Some studies have shown that bad data could potentially cost companies as much as 10–25% of their revenue. Bad data costs companies around the world $3 trillion per year. You can’t really afford to ignore it - inconsistent data means problems for your business. This step-by-step guide will help you address some of the most common data consistency issues and design a data cleansing process that delivers results. In this article, we’ll outline the steps that you can take to ensure that your data is clean, free of inconsistencies, and ready for importing or sharing among your teams. The better option? Read these tips on how to keep your data consistent so that your HubSpot database is always in tip-top shape. But losing that data and the valuable insights it provides can be a huge loss. In fact, it’s so time-consuming that many businesses find it easier to purge inconsistent records and move on rather than fix issues manually. Unfortunately, trying to clean that data by hand takes much longer than you initially thought it would - often full days or even weeks. These reasons (and many others) are why so many companies turn to tools like Insycle to help them clean up their data and install improved data-quality policies. They can’t be avoided, particularly when you're relying on humans to enter the data by hand at some point in the process. Inconsistencies in data are a reality of data collection. You might find a third of the names aren’t properly capitalized, there are issues with addresses, some fields aren’t present, or there are styling issues throughout your dataset.
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