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.
Expanding the Advantages of Data Wrangling for Your Business
Data wrangling isn’t just a preparatory step for data analysis; it’s a transformative process that yields numerous benefits for your business, streamlining operations and enhancing decision-making capabilities. Let’s delve deeper into how data wrangling can revolutionize your approach to data management and utilization!
Facilitated Analysis for Business Insight
Data wrangling converts complex, raw datasets into formats that are immediately more accessible and analyzable. This transformation enables business analysts and stakeholders to derive insights more swiftly and with greater precision. By refining the data upfront, your team can bypass the intricacies of raw datasets, focusing instead on gleaning actionable intelligence that drives strategic decisions.
Optimized Time Management
The data wrangling process significantly reduces the time spent sifting through and organizing cumbersome datasets. This efficiency allows IT professionals to dedicate more resources to pivotal tasks such as data acquisition and governance. Simultaneously, analysts and non-technical personnel benefit from quicker access to insights, facilitating a more agile response to market changes and internal decision-making requirements.
Streamlined Data Operations
One of the hallmark benefits of data wrangling is its ability to tidy up messy, unstructured data into a well-organized matrix of rows and columns. This process not only simplifies data handling but also enriches the dataset by integrating and harmonizing information from diverse sources. The result is a comprehensive dataset that offers deeper insights and intelligence, enabling a more nuanced understanding of business dynamics.
Enhanced Data Visualization
The advent of data wrangling has significantly improved the visualization of data by allowing for its exportation to various platforms, such as Microsoft Excel or sophisticated analytics tools. This compatibility empowers users to summarize, sort, analyze, and depict data in visually engaging ways, making complex datasets comprehensible at a glance. Effective visualization is crucial for communicating insights across departments and to stakeholders who may not be data-savvy.
Informed Decision-Making
At its core, data wrangling equips senior leaders and decision-makers with a robust foundation for strategic planning. By providing a clearer, more comprehensive view of the organization’s data landscape, executives can make well-informed decisions that align with the company’s objectives and market position. This data-driven approach to leadership ensures that every decision is backed by solid, actionable intelligence, positioning the business for success in an increasingly competitive environment.
The benefits of data wrangling extend far beyond mere data cleanup. This essential process enhances your business’s analytical capabilities, improves efficiency, simplifies data handling, and supports better decision-making. By investing in data wrangling, you’re not just organizing data; you’re unlocking its full potential to propel your business forward.
How Do I Perform Data Wrangling?
Our comprehensive guide delineates the 7 pivotal steps in the data preparation journey, encouraging you to iterate these stages as necessary to refine your results and achieve your analytical objectives.
Once you have collected the raw data you need, follow these steps:
1. Joining
Leverage tools like the Python Pandas Library to amalgamate diverse datasets into a centralized repository. This consolidation is foundational for subsequent operations, ensuring all pertinent data is accessible from a singular point of reference.
2. Discovery
Engage in an exploratory analysis to understand the nature and structure of your collected data. Assess how it aligns with your analytical goals and conceptualize an organization strategy that facilitates easy consumption and insightful analysis.
3. Structuring
Given that raw data often comes unstructured, imposing a coherent structure is imperative. Structuring enables the application of analytical tools and techniques, transforming the data into a format ripe for insightful analysis.
4. Cleaning
This crucial step involves purging anomalies and outliers that may distort analytical outcomes. Standardizing formats and rectifying null values enhance your dataset’s quality, ensuring consistency and reliability in your findings.
5. Enriching
Post-cleansing, scrutinize your dataset to identify potential gaps or areas necessitating augmentation. Deriving new data elements from the existing dataset or incorporating external data sources can significantly enrich your dataset, paving the way for deeper insights.
6. Validating
Guarantee the integrity, accuracy, and security of your data through rigorous validation. This entails verifying the correctness of dataset attributes, ensuring data consistency, and checking for normal distribution among other quality checks.
7. Publishing
The culmination of your wrangling efforts should be the dissemination of your refined dataset. Whether for personal use, sharing with stakeholders, or contributing to a data repository, publishing your data facilitates access and utility, readying it for analytical applications.
By navigating these seven steps, you ensure your data is not only primed for analysis but also positioned to yield the most coherent and actionable insights. This iterative process of discovery, cleaning, enrichment, and validation is what transforms raw data into a strategic asset, unlocking the full spectrum of its potential.
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 datasets 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.
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