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

What Is Data Wrangling?

Data wrangling 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, sometimes called to as data munging, data cleansing, data scrubbing, data cleaning or data remediation.

Table Of Contents

  1. Definition Of Data Wrangling
  2. 6 Basic Steps Of Data Wrangling
  3. Tools and Techniques Of Data Wrangling
  4. How Machine Learning Can Help In Data Wrangling
  5. How Express Analytics Can Help With Data Wrangling
  6. 11 Benefits of Data Wrangling
As a standalone business, various studies show different growth percentages, albeit positive, in the coming years for data wrangling. Like this one that 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.  

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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:

  1. 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
  2. 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
  3. 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
  4. 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?
  5. 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
  6. Data Publishing: Once all the above steps are completed, the final output of your data wrangling efforts are 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

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. 
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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. 

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. 
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.
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:
  • Supervised ML: used for standardizing and consolidating individual data sources
  • Classification: utilized to identify known patterns
  • Normalization: used to restructure data into proper form.
  • Unsupervised ML: used for exploration of unlabeled data

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. 

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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:

  1. 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.
  2. 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.
  3. 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.
  4. Useable data: Data wrangling improves data usability as it formats data for the end user.
  5. Helps with data flows: It helps to rapidly build data flows inside a user interface and effortlessly schedule and mechanize the data flow course.
  6. Aggregation: It helps integrate different types of information and their sources like database catalogs, web services, files, and so on.
  7. Handling big data: It helps end users process extremely large volumes of data effortlessly.
  8. Stops leakage: It is used to control the problem of data leakage while deploying machine learning and deep learning technologies.
  9. 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.
  10. 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.
  11. Overall, data wrangling improves the data analytics process.

Data Wrangling Examples and Use Cases

Outlier Detection: Detection of Outlier is a very important example and step in Data Wrangling. Outliers are those values of an attribute which lies far off from the majority values present for that attribute. Outliers can cause a Statistical or Machine Learning model to perform poorly because of the fact that they do not fall within the Normal Range of values for that attribute.
For outlier detection methods we need to know the 1st (Q1) and 3rd (Q3) Quartile of that numerical attribute.

Then we can calculate the Interquartile Range (IQR) as follows :
IQR = Q3-Q1.

If any value falls outside the Normal Range for that attribute, it is declared as an Outlier. The Normal Range is given by:

[Q1 – (1.5 * IQR), Q3 + (1.5 * IQR)]

Below Python Boxplot Images show the difference between value ranges of Price (in USD) attribute, before and after the removal of outliers.

Data Wrangling Examples with Detection of Outliers
1st Graph

As we can see, that the Maximum Price value in the 1st graph was $8000 and the there were a lot of outliers present in the data.

Data Wrangling Examples without Outliers
2nd Graph

After the removal of outliers, the Maximum Price value in the 2nd graph became $350. For this attribute, we had Q3 = $180, Q1 = $69 and IQR = $111.

Business Use Case: Outlier Detection methods are used across Industries for different purposes, namely Fraud Detection, Traffic Monitoring, Web Fault Detection, Building of Intrusion Detection Systems etc.

For Example, Banks can provide us with their Customer Transaction data where we are supposed to identify Customers who look Suspicious based on their transaction records. In this case, if we observe the average transaction amount of the customers based on a certain time span and plot the data, we can easily identify the customers whose Average Transaction Amount looks quite off as compared to the other customers. Then we can alert the Bank about those customers so that they can make further detailed enquiries about their transaction details.

Standardization of Textual Data in Python: In many cases, we perform comparison based on textual data. A common issue observed with such comparisons is lack of a standard data format for string type attributes. For example, the same words may not be detected if one starts with a capital case letter and the other does not. To overcome this, we should standardize the strings. We can convert all of them to Upper case or Lower case letters. Also we should perform Data Cleaning (major part of data wrangling) of textual data to make sure that the same format is followed across different datasets.
Data Cleaning example of two Dummy Datasets: Below is an example of two Dummy Datasets. One dataset contains records of Height in Feet and the other contains records of Date Of Births, for 5 different actors.

Standardization of Textual Data in Python

As we can see, the values in the Actor Name column in the HEIGHT dataset looks correct, but in the DOB dataset it contains unwanted characters. So if we try to create a single dataset by joining these two datasets based on the Actor Name attribute, then we get an empty dataset. This is because the values in the Actor Name column need to match in the two datasets for the join to occur successfully.

Standardization of Text Data in Python 2

To resolve this, we need to clean the values in the Actor Name column in the DOB dataset and then try to join the two datasets. We will use the re library of Python to implement Regular Expressions and come up with a cleaned set of text. We also use the string library of Python to get all the punctuations of the English language.
Next we create a Python function that takes a text as input, cleans the text and returns it. We then apply this function on the Actor Name column of the DOB dataset.

standardize text data in python

We also apply the same function to the Actor Name column of the HEIGHT dataset to make sure that the Actor Name columns in both the datasets are Standardized and the join occurs without any issue.

text normalization example

Finally we join the two datasets based on the Actor Name column to get the final dataset. This time we see that we get the desired result, and not an empty dataset as before.

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. 

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.


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


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