What is Data Cleaning?

The "least pleasurable" aspect of data scientists' jobs, according to earlier studies, was data cleaning, which took up around 60% of their time. Data scientists continue to devote a sizable portion of their working hours to data cleaning initiatives even a few years later. Despite the fact that a survey conducted in 2020 found that data scientists now only devote around 45% of their time to data preparation chores like data cleaning, this still suggests that data cleaning requires a lot of time and effort from data scientists.

 

Most people concur that the quality of your insights and analysis when utilizing data is directly related to the quality of the data you are using. In essence, the analysis produced from bad data is also bad. If you want to establish a culture in your company centered around the use of high-quality data for decision-making, one of the most crucial first steps is data cleaning, also known as data cleansing and data scrubbing.

 

Data cleaning is the process of repairing or eradicating inaccurate, corrupted, improperly formatted, duplicated, or insufficient data from a dataset. Data duplication or labeling errors are common when merging several data sources. Even though they may appear to be proper, wrong data might lead to erroneous results and algorithms.

 

The specific procedures in the data cleaning process cannot be prescribed in a single, universal fashion since they differ from dataset to dataset. However, creating a template for your data cleaning procedure is essential so that you can be sure to follow it correctly each time.

 

Data cleaning plays a crucial part in the ETL (extract, transform, load) process, helping to guarantee that information is consistent, correct, and of high quality. Despite the fact that many data scientists rank it as one of the least pleasurable duties in their work, data cleaning is essential. Additionally, by adhering to a few straightforward best practices, data cleaning may be made substantially less difficult. Continue reading to learn what data cleaning is, why it's crucial, and how to do it properly.
 

Also, read | Data Normalization

 

What is Data Cleaning?

 

Data cleaning is the process of organizing and fixing erroneous, improperly structured, or disorganized data. People could provide their phone numbers in various formats, for instance, if you ask for them in a survey. Those phone numbers need to be standardized so that they are all formatted the same before they can be utilized.

 

There are many different reasons why data might be unorganized like this. Addresses can be formatted inconsistently, records can get duplicated and need to be found and reconciled, certain records may use various phrases, such as "Closed won" and "Closed Won," to express what should be the same values, null values need to be handled appropriately, and so on.

 

Numerous techniques may be used to clean up data. Sometimes it's done manually in Excel, Python, or SQL queries. People occasionally utilize software intended for procedurally cleaning data, such as Trifacta. Additionally, it is occasionally used in ETL procedures that clean data as it is extracted from sources and loaded into warehouses.

 

Sometimes, especially when data is manually submitted by individuals, the information is just incorrect. The source of truth for revenue statistics, for instance, is frequently Salesforce data. However, this information is produced by sales representatives who complete fields in Salesforce. Dates and numbers are frequently entered incorrectly, and duplicates are occasionally made. Machine-generated data can potentially contain errors, particularly if production data is combined with data from test data sources.

 

A lot of data produced by machines is produced in a manner that is helpful to machines but not to humans. As an illustration, while logging large amounts of event data, some fields are frequently nestled inside of one another to make the data easier to store. Although this structure is frequently advantageous to robots, it is challenging for humans to analyze.

 

Also, read | What is Data Pre-Processing?

 

How do you clean data?

 

The methods used to remove inaccurate data from each dataset may vary, but you must approach these problems methodically. You'll want to keep as much of your data as you can while also making sure that your final dataset is free of errors.

 

Since mistakes are difficult to identify once the data are acquired, data cleaning is a challenging procedure. Often, there is no way to tell if a data point correctly and exactly captures the worth of anything.

 

In actuality, you might choose to concentrate on identifying and resolving data points that, in more glaring ways, disagree or don't match with the rest of your information. These data might be useless, including outliers, be presented poorly, or have missing values.

 

Based on what's acceptable, you can select from a few ways for data cleaning. A data collection that is as full as you can get should be the result; it should be legitimate, consistent, unique, and uniform.

 

Applying limitations to ensure that your data is accurate and consistent is known as data validation. It is typically used when developing surveys or other assessment tools that call for human data entry before you even begin to gather data.

 

Once your data has been gathered, it is advisable to make a backup of your original dataset and keep it secure. Duplicating the backup and working from the new copy of your dataset will allow you to restart your workflow if you make any mistakes. Examining your dataset for inconsistent, false, omitted, or outlier data is known as data screening. This can be done manually or by statistical techniques.

 

Also, read | What is Data Ingestion? Challenges and Types

 

Why is clean data important?

 

As businesses strive to leverage data analytics to improve company performance and gain competitive advantages over rivals, business operations and decision-making are becoming more and more data-driven. Therefore, clean data is essential for corporate leaders, marketing managers, sales representatives, and operational staff as well as BI and data science teams. This is true for all businesses, big and small, but it is especially true for those in the retail, financial services, and other data-intensive sectors.

 

Customer records and other company data may not be reliable if data isn't adequately cleaned, and analytics tools may produce inaccurate information. As a result, there may be operational issues, missed opportunities, poor business decisions, and misdirected plans, which might eventually drive up expenses and lower revenue and profits. According to a still-used estimate from IBM, data quality concerns cost American businesses $3.1 trillion overall in 2016.

 

A data set is cleaned up by locating and eliminating mistakes, which is the essence of data cleaning. To guarantee that the data you deal with is always accurate and of the greatest quality, data cleaning serves as its ultimate aim. Data scrubbing, data cleaning, and other similar terms are also used to describe data cleansing.

 

Utilizing specialist software to fix data inaccuracies is referred to as "computer-assisted" cleaning. Comparing inaccurate data in a database with clean data is how the program operates. Additionally, manual data entry is compared to standardizing norms. When capitalizing the names of states, it, for instance, would transform "california" to "California."

 

Experian found that 29% of businesses felt their data is inaccurate in one of its surveys. Enterprise data sets can also suffer from startling rates of quality deterioration. For instance, according to the majority of analysts, B2B customer data deteriorates at a rate of at least 30 percent yearly, and in some high-turnover businesses, it can even reach 70 percent annually.

 

Properties of clean data:

 

Measures of the cleanliness and general quality of data sets include the following properties and features of the data:

 

• accuracy

• completeness

• consistency

• integrity

• timeliness

• uniformity

• Validity

 

Data Quality Metrics are developed by data management teams to monitor these traits as well as elements like error rates and the overall amount of mistakes in data sets. Many people also make an effort to determine the commercial effect of data quality issues and the potential financial value of addressing them, in part through surveys and conversations with company leaders.

 

Also, read | A Guide to Data Federation

 

Steps in the Data Cleaning Process:

 

Depending on the data collection and analytics objectives, the extent of the data cleaning task varies. For instance, while doing a fraud detection study on credit card transaction data, a data scientist may wish to keep track of outlier numbers since they may be an indication of questionable transactions. However, the following procedures are frequently used in the data cleaning process:

 

1. Profiling and Inspection:

 

To determine the quality level of the data and to pinpoint any problems that need to be corrected, it is first examined and audited. In order to detect mistakes, inconsistencies, and other issues, this stage typically comprises data profiling, which records relationships between data pieces, evaluates data quality, and compiles statistics on data sets.

 

2. Cleaning:

 

This is the core of the data cleaning process, where inconsistent, duplicate, and redundant data are dealt with.

 

3. Verification:

 

Following the cleaning phase, the individual or group responsible for the job should review the data once more to confirm its cleanliness and ensure that it complies with internal data quality guidelines and standards.

 

4. Reporting: 

 

The outcomes of the data cleaning activity should subsequently be communicated to IT and business management in order to highlight trends and advancements in data quality. The report could include up-to-date information on the levels of data quality as well as the total number of issues that were discovered and fixed.

 

Methods For Data Cleaning

 

Through data cleaning, there are numerous methods for creating trustworthy and sanitary data. The following are a few of the data cleaning techniques:

 

• Getting rid of unnecessary observations is the first and most fundamental step in data cleaning. This procedure involves eliminating redundant or unrelated observations. Observations that don't relate to the issue at hand are referred to as irrelevant observations. A good place to start is by making sure the data is irrelevant and that you won't need to clear it again.

 

• Another strategy is to get rid of undesired outliers since they might interfere with some models. Not only will eliminating outliers help the model perform better, but it will also increase its accuracy. However, one should be certain that the removal of them is justified.

 

• When entering numbers, little errors are frequent. The numbers being input need to be transformed to actual readable data if there are any errors. To make the numbers legible by the system, all of the presented data must be transformed. All of the datasets' data types should be the same. Numeric cannot be applied to a string, and numeric cannot be a boolean value.

 

• Typos caused by human mistakes should be fixed, and this may be done using a variety of algorithms and procedures. Mapping the data and changing them into their right spelling might be one of the approaches. Models treat various values differently, therefore typos must be corrected. The spelling and case of strings found in the data are very important.

 

Also, read: Top 7 Data Cleaning Tools for 2022

 

Benefits of Data Cleaning:

 

The advantages of data cleaning for business and data management include:

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Benefits of Data Cleaning

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1. More effective decision-making:

 

Analytics apps can give better outcomes with more precise data. Because of this, companies are better equipped to decide on topics like health care and government initiatives, as well as commercial strategy and operations.

 

2. Improved sales and marketing:

 

Customer data is frequently incomplete, inaccurate, or out-of-date. The efficacy of marketing campaigns and sales activities may be increased by cleaning up the data in customer relationship management and sales systems.

 

3. Improved performance in operations:

 

Organizations may prevent inventory shortages, delivery issues, and other issues that could lead to increased expenses, decreased profits and strained customer relations by using clean, high-quality data.

 

4. Increased data utilization:

 

Data has emerged as a crucial company asset, but if it isn't utilized, it won't be able to provide economic value. Data cleaning makes data more reliable, which encourages company managers and employees to depend on it in the course of their work.
 

 

5. Decrease in data prices:

 

Data cleaning halts the spread of data mistakes and problems in systems and analytics applications. Long-term time and financial savings result from avoiding the need for IT and data management teams to continually correct the same data set issues.

 

Data governance initiatives, which seek to guarantee that the data in corporate systems is consistent and utilized appropriately, also play a significant role in data cleaning and other data quality approaches. One of the characteristics of a good data governance program is clean data.

 

Conclusion:

 

When preparing data for use in operational operations or downstream analysis, data cleaning is a crucial step. Data quality tools are the best way to do it. These tools may be used in a number of ways, from fixing straightforward typos to verifying data against a list of known true values.

 

A strong data governance structure includes data cleaning. The upkeep of the cleaned-up data comes after a company has successfully implemented a data cleaning procedure. Data cleaning is a best data management practice that may be used to maximize the use of data but must be kept up to prevent expensive re-cleaning of data.