Modern businesses collect a lot of data. In the internet era, every transaction, visit, delivery, page view, click, and share is registered as a new data point. This is extremely useful, because that information 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is the process of finding and removing problems in a database, including incorrect, corrupt, duplicated, incomplete, outdated or otherwise problematic data.
Why do businesses cleanse their data?
Businesses clean their data because clean, high-quality data boosts business performance, and poor data quality introduces risk.
Data cleansing preserves the quality of data held by an organisation. When it’s not regularly cleaned, data will degrade over time for a whole host of reasons. It might become outdated as circumstances change, or there might be duplications in unmatched data sources, data entry errors, or incomplete data.
All of these are problems that can affect the quality of the data, and data quality impacts outputs and analytics upon which business decisions are based. For example, an organisation that has an email address for every customer might decide to contact its customers via email — but it might make a different choice if it discovered that 36% of those emails were not actually attached to a real inbox.
How do you cleanse data?
Traditionally, the process of cleaning data would look something like this:
- A data audit, to determine what data is present, how it’s stored, and what the relationship between elements might be.
- Developing rules to apply during the cleansing process. These rules will need to be created while considering the purpose for the data, which can vary depending on organisational need.
- The cleansing itself, a process where the rules are applied. This will remove duplicate records, reduce misplaced capitals, find and reconcile dissimilar date formats, detect typos, and resolve other such errors of formatting, categorisation and uniformity.
- The cleansed data then undergoes verification, a process in which both computers and people double-check that the cleanse has worked as intended. If there’s any rectification required, this will be the point at which that takes place.
- Finally, a report gets created that explains the number and type of issues corrected. Ideally, this can be compared with the original data audit. Either way, this provides key stakeholders with a way to measure the progress of their data project.
Can you automate data cleansing?
Yes.
Once upon a time, our data specialists would have recommended batch cleansing your organisation’s data at least once every six months or so, depending on the data type and the use to which it’s put.
But times change, and so do data practices.
Sometimes, an organisation is looking for a one-off data cleanse in support of a specific project, and a batch data cleanse is what they need. But a lot of modern businesses actually seek a permanent solution to data quality, and the way to achieve that is via automated data cleansing.
An automated data cleansing solution is an alternative to traditional practices. Instead of manually examining and applying rules to an entire database, it’s a carefully-designed layer that works alongside the data base, all the time, and automates processes like deduplication, validation and verification. They never have to worry about how long it’s been since their last data cleanse.
By reducing the costly and inconvenient aspects of human labour associated with data cleansing, organisations can use one of these solutions to ensure their data is prepared for anything, at any time. It lifts their business performance and places them in a stronger position to adapt to today’s dynamic, changeable market conditions.
What are the benefits of data cleansing?
- Stay in touch with your customers and prospects
Contact information is often one of the earliest victims of data degradation. When you regularly cleanse your data, you maintain lines of communication. It also prevents trying to contact disconnected phone numbers, sending mail to wrong addresses, or emailing non-existent inboxes.
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. Clean, high-quality data permits the development of accurate customer profiles, and allows you to implement iterative review and refinement into marketing processes.
You can only target the right people with the right offer at the right moment if you know what all those elements are.
- It’s a key enabler of other data processes
Almost every possible business technology is underpinned by your organisational data. A new analytics system requires clean, high-quality data to produce reliable outputs. 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
We know that businesses that use data to support their business decisions perform better. They’re 19 times more likely to stay profitable, for example. But data quality is the hidden caveat in this statement. If you have poor quality, dirty data going in to an analytics system, the analyses that come out of that system are also low-quality and inaccurate.
Clean data supports profitable business.
