Data Cleansing Solutions
Improve the quality of your B2B or B2C Database with data cleansing services from Data8. Suppress and cleanse movers, deceased and goneaways.
Data naturally decays over time. Frequently cleaning and enriching your data will increase ROI, save you time and money, ensure compliance, inform better business decisions, and protect brand image.
Discover more about how Data8 can support you in achieving your data cleansing goals below.
Data Cleansing Services
PAF Addressing Services
Email and Phone Validation
Suppression & Mover Services
Preference Services
Deduplication & Merge Services
Appending & Enhancement
Happy Customers
Find out more about how we have improved the data quality and customer experience from the brands that have chosen Data8
Data Sources
At Data8, we pride ourselves on our data independence. Unlike many of our direct competitors we are not owned or influenced by a major data provider and as such we have access to the widest possible range of industry-leading data sources, including Mortascreen, TBR, NCOA, Royal Mail PAF, BT, the DMA, GAS and More…
Data Cleansing FAQs
What is data cleansing and why does my business need it?
Data cleansing is the process of identifying and correcting inaccurate, incomplete, or outdated records in your database. Over time, contact details change, customers move, and data decays, making regular data cleansing essential for maintaining accuracy, reducing wasted spend, and making better business decisions.
How often should I carry out database cleansing?
We recommend database cleansing at least once a year, though high-volume or fast-moving businesses may benefit from more frequent cleansing. Regular database cleansing ensures your records stay accurate, compliant, and actionable.
What are the signs that my data quality needs improving?
Common signs of poor data quality include high email bounce rates, undeliverable mail, duplicate records, failed communications, and inaccurate reporting. If your campaigns are underperforming or your team is working from inconsistent information, it’s time to review your data quality.
Can data cleansing be automated?
Yes, data cleansing can be fully automated using a range of secure and scalable integration methods. The three main automation options available are:
- Batch Cleanse API
- File-based exchanges
- Push-Pull integrations.
A Batch Cleanse API enables real-time or scheduled automated processing directly within your systems. File-based exchanges allow secure data transfers for bulk cleansing projects, ideal for periodic updates. Push-Pull integrations provide a flexible approach where data is automatically sent and retrieved between systems based on defined workflows.
When you choose Data8 for your data cleansing project, our technical team works closely with you to recommend the most suitable automated solution based on your organisation’s security protocols, technical capabilities, and business objectives.
How does data cleansing improve ROI and reduce costs?
By removing bad data, you reduce wasted spend on failed communications, improve targeting, and increase conversion rates. Clean data also means better decisions, fewer compliance risks, and more efficient workflows — all of which contribute to a stronger ROI.
What is involved in the data cleansing process?
The data cleansing process typically includes address validation, email and phone verification, duplicate removal, suppression screening, and deceased/goneaway identification. The goal is to ensure every record in your database is accurate, complete, and up to date.
How do I maintain data cleanliness between cleansing cycles?
Maintaining data cleanliness on an ongoing basis involves validating data at the point of capture, running regular audits, and using suppression services to flag changes. Building data cleanliness into your processes – rather than treating it as a one-off task, is the most effective long-term approach.
Data Cleaning vs. Data Cleansing
Data cleaning vs. data cleansing refers to two closely related processes in data management that improve data quality.
Data cleaning focuses on identifying and correcting errors within a dataset, such as removing duplicates, fixing typos, handling missing values, and correcting inconsistent formats.
Data cleansing, on the other hand, is a broader and more strategic process that includes data cleaning but also involves standardizing data, validating it against business rules, enriching it with external sources, and ensuring ongoing data quality management.
While the terms are often used interchangeably, data cleaning is typically tactical and project-based, whereas data cleansing supports long-term data governance and overall data accuracy across systems.