Dirty data, or data filled with errors, leads to poor marketing decisions. It's a pervasive problem that affects nearly every business. After all, data is the cornerstone of the modern information age we live in — even more so in the new marketing landscape — which relies heavily on the complex information that's more readily available today than at any point in the past.
The volume of customer data collected every day is immense, which is why it's so common for companies to have, or plan to have, a large marketing database to corral all its customer data.
In a perfect world, collected data is used seamlessly to provide marketers with the insight needed to guide campaigns, make decisions, and target audiences for promoting specific services and products. Unfortunately, dirty data costs businesses billions of dollars every year and has far-reaching consequences.
To remain competitive, prioritizing data quality is essential. Adtaxi wants to spread the word with all the essential details you need to know, from what dirty data is to how to clean data for optimal results.
The Lowdown on Dirty Data
The Data Warehousing Institute estimates that businesses in the United States lost $611 billion in staff overhead, printing, and postage due to low quality customer data. The real cost goes far beyond that, as poor data quality frustrates prospects and alienates loyal customers while eroding a company's credibility.
So, what is it? The simplest definition of dirty data is a database record that contains errors. Those errors could come from inaccuracies that existed from the start or from changes over time. As TDWI points out, roughly 2 percent of the information in customer records become obsolete within 30 days as customers go through life changes including moving, marriage, divorce, and death. Data entry mistakes and errors that occur when source systems change is also problematic.
Perils of Dirty Data
In truth, having great data is the foundation of any effective marketing campaign. It allows organizations to engage with their target audience and respond to shifts in dynamics quickly and effectively to get the most return on their investment. As mentioned, errors in the data making up your customer database can cost you. Some consequences of having bad data include:
● Wasted printing costs
● Inaccurate customer metrics
● Tracking errors
● Inaccurate marketing segmentation
● Misleading customer records
● Missed opportunities
● Decreased revenue
Characteristics of Clean Data
There's more to clean data than simply making sure it's accurate. To judge your data as clean, it should meet five criteria:
● It's valid: you can judge it as accurate or inaccurate
● It's accurate, up-to-date, and as current as possible
● It's complete: all the necessary fields contain all the necessary information
● There's no duplicate information and there are no (or minimal) errors
● All the data values are consistent — same time zone and same unit of measurement throughout the database.
How to Clean Your Data
Don't make the mistake of confusing data cleaning with data purging. You can have clean data without deleting old records, which can be useful for creating reactivation campaigns or generating scoring models. Instead, prioritize eliminating useless information with a multi-step cleansing process:
● Complete a data audit to identify discrepancies
● Set data cleaning workflow constraints as a team so the program knows what to look for and your team understands how to deal with anything that falls outside of those parameters
● Execute the data cleaning workflow
● Review the data to make sure it's correct and to manually correct anything as needed
To combat the changes that naturally occur, cleaning data should be an ongoing process for businesses and marketers alike. Collecting clean data from the start is a way to control data quality and reduce heavy data cleaning workflow.