From chaos to clarity: Tackling dirty data head-on

If you didn’t get an opportunity to hit the “reset” button at the beginning of the year, why not start while spring cleaning is in full swing? Although spring cleaning is usually associated with clearing out the cobwebs and removing that layer of dust and grime you’ve been staring at all winter — for those utilizing a customer relationship management (CRM) system, it's also a prime time to clean up dirty data.

Dirty data is essentially information in a database that is inaccurate, incomplete, outdated or even duplicated. Let’s face it, throughout the year and through ebbs and flows of the business cycle, it’s easy for dirty data to make its way into a database, just like those cobwebs that suddenly appear seemingly out of nowhere.

So, what are a few of the scenarios that might indicate your data might have a few cobwebs?

• Legacy Data: Changes in business practices or customer contact information over time can lead to working with data that’s no longer valid or particularly useful. A few examples of this include outdated data (e.g. when contact information is no longer accurate) and incomplete data (e.g. when a customer profile is missing key information).
• Data Silos: This typically occurs when different departments within a company are all responsible for collecting customer data, but use different systems or tools to store and manage it. One team may be collecting a set of specific inputs, while another has a slightly different list of information to collect, and this can quickly lead to inconsistent and incomplete data.
• Human Error: We are all human, and those pesky human errors like typos in customer names, email addresses, or phone numbers, can create duplicates, incorrect contact details and ultimately lead to failed communications.
• Inconsistent Data Entry Standards: Lack of standardized formats for entering names, dates or addresses (e.g., "NY" versus "New York") can create duplicates or mismatches.

These scenarios are just a few reasons why data hygiene and data cleansing are so important — these processes ensure CRM systems are up-to-date, accurate and reliable. While data hygiene and data cleansing may sound similar, you can think of the former as a routine cleaning to regularly identify or correct data issues, whereas the latter falls under the hygiene umbrella but is truly a deep clean that fixes or removes dirty data. In parts two and three of our data spring cleaning series, we’ll unpack the impact of dirty data on direct marketing campaigns and provide a snapshot of the results of a spring data cleansing that allowed one of our clients to more reliably send personalized communications that leveraged customer data.

At Mythic, we specialize in utilizing customer relationship management systems to help brands build lasting, meaningful connections with their target audience. By combining data-driven insights with personalized engagement, we craft tailored marketing strategies that foster loyalty, drive retention and elevate customer lifetime value.

Ready to strengthen your customer relationships? Reach out to us at newbiz@mythic.us to get started.