Data is an essential part of almost every key business decision. Effective marketing, analytics and customer engagement are only possible with quality data, making it essential to keep your data clean and up to date with an effective data management strategy.
Essentially, poor quality data almost certainly means bad results, which is where data cleansing can help.
Data cleansing is a data management process that helps ensure your dataset contains accurate and compliant information. Without clean data, businesses and organisations are unable to make informed decisions based on reliable data, and are at risk of facing GDPR fines.
For instance, data cleansing helps keep vital customer data up to date, such as if a customer has changed address, phone number, or other contact details.
Additionally, data cleansing ensures that business datasets remain GDPR-compliant. This is essential for any business that acquires and holds customer data, as non-GDPR-compliant organisations run the risk of legal intervention and brand damage, as well as substantial fines.
Besides keeping your data accurate and legally compliant, there are many benefits to data cleansing. Not only does clean data ensure that you are targeting active customers with relevant communications, but clean data can also help businesses save costs, run more efficiently, and avoid brand damage.
What is data cleansing?
Data cleansing is the process of reviewing and removing inaccurate, incomplete, or irrelevant data from your dataset. Data can become inaccurate in several ways. Over time, businesses acquire large amounts of customer and prospect data, and no matter how good the data capture and management systems are, there will be errors, duplication, or incomplete information in the dataset.
Data cleansing allows businesses and organisations to keep datasets clean and accurate by ensuring it only contains information that is meaningful, accurate and complete. This data can include customer names, phone numbers, email addresses and physical addresses, and can also include more specific customer details such as buying habits.
If data is incorrect, this can result in unreliable outcomes that can consequently cost businesses money and cause brand damage. For instance, if you were to plan a direct mail campaign, then you’d need to make sure that your customer address information was correct in order to ensure the most reliable results.
Why is data cleansing important?
As data becomes more and more central to the ways businesses operate, data cleansing plays a more important role than ever in ensuring that data is clean and accurate.
Businesses and organisations rely heavily on the quality of the data they collect and hold, particularly in the current digital age of marketing where businesses primarily engage with customers using digital communications.
Additionally, customer data must remain GDPR-compliant after the introduction of new legislation in 2018. This introduced strict new data protection laws relating to business data collection and retention processes.
There are many benefits to data cleansing that make it such an important business process. These benefits include the following.
1. Save costs
Data cleansing helps save costs that arise as a result of errors. It costs money to hold data, and if portions of that data are incorrect or irrelevant, this wastes money and budget that could be spent on retaining better quality data. It also costs to pay staff who are responsible for processing and troubleshooting data errors and inconsistencies, all of which can be avoided with a clean dataset.
2. Use your data for multichannel purposes
Data can be reused for a variety of marketing purposes, from email marketing, direct mail, customer engagement strategies and more. With a complete dataset of phone numbers, email addresses, physical addresses and additional variables, your marketing efforts can be easily widespread across different marketing channels. This makes data cleansing a useful tool for improving customer engagement.
3. Remain GDPR-compliant
As of 2018, GDPR laws have affected the way that businesses acquire and handle customer data. Data cleansing helps businesses stay compliant with the law, which not only helps prevent the possibility of lengthy and costly legal battles, but also helps protect against potential brand damage.
4. Identify gone away and deceased individuals
One of the most common mistakes businesses make when using data is to assume that every contact is active, and that individual details are up to date. Data cleansing helps businesses identify individuals who have either moved home, or individuals who have passed away. This is identified through goneaway suppression and deceased suppression services.
It is important for businesses to stay respectful of such life events. For example, sending communications to deceased contacts can be highly distressing for family and friends of the individual.
5. Make quicker business decisions
A major benefit of data cleansing is that clean data can help support better decision-making processes. Having a clean dataset gives a more accurate overview of customer information and analytics, which allows businesses to make decisions more confidently and strategically.
6. Improve work productivity
Data cleansing can help improve team productivity by keeping the datasets they work with clean and accurate. This avoids the need for workers to sift through large and potentially irrelevant datasets by ensuring it only contains high-quality information.
For larger datasets, teams may not have the appropriate resources or time to manually review customer information, making it beneficial to maintain a clean dataset from the outset.
7. Improve customer acquisition and retention
Simply put, customers can only be acquired successfully if the data you hold on them is correct. Clean prospect data ensures that you are reaching out to valid contacts and ensures that you are communicating with those customers appropriately. A consistent and clean approach to data will also help speed up the onboarding process and give the customer a better experience.
How does data cleansing work?
There is no one set way to clean data, and the data cleansing process can be different depending on the kind of dataset.
For most customer datasets, the data cleansing process involves a full review of the dataset, followed by detailed techniques to remove incorrect or duplicate data. Data cleansing also involves resolving structural errors, whereby there are inconsistencies in the formatting of data.
The data cleansing process can be quite complicated when there are many data variables involved, and many types of irrelevant data. That’s why our data management experts help businesses keep their data clean with our market-leading REaD Cleanse service.
1. Removing unwanted data
Removing irrelevant data is the most important step in the data cleansing process. This includes removing errors, unwanted and duplicate data.
It is very common for duplicate data to occur in datasets, often as a result of many datasets being combined across different platforms or departments. Not only does removing duplicate data help save businesses costs, but it can also prevent customers from receiving the same communications twice.
Irrelevant data can present itself in any kind of dataset. Data can become irrelevant if it is outdated, or contains errors such as incorrect addresses, names or numbers. Our goneaway suppression services validate data across multiple sources to keep contact addresses up to date. Data can also be irrelevant if it does not suit the needs of the business or campaigns.
For instance, if customer data exists for a demographic that does not apply to your business, consider having this removed to maintain more targeted communications.
2. Correcting formatting errors
Data errors do not occur only as a result of incorrect information, they also occur as a result of incorrect formatting. The naming conventions used in your dataset must be consistent for the information to be useful. Naming conventions can sometimes differ across platforms, causing inconsistencies when datasets are consolidated.
For instance, you may find that there are discrepancies between terms such as ‘surname’ and ‘last name’, or ‘N/A’ and ‘not applicable’. Inconsistent data formatting can mean that this data is not categorised properly.
3. Validating data
Data errors can be difficult to spot when cleansing your own data. For instance, it is hard to know whether customer contact information is still correct, or if an individual is still active.
Data validation often requires the support of professional data cleansing services to accurately validate information. Data professionals, such as REaD Group, have access to comprehensive databases in order to cross-check data and handle missing data. This includes:
- The Bereavement Register – a reliable, up-to-date record of confirmed deceased individuals.
- Gone Away Suppression File – the most accurate database for identifying people who have relocated.
How clean is your data?
Clean data is essential to business success. It is important to examine your data and evaluate the overall quality. If you suspect that performance is not reaching its potential because of low-quality data, here are some quick ways to check whether your data requires a clean-up.
Quality data check
- Is your data accurate? Check whether your data reflects the values that they should.
- Is your data complete? Check whether your data contains any missing values.
- Is your data consistent? Check whether your data has any inconsistent categories or naming conventions.
- Is your data formatted correctly? Check whether your data contains any spelling errors and uses the same units of measure throughout.
Start cleansing your data
At REaD Group, we are experts in data cleansing. We take a tailored approach to your data in order to cleanse it in a way that suits your business goals and ensures your data is high quality.