How clean is your data? Is low-quality data throwing off your analysis and impacting your decision-making? 

Data cleansing is all about fixing the data in a dataset that might be inaccurate, incomplete, a duplicate of other data, or data that is erroneous in some way. Data quality issues may also include:

• Missing data that can skew your analytics
• Inconsistencies in how data is formatted, like date formats, like using both DD/MM/YYYY and MM/DD/YYYY
• Spelling errors or grammatical errors that can impact the meaning of your data entries
• The inclusion of outlier data or other anomalies that distort your analyses or point to potential errors

Sometimes also called data cleaning or data scrubbing, data cleansing is all about uncovering these sorts of errors and taking the necessary steps to address them, either by changing the data, updating it in some way, or removing the incorrect data altogether. 

The whole purpose of data cleansing is to improve the quality of your data—and the decisions you make based on this data. It’s about making your data more useful and informative so you can have access to more accurate and consistent information.

Why Does Data Cleansing Matter?

Ensuring you’re working with high-quality data is essential—especially in the age of AI, machine learning, and predictive analytics.  High-quality data empowers your team to trust the insights garnered from these tools, all so you can decrease costs and increase revenue growth, while research shows that low-quality data costs organizations $12.9 million every year.

Data cleansing helps prevent poor data quality, which can lead you astray and even cause you to make poorly-informed strategies or the wrong decisions about the future of your company.  By gaining a deeper understanding of your organization’s data quality, you can pinpoint errors and determine whether or not your data fits the purpose you intend for it to have.

Other significant reasons data cleansing is important include:

• Ensuring operational efficiency by reducing errors and making your processes more reliable.
• Increasing customer satisfaction by providing more personalized interactions and elevating the customer experience leading to upsells cross-sells and revenue growth.
• Adhering to regulatory compliance for data protection regulations including GDPR, GCPA, and HIPAA.

Data cleansing can also lower unnecessary costs of managing and using low-quality data, like marketing to the wrong audiences or sending promotional materials to the wrong addresses. It limits wasted resources to make your operations more cost-effective. It also promotes better data integration because data is standardized across your entire organization. Data is compatible across various solutions and datasets.

Additionally, data cleaning limits inaccurate data that can create biases or imbalances in your analyses. It can even help prevent the need for discounts and reduce call center volume, saving your organization time and money.

What Qualifies as High-Quality Data?

How can you be sure that your data is high quality? Before focusing on data cleansing, it’s helpful to know what qualifies as “high-quality data.”  Data quality comes down to facts that include:

• Accuracy:  The data in your datasets and systems are a true representation of the entities and/or events they stand for, and the sources for this data are reliable.
• Consistency: Your data is the same from each system and data set to the next, without conflicting information or discrepancies for the same data values in different systems or data sets. 
• Validity: Your data exists within your predefined rules; it’s properly structured within data sets and systems, and each data point has the values it is supposed to have. 
• Completeness: The data includes all the values and kinds of data it should have—including metadata.
• Timeliness: Your data is up-to-date and available for your use, review, and analysis when you need it.
• Uniqueness: Your data isn’t duplicated within individual data sets.

Maintaining data quality and integrity isn’t just about evaluating data based on these characteristics; it takes intentional efforts across your entire organization. This means taking steps to ensure that company-wide, everyone is invested in protecting the quality of your data, with strategies like:

• Setting up well-defined data governance policies
• Making smart use of data quality alerting tools to address any anomalies in real-time
• Performing data quality audits
• Educating your team on best practices to ensure data integrity

Along with these steps, one of the most important things you can do is to cleanse data to protect high data quality standards over time. Data cleansing is essential to keep data accurate, complete, and consistent so that you can reduce costs and increase revenue.

Best Practices for Data Cleansing in 2025

How do you go about data cleansing? You can go through a methodical process to clear your data and ensure it’s of the highest possible quality. This process includes the following steps: 

1. Profiling and assessing your data: Reviewing your data to spot inconsistencies, missing information, and other outliers, looking for the areas that need attention.
2. Standardizing your data: Putting all of your data into the same format to eliminate disparities and make it easier to integrate your data into a single source This includes dates, units of measurement, and other formatting inconsistencies. Even rules of capitalization or customer information like shortening a person’s name from Jonathan to John can impact standardization.
3. Deduplicating your data: Removing duplicate data that may skew your data analysis. This ensures that every data set and each customer profile accurately and wholly reflects each entity.
4. Validating your data: Checking to see that all of your data meets your standards and rules. 
5. Enriching your data: Fleshing out your data with any additional sources you may have—filling missing gaps and extending datasets to pave the way for more in-depth analysis. Examples may include key demographic information that can improve your marketing efforts and make them more specific and personalized.
6. Transforming your data: Converting data into the formats and structures that are ready for analytics. This may include data aggregation and restructuring so it’s more usable and more practical for analysis.

Especially if you deal with large volumes of data, this may be a fairly intensive process. Here are a few thoughts on how to make the data-cleansing process as effective as possible:

Use data cleansing software to automate the process of cleansing your data: This software can help detect errors and anomalies and correct these issues so you can manage extensive amounts of data. Automated data cleaning prevents wasted resources on manual customer data correction and remediation. Accurate customer data (like you get with harpin AI) optimizes workflows and reduces overall costs.

Using data cleansing software like harpin AI also frees up your team and your resources for strategic work when you automate your customer data cleaning processes, allowing your team to focus on strategic initiatives that drive growth. And as a result? You can deliver personalized interactions with accurate customer data, fostering stronger customer loyalty and repeat business.

Data quality management solutions can clean and monitor your data quality so you can maintain high-quality data: Make the most of AI-based solutions that automatically identify data patterns and anomalies, even merging duplicate records.

Implement cross-system data integration to prevent information silos: Siloed data across multiple systems can severely impact customer experience and business operations. These data integration solutions ensure your systems can communicate and share data effectively to maintain a complete customer profile.

Establish continuous data-cleaning processes: Data cleansing is not a one-time effort but requires ongoing maintenance. Regular monitoring helps maintain data integrity as new information enters the system. Tools like harpin AI provide continuous surveillance and immediate alerting for data quality issues. You can maintain continuous data cleaning by employing strategies like:

• Implementing real-time monitoring tools that can detect and flag data anomalies as they occur
• Using automated alerts to identify potential issues like duplicate records or data inconsistencies immediately
• Enabling quick response to data quality issues through real-time notification systems

You can also:

• Use unified customer identifiers across all systems to enable seamless data integration
• Implement regular synchronization between systems to maintain data consistency and prevent fragmented customer information.
• Consider implementing a master data management system to serve as a single source of truth.

Other considerations for efficient data cleansing in 2025?

• Data cleansing is a great time to eliminate unnecessary data. If you’re hanging onto data that won’t enhance your analyses or support your business goals, this data might be irrelevant—and it could be cluttering things up. Consider cutting any data that won’t serve your present or future business goals. 
• Before you even begin to clean up your data, make sure you have a full understanding of what your objective is—so you can find the anomalies that don’t align with these objectives. 
• Make sure you document data cleaning by writing down your plan and goals for the processes, outlining your data quality standards and rules, and documenting the entire cleaning process. 
• Data cleansing is also a great time to ensure your data is updated. Check to make sure you have reliable data backups so you can restore your operations if you experience data loss, theft, or corruption.

What’s Next? Focus on Unifying entity Data with harpin AI

harpin AI is designed to make the process of unifying datasets and ensuring data quality simple and effective—by automating it all. Too many organizations rely on consumer identification data to deliver a premium, personalized product or experience to their customers. But sometimes, this data becomes disorganized and fragmented across CDPs, CRMs, warehouses, and other data systems. 

Inaccurate, invalid, and disconnected consumer data causes all kinds of issues and frustrations that can bog down call centers, hinder the customer experience, increase compliance risks, and leave ROI on the table. harpin AI helps prevent low-quality datasets and the issues it creates. 

harpin AI cleans your entity data, providing cohesive, accurate, and compliant data and creating entity profiles that deliver a clear, holistic understanding of your customers. Instead of quick fixes, harpin AI addresses the root cause of your data issues.

Discover where your organization stands when it comes to data quality. harpin AI offers a complimentary Data Quality Assessment to help you understand the current state of your customer data quality. To learn more, book a demo today.ata solution can do for you. 100% of our harpin AI partners see results within 3 weeks. Want to see firsthand how we’ll take your guest data to new heights? Book a demo today!