Best Data Cleaning Methods for Easy Data Analysis

Best Data Cleaning Methods for Easy Data Analysis

Making any decision is easy, but making it right requires accurate data. A strategist dives into insights and then, discover a strategy or solutions that is going to realistically work. Because of this accuracy, global businesses and organizations prefer hygienic data for reaching out to any final decision.

This is why the demand for clean data is steadily rising. This process is a part of data preparation, whose market value is likely to reach out to $13.51 billion in the upcoming 6 years while registering its CAGR of 18.74%. 

Data Cleansing: an Introduction

Also known as data hygiene, data cleaning or cleansing refers to an effective strategy of filtering erroneous data and fix them. Though it sounds easy, but its process is indeed technical, which involves some effective tools to increase the credibility of findings. It helps in building confidence of making right and viable decisions. 

This article will spotlight how you can try out some best practices to clean your datasets so that viable decisions can be drawn. 

Outstanding Data Cleansing Practices & Methods

Out of multiple practices, there are some proven methods of data cleansing that are worldwide accepted. Let’s catch them here. 

a. Remove Duplicate Records

Duplicate records can distort analysis results. Identify and eliminate duplicates using algorithms or software tools to maintain data integrity. Duplicate entries can mislead results of analysis, which can lead to blunders in a business. It’s necessary to eliminate dupes to make failsafe decisions. Now comes the biggest challenge, which is how to find them. So, here is the answer.

If you are versed with SQL and Python, finding duplicate records won’t be challenging. You can use SQL queries while selecting cells or columns that may have dupes. Use GROUP BY and HAVING clauses. And if you use Excel, conditional formatting with the “remove duplicates” function can help you to achieve the same result. 

Besides, advanced cleansing solutions include tools like Apache Spark can also assist in it. They have algorithms working in the background to detect and fix duplicity error in your database. There is a bonus tip in this regard. You out to validate data entries and continuously refine your database so that flawless data can lead to excellent results. 

b. Handle Missing Values

Your analysis can never lead to effective strategies unless its base is an enriched or complete database. Many methods are pervasive to handle this condition, such as deletion, and predictive modelling. Certainly, data scientists can enrich their database using various functions and techniques.

And those who are Excel entry experts, using functions like IF, ISBLANK, or IFERROR to replace or flag missing values can be helpful. Moreover, you can easily select and manage blank cells by using its ‘Go To Special’ feature. Additionally, you need to strictly follow data validation rules to avoid missing entries. These data enrichment tips can help in handling missing records in small databases. 

For larger ones, you should try advanced Excel features like Power Query. It can immediately address your missing details problem. For eliminating its flawed outcome, regular audits can help. You should update your databases to make fresh and valid decisions through analysis. 

c. Standardize Data Formats

Standardization refers to consistent format of records throughout the database. For it, normalization method is used to standardize date formats, numerical digits, and variables so that accuracy calculation can happen. 

Precisely, normalization in Excel can be done by using its functions like UPPER, LOWER, and PROPER to fix case-based errors. In addition, using CONCATENATE or TEXT functions can help in dealing with gaps between text strings. Data entry specialists also leverage Flash Fill so that an ideal and precise formatting can be applied automatically. For separating datasets in cells, use the ‘Text to Columns’ feature.  

In order to standardize dates, the DATEVALUE or TEXT function can make them all appear uniform. This is how you can consistently format data to streamline easy data analysis. 

d. Outlier Detection and Treatment

Outliers can skew analysis results. Implement robust outlier detection methods and decide whether to remove, transform, or handle outliers based on the context. Their presence may lead to skewed analysis. 

You can fix this data condition by employing statistical methods. These can be the IQR or Interquartile Range (to detect and remove extreme values), Z-scores (to identify and eliminate datasets that do not belong to a specific range), etc. Besides, clustering methods is also gaining popularity for separating outliers. Likewise, you may align tools and resources to regularly check and fix these issues. 

e. Address Typos and Inconsistencies:

Typos are typically considered as spelling mistakes that can be a valid reason for good and bad decisions. Some tools like Grammarly or Quillbot are there to ensure that your data won’t have them. Furthermore, the Microsoft and Google products also have in-built feature of auto-correction. You may use these tools to automate the procedure of removing spelling mistakes. 

f. Data Validation Rules

Validating datasets in your repository is essential to check if the collected records meet established protocols or criteria. This assurance helps in reaching out to viable decisions. Considering the value of cleansed records, Microsoft has also introduced the feature of data validation so that a non-technical person can also validate datasets by putting them into defined ranges, formats, length of the text, and custom formula for validation. This actually helps in overcoming errors and inconsistencies. 

g. Handling Categorical Data

Typically, categorical datasets represent the group of datasets. Despite being a segment, there might be corruptions in its standardization. This issue can be fixed by employing a coder or tool that is able to convert these segments into binary columns. Another method called label encoding can also be used to numerically align categories. Besides, there can be a requirement for the reclassification of those categories that look similar, but are actually difference. This challenge is observed at the time of data merger. 

Another benefit of these coding methods is that they can help you to foresee models that can help in maintaining integrity among data categories. This is indeed valuable in analysing those data segments. 

h. Use Data Cleaning Software

Many tools are available online to ensure hygienic data for in-depth analysis. It’s obvious that data cleansing software can automate the entire process of identifying and rectifying errors.  Trifacta, OpenRefine, and WinPure-like tools are everywhere on the internet, hooking data specialists to scrub a massive volume of records. The advanced tools have machine learning working in the background to automate data scrubbing. It’s a pleasure that each software comes with its unique features. So, you should determine your requirements, at first. Then, invest in a specific tool. 


Effective data cleansing methods and practices involve well-defined protocols, tools, and validation rules. The aforementioned methods and practices are proven and indeed useful to overcome errors and inconsistencies.