Raw data is often unorganized, inconsistent, and contains missing values or errors, making it challenging to analyze. This is where data wrangling comes into the picture. The process of data wrangling helps to ensure that the data is accurate, complete, and ready for data analysis. It entails the steps necessary to clean up and convert raw data into a format suitable for analysis.

In this article, we will discuss the importance of data wrangling in the data analysis process, the steps involved, how is it different from data cleaning and ETL. You will also get introduced to one of the best automated data wrangling tools. We will also provide examples of real-world data wrangling scenarios and discuss various benefits offered by this technique.

What is data wrangling?

The process of converting and processing raw data into a format suitable for analysis is known as data wrangling, also referred to as data munging. It involves identifying and fixing errors and inconsistencies in data, converting data from one format to another so that it is suitable for analysis, and consolidating data from various sources to create a single, unified dataset in order to make sure that the data is accurate, complete, and ready to be analyzed. 

Data munging demands an in-depth understanding of the raw data, the types of analysis to be conducted, and the information that needs to be omitted. To guarantee that the data is cleaned and prepared correctly, a number of robust standards and criteria must be put in place to avoid any bias. Here, automated data wrangling solutions like Fivetran, make it simple to integrate, merge, clean, and wrangle data from any source, including big data.

How does data wrangling work?

Data wrangling is a complex and iterative process that involves multiple steps to ensure that raw data is transformed into a structured and meaningful format that is ready for analysis. The steps involved in data wrangling include:

1. Data discovery

Data discovery involves identifying and locating the raw data that will be used for analysis. This may involve searching for data in different sources such as databases, spreadsheets, text files, or web pages. During this stage, data analysts will also assess the quality of the data and determine whether it is fit for purpose. This may involve evaluating the completeness of the data, assessing its accuracy and consistency, and determining whether it contains any missing values or outliers.

2. Data structuring

After gathering the data, in this stage, you need to organize the raw data into a structured format that is suitable for analysis. This may involve converting unstructured data, such as text or images, into structured data, such as tables or lists. The data team also defines the schema or data model that will be used to represent the data. This involves determining the types of data and their relationships, such as identifying primary and foreign keys or defining tables and their columns.

3. Data cleaning

Data cleaning entails locating and fixing outliers, discrepancies, and missing values in the data. This may involve removing duplicates, filling in missing data, or correcting data that is inaccurate or incomplete. In addition, standardization of data values and formats also takes place in this step, to ensure consistency across the dataset. For example, converting date formats into a standardized format or standardizing units of measurement.

4. Data enrichement

After cleaning your data and structuring it into a relevant format, in this stage the raw data is enhanced with additional information to provide more context or insights. This may involve joining data from multiple sources, such as adding geographical or demographic information to a dataset. For instance, data enrichment integrates data from a company's internal customer management systems, sales reports, and other platforms with data coming from external sources, third parties, social media, and related data from across the web.

Furthermore, data can be again transformed to make it more meaningful, such as by converting currency values into a common currency or aggregating data into meaningful categories. This step helps to provide a more complete and accurate picture of the data.

5. Data validation

Now that the data is completely cleaned and structured, it needs to be tested to ensure that it is accurate, complete, and consistent. This includes running tests or queries to identify errors or inconsistencies in the data. It is also crucial to document the testing process to ensure that it is repeatable and transparent. This helps to ensure that the data is accurate and reliable and that any issues are identified and addressed before analysis begins.

6. Data publishing

This is the last step of data wrangling which is crucial for making the insights gained from the data accessible to the people who need them and for driving informed decision-making. It includes sharing the cleaned and transformed data with stakeholders, such as analysts or business users or delivering it to AI and machine learning models to run through previously learned models or train new ones. This may involve creating reports, visualizations, or dashboards to help stakeholders understand the insights that can be gained from the data. Moreover, the data teams ensure that the data is protected and secure and that any privacy or security concerns are addressed before publishing it forward. 

Data wrangling vs data cleaning

While Data Wrangling and Data Cleaning are related processes in the data management and analysis cycle, there is a subtle difference between the two: Data cleaning is a process of identifying and correcting or removing errors, inconsistencies, and inaccuracies in the data. It includes tasks like handling missing data, correcting typos, and removing duplicates and outliers. Data cleaning aims to ensure that the data is accurate, consistent, and ready for further analysis.

Data wrangling, on the other hand, involves a broader set of tasks that prepare the data for analysis. It includes not only data cleaning but also data transformation, data integration, and data restructuring. It involves transforming and combining data from various sources, converting data from one format to another, creating new variables and features from the data, and integrating data from different sources into a unified dataset.

In other words, while data cleaning is a subset of data wrangling, data wrangling is a more comprehensive process that encompasses all tasks related to preparing raw data for analysis. Data wrangling ensures that the data is not only accurate and consistent but also in a format that is suitable for analysis and meets the specific requirements of the analysis.

To brief, data cleaning and data wrangling are both essential steps in the data management and analysis cycle. Data cleaning is focused on removing errors and inconsistencies in the data, while data wrangling encompasses all the tasks required to prepare the data for analysis, including data cleaning, transformation, integration, and restructuring.

Data wrangling vs ETL

Data Wrangling and ETL (Extract, Transform, Load) are both related to the process of preparing data for analysis, but there are some key differences between the two:

Data wrangling is a process of cleaning, transforming, and preparing raw data for analysis. It involves data cleaning, data transformation, data integration, and data restructuring. Data wrangling is typically done by data analysts and data scientists, who use tools and techniques like data visualization, statistical analysis, and machine learning to prepare the data for further analysis.

ETL, on the other hand, is a process of extracting data from various sources, transforming it into a format that is suitable for analysis, and loading it into a target database or data warehouse. ETL is typically done by IT professionals or data engineers, who use specialized tools and techniques like data mapping, data conversion, and data integration to move data between different systems.

The main differences between Data Wrangling and ETL are:

• Scope: Data Wrangling is focused on preparing data for analysis, while ETL is focused on moving and transforming data between systems.
• Users: Data Wrangling is typically done by data analysts and data scientists, while ETL is typically done by IT professionals or data engineers.

• Tools: Data Wrangling often involves the use of statistical analysis, data visualization, and machine learning tools, while ETL involves specialized tools like data mapping, data conversion, and data integration tools.

• Timeframe: Data Wrangling is typically an ongoing process that takes place throughout the analysis cycle, while ETL is typically a one-time or periodic process that is used to move and transform data between systems.

Importance of data wrangling

Data wrangling is a crucial phase in the data analysis process because unstructured, inconsistent, missing value and faulty raw data are challenging to evaluate. ETL operations need clean, organized data to start with, which is frequently not readily available. Hence, data analysts need to make sure the data is accurate, consistent, and structured, by performing data wrangling.

Here are 6 reasons why data wrangling is important for businesses:

• Ensuring data accuracy: Raw data is often unstructured and contains errors, inconsistencies, and missing values. Data wrangling helps to ensure that the data is accurate by identifying and correcting errors and filling in missing values.
• Preparing data for analysis: By converting data between different formats, normalizing data, and scaling data, data wrangling assists in turning the data into a format that is appropriate for analysis.
• Improving data quality: Data wrangling improves the quality of the data by removing duplicates, correcting typos, and removing outliers, thereby ensuring that the data is consistent and free from errors.
• Saving time: By automating the data cleaning process, data wrangling saves time and makes the data analysis process more efficient.
• Making informed decisions: Making informed decisions requires access to reliable and accurate data. This is where performing data wrangling can help save your day.
• Facilitating easier data sharing: Data can be easily shared with others for analysis when it has been properly cleaned and structured. By ensuring that the data is in a format that is appropriate for analysis and devoid of errors, data wrangling makes data sharing easier.

Benefits of data wrangling

The benefits of data wrangling are numerous, and they can have a significant impact on a business's bottom line. Some of these include:

• Good data quality: Making informed decisions requires precise and reliable data. Data wrangling helps to improve the quality of data by removing missing values, duplicates, and outliers.

• Increased efficiency: Data wrangling can automate data cleaning processes, which saves time and increases efficiency. This allows data analysts to spend more time analyzing data and making informed decisions rather than cleaning it.

• Improved decision-making: With clean and reliable data, decision-making becomes easier and more accurate. Data wrangling helps to ensure that data is consistent and free from errors, making it easier to analyze and interpret.

• Valuable insights: Data wrangling can transform and combine data from different sources, which can lead to greater insights. This can lead to new discoveries and insights that might not be visible from just one data source.

• Cost-effective: Data wrangling can be a cost-effective solution to manage data. By automating the data cleaning process, businesses can save time and reduce the costs of manual data cleaning.

• Enhanced collaboration: With clean and reliable data, collaboration becomes easier, and data can be shared across different teams and departments. This helps to create a culture of data-driven decision-making and collaboration.

• Robust data governance: Data wrangling makes sure that data is accurately labelled and standardized, which helps to improve data governance. This is critical for data privacy and regulatory compliance.

• Increased revenue: With greater insights and better decision-making, businesses can increase revenue by making more informed decisions that positively impact their bottom line.

Best data wrangling tool (Fivetran)

A business must engage in data wrangling if it wants to benefit from the best and most efficient BI and analytics. To avoid switching back and forth between data preparation and analysis, businesses invest in automated data wrangling solutions such as Fivetran that enable them to interactively modify their data as they analyze it. The cost-effectiveness and ease of use of this integrated approach to data preparation and analytics allow for quick data cleaning. 

Fivetran is a cloud-based data integration and data wrangling tool that helps organizations to streamline their data pipeline processes. Fivetran enables users to connect to over 150 pre-built connectors, which allows them to easily pull data from various sources such as databases, applications, and files.

With Fivetran, users can perform data transformations, such as filtering, sorting, and aggregating data, using SQL queries or a visual drag-and-drop interface. This allows users to transform raw data into a structured and clean format that can be used for analysis. It also offers a user-friendly interface that is easy to navigate, even for those who do not have technical expertise. Additionally, Fivetran automates the data integration process, which reduces the need for manual coding and configuration.

In addition to the above features, Fivetran is highly scalable and can handle large volumes of data. It also offers robust security features, such as encryption, access controls, and auditing, which ensures the confidentiality and integrity of data. With its ability to automatically detect schema changes and adapt to them, it ensures that data pipelines continue to run smoothly even if the source schema changes.

Fivetran offers a range of pricing plans that cater to different business needs, including small businesses, mid-sized companies, and enterprises. Overall, it is a powerful data wrangling tool that can help organizations to streamline their data pipeline processes and gain valuable insights from their data.

Examples of data wrangling

The application of data wrangling or data munging can be valuable to any sort of business that collects and manages enormous amounts of data.

Here are some examples of data wrangling tasks:

• Data Cleaning: Removing duplicates, dealing with missing values, correcting data types, and removing outliers.
• Data Transformation: Converting data into a different format, scaling numerical data, normalizing data, and encoding categorical data.
• Data Integration: Merging data from different sources, joining tables, and appending data.
• Data Reshaping: Pivoting tables, transforming data into long or wide format, and reshaping data into a format suitable for analysis.
• Feature Engineering: Creating new variables or features from existing data, deriving new data from existing data, and computing new statistics or metrics.
• Data Quality Assurance: Ensuring data is accurate, complete, and consistent across different sources and time periods.
• Data Exploration: Visualizing data, summarizing data, and identifying patterns or anomalies in the data.
• Data Preprocessing: Preparing data for machine learning algorithms by scaling data, feature selection, feature extraction, and dimensionality reduction.

These are just some examples of the many data wrangling tasks that are performed by data analysts and data scientists to prepare data for analysis. The specific tasks involved in data wrangling may vary depending on the data sources, the analysis objectives, and the analytical tools and techniques used.

Use cases of data wrangling

The importance of Data Wrangling is increasing as more and more organizations are collecting large amounts of data from various sources, and the need to extract insights from this data is becoming more critical for business success. Let’s take a look below at some of the common use cases of data wrangling.

• Business intelligence: As businesses have started leveraging and tracking larger and more complex volumes of customer and business data, the importance of data wrangling solutions increases even further. Data Wrangling is used in the business intelligence and analytics sector to clean, integrate, and transform data from various sources into a format suitable for analysis. This allows organizations to gain insights into their operations, customer behavior, and market trends.

• Healthcare: The healthcare sector continues to grow data-driven, employing analytics to boost productivity and guarantee the best possible patient care. Yet, in order to get these outcomes, healthcare professionals must manage a substantial amount of data from patient records, survey details, and scientific reports. This is where data wrangling helps to clean and integrate patient data from multiple sources to improve patient care, support clinical research, and facilitate health policy decision-making.

• Finance & banking: Today's finance institutions and banks rely on data to strengthen client connections, enhance business processes, and deliver top-notch client service. To maintain a competitive edge, these firms must manage transactional and customer data which is also crucial for identifying fraud or other risks. Data Wrangling helps to clean and integrate financial data from different sources to support financial analysis, risk management, and investment decision-making.

• Marketing: In marketing, data wrangling is used to clean and integrate customer data from various sources such as customer relationship management (CRM) systems, social media platforms, and web analytics. This helps marketers to better understand customer behavior, personalize their marketing messages, and improve their return on investment (ROI).

• Manufacturing: An enormous amount of Big Data is generated from IoT devices which need to be formatted and transformed to analyze it. Here, data wrangling is used to clean and integrate data from sensors, machines, and other devices to improve operational efficiency, reduce downtime, and optimize the supply chain. This helps manufacturers to reduce their production costs, improve product quality, and increase their profitability.

• Retail: Data from many sources, including point-of-sale (POS) systems, inventory management systems, and consumer loyalty programmes, are cleaned up and integrated via data wrangling. Retailers can use this to customize their marketing messages, enhance the effectiveness of their product offerings, and increase the efficiency of their supply chains.

Best practices for data wrangling

There are several best practices to follow when performing data wrangling:

• Understand the data: It is important to have a thorough understanding of the data before beginning of the data wrangling process. This includes understanding the structure of the data, the types of data, and any potential issues or errors.

• Document the process: Documenting the data wrangling process, including any changes made to the data can help to ensure that the data is reproducible and transparent.

• Use Consistent naming conventions: Try to use consistent naming conventions for variables and data fields which ensures that the data is consistent and comparable.

• Keep a backup: Always keep a backup of the original data before performing any data transformation or cleaning process. This can help to guarantee that the original data is maintained and can be recovered if necessary.

Conclusion 

In short, data wrangling is one of the most critical steps in the data analysis process, with the goal to transform messy, raw data into a clean, structured format that can be used for analysis and decision-making. As discussed above in the article, effective data wrangling requires the use of tools and techniques to ensure that the data is reliable and ready for further analysis and businesses prefer automated data wrangling tools like Fivetran due to it’s ease of use and robust, secure data handling operations. Check out - Fivetran Case Studies to know why companies love it.

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