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- Data that is out of date or no longer relevant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.
Also read: Top 5 Tools for a Quality Website Audit
What is Data Quality and Why is it Important?
Treating a data field before and after a quality audit. Source: WinPure
A data quality audit can help to identify and correct any issues with the data so that it is as accurate and reliable as possible. The audit process usually involves reviewing all of the data held by the organization, checking for errors and inconsistencies, and then taking steps to correct them.
Data quality audits can be time-consuming and costly, but the benefits can be significant. By improving the quality of your data, you can make better business decisions, improve your customer service, and reduce costs associated with bad data.
A data quality audit should be carried out regularly, especially if the data is to be used for decision-making purposes. It is essential to ensure that the data is reliable and accurate so that the wrong decisions are not made as a result of poor-quality information.
How to Perform a Data Quality Audit?
A classic mistake most businesses make is to hire expensive resources and invest in million-dollar technologies to improve their data infrastructure. Little do they know, a data quality audit doesn’t have to take months, or cost a fortune.
You can start your data quality analysis with data sets that affect business goals the most. Prioritization is key. Instead of attempting to fix everything all at once, start small. For example, start with your CRM, marketing, or sales data because those are critical to business success. As you work through these data sets, you will get a fair idea of the causes behind poor data. You might identify errors caused by manual data entry or system faults. Fixing the cause of poor data is the first step to ensuring quality data.
Once you’ve identified the data set you want to audit, here are three steps to follow.
Identifying Data Deficiencies
The first step in any successful audit is identifying potential deficiencies in your data. This means understanding the structure of your databases and noting any patterns that may indicate an issue with the accuracy or completeness of the data. For example, if you find that the same field has been incorrectly populated across multiple records or that certain records are missing vital information, this can indicate a problem with the accuracy or completeness of your data. It’s also important to note any discrepancies between different sources of data as this can suggest that some information is not being accurately captured by one source or another.
Also read: Top 10 Easy Data Analysis Methods and Techniques
Establishing Quality Metrics
Once you have identified areas where your data may be deficient, it’s time to establish metrics for determining how “high-quality” your data really is. These metrics should take into account both accuracy and completeness. Establishing these metrics will help you determine whether particular datasets meet your standards for quality and allow you to compare different sets of data against one another in order to make informed decisions about which dataset is most reliable. For example, if you’ve seen that your data formats lack consistency, such as names with a mix of uppercase and lowercase characters, you can then create standards as part of your quality metrics.
Testing Your Data Quality Standards
Finally, once you have established your quality metrics, testing them against actual datasets is time. This will help you determine whether the standards you have set are accurate representations of what constitutes “high-quality” data and allow you to adjust them if necessary. Testing will also help reveal any additional issues with your datasets that were not apparent during the initial assessment stage; this could include issues such as incorrect formats or outdated information that needs updating in order for the dataset to be considered “high-quality.”
Performing a comprehensive audit of your business’s data is essential for ensuring its accuracy and completeness as well as for making effective decisions based on that information. Knowing how to identify potential deficiencies in your datasets, establishing quality metrics for determining their “high-quality” status, and then testing those standards against actual datasets are all key steps in successfully performing a data quality audit.
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