Why You Need to Perform a Data Quality Audit
Imagine you’re the marketer of a B2B organization, and you need to run a campaign using the data of 2,000 customers. You prepared for the campaign months in advance, and just a week before execution, when you take a look at the data, you’re mortified to see almost 30% of the data have incomplete addresses and are messy, as in the image given below.
Your campaign has failed even before it could get started simply because you failed to perform a data quality audit. Before we talk about the audit process, it’s important to talk about data quality itself.
What is Data Quality and Why Does it Matter?
Data quality is essential for making sound business decisions. Poor-quality data can lead to inaccurate results, which can in turn impact both the bottom line and the company’s reputation. Luckily, there are many ways to prevent poor data quality and inaccuracies in databases, such as continuing maintenance of data, various data standard check-ups, and data matching software, such as WinPure, which is effective when performing data matching and cleansing activities.
Several factors can affect the quality of data. These include:
- Incorrect or incomplete data entry
- Errors in formulas or calculations
- Misinterpretation of data
- Data that is out of date or no longer relevant
- Duplicate data entries
- Human error
If left unchecked, these factors cause ‘dirty data,’ a problem that causes an average company to lose 12% of its revenue (Experian, 2017).
Moreover, problems like duplicate data can cause companies to face legal troubles with accidental violations of the sanctions list and GDPR. A customer can launch a complaint if they receive unsolicited emails (which happens when you have duplicate data), or you could even lose money on corrupted insights caused by duplicated content. Therefore, performing a data quality audit before initiating a project tied to a business goal is a critical step.
Originally published on The Tech Trend
