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Five Data Cleansing Mistakes You Need to Avoid

blog | 6 min read

Five Data Cleansing Mistakes You Need to Avoid

Five Data Cleansing Mistakes You Need to Avoid

Data is a hot topic at the moment, which is unsurprising considering the massive change in legislation we have had to adhere to, as well as all the fines and breaches that we have seen take place all within the past 12 months. 

For many businesses, data is what makes the world go around, but the handling of such has recently come under major scrutiny from regulators, heightening the importance for toeing the line.

When done correctly, data cleansing is a prime way of managing your company databases. Merging all your disparate records, eliminating any unwanted entries, and consolidating them into one single view will help your business remain compliant with regulations such as the GDPR, as well as be more efficient.

However, cleaning up your company’s database isn’t as simple as choosing any old software, as there are several data errors you should avoid to sustain business success. 

1. Buying Cheap Data

Buying leads is a common marketing strategy for many businesses, however, with so many third-party data companies selling data, it’s hard to know who is and is not legitimate. 

According to the DMA, you should avoid firms that offer you thousands of records for pennies, and instead, work with companies that will help you to select targeted prospects.

This makes sense as cheap data is often not of the highest quality, and can result in a very low conversion rate, which is ultimately a waste of time, money and resources for your business, not to mention you’re left over with a list of bad contacts you won’t get any use out of. 

In most cases, quality is better than quantity, which is especially the case when it comes to data. A bespoke, tailored list of potential customers who may be interested in your product offering will be a lot more valuable to you than a broad list of contact details, of which may not even be closely interested in what you are selling. 

2. Ignoring Suppression Files 

A suppression file identifies who in your database has died, moved away or is registered with marketing preference services, such as MPS, TPS, and CTPS

With almost 3,000 changes made to people’s personal information every day, it’s never been more important to run your database against a series of suppression files.  

This is integral to the data cleansing process, and without it, you run the risk of contacting people that shouldn’t be, including:

  • People who have passed away, which can result in upsetting bereaving family and friends.
  • People who have moved home – if they have not set up a mail redirect, will not receive any communication. As well as this, you will be sending communications to whoever has moved into the home, regardless of their interest in your product offering. 
  • People who have registered with marketing preference services, such as MPS, TPS, and CTPS. Contacting these people will open you up to hefty fines. 

Regardless of the size of your customer database, you should keep it up-to-date by actively looking for and removing suppression files. By doing this, you can save your business money, gain better visibility into who your active customers are, and protect your brand’s image from negative publicity.

3. Keeping Duplicates

Every day, your data is subject to change, with 37% of business data and 13% of consumer data decaying every single year. 

Duplicate data comes about easily; with various touchpoints making it easy to capture duplicate details. However, choosing to ignore duplicates only means your grasp and understanding of how your customers are is diminishing. 

Data analysis: Duplicate data poses what can be a significant threat to its integrity, resulting in inconsistencies and inaccuracies that will end up skewing how you perceive your data. 

Resources: Keeping unnecessary data has a big effect on your business resources, whether it’s increasing your data storage costs or causing you to spend more money on marketing communication platforms like MailChimp. 

This also impacts the database’s performance, with querying, indexing, and sorting taking much longer as your team has to spend time sifting through redundant data. 

Customer Reputation: With multiple records of the same customer, you open your business up to many reputation-damaging issues from misdeliveries to having the wrong point of contact at a business. 

Duplicate customer records will also cause a breach of accurate permission usage, right to be deleted, or subject access request (SAR), according to the Royal Mail Insight Report.

4. Ignoring outliers

Outliers are often one-offs in your database, and while it can be opportunistic to pursue them, many businesses see them as not worthwhile and subsequently ignore them.

No matter how out-of-the-ordinary an outlier might be, they help tell the full story. Choosing to disregard these will lead to inaccurate data representations and distorted results, causing biased analysis and misleading interpretations.

When examining data, outliers can provide valuable insights about your customers and business that may have been overlooked. From low customer satisfaction scores to untouched market trends, investigating them deeper will help take your business to the next level. 

5. Accepting a Fuzzy Match

Some organisations believe fuzzy matching is great for finding and matching similarities between data entries, however, it assumes that all identical matches are duplicates – which is not always the case. 

In a report published by Zoopla and the BBC, there are 2,431 UK roads named “High Street”, followed by “Station Road” (1,929), “Church Lane” (1,547) and “Church Street” (1,404). 

Most towns are so large that they have many districts containing these street names, therefore, it is wrong to make any assumption without rules-based matching using the district name, postcode, or similar. Generally, a fuzzy match looks good, however, it is making an assumption which can go very wrong. 

If your business conducts a comparison between data cleansing providers, you need to make sure it uses rules-based matching instead of fuzzy matching. At Hopewiser, we pride ourselves on only using rules-based matching to give you the most accurate possible match rate. Find out more about our data cleansing services today. 

We hope that you have found this blog helpful and that you now know what mistakes to avoid when it comes to cleaning your database. Data cleansing is vitally important to your business, and using a reputable provider can help you to get a better return from your data.


, updated 16th November 2023.