With the world of data rapidly expanding, it is becoming increasingly essential to get the right data to be organized for analysis. Business users rely on data and information to make just about every business decision. Hence, it is important to make raw data usable for analytics. Data wrangling is the process of converting and mapping raw data and getting it ready for analysis.
Definition of Data Wrangling
Data wrangling can be defined as the process of cleaning, organizing, and transforming raw data into the desired format for analysts to use for prompt decision-making. Also known as data cleaning or data munging, data wrangling enables businesses to tackle more complex data in less time, produce more accurate results, and make better decisions. The exact methods vary from project to project depending upon your data and the goal you are trying to achieve. More and more organizations are increasingly relying on data wrangling tools to make data ready for downstream analytics.
Importance of Data Wrangling
Did you know, data professionals spend almost 80% of their time wrangling the data, leaving a mere 20% for exploration and modeling?
Some may question if the amount of work and time devoted to data wrangling is worth the effort. A simple analogy will help you understand. The foundation of a skyscraper is expensive and time-consuming before the above-ground structure starts. Still, this solid foundation is extremely valuable for the building to stand tall and serve its purpose for decades. Similarly, for data handling, once the code and infrastructure foundation are gathered, it will deliver immediate results (sometimes almost instantly) for as long as the process is relevant. However, skipping necessary data wrangling steps will lead to significant downfalls, missed opportunities, and erroneous models that damage the reputation of analysis within the organization.
Data wrangling software has become such an indispensable part of data processing. The primary importance of using data wrangling tools can be described as:
- Making raw data usable. Accurately wrangled data guarantees that quality data is entered into the downstream analysis.
- Getting all data from various sources into a centralized location so it can be used.
- Piecing together raw data according to the required format and understanding the business context of data
- Automated data integration tools are used as data wrangling techniques that clean and convert source data into a standard format that can be used repeatedly according to end requirements. Businesses use this standardized data to perform crucial, cross-data set analytics.
- Cleansing the data from the noise or flawed, missing elements
- Data wrangling acts as a preparation stage for the data mining process, which involves gathering data and making sense of it.
- Helping business users make concrete, timely decisions
Data wrangling software typically performs six iterative steps of Discovering, Structuring, Cleaning, Enriching, Validating, and Publishing data before it is ready for analytics.
Benefits of Data Wrangling
- Data wrangling helps to improve data usability as it converts data into a compatible format for the end system.
- It helps to quickly build data flows within an intuitive user interface and easily schedule and automate the data-flow process.
- Integrates various types of information and their sources (like databases, web services, files, etc.)
- Help users to process very large volumes of data easily and easily share data-flow techniques.
Data Wrangling Tools
There are different tools for data wrangling that can be used for gathering, importing, structuring, and cleaning data before it can be fed into analytics and BI apps. You can use automated tools for data wrangling, where the software allows you to validate data mappings and scrutinize data samples at every step of the transformation process. This helps to quickly detect and correct errors in data mapping. Automated data cleaning becomes necessary in businesses dealing with exceptionally large data sets. For manual data cleaning processes, the data team or data scientist is responsible for wrangling. In smaller setups, however, non-data professionals are responsible for cleaning data before leveraging it.
Some examples of basic data munging tools are:
- Spreadsheets / Excel Power Query - It is the most basic manual data wrangling tool
- OpenRefine - An automated data cleaning tool that requires programming skills
- Tabula – It is a tool suited for all data types
- Google DataPrep – It is a data service that explores, cleans, and prepares data
- Data wrangler – It is a data cleaning and transforming tool
Data Wrangling Examples
Data wrangling techniques are used for various use-cases. The most commonly used examples of data wrangling are for:
- Merging several data sources into one data-set for analysis
- Identifying gaps or empty cells in data and either filling or removing them
- Deleting irrelevant or unnecessary data
- Identifying severe outliers in data and either explaining the inconsistencies or deleting them to facilitate analysis
Businesses also use data wrangling tools to
- Detect corporate fraud
- Support data security
- Ensure accurate and recurring data modeling results
- Ensure business compliance with industry standards
- Perform Customer Behavior Analysis
- Reduce time spent on preparing data for analysis
- Promptly recognize the business value of your data
- Find out data trends
Data Wrangling vs. ETL
ETL stands for Extract, Transform and Load. ETL is a middleware process that involves mining or extracting data from various sources, joining the data, transforming data as per business rules, and subsequently loading data to the target systems. ETL is generally used for loading processed data to flat files or relational database tables.
Though Data Wrangling and ETL look similar, there are key differences between data wrangling and ETL processes that set them apart.
- Users – Analysts, statisticians, business users, executives, and managers use data wrangling. In comparison, DW/ETL developers use ETL as an intermediate process linking source systems and reporting layers.
- Data Structure – Data wrangling involves varied and complex data sets, while ETL involves structured or semi-structured relational data sets.
- Use Case – Data wrangling is normally used for exploratory data analysis, but ETL is used for gathering, transforming, and loading data for reporting.
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Top Data Wrangling Skills Required
Data wrangling is one of the essential skills a data scientist must have. It is a set of tasks you need to perform so you can understand your data and prepare it for machine learning. A good data wrangler should be adept at putting together information from various data sources, solving regular transformation problems, and resolving data-cleansing and quality issues.
As a data scientist, you need to know your data intimately and look out to enrich the data. You will rarely get flawless data in real scenarios. Hence it becomes imperative to have a good know-how of the business context of the data, so you can easily interpret, cleanse and transform it into ingestible form.
Top tech companies typically look for the following skillsets in data science candidates.
- To be able to perform series of data transformations like merging, ordering, aggregating
- To use data science programming languages like R, Python, Julia, SQL on specified data sets
- To make logical judgments based on underlying business context
In order to be an excellent data wrangler, you need to learn how to keep your efforts efficient and consistent. You need data wrangling processes in place in order to make valuable insights and business decisions based on them. Help your business gain a competitive advantage over others in the industry.
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