Data Cleaning Steps & Process to Prep Your Data for Success (2022)

No matter what kind of data analytics you’re performing, your analysis and any other downstream processes are only as good as the data you start with.

Most raw data, whether text, images, video – often even data stored in spreadsheets – is improperly formatted, incomplete, or downright dirty and needs to be properly cleaned and structured before you begin your analysis.

There are a number of data cleaning, “data cleansing,” or “data scrubbing” techniques you can put to use to ensure your data is properly prepared for analysis.

  • What Is Data Cleaning?
  • Data Cleaning Tips

What Is Data Cleaning?

Data cleaning is the process of editing, correcting, and structuring data within a data set so that it’s generally uniform and prepared for analysis. This includes removing corrupt or irrelevant data and formatting it into a language that computers can understand for optimal analysis.

There is an often repeated saying in data analysis: “Garbage in, garbage out,” which means that, if you start with bad data (garbage), you’ll only get “garbage” results.

Data cleaning is often a tedious process, but it’s absolutely essential to get top results and powerful insights from your data.

This is powerfully elucidated with the 1-10-100 principle: It costs $1 to prevent bad data, $10 to correct bad data, and $100 to fix a downstream problem created by bad data.

(Video) Understanding Clean Data | Google Data Analytics Certificate

So, it’s important that you perform proper data cleaning to ensure you get the best possible results.

In machine learning, data scientists agree that better data is even more important than the most powerful algorithms. This is because machine learning models only perform as well as the data they’re trained on.

If you’re training your models with bad data, the end analysis results will not only be generally untrustworthy, but will often be completely harmful to your organization.

Proper data cleaning will save time and money and make your organization more efficient, help you better target distinct markets and groups, and allow you to use the same data sets for multiple analyses and downstream functions.

Follow the data cleaning tips and data cleaning techniques below to set yourself up for optimum analysis and results.

(Video) How to Clean Up Raw Data in Excel

Data Cleaning Steps & Techniques

Here is a 6 step data cleaning process to make sure your data is ready to go.

  • Step 1: Remove irrelevant data
  • Step 2: Deduplicate your data
  • Step 3: Fix structural errors
  • Step 4: Deal with missing data
  • Step 5: Filter out data outliers
  • Step 6: Validate your data

1. Remove irrelevant data

First, you need to figure out what analyses you’ll be running and what are your downstream needs. What questions do you want to answer or problems do you want to solve?

Take a good look at your data and get an idea of what is relevant and what you may not need. Filter out data or observations that aren’t relevant to your downstream needs.

If you’re doing an analysis of SUV owners, for example, but your data set contains data on Sedan owners, this information is irrelevant to your needs and would only skew your results.

You should also consider removing things like hashtags, URLs, emojis, HTML tags, etc., unless they are necessarily a part of your analysis.

2. Deduplicate your data

If you’re collecting data from multiple sources or multiple departments, use scraped data for analysis, or have received multiple survey or client responses, you will often end up with data duplicates.

Duplicate records slow down analysis and require more storage. Even more importantly, however, if you train a machine learning model on a dataset with duplicate results, the model will likely give more weight to the duplicates, depending on how many times they’ve been duplicated. So they need to be removed for well-balanced results.

Even simplistic data cleaning tools can be helpful to deduplicate your data because duplicate records are easy for AI programs to recognize.

(Video) Webinar: Preparing Your Data for Successful Predictive Modeling

3. Fix structural errors

Structural errors include things like misspellings, incongruent naming conventions, improper capitalization, incorrect word use, etc. These can affect analysis because, while they may be obvious to humans, most machine learning applications wouldn’t recognize the mistakes and your analyses would be skewed.

For example, if you’re running an analysis on different data sets – one with a ‘women’ column and another with a ‘female’ column, you would have to standardize the title. Similarly things like dates, addresses, phone numbers, etc. need to be standardized, so that computers can understand them.

4. Deal with missing data

Scan your data or run it through a cleaning program to locate missing cells, blank spaces in text, unanswered survey responses, etc. This could be due to incomplete data or human error. You’ll need to determine whether everything connected to this missing data – an entire column or row, a whole survey, etc. – should be completely discarded, individual cells entered manually, or left as is.

The best course of action to deal with missing data will depend on the analysis you want to do and how you plan to preprocess your data. Sometimes you can even restructure your data, so the missing values won’t affect your analysis.

5. Filter out data outliers

Outliers are data points that fall far outside of the norm and may skew your analysis too far in a certain direction. For example, if you’re averaging a class’s test scores and one student refuses to answer any of the questions, his/her 0% would have a big impact on the overall average. In this case, you should consider deleting this data point, altogether. This may give results that are “actually” much closer to the average.

However, just because a number is much smaller or larger than the other numbers you’re analyzing, doesn’t mean that the ultimate analysis will be inaccurate. Just because an outlier exists, doesn’t mean that it shouldn’t be considered. You’ll have to consider what kind of analysis you’re running and what effect removing or keeping an outlier will have on your results.

6. Validate your data

Data validation is the final data cleaning technique used to authenticate your data and confirm that it’s high quality, consistent, and properly formatted for downstream processes.

  • Do you have enough data for your needs?
  • Is it uniformly formatted in a design or language that your analysis tools can work with?
  • Does your clean data immediately prove or disprove your theory before analysis?

Validate that your data is regularly structured and sufficiently clean for your needs. Cross check corresponding data points and make sure nothing is missing or inaccurate.

(Video) 10 Super Neat Ways to Clean Data in Excel

Machine learning and AI tools can be used to verify that your data is valid and ready to be put to use. And once you’ve gone through the proper data cleaning steps, you can use data wrangling techniques and tools to help automate the process.

Data Cleaning Tips

  • Create the right process and use it consistently

Set up a data cleaning process that’s right for your data, your needs, and the tools you’ll use for analysis. This is an iterative process, so once you have your specific steps and techniques in place, you’ll need to follow them religiously for all subsequent data and analyses.

It’s important to remember that, although data cleaning may be tedious, it’s absolutely vital to your downstream processes. If you don’t start with clean data, you’ll undoubtedly regret it in the future when your analysis produces “garbage results.”

  • Use tools

There are a number of helpful data cleaning tools you can put to use to help the process – from free and basic, to advanced and machine learning augmented. Do some research and find out what data cleaning tools are best for you.

If you know how to code, you can build models for your specific needs, but there are great tools even for non-coders. Check out tools with an efficient UI, so you can preview the effect of your filters and quickly test them on different data samples.

  • Pay attention to errors and track where dirty data comes from

Track and annotate common errors and trends in your data, so you’ll know what kinds of cleaning techniques you need to use on data from different sources. This will save huge amounts of time and make your data even cleaner – especially when integrating with analysis tools you use regularly.

Wrap Up

It’s clear that data cleaning is a necessary, if slightly annoying, process when running any kind of data analysis. Follow the steps above and you’re well on your way to having data that’s fully prepped and ready for downstream processes.

Remember to keep your processes consistent and don’t cut corners on data cleaning, so you’ll end up with accurate, real-world, immediately actionable results.

(Video) Data Cleaning Steps and Methods, How to Clean Data for Analysis With Pandas In Python [Example] 🐼

MonkeyLearn is a SaaS machine learning text analysis platform with a suite of tools to help you get the most out of your clean data.

Take a look at MonkeyLearn to learn about sentiment analysis, topic categorization, keyword analysis, and dozens of other techniques that can run automatically, 24/7 on your text data, so you never miss an insight.

FAQs

Why are pre cleaning steps important to complete prior to data cleaning? ›

Using tools to pre-clean the data will make it more efficient as we can able quickly get what you need from the data available. It removes major errors and inconsistencies that are inevitable when multiple sources of data are being pulled into one dataset.

What is data cleansing and what are some best practices that should be followed in data cleansing? ›

Data cleansing best practices
  • Develop a data quality strategy. Set expectations for your data. ...
  • Correct data at the point of entry. ...
  • Validate the accuracy of your data. ...
  • Manage duplicates. ...
  • Append missing data. ...
  • Promote the use of clean data across the organisation.
9 Apr 2021

What is data cleaning explain with examples? ›

Data cleaning is a process by which inaccurate, poorly formatted, or otherwise messy data is organized and corrected. For example, if you conduct a survey and ask people for their phone numbers, people may enter their numbers in different formats.

What is data preparation and cleaning? ›

Data preparation is the process of cleaning and transforming raw data prior to processing and analysis. It is an important step prior to processing and often involves reformatting data, making corrections to data, and combining datasets to enrich data.

What is the purpose of data cleaning? ›

What is data cleaning? Data cleaning is the process of fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset. When combining multiple data sources, there are many opportunities for data to be duplicated or mislabeled.

Why is it important to clean your data? ›

Data cleansing is also important because it improves your data quality and in doing so, increases overall productivity. When you clean your data, all outdated or incorrect information is gone – leaving you with the highest quality information.

What is the first step for data analyst should take to clean their data? ›

Step 1: Remove duplicate or irrelevant observations

Remove unwanted observations from your dataset, including duplicate observations or irrelevant observations. Duplicate observations will happen most often during data collection.

What is data cleansing process? ›

Data cleansing, also referred to as data cleaning or data scrubbing, is the process of fixing incorrect, incomplete, duplicate or otherwise erroneous data in a data set. It involves identifying data errors and then changing, updating or removing data to correct them.

Which of the following can be generally used to clean and prepare big data? ›

Which of the following can be generally used to clean and prepare big data. Answer - D) Data warehouse is generally used to clean and prepare big data.

How do you write a data cleaning report? ›

It's a good idea to consider the following questions when writing the report:
  1. What types of noise occurred in the data?
  2. What approaches did you use to remove the noise? Which techniques were successful?
  3. Are there any cases or attributes that could not be salvaged? Be sure to note data excluded due to noise.

What is data cleaning in Excel? ›

A major part of Excel Data Cleaning involves the elimination of blank spaces, incorrect, and outdated information. Some simple steps can easily do the procedure of Data Cleaning in Excel by using Excel Power Query.

Which is the first step in the data preparation process? ›

Steps in the data preparation process
  1. Data collection. Relevant data is gathered from operational systems, data warehouses, data lakes and other data sources. ...
  2. Data discovery and profiling. ...
  3. Data cleansing. ...
  4. Data structuring. ...
  5. Data transformation and enrichment. ...
  6. Data validation and publishing.

What is the meaning of data preparation? ›

Data preparation is an iterative-agile process for exploring, combining, cleaning and transforming raw data into curated datasets for self-service data integration, data science, data discovery, and BI/analytics.

Which of the following is the first step of data preparation? ›

The first step is to define a data preparation input model. This means to localize and relate the relevant data in the database. This task is usually performed by a database administrator (DBA) or a data warehouse administrator, because it requires knowledge about the database model.

When you do clean the data? ›

Data cleaning is the process of editing, correcting, and structuring data within a data set so that it's generally uniform and prepared for analysis. This includes removing corrupt or irrelevant data and formatting it into a language that computers can understand for optimal analysis.

What is data quality explain? ›

Data quality is the measure of how well suited a data set is to serve its specific purpose. Measures of data quality are based on data quality characteristics such as accuracy, completeness, consistency, validity, uniqueness, and timeliness.

What are the data issues in data cleaning? ›

This process of making data accurate and consistent is riddled with many problems, few of which are mentioned below:
  • High Volume of Data: Table of Contents [hide] ...
  • Misspellings: ...
  • Lexical Errors: ...
  • Misfielded Value: ...
  • Domain Format Errors: ...
  • Irregularities: ...
  • Missing Values: ...
  • Contradiction:
22 Jan 2016

How do I clean my database? ›

Here are 5 ways to keep your database clean and in compliance.
  1. 1) Identify Duplicates. Once you start to get some traction in building out your database, duplicates are inevitable. ...
  2. 2) Set Up Alerts. ...
  3. 3) Prune Inactive Contacts. ...
  4. 4) Check for Uniformity. ...
  5. 5) Eliminate Junk Contacts.

What was the most challenging part of cleaning the data? ›

Data cleaning is tricky and time-consuming

Cleaning the data requires removal of duplications, removing or replacing missing entries, correcting misfielded values, ensuring consistent formatting and a host of other tasks which take a considerable amount of time.

Which is best tool for data analysis? ›

Top 10 Data Analytics Tools You Need To Know In 2022
  • R and Python.
  • Microsoft Excel.
  • Tableau.
  • RapidMiner.
  • KNIME.
  • Power BI.
  • Apache Spark.
  • QlikView.
22 Jul 2022

Which example qualifies as a cleaning data? ›

One of the most common data cleaning examples is its application in data warehouses. A successful data warehouse stores a variety of data from disparate sources and optimizes it for analysis before any modeling is done.

What is the main components of big data? ›

In this article, we discussed the components of big data: ingestion, transformation, load, analysis and consumption.

Which of the following method is used to produce reports about data? ›

Q.Which of the following method is used to produce reports about data.
B.executive information systems.
C.query/report writing tool.
D.all the above.
Answer» d. all the above.
1 more row

What are the three steps that are followed to deploy a big data solution except? ›

This is an actual process of getting a bigdata solution. To analyze big data sets at terabyte or even petabyte-scale by MapReduce or Spark framework.
...
3. Data processing:
  • Step 1: Data Sources.
  • Step 2: Integration and Data Storage.
  • Step 3: Data Models and Analytics.
  • Step 4: Visualization and Reporting.
23 Jul 2019

How many steps are involved in data cleaning? ›

Data cleaning in six steps.

Is data cleaning part of data analysis? ›

However, data cleaning is also a vital part of the data analytics process. If your data has inconsistencies or errors, you can bet that your results will be flawed, too. And when you're making business decisions based on those insights, it doesn't take a genius to figure out what might go wrong!

How do you clean data for beginners? ›

To clean your data, you might do some or all of the following:
  1. Delete unnecessary columns. Chances are, your dataset will contain some values that aren't relevant to your analysis. ...
  2. Identify and remove duplicates. ...
  3. Deal with missing data. ...
  4. Remove unwanted outliers. ...
  5. Fix inconsistencies.

How do you clean in Excel? ›

Using the SHIFT key, select B1 to B1000. In the example, hold “Shift” and click cell “B1000” to select cells “B1” through “B1000.” Now, type “=CLEAN(A1)” (excluding the quotes) and then press “Ctrl-Enter” to apply the CLEAN function to the entire selection and clean every data point on our list.

How do you maintain data in Excel? ›

21 Expert Excel best practices & tips
  1. Think about the order of worksheets. Put different kinds of data on different worksheets. ...
  2. Keep your timeline consistent. ...
  3. Label columns and rows. ...
  4. Avoid repetitive formulas. ...
  5. Avoid hiding data. ...
  6. Keep styling consistent. ...
  7. Use positive numbers.

What are the 5 basic steps in data analysis? ›

article Data Analysis in 5 Steps
  • STEP 1: DEFINE QUESTIONS & GOALS.
  • STEP 2: COLLECT DATA.
  • STEP 3: DATA WRANGLING.
  • STEP 4: DETERMINE ANALYSIS.
  • STEP 5: INTERPRET RESULTS.

How do you prepare data for analysis? ›

Data Preparation Steps in Detail
  1. Access the data.
  2. Ingest (or fetch) the data.
  3. Cleanse the data.
  4. Format the data.
  5. Combine the data.
  6. And finally, analyze the data.
28 Jul 2021

What are the 7 steps of data analysis? ›

Here are seven steps organizations should follow to analyze their data:
  • Define goals. Defining clear goals will help businesses determine the type of data to collect and analyze.
  • Integrate tools for data analysis. ...
  • Collect the data. ...
  • Clean the data. ...
  • Analyze the data. ...
  • Draw conclusions. ...
  • Visualize the data.
24 Aug 2021

Why is it important to process your data in research? ›

Importance of data processing includes increased productivity and profits, better decisions, more accurate and reliable. Further cost reduction, ease in storage, distributing and report making followed by better analysis and presentation are other advantages.

What is the other name for data preparation stage? ›

Data preparation, often referred to as “pre-processing” is the stage at which raw data is cleaned up and organized for the following stage of data processing.

What are the three steps for getting data ready for analysis? ›

What are the three steps of getting data ready? Extract, transform and load.

How do I prepare data analysis in Excel? ›

Simply select a cell in a data range > select the Analyze Data button on the Home tab. Analyze Data in Excel will analyze your data, and return interesting visuals about it in a task pane.

Who is responsible for metrics data preparation? ›

Manager is responsible responsible for metrics due to representation for metrics did uh representation.

What are the things you need to consider in data discovering requirements? ›

6. What is involved in collecting data – six steps to success
  • Step 1: Identify issues and/or opportunities for collecting data. ...
  • Step 2: Select issue(s) and/or opportunity(ies) and set goals. ...
  • Step 3: Plan an approach and methods. ...
  • Step 4: Collect data. ...
  • Step 5: Analyze and interpret data. ...
  • Step 6: Act on results.

Why are pre cleaning steps important to complete prior to data cleaning? ›

Using tools to pre-clean the data will make it more efficient as we can able quickly get what you need from the data available. It removes major errors and inconsistencies that are inevitable when multiple sources of data are being pulled into one dataset.

Why would someone need to process or prepare the raw data? ›

Usually, organizations must process raw data for it to become information when putting it in a repository to become useful. One notable exception is the data lake, which is a storage repository that can hold massive volumes of raw data in its native format.

How data cleaning can be handled in preprocessing? ›

Tasks in data preprocessing

Data Cleaning: It is also known as scrubbing. This task involves filling of missing values, smoothing or removing noisy data and outliers along with resolving inconsistencies.

What is data preparation explain the necessary steps of data preparation? ›

Data preparation is the process of preparing raw data so that it is suitable for further processing and analysis. Key steps include collecting, cleaning, and labeling raw data into a form suitable for machine learning (ML) algorithms and then exploring and visualizing the data.

What is the difference between data cleaning and data cleansing? ›

Data cleansing, also referred to as data cleaning or data scrubbing, is the process of fixing incorrect, incomplete, duplicate or otherwise erroneous data in a data set. It involves identifying data errors and then changing, updating or removing data to correct them.

What is the first step for data analyst should take to clean their data? ›

Step 1: Remove duplicate or irrelevant observations

Remove unwanted observations from your dataset, including duplicate observations or irrelevant observations. Duplicate observations will happen most often during data collection.

What is data cleaning in Excel? ›

A major part of Excel Data Cleaning involves the elimination of blank spaces, incorrect, and outdated information. Some simple steps can easily do the procedure of Data Cleaning in Excel by using Excel Power Query.

What are the 5 major steps of data preprocessing? ›

Let's take a look at the established steps you'll need to go through to make sure your data is successfully preprocessed.
  • Data quality assessment.
  • Data cleaning.
  • Data transformation.
  • Data reduction.
24 May 2021

What was the most challenging part of cleaning the data? ›

Data cleaning is tricky and time-consuming

Cleaning the data requires removal of duplications, removing or replacing missing entries, correcting misfielded values, ensuring consistent formatting and a host of other tasks which take a considerable amount of time.

Why do we pre process data? ›

Data preprocessing is a required first step before any machine learning machinery can be applied, because the algorithms learn from the data and the learning outcome for problem solving heavily depends on the proper data needed to solve a particular problem – which are called features.

Which of the following is the first step of data preparation? ›

The first step is to define a data preparation input model. This means to localize and relate the relevant data in the database. This task is usually performed by a database administrator (DBA) or a data warehouse administrator, because it requires knowledge about the database model.

Why is it important to process your data in research? ›

Importance of data processing includes increased productivity and profits, better decisions, more accurate and reliable. Further cost reduction, ease in storage, distributing and report making followed by better analysis and presentation are other advantages.

Which example qualifies as cleaning data? ›

One of the most common data cleaning examples is its application in data warehouses. A successful data warehouse stores a variety of data from disparate sources and optimizes it for analysis before any modeling is done.

What does data processing mean? ›

data processing, manipulation of data by a computer. It includes the conversion of raw data to machine-readable form, flow of data through the CPU and memory to output devices, and formatting or transformation of output. Any use of computers to perform defined operations on data can be included under data processing.

Which of the following can be generally used to clean and prepare big data? ›

Which of the following can be generally used to clean and prepare big data. Answer - D) Data warehouse is generally used to clean and prepare big data.

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