Modern businesses collect a lot of data. This is useful, because that data can tell you a lot about how your business is performing and what strategies might improve that performance.

But collecting a lot of data is no guarantee of data quality. And that’s where data cleansing comes in.

Data cleansing, or data cleaning, is the process of finding and removing problems in a database, including incorrect, corrupt, duplicated, incomplete, outdated or otherwise problematic data.

How do you cleanse data?

Traditionally, the process of cleaning data would look something like this:

  1. A data audit, to determine what data is present, how it’s stored, and what the relationship between elements might be.
  2. Developing the rules to apply during the cleansing process.
  3. The cleansing itself, a process where the rules are applied.
  4. The cleansed data then undergoes verification. If there’s any rectification required, this will be the point at which that takes place.
  5. Finally, a report gets created that explains the number and type of issues corrected.

Why does a business cleanse its data?

  • Stay in touch with your customers and prospects

Contact information is often one of the earliest victims of data degradation. Data, when collected, represents a snapshot of that contact’s information at that moment in time, and it changes all the time. Let’s take contact emails as an example. If you have a contact’s work email in your database, there’s around a 15% chance it becomes outdated each year.

  • Target customers effectively

For sales and marketing applications, clean data is essential—and it goes well beyond just staying in touch with your customers. Marketers need clean data to create customer personas and target effectively.

  • Enable other processes

Almost every possible business technology is underpinned by your organisational data. A CRM system with all the bells and whistles imaginable will not preserve customer relationships without high quality data. And if you’re implementing an AI solution? Well, clean, high-quality data part of the roadmap for that, too!

  • Clean data makes businesses perform better

Data driven businesses are 19 times more likely to stay profitable. If dirty data enters an analytics system, the analyses that emerge from that system are inaccurate.

Clean data supports profitable business.

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