What is data cleaning?

Data mining is concerned with extracting valuable information from the data, which can help organizations make business decisions. But before performing data mining, we have to clean the data. Data cleaning deals with cleaning the data and making it suitable to perform analysis. It includes eliminating the wrong data, raw data organization, and filling the rows in which null values are present.

When you perform data cleaning, you are converting the data to be in the proper format to obtain valuable information from the data. When you understand the quality of data, you can get accurate analysis from the data. You need to prepare the data in such a manner that the data can discover basic patterns.

When you perform data cleansing before data mining, you give the user the ability to find incomplete or inaccurate data before business analysis. Performing data cleaning in data mining can be a hectic task since it requires IT resources to evaluate your data. Also, data cleaning before data mining proves to be a time-consuming task and creates difficulty for data analysts. The improper data quality will make your final analysis suffer in terms of accuracy, or it could lead to generating improper conclusions.

Common inaccuracies in data include typographical errors, entries that are misplaced & missing values. Data that includes these kinds of errors are termed "dirty data."According to a survey, it is proved that only 3% of the data can meet all the basic quality standards. In today's world, a vast amount of data are sourced from multiple platforms. Hence, a data cleansing tool has become essential for ensuring the accuracy & efficiency of the data. Therefore, data quality has been an essential element, and it has become the company's most important priority.

Manual data cleansing is a very time-consuming process and hence data cleansing tools were introduced to save the time of the analysts in cleaning the data.

Data cleaning deals with the removal of incorrect, duplicate, and corrupted data from a data set. When you combine data from multiple sources there is a possibility that your data might suffer from duplication. If your data is not proper it will create a huge impact on the outcomes and algorithms. Data cleaning processes differ from dataset to dataset. All good decisions & bad decisions are dependent upon the quality of the data. When data is not cleaned, a lot of errors are present in the data. To clean those errors there is a lot of costs involved and it takes a lot of time to correct those errors. With the help of data cleaning, you can be ensured that your data can be trusted and can be used for decision making. When data can be trusted decisions can be taken more accurately.

What is the primary difference between data cleaning and data transformation?

Data cleaning deals with the removal of data that does not fit into your dataset, while data transformation deals with the conversion of data into one format or another. The data transformation process can also be termed as data wrangling, data mugging.

Process involved in cleaning data

Monitor your data regularly and keep track of all the errors, when you monitor errors it becomes very easy to identify corrupt information.
Validate the accuracy of data
Scrub for duplicate data.

Steps involved in cleaning data

Steps involved for data cleaning might vary according to the types of data that that your organization stores, but the basic steps involved in data cleaning are described below:

Step 1: Removal of duplicate and irrelevant observations

Eliminate unwanted observations which are present in your dataset which includes duplicate and irrelevant observations. Duplicate observations occur while data collection. When data are collected from multiple data sources there is a high possibility for duplicate data to be present in your data set.

An observation can be termed as "irrelevant observation" when it do not fit into any category which is taken into consideration for performing analysis. For example: if you want to analyze data related to cancer disease but your dataset also includes data related to malaria disease, you may eliminate data that are not needed. By doing this you can make your analysis more efficient and minimize errors in your analysis.

Step 2: structural errors must be fixed.

Fixing structural errors involves fixing naming conventions, typo errors & incorrect capitalization. These inconsistencies can cause incorrect results. For Example, you may find "null" and "nil" both appearing, they must be categorized into the same category.

Step 3: Unwanted outliers must be filtered.

In your data set, if you find some observations that do not help perform analysis, you may consider removing those observations. By doing so, you would be able to improve the performance of your data.

The presence of an outlier in a dataset does not mean that your data is incorrect.

Step 4: All missing data must be handled.

Missing data cannot be ignored because algorithms do not accept missing data. below given are the ways to deal with missing data:

Drop observations that consist of missing values, but this will result in loss of information. Go for this approach only when the data which you are dropping does not have an impact on other attributes which are present in your dataset.

Fill the values of the missing rows by calculating the mean or mode of that entire column. The values which are obtained can then be used to fill the missing observations.

Step 5: perform data validation and QA

When your data cleaning process ends you must be able to answer the following questions:

Does the data make sense?
Does the data consist of any appropriate rules?
Are you able to draw any conclusions with the help of your data?
Did you find any trend in your data?

Incorrect data can generate false conclusions which in turn can lead to poor decision making.

Components of data quality

To determine the data quality there is a need to examine the characteristics of the data which you have gathered/collected.

Following are characteristics of quality data:

Validity ? it can be measured in terms of the percentage to which your data conforms to the business specifications.

Accuracy ? it can be defined to ensure that all your data are close to the true values.

Completeness ? your data must be complete with all the necessary data.

Consistent ? the consistency must be maintained within the dataset or across multiple data sets.

Advantages of data cleaning

When your data is clean it increases overall productivity which allows you to take quality decisions for organizations. The benefits of data cleaning are as follows:

Removal of errors when multiple data sources are participating in data collection.
The presence of fewer errors makes clients happy and removes unwanted frustrations which an employee's faces
Mapping your data to different functions becomes less complex.

The following are the advantages of data cleaning:

Improvement in decision making ? the quality of data plays an important role in decision making. The company cannot afford to waste their time and energy on cleaning the data.

Boosted efficiency ? utilization of clean data is beneficial for both company’s external and internal needs. When data is cleaned properly valuable insights can be extracted which would be useful for internal needs and processes.

Competitive edge ? when an organization can meet their customer needs, the organization will rise higher when compared with their competitors. With the help of data cleansing tools reliable and complete insights can be identified which can help meet customer needs.Faster response rates, quality data & and customer experience can improve when the data is cleaned.

Tools and softwares available for data cleaning

There are various softwares present like tableau, which provides various ways to clean and combine your data. The tableau consists of 2 products:

Tableau prep builder.
Helps to build data flows.

Tableau prep conductor.
Can be used to schedule, manage & monitor flows across the organization

With the help of a data scrubbing tool, data administrators can save a vast amount of time by helping analysts to start their analyses faster. However, to make efficient and effective decisions, it is necessary to understand the quality of data and tools needed to create, manage & transform the data.

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