Data cleansing

What is data cleansing?

Data cleansing is a data quality improvement process that involves removing duplicates, errors, and inconsistencies to make it more usable. Most datasets that businesses handle can come with missing values, incorrect formatting, typos, repeated entries, and other errors that reduce its accuracy and make it difficult for analysis. This is why data cleansing is one of the most important data processing requirements.

Data cleansing is also an important function in ETL (extraction transformation and loading process) and data transformation process, where it happens during the second step ‘transform’. After extracting and collecting data from multiple sources, you might expect the above inaccuracies and misalignment in the data, making it ready for refinement before being loaded into the destination.

While data cleaning and data cleansing are often used interchangeably, they both refer to the same data refinement process, and their goal is to have clean, standardized data suitable for analysis.

Why is data cleansing important?

Data cleaning is important for many reasons, from adhering to governance requirements to improving the quality of business decisions made.

Improve decision making: data cleaning removes any value that might skew your analytics outcomes, guaranteeing reliable insights to make impactful decisions. 

Reduce costs: dirty data is often expensive to handle and leads to both internal and external spending. If you don’t clean up the data long enough, it can get expensive to fix them, whereas, the dirty data could also lead to errors that could have cost implications. Regular data cleaning minimizes both of these expenses.

Get more value from marketing and other efforts: Marketing campaigns require accurate data to work with, else the effectiveness and ROI get impacted directly. You will require cleaned customer data if you are performing research and segmentation to create tailored campaigns. 

Improve customer satisfaction: By targeting the right customers, knowing their correct pain points, and fixing them can lead to improved customer satisfaction.

Security and governance: Companies with compliance requirements must maintain data in required formats to avoid penalties and other repercussions. So, data cleaning is a necessity that must be performed regularly.

Integral for machine learning and data science activities: Outliers and other missing values can often affect machine learning model performance and accuracy. That’s why data scientists must perform meticulous data cleaning to improve the ML model output.

Steps involved in data cleansing

Following are some steps involved in the data cleaning cycle. This process is a cycle, since it's more of a continuous process.

1. Data profiling is an overview process of data cleaning to understand data quality and cleaning issues with the datasets. It involves analyzing errors, inconsistencies, and other outliers using profiling tools like Excel, SQL, or other querying tools.

2. Addressing missing data requires removing entries with no values and replacing it using imputation, mean/median, or other default values.

3. Removing duplicates requires going through records, looking for unique identifiers and removing duplicates or merging them.

4. Standardizing data formats means removing formatting inconsistencies and adapting universal standards for everything, especially for phone number, address format, dates, etc.)

5. Error correction is the next important step in data cleansing, which is about correcting spelling mistakes, typos, and other conflicting errors. 

6. Outlier removal is also a part of data cleansing where you use visualization or statistical methods to remove outlier values that don’t fit the required context.

7. Maintain documentation to record every data cleansing process so anyone can keep track of the data processing steps it has gone through.

8. Continuous monitoring must be in place to ensure that the cleaned data is consistent and there aren’t any deviations from the corrected value.

Tools required for data cleansing

Following are some of the best data cleansing tools.

Basic data cleansing tools

If you have small datasets and need to do temporary data cleaning, basic data processing systems like Excel are sufficient.

• Microsoft Excel (suitable for manual cleansing, if you have small datasets.)

• OpenRefine (another basic tool with data cleansing and refinement functions).

Advanced data cleansing tools

These tools come with in-depth data cleaning features and are suitable for complex data sets. But, most of them are paid and require training to use.

• Talend data quality

• Alteryx

• Informatica data quality

• SAS data management

Cloud-based data cleansing tools

Some of the cloud based data cleaning tools that are cost-effective and easy to access tools that come with pay-as-you-use payment options are:

• Google DataPrep

• Snowflake (data cleaning is an add-on function).

• TIBCO clarity

Open-source data cleansing tools

These are free to use, or there might be a nominal cost. 

• OpenRefine

• IBM InfoSphere Datastage

• TIBCO

Data cleansing is a continuous process and needs a standardized procedure, tools, and awareness training in place. With your unique data cleansing approach, it’s much easier to handle this complicated task and never worry about inconsistent or error-filled insights.