Dirty data is a term used to describe ineffective data entered into an organization’s database. Data is a powerful tool that, when processed and synthesized, effectively transforms into information used to drive business decisions. If you’ve been following this blog, we’ve talked about the benefits of using behavioral tracking to collect data on your targeted audience through their engagement with your digital ads, website, transactions with your practice, and customer experience surveys. Ideally, the data collected would be housed in a customer relationship management system (CRM) or another data platform. However, simply entering data into your CRM is not going to cut it; the quality of the data is essential.
Creating A Mess
Dirty data, by definition, is data that is incomplete, inaccurate, duplicated, or inconsistent. Incomplete data is referring to missing information in your CRM, like a patient’s missing email address or phone number. Erroneous data results from phone numbers, addresses, and information of the like not being updated when they change. Duplicated and inconsistent data can result from patient data entered in with misspelled names, reentry as a new patient, or if the information is input differently across multiple platforms. The root cause of data errors can be boiled down to two primary sources, System problems and the people entering the data. System errors stem from multiple software integrations like when transferring data into a new platform when upgrading. Other contributing factors to dirty data are inadequate system setups like when choosing information fields or the lack of routine maintenance on the data itself. People can also affect the cleanliness of data entry by manual entry mistakes, lacking in data collection strategy, or not comprehending the importance of quality data.
There can be significant implications caused by dirty data, and many of them can affect your bottom line. Having erroneous data can cause inefficiencies and be a source of slow productivity. It also can create extra work as data that is dirty is ineffective and useless. If dirty data supports business decisions, it could lead to ineffective strategies and negative results. Without accurate data, strategies like content marketing and automation can’t deliver relevant and hyper-targeted communications to your current or potential clients, as precise segmentation will be a task that would be virtually impossible.
As you begin thinking about different marketing strategies to implement into your private practice, try to think about their dependence on data to function. Consider having a specialist design your database infrastructure to ensure proper execution and conduct training on system procedures. Adequate education on the platform can be a crucial factor for team buy-in. Don’t forget to check out my latest blog, Testing with Marketing Experimentation.