One of the consequences of merger and acquisition activity is that insurers inherit a variety of legacy systems. And, in the case of IT systems, more isn't necessarily better. Duplicate maintenance and system support, disjointed business processes, and increased overhead expenses are just a few of the challenges compounded by multiple systems.Consequently, many insurers are searching for ways to minimize operational complexity, integrate and consolidate systems, and reduce associated costs.
When system consolidation is the chosen path, insurers look to migrate differing lines of business or business processes to a common IT platform. The concept, while simple to understand, is difficult to achieve.
The insurance industry is built on a rich history of data dating back decades. And, because data is the lifeblood of our industry, insurers must carry the data forward and have it readily available for historical reference as well as current day processing.
The effort to consolidate data from various legacy systems, ensure data integrity and implement data format commonality are challenges that leave many insurers putting off their inevitable data conversion effort.
Viewed by many insurance executives as a daunting task that had to be done, the traditional data conversion approach was to throw IT workers at the effort in hopes of getting this unpleasant task done quickly.
Unfortunately, this haphazard "get it done quickly" method typically failed to address the critical details required for successful data conversions.
Common results included major cost overruns, timeframes longer than expected and results lacking acceptable data quality.
Moreover, the conversions often were incomplete due to the conversion team's lack of understanding of data rules and business objectives. By underestimating the complexity of data conversions and viewing them as nothing more than a necessary evil, insurers often put their data assets at risk.
Insurance executives need to examine other approaches for data conversion. For starters, there is no need to rewrite the rules of conversion each time an insurer needs to migrate data from one system to another.
By capturing results and applying lessons learned from previous conversions, data conversion software can be built that provides automation and experience reuse-saving time and money.
To further simplify data conversions, the projects should be approached from a business perspective, rather than as an IT project. After all, it's just data, and the way it is stored is not a particularly difficult IT challenge.
Approaching data conversion from the business perspective ensures that those who know the data are the ones driving the conversion methodology.
These subject-matter experts apply business rules to dictate the transformation requirements between the old system (the source) and new system (the target).
And, because business experts are intimately familiar with the data, they also know what results to expect and can validate the conversion's success. Utilizing this expertise accelerates the confirmation of the conversion results and facilitates easier data cleanup.
Traditionally, conversions were done via a file-to-file process where data is lifted from the old system, massaged a bit and loaded to the new system. Soon after, the nightly cycle was run and "suspect data" was uncovered, detected as invalid, inconsistent or non-processable. Oftentimes, processing cycles were delayed, billing notices couldn't be generated or were incorrect, agents couldn't be paid, and customer service was inundated with irate clients voicing their concerns about the company's lack of service.
Making matters worse, there were cases where the cycle simply couldn't be run at all, and the converted system was replaced with the prior production system environment.
To avoid these common data conversion hurdles, the automated process to migrate data from the old to the new system should essentially replicate the manual process as though the customer service representative was keying the data into the workstation.
With this approach, the data being moved to the new system is subject to the appropriate edits and quality checks to ensure the highest quality data, while minimizing questionable data and processing disruption.
Consider investing resources at the beginning of the conversion project to better understand the shape and condition of the data to be converted. Investment in data cleanup upfront, prior to conversion, will avoid costly processing and data errors with the new system.
Each data conversion doesn't need to feel like the insurer's first. Upfront preparation, good planning, a collaborative effort between business groups and IT, implementation of best practices and effective use of automation techniques and tools can help the insurer avoid the all too common headaches of data conversion.
Jerry Kaul is vice president of marketing and business development with Universal Conversion Technologies.
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