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Data is the lifeline of modern businesses, but it’s also a double-edged sword. On one hand, data has the potential to provide valuable insights and drive better decision-making. On the other hand, data is often messy, inconsistent, and unstructured, which makes it difficult to use. This is where data wrangling comes in.
Data wrangling, also known as data munging, is the process of cleaning, transforming, and organizing raw data into a structured format that can be easily analyzed and used for business purposes. It’s a crucial step in the data analytics process, as it helps to ensure that data is accurate, consistent, and ready for analysis.
The need for data wrangling arises from the fact that data is often collected from multiple sources, such as customer databases, social media, and website analytics, and it’s rarely in a format that can be easily analyzed. Data wrangling helps to standardize and clean this data, making it easier to use and increasing the chances of finding valuable insights.
There are several data wrangling techniques that can be used to clean and transform data, such as:
- Removing duplicates and irrelevant data: This helps to reduce the size of your data set and improve the accuracy of your analysis.
- Filling in missing values: This helps to ensure that your data is complete and consistent.
- Handling outliers: Outliers can have a significant impact on your analysis, so it’s important to handle them appropriately.
- Converting data into a standardized format: This helps to ensure that data from different sources can be easily combined and analyzed.
- Removing errors and inconsistencies: This helps to improve the accuracy of your analysis and ensure that your results are trustworthy.
With Relational Junction as your Swiss Army Knife for all things data management, moving data to one place for your further analysis is our specialty. Our solution helps combine structured and unstructured data retrieved from source systems, with the data warehouse of your choice as the final destination. Using SQL, data cleansing can be done in this process, speeding up your time to insights.
Conclusion
Data wrangling is an essential step in the data analytics process. It helps to clean, transform, and organize raw data, making it easier to use and increasing the chances of finding valuable insights. By investing in data wrangling, businesses can unlock the full potential of their data and drive better decision-making.
Not sure where to start?
We can help. Schedule a demo of Relational Junction today to discuss how we can help create a unified view of your data by bringing it all to one place with instant connections to on-premise or cloud enterprise applications or databases.