Manual data cleaning takes a long time and might be daunting. That is why data cleaning is likely to be outsourced by large corporations or they need data scientists and data analysts to perform this task. This article will go over the reasons why data cleansing is so important in the corporate world and how to apply SQL to this process.
Data cleaning is the process of removing dirty data from datasets, including
Data cleaning is a crucial step to improving your analysis which leads to accurate and valuable insights.
Imagine that you want to analyze how much potential customers spent on your services.
With the dirty information that is duplicated and incomplete, you cannot calculate the right numbers. As a result, you end up developing poor strategies and arriving at unwise decisions.
Below are several reasons why we should clean data before analyzing it:
For example, email campaigns
A company may have datasets that are outdated, yet it is still being used for email advertisements. This might result in a slew of unforeseen costs.
Data cleaning guarantees that only the appropriate people sign up for your email list. One common mistake we find is companies sending the incorrect people because they were accidentally added to their mailing list. This is considered spam.
Duplicate data clutter the workplace, resulting in unproductive operations. Businesses must simplify their processes to the greatest extent feasible. Profits are greater when total expenses are lower. Managers will also benefit from data purification when deciding on roles inside their departments. Job descriptions should be updated on a regular basis, however, clutter obscures this necessity.
Businesses that use the right analytics and cleaning technologies will have a higher chance of seeing new possibilities. For example, there may be a need for a particular product that they might give, but this information is obscured by obsolete, irrelevant statistics.
You’ll miss out on prospective prospects and current customers if faulty data leads to a poor customer experience.
The modern buyer has more power over their purchasing experience than ever before. When they’re considering buying from you, they expect a hassle-free experience.
However, something as basic as mispronouncing a prospect’s name might be the difference between completing a transaction and losing a potential customer to your competitors.
Clean data is critical to the effectiveness of every contact, whether your agents are speaking with a prospect for the first time or an account executive follows up on an established customer.
Executives and key stakeholders used to make significant long-term business choices based on instinct and intuition.
When it comes to critical decision-making, data is now so easily available that there is no need for guesswork.
Here’s the issue:
If your executive team is relying on erroneous data, they may make rash and perhaps destructive long-term choices.
The good news is that clean data gives decision-makers the tools they need to report accurately and completely.
Here are steps to clean data that you need to keep in mind. The more the data is cleaned, the more insights your company will have.
Before analyzing data, check your data carefully to see whether it has duplicates or irrelevant data or not. Data collection is one of the reasons causing this problem.
Duplicated values and irrelevant data can lead to wrong statistics, so getting rid of bad data can help improve performance and its accuracy.
SELECT * FROM
(select *, count(*) over(partition by [column], [column], [column] order by [column] ) as rowRank
from {schema}.{table}) x
WHERE rowRank > 1;
DELETE * FROM
(select *, count(*) over(partition by [column], [column], [column] order by [column] ) as rowRank
from {schema}.{table}) x
WHERE rowRank > 1;
Depending on your purpose to get data, you should consider whether outliers affect your results or not.
If you are analyzing customers who are from 18 to 30 years old and your datasets have the elderly, please ensure that you will eliminate these values.
Sorting data in both DESC and ASC is the fastest way to find outliers. This enables you to quickly skim the highest and lowest values and find unexpected data.
SELECT [column], [column]
FROM {schema}.{table}
ORDER BY [column] DESC / ASC
Interquartile range (IQR) is one of the statistical methods that help us identify outliers.
NTILE(num_buckets integer) is the window function from postgreSQL( SQL) that allows us to separate our values into many groups. Therefore, if we need to divide our data into four quartiles we would use NTILE(4)
Outliers are numbers that are more than 1.5 times the range of our first and third quartiles.
SELECT
customer_name,
age,
NTILE(4) OVER (ORDER BY age) AS age_quartile
FROM customer_information
First, understand that there is no good approach to handle missing data. However, many algorithms won’t allow missing values, you can’t disregard it.
There are a few options for dealing with missing data.
The fastest way to get rid of missing data, use deletion methods. This method can be appropriate if after deleting, your analysis results do not affect.
As a second option, you can fill in missing values based on other observations; however, you risk losing data integrity because you’re working with assumptions rather than actual observations.
We begin by checking the data type of our date column. For example, dates can be ‘1/12/2021’ , ‘12/1/2021’, ‘1/12/21’, ‘2021/12/1’,… and as a human being, we can understand that these are DATES. However, it doesn’t mean that Python / PostgreSQL are aware of the dates as such.
It can recognize these dates as objects / text. Therefore, our date columns should be converted to datetime “yyyy-mm-dd”
alter table [table_name]
add [new_column] TIMESTAMP
UPDATE [table_name]
SET [new_column] = CAST( [old_column] AS TIMESTAMP)
The importance of correcting typos caused by human mistakes cannot be ignored, and typos may be corrected using a variety of algorithms and procedures. One way is to map the values and then convert them to their proper spelling.
Create FUNCTION in SQL
CREATE OR REPLACE FUNCTION public.to_clean_name(__name text)
RETURNS text
LANGUAGE plpgsql
AS $function$
BEGIN
IF ( __name in ( ‘free-trial’) ) THEN
RETURN ‘freemium’;
ELSIF ( __name in (‘standardize’) ) THEN
RETURN ‘standard’;
ELSIF ( __name in (‘advanced-service’) ) THEN
RETURN ‘advanced’;
END IF;
RETURN __name;
END;
$function$
As part of basic validation, you should be able to answer these questions at the end of the data cleansing process:
After having data cleaning with sql, you are now ready to analyze datasets with advanced styling options and identify valuable insights through Dataflake
