Powering Intelligent Business Decisions Through Data Wrangling

Today, in this data-centric world, data wrangling is extremely valuable. But how does data wrangling transform your business landscape? It enhances the accuracy of data and, thus, helps in data-based strategic decision-making for your business.

As businesses/organisations/firms increasingly depend on data for compliance and strategic gains, data science training is crucial for mastering data wrangling. The journey of this process unfolds to disclose its significant role in influencing the upcoming trends in analytics and maintaining regulatory compliance standards.

Data Wrangling: Overview & Strategic Value

What is data wrangling? It is a structured/organised process that transforms raw data into a format suitable for analysis—it is central and a key process in transforming messy, raw data into polished and analysis-ready form.

Moreover, data wrangling:

• Supports data-driven strategies/approaches and ensures regulatory compliance.
• Aids in maintaining data governance and makes sure data management aligns with legal requirements.
• Assists in preventing potential legal problems and improves the credibility and trustworthiness of the organisation.

Key Advantages of Data Wrangling for Analysis

Data wrangling has various advantages that improve the analytical process:

• Improved data quality: Thorough, meticulous cleaning and structuring of data removes duplicates, errors, and inconsistencies, generating pristine/error-free data. This results in reliable and accurate datasets, crucial for any analytical task and highly important for conducting accurate analyses and making sound business decisions.
• Boosted analytical efficiency: Streamlining data preparation lowers the time and effort required to get data ready for analysis. This efficiency allows data professionals to focus more on extracting insights and spend less time cleaning and organising data.
• Seamless data integration: Unifying disparate data formats creates a single, cohesive dataset for comprehensive analysis. This integration offers special relevance in today's data-driven world, where information is often obtained from multiple channels.

Also Read: Exploring the Key Advantages of Data Science Courses for Career Growth

Data Wrangling Procedure: Steps & Significance

What steps shape successful data wrangling? Let us delve into the core steps of data wrangling and their importance:

1. Data Collection

The initial/first step is data collection, in which raw data is gathered from varied sources like databases, APIs, and web scraping. Effective data collection is vital since the format and quality of the collated data can vary greatly, making it crucial to prepare the data for analysis. This also ensures a comprehensive dataset to work with, laying the groundwork for subsequent steps.

2. Data Cleaning

The next step is cleaning by identifying and addressing missing values, rectifying errors, and removing duplicates. This step ensures that the data is reliable and accurate, necessary for any analysis. Without cleaning the data, any conclusions or insights derived could be wrong/misleading/flawed.

3. Data Structuring

Structuring the data after cleaning helps organise the data into an appropriate format for analysis. This might involve reorganising the data into columns and rows and integrating them from different/multiple sources. As such, structuring makes data easier to analyse and interpret, facilitating more meaningful insights.

4. Data Enriching

Data enrichment involves augmenting the dataset. This is done by adding more information or merging with outside sources, such as calculated features and data from other datasets that give more context/extra reference. Enriching data enhances both its detail and diversity, providing a more comprehensive analysis.

5. Data Validating

Validation ensures processed data fulfils quality standards, checking for precision, consistency, and completeness. The techniques include statistical analysis and rule-based evaluation. This step confirms data integrity, ensuring reliability for further use.

6. Data Storing

The wrangled data is stored for future use in a data warehouse, database, or a suitable file format. Proper storage allows simple and easy retrieval for further processing, analysis, and reporting to ensure the data remains accessible and secure.

7. Documentation

Clear and thorough documentation explains the changes made to the data. This helps future users understand the steps taken so they can repeat the work or build on it. Documentation is highly important for maintaining transparency, ensuring consistency, and facilitating collaboration.

Best Practices for Efficient Data Wrangling

Recognising, adopting, and adhering to best practices in data wrangling can greatly boost your efficiency, accuracy, and effectiveness in handling data. Follow these guidelines to streamline the process and ensure high-quality data outcomes:

Planning & Documentation

Before starting:

• Outline objectives and establish a clear workflow.
• Identify data sources and define steps needed to achieve goals.

Keeping detailed and elaborate records/documents of transformations applied creates a valuable resource for future reference or troubleshooting.

Data Cleaning & Testing

Thorough data cleaning ensures the collated data is error-free. This involves handling missing values and standardising data formats. Testing cleaned data is another best practice that cannot be overlooked. Verifying data meets predefined objectives by employing statistical tests or data visualisation tools. This step confirms data reliability and readiness for analysis.

Choosing the Right Tools & Maintaining Organisation

Choosing the appropriate tools for data wrangling tasks is a strategic decision. What do you need to consider when choosing the tool? Opt for:

• Tools that influence the process's efficiency.
• Tools that provide functionalities tailored to different needs, such as Python, R, and SQL.
• Tools that align with your specific data requirements and workflow.

Furthermore, maintaining organisation throughout the process is key, using consistent naming conventions, version control, and backup procedures to ensure data security and accessibility.

A Brief Look at Data Science Careers

What makes a successful data science professional?

• Proficiency/competency in data science programming languages.
• Robust understanding and knowledge of data manipulation techniques.
• Strong critical thinking skills and the ability to match data insights/findings with business goals.

What educational qualifications open these opportunities?

• A bachelor's degree in computer science, information technology, or any other related field.
• A master’s degree will be preferred by some employers.

In India, skilled data wrangling and data science professionals can expect competitive salaries. Some of the prominent roles/jobs and their respective salary ranges:

• Data analysts: ₹4,00,000–₹7,00,000 annually
• Data engineers: ₹8,00,000–₹15,00,000 annually
• Data scientists: ₹9,00,000–₹16,00,000 annually

These numbers emphasise the profitable potential of data science jobs and the value placed on data wrangling skills. To understand the differences between these roles and choose the right career path, read our blog "Data Scientist vs Data Analyst vs Data Engineer: Career Paths in India."

Turn Your Career Around With Data Wrangling Skills

The future of data wrangling is filled with promise. But what does it mean to you as a data science professional? Expertise in this process puts you at the frontline of data-driven innovation, ready to define and influence the future of analytics and decision-making.

At Aimore Technologies, one of Chennai’s best software training institutes with placement, we empower you to develop strong data wrangling skills for career advancement. Our specialised courses and hands-on, industry-relevant/-aligned IT training and placement assistance hone your skills for a successful career in data science and analytics.

Aiming to achieve data wrangling mastery? Let us connect!