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3 Causes Of Dirty Data

3 causes of dirty data

Over the years, life science organizations and health payers alike have attempted to perfect their strategies for communicating with their audiences. While everything may seem as though it’s running smoothly for a while, it’s common for tactics to hit a snag along the way. Searching for the cause of the issue can cost organizations and insurance companies time, money and valued clients.

Sometimes, the error is caused by something as simple as a data mistake. These problems should demonstrate to life science organizations and health payers why keeping their information clean is an important task. Here are some examples of causes of dirty data:

1. Incomplete information
We’ve all started a task we didn’t finish. The same can be said of healthcare providers who begin to share their information – name, practice, specialty, etc. – but stop before they complete the job. As a result, life science organizations and health payers are left with records that are incomplete. Without the required information for making contact, these groups are stuck in terms of marketing outreach. Complete profiles of data will help life science organizations and health payers better interact with their consumers.

“Marketers are left to find the commonalities and inaccuracies between the two profiles.”

2. Duplicate profiles
Remembering login credentials can be tough, leading people to create a new account although an older one already exists. Sales and customer representatives may create multiple entries in an organization’s customer database for the same prospect or customer. In these cases, life science organizations and health payers are left with duplicate profiles. What’s worse is if the information in each differs. Marketing and sales members are left to test out the data listed to find the commonalities and inaccuracies between the two. Taking the time to sift through these similar profiles can be overwhelming, especially if the person is no longer a lead for health payers and life science organizations.

3. Incorrect information
Over time, people’s lives change. A marriage can bring a new last name, a move can prompt a new phone number and address or an online threat can be the impetus for a different email address. Remembering to go back and alter this information is challenging, especially after sharing this data with so many sites and vendors as the years passed. While some of the most necessary companies – electric, water, internet – will be the first to be altered, others may be pushed from people’s memory until they receive a notification from the organization itself. Life science groups and health payers dealing with this information could find themselves stumped and out of luck.

The importance of clean data
It is crucial for life science organizations and health payers alike to take the actions necessary to ensure their consumer information is as accurate and useable as possible. This means communicating with clients on a regular basis, and providing a quick and easy method to update their information.  Taking these steps will ensure out-of-date data is corrected quickly and incomplete profiles are finished in a timely manner.

There are many positives to be gained by keeping data clean, according to Strategic Fulfillment Group. Not only can marketers increase efficiency in their efforts, but life science organizations and health payers can make more data-driven decisions, improve their overall reputation and provide a more satisfactory experience to clients.

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