Complex and intricate datasets can hinder data analysis and business processes. To make data usable for the end-processes, it has to be transformed and organized according to the target system’s requirements. Data wrangling improves data usability by transforming it to make it compatible with the end system.
But what exactly is data wrangling, and why is it so important? Read on to find out.
What Is Data Wrangling?
Data wrangling (also called data munging) is the process of taking disorganized or incomplete raw data and standardizing it so that you can easily access, consolidate, and analyze it. It also involves mapping data fields from source to destination.
From a practical perspective, this may mean targeting a field, row, or column in a dataset, and implementing an action like joining, parsing, cleaning, consolidating, or filtering to produce the required output.
Your company can then use this wrangled data to process it further for business intelligence (BI), reporting, or improving business processes.
Why Do You Need Data Wrangling?
Did you know, data professionals spend almost 73% of their time just wrangling the data? This means it’s an indispensable aspect of data processing. Accurately wrangled data ensures that quality data is entered into analysis or downstream processes for consolidation and collaboration.
Data wrangling can be arranged into a consistent and repeatable procedure using integration tools with automation capabilities that clean and convert source data into a format that be reused as per the end requirements. After converting data to a standard format, you can perform crucial, cross-data set analytics.
5 Key Steps for Data Wrangling
Just like most data analytics processes, data wrangling is an iterative process in which you have to perform these five steps recurrently so as to yield the results you want.
· Understanding Data
The first step is to understand the data in great depth. Before applying procedures to clean it, you must have a clear idea of what the data is about. This will help you find the best approach for productive analytic explorations. For instance, if you have a customer dataset, and you learn that most of your customers are from one part of the country, you’ll keep that in mind before progressing further.
In most cases, you’ll have raw data in a disorganized manner. There won’t be any structure to it. In this step, you have to restructure it for easy accessibility, which might mean splitting one column or row into two or vice versa – whatever is needed for better analysis.
Almost every dataset includes some outliers that can skew the outcomes of the analysis. You’ll have to clean the data for optimum results. In this step, the data is cleansed exhaustively for superior analysis. You’ll have to change null values, remove duplicates and special characters, and standardize the formatting to improve the consistency of the data. For example, you may replace the many different ways that a state is recorded (such as CA, Cal, and Calif) with a single standard format.
After cleansing, your data has to be enriched, which means taking stock of what’s in the dataset and strategizing how to make it better by adding supplementary data. For instance, a car insurance company might want to know crime rates in the neighborhoods of their users to estimate risk better.
Validation rules denote some repetitive programming steps that are used to authenticate the reliability, quality, and safety of the data you have. For instance, you’ll have to determine whether the fields in the dataset are precise by cross-checking data or observing whether the attributes are normally distributed.
Data Wrangling Use-Cases
Two of the most common use-cases of data wrangling are:
· Fraud Detection
Using a data wrangling tool, you can perform the following:
- Distinguish corporate fraud by identifying unusual behavior by examining intricate information like multi-party and multi-layered emails or web chats.
- Support data security by allowing non-technical operators to examine and wrangle data quickly to keep pace with billions of daily security tasks.
- Ensure precise and repeatable modeling outcomes by standardizing and quantifying structured and unstructured datasets.
- Enhance compliance by ensuring your business is complying with industry and government standards by following security protocols during integration.
· Customer Behavior Analysis
A data wrangler can help your business get more precise insights quickly via customer behavior analysis. It empowers the marketing team to take business decisions into their hands and make the best of it. You can use it to:
- Decrease the time spent on data preparation for analysis
- Quickly understand the business value of your data
- Allow your analytics team to utilize the customer behavior data directly
- Empower data analysts to find out data trends via data discovery and visual profiling
For a business that wants to enjoy the finest and result-driven BI analytics, data wrangling is an essential part of the data process. You can perform data wrangling using automated tools, such as Astera Centerprise. The software not only extracts data, but also transforms, cleans, and structures data so it can be fed into analytics and BI apps using pre-built connectors.
With the Instant Data Preview feature, Astera Centerprise offers you an insight into the validity of your data mappings. You can easily examine a data sample being processed at every step of the transformation process. As a result, you can quickly detect and correct any mapping errors before the task is performed.