data varies from one company to another, addsColby.

"In general, most people would agree they want to see somethingabove 90 percent accuracy," she says. "That's not exactly idealeither. One organization looked at an 80/20 rule, but frankly Idon't think that's a standard anyone should work toward."

One of the biggest challenges Indiana Farm Bureau Insurance(IFBI) has faced is the quality of data collected from policyapplications, according to Jim Putka, executive director, systemsdevelopment for IFBI.

"One of our problems—both on the p&c sideand the life side—is getting quality and complete applicationdata," says Putka. "It's an issue with the intake of data becausewe have to make underwriting decisions. The quality of data isparamount."

IFBI works with both independent and captive agents and has hadto deal with issues relating to the accuracy of the data coming inon the applications and also the completeness of the data.

"If the data is not complete, there is significant productivitylost because there is a back and forth among the underwriter, theagent, and the CSR," says Putka. "If the data is inaccurate thereare delays and those delays have a ripple effect in terms ofbilling. Inaccurate and incomplete data may turn into unexpected,billed premium amounts for the insured."

The carrier has used the Exceed billing solution from CSC forits homeowners policies and has had success with the quality ofdata it brings in that the carrier is working on an implementationof Exceed for its personal auto business.

"What we found as an Exceed customer is that we are getting amuch better experience on the homeowners' side," says Putka. "Weheard lots of complaints on the auto side, which is serviced by ourlegacy application, and also our life side."

The CSRs are excited about adding Exceed for personal auto,according to Putka. One plus is that any policy changes arereflected immediately in billing. A second benefit isstraight-through processing.

The system allows the carrier to work the quotes and run themthrough various iterations so the IFBI data is complete andaccurate and can proceed smoothly to the next step.

"We anticipate we are going to have dramatic savings once weimplement Exceed for personal auto in terms of the amount of timeit takes for us to issue a policy," says Putka. "It is going toeliminate the back and forth among the underwriters, the CSRs andour agents."

While the Exceed system handles the process from quote to issue,it ensures the information is complete, even though humans areinputting the data, according to Putka.

"[The system] takes a look at the VIN and make sure it matches,"he says. "It has various sub-systems that feed data and we are ableto scrub the address and check if it is the name of an existingcustomer. There are edits built in that enforce the business rulesbefore an application can move onto the next stage. We believethere will be a dramatic drop in the number of applications thatare inaccurate or incomplete."

IFBI sets a target in terms of the number of applications thatcome in clean, explains Putka.

"We keep a metric in terms of the apps that are unclean orinaccurate," he says. "We have a baseline where we can compare ourperformance with Exceed."

DATA LEADER

A data leader should play a prominent role in the structure ofan insurance carrier—certainly more important than it is viewed bymost, points out Loughrige.

"Larger carriers have them, but smaller ones don't have asstrong a role for a data leader," he says. "Companies need to havea data architect and a data base administrator."

Loughrige believes most companies do a better job in the DBAworld and DBAs are more commonly found among carriers. For example,if a carrier has a performance issue on a query, it usually meansthere is something wrong with the query and those problems tend tobe addressed more readily.

Loughrige maintains the data architect sets up the logical flowof data and sites the example of the architect vs. thecarpenter.

"If you have a problem building something you call thecarpenter," he says. "You need someone dealing in the physicalworld. Sometimes the solution lies in the design, though. Sometimesyou just have a bad design."

The reason architects aren't always viewed as the go-to personis because they tend to deal more in imagination, according toLoughrige. But good CIOs take the data architect and focus onsolving a real problem today rather than the future.

"If people have a problem they want it solved today," he says."Sometimes people think they have programming or applicationproblems, but I think they always need to ask: Do I have anunderlying data problem that just looks like something else?"

If the data initiative is being run through the IT department,Pauli is skeptical about the possibility of success.

"[Data initiatives] have to be at the C-level and one or morebusiness heads have to be on board," she says. "You need enterprisegovernance. Otherwise, in the day-to-day push to run an insuranceoperation, you will naturally revert to what you've been doing forthe last10 years. This can't be an IT project; it has to be abusiness project."

Such direction doesn't preclude an enterprise data strategy,offers Pauli. For example, she looks at a staple of all insuranceinformation: the policyholder's address.

"The claims people may put address over here and have adifferent configuration," she says. "But an address is an address.Having standards doesn't preclude doing different things with it.That's where you need open data architecture and working withtechnology providers to develop integration layers."

Carriers also need a collaboration with the company experts inthe data area.

"I don't think you can pull the bunny out of the hat," saysPauli. "You need brilliant IT people, but if you live and die data,they are going to bring something to the party more than if you aresimply an insurance IT executive."

SOCIAL MEDIA

Carriers are using a variety of monitoring tools to understandwhat customers or non-customers are saying online about theircompany, explains Colby. One such partnership between Attensity andPegasystems uses Attensity as a monitoring engine, and pushes thedata to Pega, which automatically generates business rules andreacts to social media.

"In terms of reacting to [social media], it's compelling," shesays. "It's not just listening, it's engaging, but ideally in a waywhere you are not just doing it through human intervention."

As for how social media relates to data management and dataquality, Colby believes much of the interest she is seeing in datamanagement is actually in the unstructured data space.

"Using more of the advanced search capabilities, it's been ofhuge interest in the past year alone to figure out the benefit ofusing a capability such as this. You can interrogate any source youwant," says Colby. "Whether it's an inbound document you're gettingin electronic format, claims notes that may be somewhat structuredor if it's coming through your social media channels, you can startto identify patterns in the data and not necessarily structure it,but look for similar patterns and values. In my mind, it's usingunstructured data capabilities to look at internal or externalsources."

Carriers need to take a look at the social media data that isaccumulating and how that relates to big data issues.

"If you have all that information that's available on theInternet through social media, you have to start thinking about howyou are going to use the data," says Pauli. "It's a kick in thepants to start thinking about data in a different way and trying toget to that enterprise strategy. These are expensive and multi-yearstrategies.