This post is a slight deviation from our usual Project Management topics. Many of the projects that i have been are severely haunted by data issues.
Data is the heart and soul of any business. Everyone needs data that is clean, consistent, reliable and secured. Quality and Utility of data improves as and how we add volumes of proper and relevant information.
Insurance carriers like any other industry are often plagued with bad, inconsistent or non reliable data which may have happened due to various factors like application shortcomings, poor system and/or data model design, End user competence levels, user application knowhow etc.
What exactly is Bad data?
Data which is not normalized, inconsistent and thus non reliable is bad data. To give you a simple example, Same information can be represented in multitude of ways e.g. Contacts information stored in the system can be saved based on individual user's preferences.
e.g. Doctor identifier can be stored in system as "Doctor", "Dr", "Dr.", "Doc" etc. So if we need to run a report on all doctors in Systems or if we need to convert this data, we need some logic to handle care this inconsistency.
This is just one example of a potential data issue. There are multitude of other cases that are party to this bad data issue.
Why do we need good data?
Like we said earlier, an application is a bare body skeleton without data. Data is used for various purposes by insurance organizations for e.g.
- First and foremost Policy, Billing and Claims processing
- Customer service
- Internal Organizational and statistical analysis
- Risk analysis
- Predictive analytics
- Reporting to 3rd party agencies
- State and federal regulatory and compliance reporting
- Sending data across systems to interchange information e.g ISO, DMV etc
Impacts
Impacts of bad quality data are
1> Coverage Denial
Insureds may be incorrectly denied coverage, if policy number, date of loss, cancellation, reinstatement, renewal, coverage etc data is incorrectly entered or not entered when it should (No Mandatory checks in the System)
2> Incorrect Reporting
Incorrect data may cause claims to be incorrectly reported to 3rd party agencies which may lead to potential issues for the insureds and the carrier alike.
3> Claims Leakage
Claims may get incorrectly paid if policy, claims data is incorrect leading to leakage
4> Operational Efficiency
Severely impacts the organizational efficiency of Intake and operations groups e.g Claims System forces Intake to capture claims story as long winded notes rather than structured data. Consequently when Customer service or executive Management when references this claim file has to read through pages of data to get a gist of the claim or even report some status to the customer.
5> Functional Impacts
Data logic errors, poor system designs and incorrectly written business rules can cause problems for eg today's claims and policy systems use address standardizations or postal address lookups based on the address data entered in the system. If there is no uniformity in the city, street, zip entered throughout the system, then good luck with doing a proximity search when looking for a company prefered low cost Auto Bodyshop within 5 miles of your home location.
6> Third Party Impacts
There have been cases where an insured's policy was not recognized by DMV and have caused insured problems when he was stopped during a routine traffic check.
7> Statistical Impacts
Internal actuary and management level executives do a business health check and future policy rate predictions to be competitive based on the Loss ratios, premiums, outstanding payments etc. Sometimes incorrect or poorly designed data structure prevent this. This impacts the organizational data quality and process effectiveness.
8> Conversion Issues
When an old application is retired and data is moved to a new and better system, bad data is the worst possible issue that development and business Team have to tackle. In this case, each scenario has to be correctly identified and workaround have to be reached and agreed in order for the data conversion to proceed.
Handling data Issues
All these and other such problems are surely avoidable or can be solved if steps are taken to resolve the issues identified.
The first and foremost thing that has to happen in this case is proper and effective investment in the area of technology, leverage business knowledge and expertise and finally effective data management to reach the end goal.
Usage of the ACORD Data Model
Many of the companies who go for system transformation or remodeling do so because
- Many who move away from legacy system need to understand the power and importance of good data. For this it is important to have a consistent and a normalized Claims, policy and billing data structure. There is a generic data structure that is proposed by ACORD
It makes more sense to effectively leverage new technology for data interchange, data setup and storage e.g buying a leading top of the market claims system rather than investing in legacy system short term fixes that offer no long term gains, are fairly costly and therefore a bad investment.
Make sure you have your key stakeholders involvement. Their go ahead, business vision and knowhow is extremely important to guarantee success in your project ventures.
No comments:
Post a Comment