Data Cleaning in Data Analytics

Data Cleaning in Data Analytics

Imagine  building a house on a bad foundation, no matter how good the plans are, the building is at risk and can collapse anytime. The same principle applies to data analytics. No matter what kind of data analytics you’re performing, your analysis and any other downstream processes are only as good as the data you start with. Most raw data, like text, images, video, or even data stored in spreadsheets, is often improperly formatted, incomplete, or downright dirty. It needs to be properly cleaned and structured before you can begin your analysis. 

By using effective data cleaning techniques, you can enhance the quality of your data without altering its original meaning, ensuring a solid foundation for accurate and reliable insights. If the data is inaccurate or incomplete then the insights from it are unreliable. To avoid that cleaning is the major step in data preparation.

What is data cleaning?

Data Cleaning  also known as data cleansing and data wrangling is one of the important steps to prepare data for analysis. It is the process of finding ,editing, correcting and structuring the  inaccurate, incomplete and irrelevant records from the dataset. This also includes removing the corrupt data and formatting it into a language that the computer understands.

Most projects need 80 % of cleaning the data which is tedious , but this process is important to get top results and accurate insights from the data.

Why does cleaning data matter?

• Accurate Insights : Messy data leads to meaningless results . Clean data ensures that insights are accurate and reliable.

• Organized Data : Businesses often deal with large amounts of data everyday and cleaning helps to organize this data and make it easy to analyze and work with.

• Cost Savings :  Regular data cleaning  can reduce  errors early and prevent fixing the data down the line.

• Improves Productivity : Cleaning the data early saves time and improves efficiency ,allowing you to focus on analysis rather than fixing issues.

• Better Mapping : Clean data helps in building strong applications enhancing overall data utilization and effectiveness.

How to Perform Data Cleaning 

• Trim the Inconsistencies :  Start by removing duplicate information and any that is not needed. Clear out repeated values to streamline your dataset.

• Tackle Structure Errors : Fix the errors like typos and inconsistent capitalization make sure there is a uniform format of labels .

• Standardize the Units : Standardize the data types and units . Ensure that numerical data uses the same format and also consistent date types.

• Handle Outliers : Identify outliers and remove them if they do not add value to your analysis.

• Convert Types and Clean Syntax : Correct and convert the data types appropriately ,like changing numbers to integers and text to string . Clear the leading and lagging spaces .

• Deal with missing data : If there is missing data, try to find the relationship with other columns and fill or  convert all the missing gaps with “missing” or “0”  to maintain clarity .

• Validate your dataset : After cleaning, validate your  dataset to ensure all corrections and standardizations are correct. Check the reliability of your data  source to confirm reliability.

Tools for Data Cleaning 

There are many tools that helps in data cleaning process:

• Microsoft Excel : Excel provides various functions for data cleaning , like removing duplicates, replacing text and shaping the columns. It has an advanced tool called Power Query which helps in cleaning the data.

• Programming Languages : Programming languages like R,SQL,Python and Ruby are commonly used to write scripts to automate data cleaning. Among the most popular Python libraries  Pandas and Numpy are particularly powerful for their powerful data manipulation capabilities.

• Data Visualization Tools : Bar Charts and Scatter Plots helps identify the errors in the dataset. From the scatter plot you can check the outliers and from the bar chart you can get the mislabeled values.

• Proprietary Software : Tools like OpenRefine and Trifacta are designed to make data cleaning accessible for non-technical users, offering intuitive interfaces and powerful features for efficient data preparation.

Data Cleaning Tips

• Create the right process and use it consistently

Develop a data cleaning process that aligns with your data, your requirements, and the tools you'll use for analysis. Remember, this is an iterative process; once you establish your specific steps and techniques, you must apply them consistently to all future datasets and analyses.

• Use tools

There are many data cleaning tools available, ranging from to advanced and machine learning-augmented. It's best to research and identify free and basic  tools that best suit your needs.

If you're proficient in coding, you can create custom models to meet your specific requirements. However, excellent tools are also available for non-coders. Look for tools with an intuitive UI that allows you to preview the effects of your filters and quickly test them on different data samples.

• Pay attention to errors and monitor the sources of unclean data

           Keep a record of common errors and trends in the data and annotate them so you will know what type of cleaning techniques need to be used from different data sources. This will save time and make data more cleaner especially when integrating with analysis tools you use regularly   

  In summary, data cleaning is essential for ensuring the reliability and accuracy of insights in data analytics. It serves as the foundation for maintaining data integrity and relevance, emphasizing the importance of starting with high-quality data. Despite its time-consuming nature, meticulous attention to various facets such as pruning, outlier detection, and standardization is crucial. The availability of tools like MS Excel, Python, and proprietary software simplifies the data cleaning process, enabling analysts to navigate vast datasets with confidence and precision.