What is Data Wrangling? What are the Steps in Data Wrangling?

What is Data Wrangling?

Data wrangling, also known as data blending or data remediation, is the practice of converting and then plotting data from one “raw” form into another.
The aim is to make it ready for downstream analytics. Often in charge of this is a data wrangler or a team of “mungers”.
As any data analyst will vouch for, this is where you get your hands “dirty” before getting on with the actual analytics with its models and visual dashboards.
Data wrangling encompasses all the work done on your data prior to the actual analysis.
It includes aspects such as weighing data quality and data context and then converting the data into the required format.
Data wrangling is sometimes called to as data munging, data cleansing, data scrubbing, or data cleaning.
As a standalone business, various studies show different growth percentages, albeit positive, in the coming years for data wrangling.
This one forecasts that the data wrangling market, currently at about over the US $1.30 billion, will touch $ 2.28 billion by 2025, at a CAGR of 9.65% between 2020 and 2025. 
By and large, data wrangling still remains a manual process. When humans are involved with any process, two things are bound to happen – expenditure of time, and errors getting in.
If your enterprise does not have a dedicated team of wranglers, it is then left to your data analysts to do this work.
Industry surveys have shown that between 70 to 80% of a data analyst’s time goes into data wrangling, or just getting the data ready. That’s an awful “waste” of “qualified” time. 
In an earlier post, we had talked about how “dirty” data or poor data riddled with inaccuracies and errors was responsible for erroneous analysis. This leads to time loss, missed objectives, and loss of revenue.
Getting your data “prepped” for analysis is THE most important one in the data analytics process; it just cannot be emphasized enough. Without this step, algorithms will not derive any valuable pattern.  

Detect and manage outliers in data wrangling efficiently. Talk to our experts for machine learning solutions

Importance of Data Wrangling in Data-Driven Marketing

Data wrangling has been the complete domain of trained and professional data scientists; this process may require up to 80% of the analysis cycle.   

Identifying new and understanding present customers

Marketers use customer information from different devices, applications, and touchpoints to improve the complexity of customer segmentation and obtain a clearer understanding of their audiences by disclosing their changing behaviors and interests rapidly. 

Data wrangling tools rapidly format siloed information from social media, transactional, and various origins to convert customer reactions into insights that are used to boost marketing efforts. 

Boost customer loyalty

Customer information can be used by marketers to cultivate stronger connections with fresh and present customers. 

By examining information from each interaction a client has had with a business, marketers can offer tailored client experiences according to real-time intelligence.   

Data Wrangling Challenges

There are many challenges associated with data wrangling, particularly when creating a datasheet that outlines the flow of business:

Examining use cases: The data requirements of stakeholders depend purely on the queries they’re trying to address using data.

Analysts should recognize use cases thoroughly by researching what subset of entities are suitable, whether they are attempting to forecast the likelihood of an event or evaluating a future amount. 

Inspecting identical entities: After downloading impure data, it’s difficult to judge what’s unrelated and what’s related.

For instance, we consider “consumer” as an entity. The data sheet may have a consumer named “Simon Joseph.” Another column might contain a different consumer, “Simon J.”

In these situations, you have to deeply examine numerous factors while concluding the columns. 

Exploring data: Data can be in big files. So, it’s challenging to make a model and feature selection.

Eliminate redundancies in the data ahead of exploring the relationships between the results.

For instance, there may be two columns for color, one in French and another in English.

It might result in complicated data when you aren’t eliminating such redundancies. 

Preventing selection bias: Selection bias happens when collected data doesn’t describe the future or true population of cases.

Make sure that the training sample data describes the implementation sample.

One of the biggest challenges in machine learning today continues to be in automating data wrangling. One of the main hurdles here is data leakage.

The latter refers to the fact that during the training of the predictive model using ML, it uses data outside of the training data set, which is unverified and unlabeled.

Data Wrangling vs. Data Cleaning vs. Data Mining

The activity of transforming and mapping data from one raw form to another is called data wrangling.

This involves feature engineering, aggregation and summarization of data, and data reformatting. 

Data cleaning is the activity of taking impure data and storing it in precisely the same format, erasing, adjusting, or improving issues associated with data validity.

The data cleaning process can start only after reviewing and characterizing the data source.  

Data mining is a narrower field that identifies invisible patterns in massive datasets for forecasting results in the future.

However, data cleaning is the preparation of data for the data mining process if the most important information can be extracted from the given data source.

Important Characteristics of Data Wrangling

Useable data

Data wrangling formats the information for the end user, which enhances data usability. 


It helps in merging various forms of data and their origins including files, online services, and database catalogs. 

Data preparation

Data preparation is challenging to achieve better results from deep learning and ML initiatives, so data munging is important. 

Faster decision

Wrangling the data enables the wrangler to make faster decisions while enriching, cleaning, and converting the data into the best image.


Data wrangling techniques like automated data integration tools clean and convert raw or impure data into a standard form that can be used frequently according to end needs.

Businesses use this standardized data to adopt challenging cross-dataset analytics. 

Saves time

As mentioned earlier, data analysts spend enough time sourcing data from numerous origins and updating data sets instead of conducting fundamental analysis.

Data wrangling offers errorless data to analysts quickly.

What are the Tools and Techniques of Data Wrangling?

It has been observed that about 80% of data analysts spend most of their time in data wrangling and not the actual analysis.

Data wranglers are often hired for the job if they have one or more of the following skillsets: Knowledge in a statistical language such as R or Python, knowledge in other programming languages such as SQL, PHP, Scala, etc.

They use certain tools and techniques for data wrangling, as illustrated below:

  1. Excel Spreadsheets: this is the most basic structuring tool for data munging
  2. OpenRefine: a more sophisticated computer program than Excel
  3. Tabula: often referred to as the “all-in-one” data wrangling solution
  4. CSVKit: for conversion of data
  5. Python: Numerical Python comes with many operational features. The Python library provides vectorization of mathematical operations on the NumPy array type, which speeds up performance and execution
  6. Pandas: this one is designed for fast and easy data analysis operations.
  7. Plotly: mostly used for interactive graphs like line and scatter plots, bar charts, heatmaps, etc

R tools

  1. Dplyr:  a “must-have” data wrangling R framing tool
  2. Purrr: helpful in list function operations and checking for mistakes
  3. Splitstackshape: very useful for shaping complex data sets and simplifying visualization
  4. JSOnline: a useful parsing tool

Why the Need for Automated Solutions?

The introduction of artificial intelligence (AI) in data science has made it imperative that data wrangling is done with the strictest checks and balances.
Machine learning (ML), a subset of AI, requires a feed of vast amounts of data for an enterprise to derive the maximum value from its data.
There just can’t be a time lag between these two processes – data wrangling and data analytics – especially if AI is involved. 

What are Some Good Practices for Data Wrangling?

Data wrangling can be done in numerous ways. The exact approach can change according to Who the data is given to (a business or an individual). 

To achieve effective outcomes, there are a few good practices one should know: 

Select the proper data

Data selection is very important to choose the data that is needed at present for a particular reason and make it easily available for later use if required. 

You need to remember a few tips for choosing the proper data:

  1. Skip data with multiple nulls, repetitive, or identical values
  2. Avoid calculated or derived values and select ones that are near the source
  3. Retrieve information from different platforms
  4. Refine the information to select a subject that matches the rules and conditions
Recognize the data  

When you know the information, you can decide on a suitable quality level for the intended use of the data. 

You need to remember the following points:

  1. Study the data, file formats, and database
  2. Use visualization features to examine the present condition of the data
  3. Use profiling to produce Data Quality Metrics
  4. Understand the limitations of data
Reassess your work

Even though a company may have tight guidelines for data wrangling, experts may observe that after the completion of the process, there is still an opportunity for improvement.

Moreover, the wrangler may encounter operation errors. 

Upon finishing the project, reassess the data to make sure that it is of good quality and well organized.    

What are the 6 Steps in Data Wrangling?

It is often said that while data wrangling is the most important first step in data analysis, it is the most ignored because it is also the most tedious.

To prepare your data for analysis, as part of data munging, there are 6 basic steps one needs to follow.

They are:

Data Discovery: This is an all-encompassing term that describes understanding what your data is all about. In this first step, you get familiar with your data.

Data Structuring: When you collect raw data, it initially is in all shapes and sizes, and has no definite structure.

Such data needs to be restructured to suit the analytical model that your enterprise plans to deploy

Data Cleaning: Raw data comes with some errors that need to be fixed before data is passed on to the next stage.

Cleaning involves the tackling of outliers, making corrections, or deleting bad data completely

Data Enriching: By this stage, you have kind of become familiar with the data in hand.

Now is the time to ask yourself this question – do you need to embellish the raw data? Do you want to augment it with other data?

Data Validating: This activity surfaces data quality issues, and they have to be addressed with the necessary transformations.

The rules of validation rules require repetitive programming steps to check the authenticity and the quality of your data

Data Publishing: Once all the above steps are completed, the final output of your data wrangling efforts is pushed downstream for your analytics needs.

Data wrangling is a core iterative process that throws up the cleanest, most useful data possible before you start your actual analysis.

6 Steps in Data Wrangling

When Should You Use Data Wrangling?

You need to use data wrangling when you obtain data from multiple origins and require modification before adding it to a database and executing queries.

Listed below are a few examples of when data wrangling would be useful:

Digitizing records: Many people will write addresses, dates, and other details in multiple ways, so after the data is digitized, it must be standardized. 

Optical character recognition (OCR): This automated method is used when transferring data from paper manually would be too costly.

OCR can digitize data automatically, even though mistakes will still need to be wrangled. 

Gathering information from various countries: Various formats are used for data entry in various countries.

For instance, Denmark doesn’t use commas to separate numbers but instead uses a period (35.000 = thirty-five thousand).

Data from various origins like this has to be standardized to be queried together in a single big database. 

Scraping data from websites: Data on websites is kept and displayed in a way that is readable and usable by humans.

When data is scraped from websites, it has to be organized into a format that is fit for querying and databases.   

Additionally, data wrangling is also used to:

  1. Save steps related to the preparation and implementation of comparable datasets
  2. Find duplicates, anomalies, and outliers
  3. Preview and offer feedback
  4. Reshape and pivot data
  5. Aggregate data
  6. Merge information across different origins via joins
  7. Schedule a procedure to execute a trigger-oriented or time-oriented event

When Shouldn’t You Use Data Wrangling?

Corporate users use data wrangling to manipulate data.

To understand whether you have to wrangle data, you have to decide what you are going to do with the data and whether it is feasible in the present condition of the data.

How Machine Learning can help in Data Wrangling

The development of automated solutions for data munging faces one major hurdle: the cleaning of data requires intelligence and not a mere repetition of work.
Data wrangling means having an understanding of what exactly you are looking for in order to resolve the variances between data sources or say, the conversion of units. 
A typical munging operation consists of these steps: extraction of the raw data from sources, the use of an algorithm to parse the raw data into predefined data structures, and moving the results into a data mart for storage and future use. 
The few data munging automated software that are available today use end-to-end ML pipelines.
But these are far and few in-between. The market certainly requires more automated data wrangling software. 
These are the different types of machine learning algorithms:
  1. Supervised ML: used for standardizing and consolidating individual data sources
  2. Classification: utilized to identify known patterns
  3. Normalization: used to restructure data into proper form.
  4. Unsupervised ML: used for exploration of unlabeled data

What are the Various Use Cases of Data Wrangling?

Some of the frequently seen use cases of data wrangling are highlighted below:

Financial insights

Data wrangling can be used to identify insights invisible in data, forecast trends, and predict markets. It assists in making investment decisions.

Unified format

Various departments of the organization use multiple systems to collect data in numerous formats.

Data wrangling aids in the unification of data and converts it into a single format to offer a comprehensive view. 

Industry Use Cases of Data Wrangling

Listed below are a few examples:


Helps finance and banking companies access, govern, and manage good-quality data to estimate, manage, and track dangers for continuous credit, operational, and market risk management needs. 


It helps pharma and healthcare businesses boost research and development, speed up drug detection, and provide breakthrough therapies quickly.


Supports underwriting and risk management use cases.


Helps manage different use cases including operational intelligence, asset management, and supply chain optimization. 

Public sector

Supports multiple use cases including case management, cybersecurity, and enhancing the audience experience.

As it is, a majority of industries are still in the early stages of the adoption of AI for data analytics.

They face several hurdles: the cost, tackling data in silos, and the fact that it is not really easy for business analysts – those who do not have a data science or engineering background – to understand machine learning. 

Express Analytics Webinar: Don’t Wrestle With Your Data. The What, Why & How Of Data Wrangling

While a lot of effort has been put into automating data analytics by using advanced technologies such as AI and ML, the same cannot be said for the process of data munging. Automated solutions here are the need of the hour.
Most enterprises continue to use the traditional extract-transform-load (ETL) tools for the same. They cannot be blamed because there just ain’t enough data-wrangling automated solutions in the market.

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The use of open source languages

A few data experts have started using open source programming languages R and Python and their libraries for automation and scaling.
Using Python, straightforward tasks can be automated without much setup. Again, things here are still at a nascent stage. 

11 Benefits of Data Wrangling

Data wrangling is an important part of organizing your data for analytics. The data wrangling process has many advantages.

Here are some of the benefits:

Saves time: As we said earlier in this post, data analysts spend much of their time sourcing data from different channels and updating data sets rather than the actual analysis.

Data wrangling offers correct data to analysts within a certain timeframe.

Faster decision making: It helps managements take decisions faster within a short period of time.

The data wrangling process comes with the objective of obtaining the best outputs in the shortest possible time.

Data wrangling assists in enhancing the decision making process by an organization’s management.

Helps data analysts and scientists: Data wrangling guarantees that clean data is handed over to the data analyst teams.

In turn, it helps the team to focus completely on the analysis part. They can also concentrate on data modeling and exploration processes.

Useable data: Data wrangling improves data usability as it formats data for the end user.

Helps with data flows: It helps to rapidly build data flows inside a user interface and effortlessly schedule and mechanize the data flow course.

Aggregation: It helps integrate different types of information and their sources like database catalogs, web services, files, and so on.

Handling big data: It helps end users process extremely large volumes of data effortlessly.

Stops leakage: It is used to control the problem of data leakage while deploying machine learning and deep learning technologies.

Data preparation: The correct data preparation is essential in achieving good results from ML and deep learning projects, that’s why data munging is important.

Removes errors: By ensuring data is in a reliable state before it is analyzed and leveraged, data wrangling removes the risks associated with faulty or incomplete data.

Overall, data wrangling improves the data analytics process.

How Express Analytics can help with Your Data Wrangling Process

Our years of experience in handling data have shown that the data wrangling process is the most important first step in data analytics.

Our process includes all the six activities enumerated above like data discovery, etc, to prepare your enterprise data for analysis.

Our data wrangling process helps you find intelligence within your most disparate data sources.

We fix human error in the collection and labeling of data and also validate each data source.

All of this helps place actionable and accurate data in the hands of your data analysts, helping them to focus on their main task of data analysis.

Thus, the EA data wrangling process helps your enterprise reduce the time spent collecting and organizing the data, and in the long term helps your business seniors take better-informed decisions.

Click on the banner below to watch our three-part webinar – Don’t wrestle with your data: the what, why & how of data wrangling. In each of these webinars, our in-house analysts walk you through topics like, “How to craft a holistic data quality and management strategy” and “The trade-off between model accuracy and model processing speed”.

Click to watch our 3-part free webinar series on the Why, What & How Of Data Wrangling.

In conclusion: Given the amount of data being generated almost every minute today, if more ways of automating the data wrangling process are not found soon, there is a very high probability that much of the data the world produces shall continue to just sit idle, and not deliver any value to the enterprise at all. 

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