What is Data Wrangling? Steps, Solutions, and Tools

These days almost anything can be a valuable source of information. The primary challenge lies in extracting the insights from the said information and make sense of it, which is the point of Big Data. However, you also need to prep the data at first, and that is Data Wrangling in a nutshell.

The nature of the information is that it requires a certain kind of organization to be adequately assessed. This process requires a crystal clear understanding of which operations need what sort of data.

Let’s look closer at wrangled data and explain why it is so important.

What Is Data Wrangling? Data Wrangling Definition

Data Wrangling process (also known as Data Munging) is the process of transforming data from its original “raw” form into a more digestible format and organizing sets from various sources into a singular coherent whole for further processing.

What is “raw data”? It is any repository data (texts, images, database records) that is documented but yet to be processed and fully integrated into the system.

The process of wrangling can be described as “digesting” data (often referred to as “munging” thus the alternative term “data wrangling techniques”) and making it useful (aka usable) for the system. It can be described as a preparation stage for every other data-related operation.

Wrangling the data is usually accompanied by Mapping. The term “Data Mapping” refers to the element of the wrangling process that involves identifying source data fields to their respective target data fields. While Wrangling is dedicated to transforming data, Mapping is about connecting the dots between different elements.

What is the Purpose of Data Wrangling?

The primary purpose of data wrangling can be described as getting data in coherent shape. In other words, it is making raw data usable. It provides the substance for further proceedings.

As such, Data Wrangling acts as a preparation stage for the data-mining process. Process-wise these two operations are coupled together as you can’t do one without another.

Overall, data wrangling covers the following processes:

  • Getting data from the various source into one place
  • Piecing the data together according to the determined setting
  • Cleaning the data from the noise or erroneous, missing elements

It should be noted that Data Wrangling is a somewhat demanding and time-consuming operation both from computational capacities and human resources. Data wrangling takes over half of what data scientist does.

On the upside, the direct result of this profound — data wrangling that’s done right makes a solid foundation for further data processing.

Data Wrangling Steps

Data Wrangling is one of those technical terms that are more or less self-descriptive. The term “wrangling” refers to rounding up information in a certain way.

This operation includes a sequence of the following processes:

  1. Preprocessing — the initial state that occurs right after the acquiring of data.
  2. Standardizing data into an understandable format. For example, you have a user profile events record, and you need to sort it by types of events and time stamps;
  3. Cleaning data from noise, missing, or erroneous elements.
  4. Consolidating data from various sources or data sets into a coherent whole. For example, you have an affiliate advertising network, and you need to gather performance statistics for the current stage of the marketing campaign;
  5. Matching data with the existing data sets. For example, you already have user data for a certain period and unite these sets into a more expansive one;
  6. Filtering data through determined settings for the processing.

Data Wrangling Machine Learning Algorithms

Overall, there are the following types of machine learning algorithms at play:

  • Supervised ML algorithms are used for standardizing and consolidating disparate data sources:
    • Classification is used to identify known patterns;
    • Normalization is used to flatten the independent variables of data sets and restructure data into a more cohesive form.
  • Unsupervised ML algorithms are used for the exploration of unlabeled data:


How Data Wrangling solves major Big Data / Machine Learning challenges?

Data Exploration

The most fundamental result of data mapping in the data processing operation is exploratory. It allows you to understand what kind of data you have and what you can do with it.

While it seems rather apparent — more often than not this stage is skewed for the sake of seemingly more efficient manual approaches.

Unfortunately, these approaches often leave out and miss a lot of valuable insights into the nature and the structure of data. In the end, you will be forced to redo the thing properly to make possible further data processing operations.

Automated Data Wrangling goes through data in more ways and presents many more insights that can be worthwhile for business operation.


Unified and Structured Data

It is fair to say that data always comes in as a glorious mess in different shapes and forms. While you may have a semblance of comprehension of “what it is” and “what it is for” — data, as it is in its original form, raw data is mostly useless if it is not organized correctly beforehand.

Data Wrangling and subsequent Mapping segments and frames data sets in a way that would best serve its purpose of use. This makes datasets freely available for extracting any insights for any emerging task.

On the other hand, clearly structured data allows combining multiple data sets and gradually evolve the system into more effective.

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Data Clean-up from Noise / Errors / Missing Information

Noise, errors, and missing values are common things in any data set. There are numerous reasons for that:

  • Human error (so-called soapy eye);
  • Accidental Mislabeling;
  • Technical glitches;

Its impact on the quality of the data processing operation is well-known — it leads to poorer quality of results and subsequently less effective business operation. For the machine learning algorithms noisy, inconsistent data is even worse. If the algorithm is trained in such datasets — it can be rendered useless for its purposes.

This is why data wrangling is there to right the wrongs and make everything the way it was supposed to be.

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In the context of data cleaning, wrangling is doing the following operations:

  • Data audit — anomaly and error/contradiction detection through statistical and database approaches.
  • Workflow specification and execution — the causes of anomalies and errors are analyzed. After specifying their origin and effect in the context of the specific workflow — the element is then corrected or removed from the data set.
  • Post-processing control — after implementing the clean-up — the results of the cleaned workflow are reassessed. In case if there are further complications — a new cycle of cleaning may occur.

Minimized Data Leakage

Data Leakage is often considered one of the biggest challenges of Machine Learning. And since ML algorithms are used for data processing — the threat grows exponentially. The thing is — prediction relies on the accurateness of data. And if the calculated prediction is based on uncertain data — this prediction is as good as a wild guesstimation.

What is Data Leakage? The term refers to instances when the training of the predictive model uses data outside of the training data set. So-called “outside data” can be anything unverified or unlabeled for the model training.

The direct result of this is an inaccurate algorithm that provides you with incorrect predictions that can seriously affect your business operation.

Why does it happen? The usual cause is a messy structure of the data with no clear border signifiers where is what and what is for what. The most common type of data leakage is when data from the test set bleeds into the training data set.  

Extended Data Wrangling and Data Mapping practices can help to minimize its possibility and subsequently neuter its impact.

Data Wrangling Tools

Basic Data Munging Tools

Data Wrangling in Python

  1. Numpy (aka Numerical Python) — the most basic package. Lots of features for operations on n-arrays and matrices in Python. The library provides vectorization of mathematical operations on the NumPy array type, which improves performance and accordingly speeds up the execution.
  2. Pandas — designed for fast and easy data analysis operations. Useful for data structures with labeled axes. Explicit data alignment prevents common errors that result from misaligned data coming in from different sources.
  3. Matplotlib — Python visualization module. Good for line graphs, pie charts, histograms, and other professional grade figures.
  4. Plotly — for interactive, publication-quality graphs. Excellent for line plots, scatter plots, area charts, bar charts, error bars, box plots, histograms, heatmaps, subplots, multiple-axis, polar graphs, and bubble charts.
  5. Theano — library for numerical computation similar to Numpy. This library is designed to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently.

Data Wrangling in R

  1. Dplyr – essential data-munging R package. Supreme data framing tool. Especially useful for data management operating by categories.
  2. Purrr – good for list function operations and error-checking.
  3. Splitstackshape – an oldie but goldie. Good for shaping complex data sets and simplifying the visualization.
  4. JSOnline – nice and easy parsing tool.
  5. Magrittr – good for wrangling scattered sets and putting them into a more coherent form.


Staying on your path in the forest of information requires a lot of concentration and effort. However, with the help of machine learning algorithms, the process becomes a lot simpler and manageable. 

When you gain insights and make your business decisions based on them, you gain a competitive advantage over other businesses in your industry. Yet, it doesn’t work without doing the homework first and that’s why you need data wrangling processes in place. 

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Benefits and Challenges of Big Data in Customer Analytics

“The world is now awash in data, and we can see consumers in a lot clearer ways,” said Max Levchin, PayPal co-founder.

Simply gather data, however, doesn’t bring any benefits, it’s the decision-making and analytics skills that help to survive in the modern business landscape. It’s not something new, but we need to know how to construct engaging customer service using the information we have at hand. Here’s where Big Data analytics becomes a solution. 

These days, the term Big Data is thrown around so much it seems like it is a “one-size-fits-all” solution. The reality is a bit different, but the fact remains the same — to provide well-oiled and effective customer service, adding a data analytics solution to the mix can be a decisive factor.

What is Big Data and how big is Big Data?

Big Data is extra-large amounts of information that require specialized solutions to gather, process, analyze, and store it to use in business operations. 

Machine learning algorithms help to increase the efficiency and insightfulness of the data that is gathered (but more on that a bit later.)

Four Vs of Big Data describe the components:

  • Volume — the amount of data
  • Velocity — the speed of processing data
  • Variety — kinds of data you can collect and process
  • Veracity — quality, and consistency of data

[Source: IBM Blog]

How big is Big Data? According to the IDC forecast, the Global Datasphere will grow to 175 Zettabytes by 2025 (compared to 33 Zettabytes in 2018.) In case you’re wondering what a zettabyte is, it equals a trillion gigabytes. IDC says that if you store the entire Global Datasphere on DVDs, then you’d be able to get a stack of DVDs that would get you to the Moon 23 times or circle the Earth 222 times. 

Speaking regarding single Big Data projects, the amounts are much smaller. A software product or project passes the threshold of Big Data once they have over a terabyte of data.

Class Size Manage with
Small < 10 Gb Excel, R
Medium 10 GB – 1 TB Indexed files, monolithic databases
Big > 1 TB Hadoop, cloud, distributed databases

Now let’s look at how Big Data fits into Customer Services.

Big Data Solutions for Customer Experience

Data is everything in the context of providing Customer Experience (through CRMs and the likes), and it builds the foundation of the business operations, providing vital resources.

Every bit of information is a piece of a puzzle – the more pieces you have, the better understanding of the current market situation and the target audience you have. As a result, you can make decisions that will bring you better results, and this is the underlying motivation behind transitioning to Big Data Operation.

Let’s look at what Big Data brings to the Customer Experience.

Big Data Customer Analytics — Deeper Understanding of the Customer

The most obvious contribution of Big Data to the business operation is a much broader and more diverse understanding of the target audience and the ways the product or services can be presented to them most effectively.

The contribution is twofold:

  1. First, you get a thorough segmentation of the target audience;
  2. Then you get a sentiment analysis of how the product is perceived and interacted with by different segments.

Essentially, big data provides you with a variety of points of view on how the product is and can be perceived, which opens the door to many possibilities of presenting the product or service to the customer in the most effective manner according to the tendencies of the specific segment.

Here’s how it works. You start by gathering information from the relevant data sources, such as:

  • Your website;
  • Your mobile and web applications (if available);
  • Marketing campaigns;
  • Affiliate sources.

The data gets prepared for the mining process and, once processed, it can offer insights on how people use your product or service and highlight the issues. Based on this information, business owners and decision-makers can decide how to target the product with more relevant messaging and address the areas for improvement. 

The best example of putting customer analytics to use is Amazon. They are using it to manage the entire product inventory around the customer based on the initial data entered and then adapting the recommendations according to the expressed preferences.

Sentiment Analysis — Improved Customer Relationship

The purpose of sentiment analysis in customer service is simple — to give you an understanding of how the product is perceived by different users in the form of patterns. This understanding lays a foundation for the further adjustment of the presentation and subsequently more precise targeting of the marketing effort.

Businesses can apply sentiment analysis in a variety of ways. For example:

  • A study of interaction with the support team. This may involve semantic analysis of the responses or more manual filling-in of the questionnaire regarding an instance of the particular user.
  • An interpretation of the product use via performance statistics. This way, pattern recognition algorithms provide you with hints at which parts of the product are working and which require some improvements.

For example, Twitter shows a lot of information regarding the ways various audience segments interact and discuss certain brands. Based on this information, the company can seriously adjust their targeting and strike right in the center.

All in all, sentiment analysis can help with predicting user intent and managing the targeting around it.

Read our article: Why Business Applies Sentiment Analysis

Unified User Models – Single Customer Relationship Across the Platforms – Cross-Platform Marketing

Another good thing about collecting a lot of data is that you can merge different sets from various platforms into the unified whole and get a more in-depth picture of how a given user interacts with your product via multiple platforms.

One of the ways to unify user modeling is through matching credentials. Every user gets the spot in the database and when the new information from the new platform comes in is added to the mix thus you are can adjust targeting accordingly.

This is especially important in the case of eCommerce and content-oriented ventures. The majority of modern CRM’s got this feature in their bags. 

Superior Decision-Making

Knowing what are you doing and understanding when is the best time to take action are integral elements of the decision-making process. These things depend on the accurateness of the available information and its flexibility regarding the application.

In the context of customer relationship management (via platforms like Salesforce or Hubspot), the decision-making process is based on available information. The role of Big Data, in this case, is to augment the foundation and strengthen the process from multiple standpoints.

Here’s what big data brings to the table:

  1. Diverse data from many sources (first-party & third-party)
  2. Real-time streaming statistics
  3. Ability to predict possible outcomes
  4. Ability to calculate the most fitting courses of actions

All this combined gives the company a significant strategic advantage over the competition and allows standing more firmly even in the shake market environment. It enhances the reliability, maintenance, and productivity of the business operation.

Performance Monitoring

With the market and the audience continually evolving, it is essential to keep an eye on what is going on and understand what it means for your business operation. When you have Big Data, the process becomes more natural and more efficient:

  • Modern CRM infrastructure can provide you with real-time analytics from multiple sources merged into one big picture.
  • Using this big picture, you can explore each element of the operation in detail, keeping the interconnectedness in mind. 
  • Based on the available data, you can predict possible outcome scenarios. You can also calculate the best courses of action based on performance and accessible content.

As a direct result, your business profits from adjusted targeting on the go without experiencing excessive losses due to miscalculations. Not all experiments will lead to revenue (because there are people involved, who are unpredictable at times), but you can learn from your wins as well as from your mistakes. 

Diverse Data Analytics

Varied and multi-layered data analytics are another significant contribution to decision-making.

Besides traditional descriptive analytics that shows you what you’ve got, businesses can pay closer attention to the patterns in the data and get:

  • Predictive Analytics, which calculates the probabilities of individual turns of events based on available data.
  • Prescriptive Analytics, which suggests which possible course of action is the best according to available data and possible outcomes.

With these two elements in your mix, you get a powerful tool that gives multiple options and certainty in the decision-making process.


Cost-effectiveness is one of the most biting factors in configuring your customer service. It is a balancing act that is always a challenge to manage. Big Data solutions make the case of making the most out of the existing system and making every bit coming into count.

There are several ways it happens. Let’s look at the most potent:

  1. Reducing operational costs — keeping an operation intact is hard. Process automation and diverse data analytics make it less of a headache and more of an opportunity. This is especially the case for Enterprise Resource Planning systems. Big data solutions allow processing more information more efficiently with less messing around and wasting opportunities.
  2. Reducing marketing costs — automated studies of customer behavior and performance monitoring make the entire marketing operation more efficient in its effort thus minimizing wasted resources.
These benefits don’t mean that big data analytics will be cheap from the start. You need proper architecture, cloud solutions, and many other resources. However, in the long-term, it will pay off. 

Customer Data Analysis Challenges

While the benefits of implementing Big Data Solutions are apparent, there are also a couple of things you need to know before you start doing it.

Let’s look at them one by one.

Viable Use Cases

First and foremost, there is no point in implementing a solution without having a clue why you need it. The thing with Big Data solutions is that they are laser-focused on specific processes. The tools are developed explicitly for certain operations and require accurate adjustment to the system. These are not Swiss army knives — visualizing tools can’t perform a mining operation and vice versa.

To understand how to apply big data to your business, you need to:

  • Define the types of information you need (user data, performance data, sentiment data, etc.)
  • Define what you plan to do with this data (store for operational purposes, implement into marketing operation, adjust the product use)
  • Define tools you would need to do those processes? (Wrangling, mining, visualizing tools, machine learning algorithms, etc.)
  • Define how you will integrate the processed data into your business to make sure you’re not just collecting information, but it is useful.
Without putting the work into the beginning stages, you risk ending up with a solution that would be costly and utterly useless for your business. 

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Because big data is enormous, scalability is one of the primary challenges with this type of solution. If the system runs too slow or unable to go under heavy pressure — you know it’s trouble.

However, this is one of the simpler challenges to solve due to one technology — cloud computing. With the system configured correctly and operating in the cloud, you don’t need to worry about scalability. It is handled by internal autoscaling features and thus uses as much computational capacity as required.

Data Sources

While big data is a technologically complex thing, the main issue is the data itself. The validity and credibility of the data sources are as important as the data coming from them. 

It is one thing when you have your sources and know for sure from where the data is coming. The same thing can be said about well-mannered affiliate sources. However, when it comes to third-party data — you need to be cautious about the possibility of not getting what you need.

In practice, it means that you need to know and trust those who sell you information by checking the background, the credibility of the source, and its data before setting up the exchange.

Data Storage

Storing data is another biting issue related to Big Data Operation. The question is not as much “Where to store data?” as “How to store data?” and there are many things you need to sort out beforehand.

Data processing operation requires large quantities of data being stored and processed in a short amount of time. The storage itself can be rather costly, but there are several options to choose from and different types of data for each:

  1. Google Cloud Storage — for backup purposes
  2. Google DataStore — for key-value search
  3. BigQuery — for big data analytics
This solution is not the only one available but this is what we use at the APP Solutions, and it works great. 


In many ways, Big Data is a saving grace for customer services. The sheer quantity of available data brims with potentially game-changing insights and more efficient working processes.

Discuss with your marketing department what types of information they would like and think of the ways how to get that user data from your customers to make their journey more pleasurable and customized to their likes. And may big data analytics and processing help you along the way.

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