The Importance of Data Cleaning in SQL

In each & every industry data collected as messy and imperfect it could be human error or technical error sometime because of large data structure. Before we analyze data need to make sure data is clean and ready to use for further analysis so we can create best insights as per client requirements. That way we can save time and resources.

What is data cleaning?

Data cleaning is the process of fixing or removing incorrect, incomplete, duplicate, corrupted, incorrectly structured, or otherwise erroneous data in a data set. These data messed up by human, data entry or may be because of large data structure , sometime because of different terminology. Because of those error gives wrong analysis & wrong results. Part of the data preparation process, data cleansing allow us to accurate data that generates correct visualizations, models, and business decisions.

Why is data cleaning important?

Analyses and algorithms are only as good as the data they’re based on. On average, organizations believe that nearly 30% of their data is inaccurate. This dirty data costs company 12% of their overall revenue and they’re losing more than just money.

Cleansing produces consistent, structured, accurate data, which allows for informed, intelligent decisions. It also highlights areas for improvement in upstream data entry and storage environments, saving time and money now and in the future.

Data cleaning can also increase productivity. For example, customer data can often be inconsistent or out of date. Cleaning CRM and sales data can improve the productivity of sales and marketing efforts by eliminating the need to wade through outdated or incorrect data.

Why you should learn how to clean data with SQL

Data cleaning can be done in a scripting language such as R or Python. Many BI platforms also have built-in operations for data cleaning. If there are other ways to clean data, what makes SQL so important?

For most companies, data is stored in databases or data warehouses. Data can be collected from various data sources and loaded into data warehouses via ETL or ELT tools. Then, data workers can retrieve data from data warehouses and build reports or applications. The process from data sources to data applications is called a data pipeline.

In a data pipeline, messy data usually exists in data sources or data warehouses. Many ETL tools support writing SQL to transform and clean data before loading it to data warehouses. Both ETL and ELT require writing SQL to transform or clean data.

SQL is a necessary process in most data pipelines. Using SQL to clean data is much more efficient than scripting languages if your database is built on cloud.

In addition, DBT (data build tool) has recently become a popular tool for speeding up the process of data transformation and building data pipelines. It allows you to create your entire transformation process in your data warehouse with SQL. Many data engineers use it to transform and clean data in data warehouses.

Data Cleaning Steps:

People also prefer to say data cleaning or scrubbing. Data cleaning process includes multiple steps to remove duplicates, errors, null values etc. it is vary depending on the data set and what is the source of that data sets.

Lets see step by step process for data cleaning.

1.      Delete Duplicate data

2.      Delete irrelevant data

3.      Manage incomplete data

4.      Manage outliers

5.      Standardize/Normalize data

6.      Validate data

1. Delete duplicate data : Duplicate data occurs when multiple entry of the same or similar records found in data set. Duplicate data gives inaccurate and wrong analysis so make sure before we use data you can check duplicates, it could me rows, columns.

To identify and remove duplicates or repetitive records we can use DISTINCT function or Group by Function for specific column and selected distinct values.

Select Distinct column1, column2,…

From table_name ;

1. Delete irrelevant data: Data that’s not relevant to the problem being solved can slow down processing time. Removing these irrelevant observations doesn’t delete them from the source but excludes them from the current analysis. Keep in mind, if a certain variable isn’t needed, it might be correlated with the outcome being investigated

DELETE FROM table_name WHERE condition;

Two important commands for data removal are DELETE and DROP TABLE. These commands play crucial roles in maintaining database integrity and managing database structures.

• Delete command : use to remove rows from table but structure will remain same.

• Drop Command: remove various elements from a database, such as columns, constraints, indexes, tables, views, and databases.

1. Manage Incomplete data: Data might be missing values for a few reasons (e.g., customers not providing certain information), and addressing it is vital to analysis as it prevents bias and miscalculations. After isolating and examining the incomplete values, which can show up as “0,” “NA,” “none,” “null,” or “not applicable,” determine if those are plausible values or due to missing information. While the easiest solution might be to drop the incomplete data, be aware of any bias that might result in that action. Alternatives include replacing null values with substitutes based on statistical or conditional modeling or flagging and commenting on the missing data.

SELECT id, name, email, year, country, 

  IF(state IN ('Not Applicable', 'N/A'), NULL, state)  AS new_state 

FROM customers;

 This statement will create new column name new_state with replaced values with NULL where ‘Not applicable’ & ‘N/A’ are available in customer data set

4.      Manage Outlier: Data points that are far removed from the rest of a population can significantly distort the reality of the data. These outliers can be identified with visual or numerical techniques including box plots, histograms, scatterplots, or z-scores; when part of an automated process, it allows for quick assumptions, testing those assumptions, and resolving data issues with confidence. Once identified, outliers can be included or omitted depending on how extreme they are and what statistical methods are being used in an analysis.

 You can identify and address outliers by calculating summary statistics and then removing or adjusting values that fall outside an acceptable range.

5.      Standardize/Normalize data: Standardize formats, units, or values to ensure consistency. It’s important to correct errors and inconsistencies including typography, capitalization, abbreviation, and formatting. Look at the data type for each column and make sure entries are correct and consistent, which may include standardizing fields, and remove unwanted characters such as extra whitespaces.

We can use some SQL functions like TRIM(to remove leading and trailining spaces),UPPER,LOWER ( to convert text to specific case )and another function like REPLACE (use to replace specific character from data).

UPDATE your_table

SET column_name = TRIM(column_name);

UPDATE your_table

SET column_name = UPPER(column_name);

UPDATE your_table

SET column_name = REPLACE(column_name, 'old_value', 'new_value');

 Some of records likely have a different formats in table or columns. We can use TO_DATE function to convert date string to a specific date formate like DD:MM:YYYY or as per standard.

Update your_table

Set date_column = to_date(date_column, 'yyyy-mm-dd');

6. Validate Data:

The last step of data cleaning is to validate our data, at the end of the data cleaning process, you need to be sure that your data is valid, accurate, complete, consistent, and uniform.

Some questions you can ask yourself are:

· Do you have enough data?

·        Does the data follow the constraints for its field?

·        Does your clean data prove or disprove your theory before analysis?

·        Can you find trends in the data to help you form your next theory?

·        Does the data make sense?

·        If not, is that because of a data quality issue? 

This happens throughout an automated data cleansing process, but it’s still important to run a sample to ensure everything aligns. This is also an opportunity to document what tools and techniques were used throughout the cleansing process.

Validating data can prevent false conclusions based on low-quality data.

There’s no single correct way to clean your data. These 6 steps are a good guideline for what procedure you should follow and what issues to look for in your data.

First we have to understand where data coming from, that way your data cleaning process will be much easier for further analysis.

Conclusion:

In sum-up I can say that SQL data cleaning is a crucial role in ensuring efficient analysis as its very important for reliable and accurate insights for clients. SQL is also provide powerful tools and function to address quality issues effectively. By leveraging these capabilities such as data standardization engineers can improve the quality of their data sets and drive through accurate result and trustworthy analytics.

              Thank you for taking time out to read my blog, Hope it is helpful for your study.