If your company collects large amounts of data online, then data wrangling
is an essential process you will need to carry out once you have the raw data you need. There are seven steps to successful data wrangling, and we provide these below.
Data wrangling, also known as data munging, is a critical process that follows data harvesting. Here are some frequently asked questions about data wrangling, what it is, and why
and how you should do it.
What is data wrangling?
Data wrangling is the process of cleaning, restructuring and enriching data. It can turn, or map, large amounts of raw data into a different format that makes the data more useful for the purposes of consumption and analysis, by better organizing it. It can combine diverse data into indexed and searchable data sets.
Once you have performed the necessary data extraction from the web, data wrangling should be the next task on your agenda. When you collect raw data sets, they can be untidy and complex. Data wrangling unifies and sorts the data so it is easy to access and translate into actionable insights.
Through data wrangling
, data sets can be transformed into usable and functional formats, with any bad data corrected or removed. Those who collect the data, or other non-technical stakeholders within the company, can then more quickly and easily understand the data and make better decisions based on it.
Which industries use data wrangling methods?
Any business that collects data online
should carry out data wrangling after extracting the necessary raw data. Companies within the e-commerce or travel industries, for example, regularly collect price comparison data. This gives them the insight and business intelligence they need to make informed decisions about how to price their products and services.
But large amounts of raw data that follows no consistent structure and may contain objects that do not belong are not very usable for analysis and strategic purposes. Data wrangling helps businesses turn data into actionable insight that can be acted on quickly. This is especially useful if organizations want to implement surge pricing or flexible pricing strategies, in order to react in real-time to changing market conditions and their competitors’ actions.
Why is data wrangling so important?
With data being used to inform almost every business decision, data needs to be prepared in a way that makes it usable and analyzable. Data on the web is increasingly diverse and unstructured, and without proper data preparation, data-related projects can fail.
Analysis and decision making may take too long to be meaningful, the data could be biased without you realizing, you could read the data the wrong way and make poor decisions as a result.
You need to spend time cleaning and organizing raw data before it can be consumed and scrutinized. At the same time, with data informing just about every business decision, business users have less time to wait on technical resources for prepared data.
Visualization and statistical applications usually need data sets that are structured and organized first, in order to provide the analysis you require. Converting your raw data into indexed, searchable sets of data enables you to gather intelligence, learn from it and make informed strategic decisions.
What are the benefits of data wrangling for my business?
Business analysts and stakeholders within your organization will be empowered to analyze complex data quickly and efficiently, once raw data has been wrangled and transformed.
Efficient use of time
Data wrangling means spending less time organizing unruly data before it can be used. IT professionals can focus on data acquisition and administration responsibilities, while analysts, non-technical people and other stakeholders can get insights faster and make informed decisions based on easily readable and digestible data.
Simple data handling
Data wrangling transforms raw data, which is messy and unstructured, into neat data arranged in rows and columns. It blends and enriches data so that it is more useful, meaningful and simpler to handle. Data from a variety of sources can be gathered together to provide deeper intelligence than more limited data.
Clearer visualization of data
You can export wrangled data into the platform of your choice, whether that is Microsoft Excel or any other analytics visualization tool. This can help you to summarize, sort, analyze and visualize your data.
Better decision making
Senior leaders within your organization are better equipped to make business decisions based on the large amounts of data you collect and process.
How do I perform data wrangling?
Our step-by-step guide to wrangling data below shows the 7 key steps in any data preparation process. You should repeat these steps as many times as you need to in order to achieve the results you want.
Once you have collected the raw data
you need, follow these steps:
You can use Python Pandas Library (see below) to merge your data sets, bringing them all together in one place.
Look at what data you have, and how you would like to organize it in order to make it easy to consume and analyze.
Since raw data is usually lacking in structure, it needs to be given a structure to allow for better analysis.
Remove any outliers within your data set, which can skew your results when you analyze your data. Change any null values and standardize the format of the data, to improve the quality and consistency.
Once you have cleaned your data, you will need to check what you have and decide whether you need additional data, for example by deriving new data from the existing data set, in order to achieve your goals.
Verify the consistency, quality and security of your data by validating your data. You can do this by checking whether the fields in your data sets are accurate or whether attributes are normally distributed, for example.
Publish the newly wrangled data somewhere so it can be used by you or other stakeholders in the future.
How can I perform data wrangling methods using Python?
Python is a programming language that can help you perform data wrangling. The Python Pandas Library
has built-in features that allow you to apply data transformation methods like merging, grouping and concatenating data so you can achieve your analytical goal.
Merging two or more data sets brings them into one place for easy analysis. Grouping data allows you to organize data by a certain characteristic, such as year, while concatenating data combines different data objects together so you can see them side by side.
To learn more about data wrangling with Python, read this tutorial
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