Data hygiene is integral to data management, and ensuring data quality is key to any successful data-driven business. A data set can be considered bad if it contains duplicate records, missing or incorrect data points, or unstructured and inaccurate data. Even more challenging, it takes almost no time for information to become outdated. For example, when a member relocates, creates a new email address, or changes their phone number, your data instantly becomes outdated.
To begin evaluating the status of your data, perform an audit or data analysis. Data auditing involves running validation tests to compare data against known values, in addition to looking for anomalies and inconsistencies in the data set. You can also check records for completeness, integrity, and accuracy by examining fields that should contain certain data types, such as numbers or text strings. The first step in this process is to identify errors to resolve. Once you know what needs to be corrected, you can take corrective action manually or using automated tools. Learn more about tackling common data hygiene challenges.
Record and database size largely determines the most effective and realistic data-cleaning method. Most of the time, manual cleaning is not the ideal method for cleaning up your data. Manual cleaning may be attainable if your record size is less than 5,000 records, though it will demand a significant investment of time and preserves the potential for human error. Automated hygiene tools are becoming the way of the future and the best approach to ensure your organization operates more effectively and efficiently. Data hygiene may seem tedious and painful; however, dedicating resources to data cleaning saves time and money in the long run by ensuring accuracy and organizational success. Maintaining accurate data records will help you make better decisions, improve customer relationships, and increase overall business performance.
Still not convinced data cleaning is worth the time and investment? Uncover how much dirty data is costing your organization with the Dirty Data Cost Calculator.
After your first cleaning, the data will only get easier to maintain. You should experience significantly fewer bounce backs and return-to-sender mailings, allowing your focus to shift toward effectively reaching your organizational goals. It is still recommended your data undergo a thorough clean once or twice a year, but it will get much easier to identify issues after you perform your first clean.
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Bumblebee was built by a team of data enthusiasts, and Transformers fans, who knew there was a better way to help organizations have fast, easy, and affordable access to clean contact data. Our mission is to remove the burden of manual data cleanup so that organizations can focus on reaching their goals.