Businesses have relied on experts in data science and analysis for a long time to help them comprehend and make use of the information at their disposal. This demand has increased with the abundance of data brought on by the creation of smart gadgets and other technological developments.
It’s tough to pinpoint just one data science skill as being crucial for business professionals. Ideas are only as useful as the evidence they are based on, that much is certain. This means it’s crucial for businesses to hire people who know what clean data looks like and how to transform raw data into forms that can be used. Data wrangling is used in this situation.
The definition of data wrangling, its main procedures, and its importance to business are described in this blog post.
What is Data Wrangling?
To make complicated data sets more accessible and understandable, data wrangling is the act of cleaning up errors and merging different complex data sets. Large amounts of data need to be stored and organised for analysis because the amount of data and data sources available today are expanding quickly.
Data wrangling, also referred to as data munging, is the act of rearranging, changing, and mapping data from one “raw” form to another in order to increase its value and usability for a range of downstream uses, including analytics.
It is the process of preparing raw data for analysts to use in quick decision-making by cleaning, organising, and changing it into the necessary format. Data wrangling, often referred to as data cleaning or data munging, enables businesses to handle more complex data in less time, provide more accurate results, and make better decisions. Depending on your data and the objective you’re trying to achieve, the precise procedures change from project to project. To prepare data for downstream analytics, an increasing number of organisations are turning to data wrangling solutions.
The Importance of Data Wrangling
The data that underlies whatever analyses a corporation conducts will ultimately limit them. Analyses will be flawed if data is inaccurate, untrustworthy, or incomplete, which will reduce the value of any insights discovered.
By making sure that data is in a trustworthy state before it is examined and used, data wrangling aims to eliminate that risk. As a result, it plays a crucial role in the analytical process.
It’s crucial to keep in mind that data wrangling can be time-consuming and resource-intensive, especially when done manually. Determining that data must contain specific information or be in a particular format before it is uploaded to a database, for example, might assist organisations to streamline their workers’ data cleanup procedures. Understanding the processes of the data wrangling process is crucial for this reason.
The Steps to Perform Data Wrangling
The exact tasks required in data wrangling depend on what transformations you need to carry out to get a dataset into better shape. For instance, if your source data is already in a database, this will remove many of the structural tasks. But if it’s unstructured data (which is much more common) then you’ll have more to do.
The following steps are often applied during data wrangling. But the process is an iterative one. Some of the steps may not be necessary, others may need repeating, and they will rarely occur in the same order. But you still need to know what they all are!
Step 1: Data Discovery
Data extraction isn’t always included in the definition of data wrangling. But in our view, it’s a crucial component of it. Without first gathering the data, transformation is impossible. Planning is required at this point. You must choose the facts you require and the sources from which to gather them. The data will then be extracted in a raw format from its source. This could be a repository run by a third party, a website, or some other place. Roll up your sleeves if the data is unstructured and raw because there is work to be done!
Step 2: Data Structuring
When raw data is gathered, it comes in a variety of sizes and forms. It lacks a clear structure, which indicates that it lacks a model and is wholly disorganised. Giving it a framework enables better analysis and allows it to be reformed to fit in with the analytical model used by your company.
Unstructured data frequently has a lot of text and contains elements like dates, numbers, ID codes, etc. The dataset has to be parsed at this point in the Data Wrangling procedure.
This is the procedure used to extract pertinent information from recent data. When working with code that has been scraped from a website, for instance, you might parse the HTML code to extract the information you require and discard the rest.
This will produce a spreadsheet with more valuable data and more user-friendly columns, classes, headings, etc.
Step 3: Data Cleaning
The terms “Data Wrangling” and “Data Cleaning” are frequently used interchangeably. But they are two quite distinct procedures. Cleaning is merely one part of the total Data Wrangling process while being a difficult procedure in and of itself. Raw data typically contains a number of inaccuracies that must be corrected before moving on to the next step. This is accomplished by sanitising and cleaning up the dataset using algorithms.
Every piece of data is meticulously inspected and redundant information that doesn’t suit the data for analysis is deleted, resulting in high-quality analysis. Data with Null values must be converted to either an empty string or zero, and formatting must be standardised to improve the quality of the data. Making ensuring there are no ways that the final data that will be used for final analysis could be impacted is the aim of data cleaning or remediation.
Step 4: Data Enriching
You have a thorough comprehension of the data at hand at this point in the Data Wrangling procedure. Do you want to accentuate or enrich the data at this point? Do you want more data to be added to it?
You can increase the precision of your analysis by combining your raw data with extra data from different sources, such as internal systems, outside providers, and so on. Alternatively, you could just want to fill in any informational gaps. Putting together two client information databases, for instance, where one has addresses and the other doesn’t.
If the current data does not satisfy your criteria, you may choose to take the optional step of enriching the data.
Step 5: Data Validating
The validation step verifies the accuracy of the result because data is heavily modified during the wrangling process. Has any crucial information unintentionally changed? Has standardisation been fully implemented so that nothing is found when you search for a format you wanted to get rid of? Any mistakes that went unnoticed? The analysis’s results will be impacted by even minor mistakes, hence a careful and exhaustive quality check is required.
Data quality criteria are used for the examination and evaluation of the quality of a certain data set. Following data processing, the quality and consistency are checked, creating a solid barrier to security concerns. These must follow syntactic rules and be conducted along various dimensions.
Step 6: Data Publishing
All the steps have been finished by this point, and the data is prepared for analysis. The freshly wrangled data has to be published somewhere where you and other stakeholders can readily access it and use it.
The information can be added to a fresh architecture or database. The end result of your work will be high-quality data that you can utilise to obtain insights, produce business reports, and more, provided the other steps were appropriately carried out.
Even more data processing could be done to produce more elaborate and substantial data structures, like data warehouses. The options are limitless at this point.
Data Wrangling Tools
Before data is fed into analytics and BI programmes, it can be collected, imported, organised, and cleaned using a variety of data wrangling tools. Using software that enables you to evaluate data mappings and examine data samples at each stage of the transformation process, you can employ automated techniques for data wrangling. This makes it easier to identify and swiftly fix data mapping issues. Businesses that deal with extraordinarily huge data volumes must automate data cleaning. The data team or data scientist is in charge of wrangling when manual data cleansing procedures are involved. However, in smaller setups, cleansing data before exploiting it is the responsibility of non-data specialists.
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
Obtaining accurate data may require a significant amount of time and effort. To demonstrate that cascading the outcomes in the processing phase, there must be hard labour. The intense data wrangling procedure transforms the data into various insights, refocusing the entire effort to provide beneficial results. For the construction process to continue and better serve present and future generations, a strong foundation is required. Once the data is placed with the right code and infrastructure, results can be driven quickly. Skipping it will cause the entire process to go absurdly wrong, which will seriously damage the analytics reputation inside the company.
High-quality data are the cornerstone of data science. As a result, optimised data can be used to produce optimised results, and vice versa. So, before processing it for analysis, wrangle the data.
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