Data cleaning is one of the important processes involved in data analysis, with it being the first step after data collection. It is a very important step in ensuring that the dataset is free of inaccurate or corrupt information.

It can be carried out manually using data wrangling tools or can be automated by running the data through a computer program. There are so many processes involved in data cleaning, which makes it ready for analysis once they are completed.

This article will cover what data cleaning entails, including the steps involved and how it is used in carrying out research. 

What is Data Cleaning?

Data cleaning is the process of modifying data to ensure that it is free of irrelevances and incorrect information. Also known as data cleansing, it entails identifying incorrect, irrelevant, incomplete, and the “dirty” parts of a dataset and then replacing or cleaning the dirty parts of the data. 

Although sometimes thought of as boring, data cleansing is very valuable in improving the efficiency of the result of data analysis. It generally helps to improve data quality, and the process can be automated or done manually.

The process of data cleansing may involve the removal of typographical errors, data validation, and data enhancement. This will be done until the data is reported to meet the data quality criteria, which include; validity, accuracy, completeness, consistency, and uniformity.

Why Do We Clean Data?

In most cases, some of the datasets collected during research are usually littered with “dirty” data, which may lead to unsatisfactory results if used. Hence, the need for scientists to make sure that the data is well-formatted and rid of irrelevancies before it is used.

This way, they are able to eliminate the challenges that may arise from data sparseness and inconsistencies in formatting. Cleaning in data analysis is not done just to make the dataset beautiful and attractive to analysts, but to fix and avoid problems that may arise from “dirty” data.

Data cleansing is very important to companies, as lack of it may reduce marketing effectiveness, thereby reducing sales. Although the issues with the data may not be completely solved, reducing it to a minimum will have a significant effect on efficiency

Data Cleaning Steps 

Understanding the what and why behind data cleaning is one, going ahead to implement it is another. Therefore, this section will be covering the steps involved in data cleaning, and further explanations on how each of these steps is carried out.

• Removal of Unwanted Observations

Since one of the main goals of data cleansing is to make sure that the dataset is free of unwanted observations, this is classified as the first step to data cleaning. Unwanted observations in a dataset are of 2 types, namely; the duplicates and irrelevances.

• Duplicate Observations

A data is said to be a duplicate if it is repeated in a dataset, with it having more than one occurrence. This usually arises when the dataset is created as a result of combining data from two or more sources.

This can also occur in some other cases, including when a respondent makes more than one submission to a survey or error during data entry.

• Irrelevant Observations

Irrelevant observations are those that don’t actually fit the specific problem that you’re trying to solve. Like having the price when you are only dealing with quantity.

For example, if you were building a model for prices of apartments in an estate, you don’t need data showing the number of occupants of each house. Irrelevant observations mostly occur when data is generated by scraping from another data source.

• Fix Data Structure

After removing unwanted observations, the next thing to do is to make sure that the wanted observations are well-structured. Structural errors may occur during data transfer due to a slight human mistake or incompetency of the data entry personnel. 

Some of the things one should look out for when fixing data structure include; typographical errors, grammatical blunders, and so on. The data structure is mostly concerned with categorical data. 

Here, we correct misspelled words and summarize category headings that are too long. This is very important because long category headings may not be fully shown on the graph.

For better illustration, consider the graph below showing the total contract sum and the amount paid by a commissioner who carried out some projects in a community. 

From the graph above we observe that

• “school” should be capitalized to give us “School”
• “Establishment of…” is too long and should be summarized so that the heading can fully show on the graph.
• Also “Hspital” should be “Hospital”.

After eliminating the inconsistency in the data structure, the bar graph becomes cleaner. 

• Filter-out Outliers

In order to improve the performance of your model, you should remove outliers. Outliers are data points that differ significantly from other observations in a data set.

Outliers are very tricky, in the sense that they are of the same type with other observations, making them look wanted but hugely different from the others. For example, a particular data point may be numerical like other observations in the data set but may turn out to be a big 1000 with the rest between the range 1-10. 

Although problematic to some models, there should be a valid reason for removing an outlier. Outliers may arise from a measurement error that is unlikely to be real data, while it may also be as a result of scraping a bigger dataset.

Outliers may give more insight into your model the way the other observations can’t. Hence, you should be careful when removing outliers from your data.

• Handle Missing Data

You may end up with missing values in your data due to errors during data collection or non-response bias from respondents. You can avoid this by adding data validation to your survey.

However, now that you already have missing data, how do you handle them?  

There are 2 common ways of handling missing data, which are; entirely removing the observations from the data set and imputing a new value based on other observations. 

• Drop Missing Values

By dropping missing values, you drop information that may assist you in making better conclusions on the subject of study. It may deny you the opportunity of benefiting from the possible insights that can be gotten from the fact that a particular value is missing.

For example, when collecting the scores of students in various exams, student A’s score mag be missing in mathematics because he didn’t sit for the exam. Assume that this happened because he was sick, and sick students are allowed to rest the exam at a later date.

If the whole observation was deleted, we can not detect that he was sick. 

• Inpute Missing Values

Consider another student B who wrote the exam but his score was missing because the teacher forgot to enter her score. If the teacher imputes a random score for her, it may end up rendering the data incorrect. 

Student B may have scored higher or lower than the score the teacher randomly assigns to her. 

Therefore, if data is missing, you should always indicate it in your dataset. You can indicate missing values by simply creating a Missing category if the data is categorical, or flagging and filling with 0 if it is numerical.

This way, the algorithm will be aware that there are missing values in the dataset.

Raw vs Clean Data

When working on analyzing a dataset, it is important for the analyst to be aware if he or she is dealing with raw or clean data. This will help ensure that one doesn’t encounter problems when analyzing the data.

Here are some things you should look out for to confirm whether you are dealing with raw or clean data. 

• Definition

Raw data is the data that is collected directly from the data source, while clean data is processed raw data. That is, clean data is a modification of raw data, which includes the removal of irrelevances and inaccuracies.

• Format

As the name implies, raw data is usually in its raw format, which in most cases cannot be understood by laymen and will need some modification before it can be analyzed. Clean data, on the other hand, is usually in an analyzable format and can even be understood by laymen even without visualization.

• “Dirtiness”

Raw data is full of irrelevances, errors, and corrupt information, while clean data has been modified to eliminate these “dirtiness”. When reading raw data, you may encounter missing observations, inconsistencies, and errors. This is not the case when dealing with clean data.

How to Collect Clean Data with Formplus (Step by Step Guide) 

Follow these 5 simple steps to collect clean data with Formplus.

Step 1- Create an Online Data Collector

Collect clean data with forms or surveys generated on Formplus through one of the following options:

• Use an Existing Template

Get a head start by using a template designed by a team of clean data collection experts. To do this, go to Templates and choose from any of the available templates.

• Start From Scratch

To create a new survey from scratch on Formplus, go to your  Dashboard, then click on the Create new form button. 

Alternatively, go to Forms in the top menu, then click on the Create Form button.

Step 2 – Choose the Right Form Fields 

The next step is to add questions so you can collect data from your survey. You can do this by going to the left sidebar in the form builder, then choose from any of the available 30+ form fields. 

Ensure that respondents give correct responses to your questions by choosing the right form field. For example, when asking for respondents’ email addresses, go to Inputs>Email. 

If you click on Short Text, the respondent will be able to give an irrelevant response instead of an email address.

Step 3 – Validate Your Questions

You can add form validation to your questions to avoid No-response biases and irrelevant responses. As seen in the image below, you can choose to make a question required to avoid non-response bias and also validate your questions to ensure that only relevant data is collected.

Step 4  – Customize Your Survey

After validating your question, you can click on the Save button in the top-right corner of the form builder and you will be automatically directed to the Customise page. On this page, you can fine-tune the design and look of your survey. For instance, you can add a logo, color, font, background image, etc. using the built-in Formplus features. Alternatively, you can add your own custom CSS. 

Step 5 – Start Collecting Clean Data 

After satisfactorily beautifying your survey, you can preview and start collecting clean data. With Formplus, you have various sharing options to choose from. This includes sharing via email, customized links, social media, etc.

You can send personalized email invites to respondents with prefilled respondent details to avoid entry of incorrect data. With prefilled surveys, personal details like respondent’s name, email address, and phone number will be prepopulated.

Formplus Features that Supports Clean Data 

• Export Data as CSV to Excel

You can easily export the data collected from Formplus into Excel, where you can perform data cleansing. This can be done easily by existing the data into your local storage for use on Excel or through easy Microsoft OneDrive integration.

• Captcha Check

Prevent spam bots from filling your survey with irrelevant data by enabling the Captcha feature. With Captcha check, you can ensure that only humans submit responses to your survey. 

• Private Forms

Ensure that only authorized personnel can fill your forms by creating private forms with Formplus. You can grant specific people access to your forms by adding them to your Formplus account as users.

• Offline Capability

Increase the response rate on your surveys by taking advantage of the offline capability of the forms created on Formplus. Respondents who have limited access to an internet connection can fill your survey offline and it will be automatically synced once they have access to an internet connection.

• Form Filter

Formplus automatically filters the kind of responses that can be submitted by respondents. 

• Data Report

Make better decisions by receiving reports and actionable insights on your data with Formplus Analytics. The analytics dashboard reveals information like the number of submissions received, the location of the respondents, and the type of device used.

You can also customize your analytics dashboard so that you can have access to more robust data.

• Geolocation/ Location Data

Discover respondents’ location with the geolocation feature on Formplus. With this feature, you can detect the location where people are filling your forms or surveys from. 

This can be very useful in data cleaning if the information on a respondent’s location is missing. 

Advantages of Data Cleaning 

• Improved Decision Making

Data cleansing will help eliminate inaccurate information that may lead to bad decision making. With up-to-date information on the market, for instance, a business owner can properly decide whether to make a sale or purchase.

• Revenue Booster

Businesses who have the correct data on the demography of their target audience can employ the right marketing tactics. This will help generate more customers, sales, and higher revenue. 

• Cost-effective

When working with the right database for marketing, businesses are sure of getting a high engagement rate, giving back the required value for their money. This will help save costs spent on ineffective marketing practices.

• Increases Productivity

With accurate and updated information, employees will spend less time contacting expired contacts or customers with stale information. For example, if support tickets are not updated when completed, employees will waste time contacting customers when they don’t need to.

• Boosts Reputation

Having clean and error-free data will help boost trust and reputation, especially for companies that specialize in sharing data with the public. If you provide clean data to people, they will trust you as a reliable data bank.

Disadvantages of Data Cleaning 

• Analysts may lose out on actionable insights due to incomplete data. This is very common in cases where missing observations and outliers are dropped.
• It may lead to an even bigger problem when automated. Some automated data cleaning tools are not very smart and may end up mishandling some observations in the dataset.
• It is time-consuming. Data cleaning may take a lot of time, especially when dealing with large data.
• The process is very expensive.

Conclusion 

With the alarming increase in digitization, data is perhaps one of the most valuable things right now. One of the most interesting things about data in this era is its ease of accessibility-online through social media, search engines, websites, etc.

However, the challenge a lot of us face is that most of the  data is either incorrect or full of irrelevancies. Therefore, in order to leverage on the easily accessible huge data, we need to take our time to clean it.

Data cleaning is arguably one of the most important steps towards achieving great results from the data analysis process. In simple terms, if the data isn’t cleaned, data analysis will not yield a perfect result.