Keeping up with the Joneses
Have you ever had to make contact with a client regarding their policy only to find out that their number or email address has changed? What about making that courtesy follow-up call that another team member had already made without your knowledge?
Due to the uncertainty and frequency of clients changing their details, whether it may be their number, email address or even workplace, this can lead to dirty data which can contain errors such as spelling mistakes or punctuation errors, incorrect data, or even data that has been duplicated.
Vital importance
The policyholders contact data is the most vital information to a variety of departments within an insurance organisation. Should any of this data be inaccurate; a broker or adviser may take on an unknown risk, provide an incorrect rate quote for a new policy and won’t be able to report the correct taxes.According to Larry Seltzer who wrote an article Health insurers getting bad data from healthcare.gov, insurers told the Wall Street Journal that they were receiving erroneous application data from the troubled healthcare.gov site.
The data included duplicate enrolments, spouses reported as children, missing data fields and suspect eligibility determinations. One company also reported that some applications contained three spouses per application.
Stripped of impurities
Brokers and advisers need to treat data as an asset; they must know how to cleanse dirty data and how to maintain it. Data cleansing, also known as data scrubbing, is the process of amending, removing and even merging of dirty data.
By cleansing data; you improve the data quality, however instead of doing a data cleanse which may happen quarterly, implementing a data strategy process - which could be integrated as part of daily routines - is best practice.
Start internally
In any data strategy development process, the first place to begin would be to start analysing internal data to source where any errors may occur, what types of mistakes are usually made, the effect it has on different departments and on the organisation as a whole.
A data strategy process of possible solutions can then be determined around this analysis. Although there are no conventional solutions or strategies for every organisation due to their own data quality needs, by not addressing data quality issues, you will be facing a tedious task ahead on a tremendous scale further down the line.
Look towards combinations
These guidelines may be combined with one another to improve data quality:
• Create a backup: before you start to clean your data or make any other changes, it is vital that you keep a copy as a backup in the event something does not go according to plan. Backing up on a regular basis can prevent you from data loss and implications;
• Maintain a style sheet: in order to make data entry easy and to maintain consistency for crucial data quality, having standard data entry formats which have mandatory fields for data information is advised;
• Validate information: to ensure the accuracy of contact data, encourage the policyholder to complete mandatory fields and to verify it at the point of capture for clarification;
• Analyse data: although manual processes are very time consuming, these can be implemented over a certain timeframe depending on the size of the organisation or database. With manual processes, the possibility of human error could creep in; however, use of correct software tools could automate processes; and,
• Educate staff: you may want to host training sessions, create a procedure manual or video to educate new staff members on the importance of data quality.
Brokers and advisers need to acknowledge why this is all so essential for business along with the negative effects that dirty data can create as well as the benefits of clean data, which can increase the operational proficiency and the policyholders overall experience.
