Data Cleansing: what is it and why is it important?

Data is an essential part of almost every key business decision. Effective marketing, analytics and customer engagement are only possible with data quality, 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 catching and eliminating data quality issues, 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, adhere to data governance, 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 inaccuracies can arise because of input errors, formatting errors, processing errors as well as day to day changes such as people who have moved homes, died, changed contact details and more.

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?

By following the recommended steps, you can ensure that your data is error-free, complete, and ready for use in analysis or decision-making. Here's a step-by-step guide to the data cleansing process:

1. Profiling data

Before you start cleaning, it’s essential to understand the state of your data. This step, called data profiling, involves analysing your dataset to identify its structure, patterns, inconsistencies, and potential issues.

Profiling helps you get an initial understanding of your data. This allows you to gauge its current state and pinpoint errors, such as missing values, duplicates, and outliers. Once this is understood, you can begin to prioritise the areas that need the most attention.

2. Removing unwanted data

Once the data has been profiled, focus on removing unwanted data. This step involves eliminating redundant or irrelevant information that doesn’t add value to your objectives. Common examples of unwanted data include:

• Duplicate entries should be removed to avoid skewing analysis
• Outdated records can be purged to ensure relevance
• Unnecessary fields or categories should be dropped to streamline the dataset

Removing unwanted information ensures that your dataset is lean, focused, and meaningful.

3. Correcting formatting errors

Consistent formatting is key for data reliability. Inconsistent or poorly standardised data not only hinders compatibility between systems but also creates challenges for analysis, reporting, and automation. Formatting errors are common and often stem from human error. In organisations with multiple departments, data silos frequently arise, leading to inconsistent formatting across datasets.

Some examples of common formatting issues include:

• Inconsistent date formats: Variations such as MM/DD/YYYY vs. DD-MM-YYYY can cause confusion in interpretation and analysis
• Inconsistent abbreviations: Differences like "Jan" vs. "January" can create difficulties in aggregation or filtering
• Improper capitalisation: Variations in names or text fields (e.g., "john doe" vs. "John Doe") can impact readability and search accuracy
• Misaligned numerical values: Variations such as "00123" vs. "123" can affect sorting and validation

Addressing these errors is critical for ensuring data is accurate, easy to work with, and dependable. Clean, standardised data saves time, improves operational efficiency, and supports better decision-making across the board.

4. Handling missing data

Data cleansing involves not only correcting the issues in the existing data, but also identifying and addressing any missing information. Missing values can significantly distort analysis if not addressed, leading to issues such as inaccuracies in sales trends, flawed customer insights, and errors in calculated averages or totals.

Solutions include data enrichment, where missing information is supplemented using external sources or third-party data. Another approach is imputation, which involves replacing gaps with averages, medians, or predicted values, or flagging incomplete records for review.

For critical fields, manual input or external data sources may be used to fill gaps. The best approach depends on the significance of the missing data, and its impact on your analysis.

5. Validating the data

Once errors have been corrected, the next step is to validate the data to ensure it aligns with your business rules and standards. Validation is essential to confirm that the dataset is not only clean but also accurate and reliable for its intended purpose.

Validation checks may include:

• Verifying numerical ranges: Ensuring values like sales figures or stock quantities fall within realistic limits
• Pattern matching: Checking that fields such as email addresses, phone numbers, or IDs follow standard formats
• Cross-field consistency: Ensuring related fields align logically, such as verifying that delivery dates don’t precede order dates
• Data type validation: Confirming fields are in the correct format, such as numbers, text, or dates, as required

By performing these checks, you safeguard the integrity of your data, reduce the risk of downstream errors, and build confidence in the analysis and decisions based on the dataset.

6. Enriching the data

Once the dataset is clean, consider enriching the data by adding more value. This can involve integrating data from additional sources to fill gaps or enhance context, such as appending demographic or geographic data to customer records. Enrichment not only improves the completeness of your data but also provides deeper insights for analysis.

7. Auditing and automating

Data cleansing isn’t a one-time process—it requires ongoing data quality management to ensure long-term accuracy and reliability. Regular audits and automation play a key role in maintaining high data standards and developing a culture that prioritises data quality.

Practical advice for auditing includes:

• Schedule periodic reviews: Regularly review datasets to uncover recurring issues, such as duplicates or outdated entries, and address them proactively
• Use data profiling tools: Leverage tools, such as Online, to analyse data patterns, monitor quality metrics, and detect inconsistencies or anomalies
• Investigate root causes: Focus on identifying and resolving the underlying causes of errors, such as flawed processes or lack of standardisation, to prevent them from recurring

Data quality automation can take care of repetitive tasks like deduplication, format standardisation, and real-time validation, saving time and cutting down on errors. It keeps data cleansing in check by flagging issues early and handling them before they turn into bigger problems, so you can focus on more important work. For example, Connect offers ongoing data cleansing with always-on technology.

8. Documenting the process

Finally, document the steps and decisions made during cleansing. This creates a reference for future efforts and ensures transparency in data management. Start by noting every step you took, from identifying errors to deciding on specific solutions. Be clear about the methods you used, like why you chose a particular imputation technique or how formatting was standardised. Include details about tools or scripts, and make sure to capture key decisions and their rationale.

Good documentation doesn’t just help you; it ensures others can follow the process too. Whether it’s for troubleshooting, future updates, or marketing campaigns, having a clear reference means less guesswork and more alignment.

Case Study: Improving Macmillan's Data Quality

Macmillan, one of the UK’s foremost cancer support charities, leveraged our data cleansing services to enhance the efficiency of their direct mail and campaign efforts. By removing outdated information and improving data quality, Macmillan could concentrate on their core mission — supporting those in need — while simultaneously increasing their fundraising potential.

Read the full Macmillan case study.

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.

Clean Data, Clear Decisions - Starting Improving Your Data

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. We have access to the UK’s largest customer database, with variables including homemover data and deceased suppression data in order to validate accurate and active customer information.

Explore our data cleansing solutions, or get in touch with our team to find out more.

Online - our leading data management platform

Manage your customer data online with an all-in-one solution, with the tools to optimise the accuracy, value and compliance of your data.

Online

Connect - the automated data cleansing solution

Experience seamless data cleansing with always-on technology. Connect maintains data accuracy on the go, helping you target the right customers.

Connect

Datawise - embedded CRM data cleansing tool

Cleanse your data directly in your CRM platform to ensure accuracy and completeness, removing the need to extract your customer data for cleansing.

Datawise