Data cleansing: What is it and why does it matter?

Data cleansing is the process of fixing or removing improperly formatted, inaccurate, incomplete, irrelevant, or duplicate data. The main goal of data cleansing is to ensure data is high-quality, reliable, compliant, and ready for data analysis. 

Let’s explore how data cleansing fits into the data lifecycle, the methods involved, and how you can create a data cleansing strategy for your own business operations. 

Data cleansing: What it is and what it isn’t 

Data cleansing is regularly used interchangeably with other terms like data cleaning and data transformation, and while all of these terms fall under the data science umbrella, they do have some distinct differences.

Data cleansing vs data cleaning

Data cleaning also known as ‘data scrubbing,’ is specifically focused on fixing errors in a dataset, such as removing duplicates and dealing with missing values.

By contrast, data cleansing is more broad in scope. It also addresses errors, but it takes things further by standardizing data and ensuring all of the information is complete and aligned with a business’s goals.

Data cleansing vs data enrichment

Data enrichment is the process of enhancing data to make it more helpful for an organization’s goals. For example, adding context to an email list to support sales reps.

Data cleansing, on the other hand, is focused on preparing and standardizing data. It’s a precursor in the process of data quality assurance — you can’t begin to enrich data until you’ve ensured all of that data is clean and reliable. 

Data cleansing vs data transformation

Data transformation is the process of changing a data’s format and structure to align it with an organization’s predefined criteria. It typically doesn’t deal with duplicates, errors, or mistakes.

In short, data cleansing fixes mistakes and ensures data is useful, while data transformation converts that cleansed data into a business-ready format. 

Data cleansing vs data profiling

Data profiling is a method for analyzing data in bulk to discover anomalies, errors, and missing information. In essence, it quickly reveals all of the problems with data so they can be rectified. 

Data cleansing comes after this step. In essence, it fixes all of the problems identified by data profiling. Both stages are essential for data quality assurance. 

Why is data cleansing important? 

Data is everywhere these days. CRM solutions, point of sale (POS) systems, the Internet of Things (IoT), social media, and smart sensors all produce a steady stream of information that companies must leverage to keep a competitive edge. 

Part of the challenge is accessing data and unifying it to create a single source of truth. Tellingly, a staggering 40% of business-critical data is trapped in data silos, according to a 2022 study by Experian. But aggregating this data is only half the battle. 

Collating unstructured and structured information from multiple data sources often results in unstandardized, messy data that is difficult to analyze and interpret. This makes it hard for companies to use the information they possess to drive improved decision-making. 

Bad data costs companies an estimated 3.1 trillion per year. Without reliable, consistent data at their disposal, organizations miss key insights that can help them remain agile and stay competitive. Data cleansing is a way to turn chaos into data confidence. It ensures that all data is consistent, fully complete, and aligned with the business’s needs.

In doing so, it allows organizations to derive actionable insights from the data they own, helping to improve profitability and create better customer relationships. It also supports data governance and compliance by ensuring sensitive data and personal information is categorized and accessible. 

How to cleanse your data

Cleaning dirty data requires a systematic approach. You’ll need to start by analyzing your data on a broad scale to find out where the problems lie. After you’ve found where the problem(s) lie, you can start to resolve these issues. Here’s how you can do this step-by-step. 

Step 1: Create profiles for your data

First, you need to understand the data you’re working with. This will help you make informed decisions about what’s essential, what can be removed, and where key insights will lie. 

Begin by analyzing and examining your dataset. Try to identify the likely problem areas, considering structure, content, and the relationship between data points. Keep your organization’s goals in mind, as this context will inform your analysis. 

Step 2: Remove data duplicates and irrelevant data

Before you tackle formatting and structure, start by removing any unneeded items from your dataset.

This could be duplicate and redundant data, which is especially common when you’re scraping and aggregating data from multiple sources such as POS, CRM, and ERP systems, as these solutions tend to create repeat entries due to variations in details. 

For instance, ‘John Smith’ vs ‘J. Smith’ would result in two observations when only one is needed. 

Irrelevant data refers to data that isn’t useful for your intended goal. For instance, if you’re exploring sales trends, a column with customer’s middle names is unlikely to be relevant and can be removed. 

Deleting duplicate and irrelevant data will help you avoid distractions, making it easier to find where the value lies in your dataset. 

Step 3: Tackle simple errors

Next, tackle basic data errors. This includes poor capitalization, typographical errors, and syntax issues, and can occur often for manual data entry sets. These problems might be easy for us to spot, but databases won’t know that. 

For example, a simple typo in like ‘Califronia’ in a data set should be ‘California,’ which creates two entries for that specific data point, where only one is needed. 

Once you identify common misspellings and capitalization errors, these can easily be rectified with a find-and-replace function on your chosen solution. Making these simple fixes will keep your data consistent, which is especially important for compliance and if the information is relevant to customers. 

Step 4: Standardize formats

One of the most common (and often difficult to avoid) data problems is inconsistent formatting of numbers and names. This is especially common with currencies, phone numbers, and dates. For instance, consider these five date variables:

• 17/02/2025
• 02/17/2025
• February 17th 2025
• 17th February 2025
• 17 Feb 2025

Each of these dates means exactly the same thing, but they’ll appear as distinct values in your dataset, so they should be sorted and merged. Decide upon universal formats and apply them across the board for consistency. 

A good example of this is date formats used in different countries. For example, America, Australia, and South Korea all used different date formats — which is why its important to create standardized formats.

• Country: USA
  • Date format: MM/DD/YYYY
• Country: Australia
  • Date format: DD/MM/YYYY
• Country: South Korea
  • Date format: YYYY-MM-DD

Step 5: Evaluate anomalies

Outliers can skew your data and lead to inaccurate predictions. For instance, an unusually high customer order quantity could give you a false impression of business performance.

To start, collate all of the data points that fall outside of an expected range. Using visualization tools like box plots or scatter plots can make it easier to visually spot anomalies. You can then decide to remove or keep them. 

Remember that outliers aren’t always mistakes. It’s essential to validate each anomaly and check whether it’s truly an error. Some observations may seem extreme, but if it turns out the data is factual, it could provide new opportunities for analysis. 

Step 6: Take care of missing data

Missing data can introduce a wealth of problems. If you don’t have the complete picture when you start data analytics, you risk making false predictions and missing out on critical insights. If you identify missing data, there are a few options to consider: 

• Replace the data with the most common value in the dataset
• For numerical values, calculate the median or mean of all other observations to arrive at an average
• Delete or remove the observation with the missing data

The real solution to missing data is tackling it at the source and implementing policies to ensure all of your manual entry observations are comprehensive. 

Step 7: Validate your data

Finally, validate that all of the previous steps are complete and in line with your requirements. Double-check that:

• There are no duplicate records or irrelevant data
• There are no remaining simple spelling or grammar errors
• Formats are standardized in line with your business requirements
• Data is structured correctly
• Anomalies are either acknowledged or removed
• Missing data is handled
• Data makes sense in the context of your business goals

Once this is complete, you should have a cleansed, validated data set that will put your business in the best position for analysis and reporting. 

Data quality benchmarks to look out for

There are six key benchmarks that make for high-quality, reliable data. 

1. Validity: Does the data align with business rules and allowable parameters? 
2. Accuracy: Is every single detail of the data correct? Are the data points correct?
3. Completeness: Are there any missing values within the data? 
4. Uniformity: Is the data consistently formatted? 
5. Relevance: Does the data support the goals of our analysis? 
6. Consistency: Are there any contradictions or conflicts between data sets? 

Apply these metrics as a final check when you’re validating your observations. 

How data cleansing tools can help 

Manually cleansing data is extremely challenging, and there are many tools that can help. These solutions can support everything from

• Data management software: This can help you unify, cleanse, and standardize your data fast. Some data management software will have advanced functions that allow you to profile data, remove duplicate values, identify anomalies, and fix formatting issues using artificial intelligence (AI) models. 
• ETL (Extract Transform, Load) Tools: ETL tools extract data from various sources and transform it into a standardized format. They then load it into a data warehouse to prepare the information for further cleansing. 
• Data profiling tools: A data profile tool analyzes the quality of your data, identifies duplicate records, and finds inconsistencies. You can then use this information to make the cleansing process more efficient. 
• Open-source tools: These solutions are free and customizable, meaning you can set them up for your specific data cleansing requirements. That said, they require a lot more technical expertise to operate.  

Open-source tools are a great way forward for small businesses, but if you want something comprehensive to help you save time managing large datasets, an all-in-one data management solution is the way to go. 

Data cleansing hurdles and how to overcome them 

Accurate data cleansing can be a challenging process. It’s time-consuming, prone to errors, and hard to scale when done manually, making it hard for organizations to keep pace.

To help, we’ve put together three common challenges along with some best practices for data cleansing. 

1. Time and resource constraints

The data cleansing process is resource-intensive when you go it alone, especially if you’re managing large, diverse data sets. 

The best course of action is to automate repetitive cleaning tasks like data duplication and standardization with a data lifecycle management solution. 

This will let you spend more time using data to support business intelligence. Another benefit here is that a cloud data lifecycle management (DLM) solution will scale alongside your needs. 

2. Difficulty identifying root causes

Data quality issues can come from anywhere, and it’s often difficult to pinpoint why an error is occurring.

Implement a data auditing process to address this challenge. Look for patterns and anomalies that crop up repeatedly, and make this the basis of your investigation. Monitoring tools can also flag errors in real-time, which makes it easier to tackle problems proactively. 

If you can’t identify the source of a problem, it’s best to start at the beginning. If multiple teams and departments are manually inputting data, it’s worth setting up policies and a helpful knowledge center to ensure everyone is entering information in a standardized way. 

3. Maintaining data integrity

Data integrity is a tricky topic within data cleansing. On the one hand, you’re cleaning data to improve its quality. On the other, if you make a wrong decision, you risk ruining the integrity of that dataset entirely. 

It’s a good idea to create data backups before you make any significant changes so you can restore the data to its original form should something go wrong. You should also establish clear data validation rules during the initial profiling stage to reduce the risk that decisions to remove data will be based on subjective opinion.

Summing up

Data cleansing is a key step in the data lifecycle management. It ensures your information is reliable, consistent, and ready to be mined for insights. 

The key takeaway: is to take a systematic approach to cleansing. Start with the broad strokes and identify your scope. Then, work through each of the common issues one by one. Along the way, try to keep decisions quantifiable rather than relying on opinions. This will keep your data integrity in check. 

How RecordPoint can help 

With RecordPoint, you can unify, clean, transform, and leverage all of your business data wherever it resides. Our platform will collate your structured and unstructured data under one roof, and you won’t need to move a thing. 

Once you’ve aggregated your siloed data, our machine learning (ML) model will help you clean, classify, and standardize your records for reliability. Our solution will notify you of common errors, inconsistencies, and anomalies and provide actionable steps to rectify the issue. And, as our ML model learns with time, it’ll continually get better at understanding your business needs. 

RecordPoint will help you stay compliant, control your information, and derive insights from the high-quality data you possess. Book a demo today to learn more.