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How Insurance Companies Can Cut Costs Without Jeopardizing Data Quality

by Jessica Dugas, on February 16, 2017

In the insurance industry, data is a vital tool to make decisions around risk, claims, sales and pricing. However, collecting, managing and analyzing data can be extremely costly- but it certainly doesn't have to be. With our experience working with insurance companies, we've come up with three key ways insurance companies can cut costs without affecting their data quality or security- and quite possibly improve it!

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Move to The Cloud

One of the greatest ways to cut costs for big data use is by ditching your traditional in-house server for a cloud-based one.

Scalability is the number one reason to move to the cloud. Insurance companies produce mass amounts of data daily, so being able to easily and quickly scale up when your data warehouse is full is essential.

No hardware maintenance is another great reason to switch to the cloud. Hardware updates can be very costly especially as your server ages. Ditching your in-house server for data collection in the cloud will eliminate hardware, in turn saving you a lot of money.

 

Overarching Data Collection

Departments and teams within insurance agencies commonly use different systems to store, manage and analyze their data. This makes it extremely difficult to share data across the company, not to mention the potential for massive data quality issues. 

Implementing an overarching, holistic data system breaks the data silo mold, giving more flexible access to cross-departmental data. This cuts the time teams need to rewrite and organize information to share to coworkers in other teams. In addition to saving time, this also gives your company a more firm pulse on the business, and can show how unrelated departments might be impacting each other. 

 

Utilize Automation

Lastly, another great way to cut costs in data is to utilize automation- and, no, we aren't talking about replacing your agents with robots. The kind of automation we're talking about complements your employees, instead of replacing them. This reduces the time your employees spend on automatable tasks, freeing them up for the more important work that can't be automated. And as we all know, time is money!

Automating simple, repetive tasks can not only relieve some stress, but saves the company money by reducing the time spent on them. Some examples could be report generation, daily data snapshots, and source integration- but the sky is the limit!

 

 

Do you need help implementing any of these strategies?

We've got you covered. Check out our data services for insurance

 

Topics:Data Science