Data wrangling: cos'è, come funziona e a cosa serve

The Data Wrangling transforms raw, messy, incompatible data into a coherent, usable whole.

In a world where data is the new gold, Data Wrangling is the art of refining this precious raw material into gems of knowledge. Through a meticulous process of data cleaning, transformation and integration, Data Wrangling lays the foundation for analysis andvalorisation of information.

In this study you will find clarifications on the meaning, logic and applications of Data Wrangling processes and some useful onespractical tips for automation part or all of such flows through the use of Connecteed.
 

Data Wrangling: what is it

Data Wrangling, also known as Data Munging, is the process oftransformation and mapping of raw data from different sources into a more usable and valuable format for analysis and downstream processing. It involves cleaning, structuring and enriching data to make it ready for use in specific applications.

Data Wrangling addresses the challenges posed by incomplete, inconsistent or unstructured data by transforming it into aconsistent and reliable format. It requires a combination of technical and domain skills to understand the data, identify anomalies, and apply appropriate transformations.
 

Data Wrangling: how it works

The Data Wrangling process involvesdifferent interconnected phases, each of which plays a crucial role in transforming raw data into a usable format. Let's explore the key phases of Data Wrangling together:
 

1. Data exploration

The first phase of Data Wrangling involves exploring and understanding the raw data. This involves examining the structure, content and quality of the data. Analysts identify data sources, evaluate their relevance and determine requirements for further processing. Data exploration also helps iidentify any quality problems, such as missing values, outliers, or inconsistencies.
 

2. Data cleansing

Data cleansing is a fundamental step in Data Wrangling. It involves identifying and dealing with incomplete, incorrect, or duplicate data. Data cleaning techniques include removing or replacing missing values, correcting spelling errors, normalizing formats, and resolving inconsistencies. The goal isimprove quality and integrity of data to ensure accurate analysis results.
 

3. Data transformation

Data transformation involves changing the structure or format of data to fit the specific needs of your analysis. This may involve aggregating data, creating new variables, encoding categorical variables, or normalizing values. Data transformations aim tomake the data more meaningful, comparable and suitable for the intended analysis.
 

4. Data integration

Data integration involves combining data from different sources to create a single, coherent data set. This requires mapping data schemas, reconciling differences between sources, and managing primary keys and dependencies. Data integration ensures that all relevant data isavailable in one place, allowing for a complete and holistic analysis.
 

5. Data validation

Data validation is the process of verifying the accuracy, completeness and consistency of data after the cleaning and transformation phases. It involves checking data for compliance with business rules, referential integrity constraints, and data quality requirements. Data validation ensures that the data is reliable andready for analysis and decision making.
 

Data Wrangling: a cosa serve

Data Wrangling plays a critical role in making data usable and valuable to organizations. Here are some of the main benefits of Data Wrangling:

1. Reliable data for analysis.
   Data Wrangling transforms raw, messy data into a clean, consistent and reliable format. This provides a solid foundation for data analysis, ensuring that the information extracted is accurate and meaningful. High-quality data leads to better decisions and more effective business outcomes.

2. Efficiency and time saving.
   Data Wrangling automates and simplifies the data preparation process, which would otherwise be time-consuming and error-prone. Through Data Wrangling tools and techniques, analysts can quickly clean, transform and integrate large volumes of data, saving valuable time and resources.

3. Enabling advanced analytics.
   Data Wrangling unlocks the potential of data for advanced analytics, such as machine learning, predictive analytics and artificial intelligence. By preparing data in a structured and compatible format, Data Wrangling lays the foundation for the application of sophisticated algorithms and models, allowing you to extract deeper and more valuable information from your data.

4. Collaboration and data sharing.
   Data Wrangling promotes collaboration and data sharing within organizations. By creating a central repository of clean, consistent data, Data Wrangling allows teams to access and use the same data, regardless of department or location. This fosters consistent and aligned decision making across the organization.

5. Agility and adaptability.
   Data Wrangling makes organizations more agile and adaptable in an ever-changing data landscape. With the ability to quickly integrate new data sources and adapt to changes in business requirements, data wrangling allows organizations to remain responsive and take advantage of new opportunities as they arise.
    

Connecteed as a tool for Data Wrangling

Connecteed emerges as a powerful platform for Data Wrangling, simplifying and automating the data transformation process. With its advanced features and intuitive interface, Connecteed allows analysts toefficiently manage the workflow del Data Wrangling.

Here's how Connecteed supports the different phases of Data Wrangling:

1. connectivity of data sources.
   Connecteed offers built-in connectors for a wide range of data sources, including databases, flat files, APIs, and cloud systems. This allows users to easily access and integrate data from different sources, eliminating the need to write complex code.

2. Data cleansing and transformation.
   The platform provides a rich library of data cleansing and transformation functions. Users can apply common operations such as removing duplicates, handling missing values, standardizing formats, and creating new variables through an intuitive drag-and-drop interface. Connecteed simplifies the data cleansing process, ensuring consistent, high-quality data.

3. Automation and scheduling.
   Connecteed enables the automation of Data Wrangling workflows through scheduling capabilities. Users can set Data Wrangling jobs to run automatically at defined intervals or triggered by specific events. This ensures that data is constantly updated and ready for analysis, without requiring manual intervention.

4. Data governance and collaboration.
   The online application promotes data governance and collaboration by providing features for version management, access control and workflow sharing. Users can track changes to workflows, control access to sensitive data, and collaborate with colleagues by sharing and reusing data wrangling workflows. This fosters a controlled and collaborative Data Wrangling environment.

By choosing Connecteed as a Data Wrangling tool, organizations cansimplify and accelerate the data preparation process.
 

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Data Wrangling is a fundamental aspect of data management in the digital age. Transforming raw, messy data into a fcleanly shaped, coherent It isusable, Data Wrangling lays the foundations for all subsequent processing phases of the raw material collected through all the acquisition channels used.

By leveraging powerful tools like Connecteed, organizations can simplify and automate the data wrangling process. By connecting disparate data sources, applying data transformations, and fostering collaboration, Connecteed enables organizations toexploit the full potential of their data: activate your free demo now and discover all the advantageous features of this professional tool.