Data Transformation, Wrangling and Cleanup

In today's world, the volume of data is growing daily, and companies face enormous amounts of diverse information from various systems that need to be processed, standardized, and consolidated. Working with data processing and transformation is quite labor-intensive, and if done manually, errors are guaranteed.

This is where specialized systems designed for data transformation and cleaning come to the rescue. Data processing systems can be tailored for different tasks, such as reporting, data analytics, business intelligence, etc. However, their primary function is to transform incoming data and output it in the form of tables/diagrams or save it in a structured format.

The scope of applications that deal with data transformation is vast, ranging from inventory reconciliation to supplier price list processing.

What is Data Transformation?

Data transformation is the sequential process of converting data from one format or structure to another. Typically, data transformation includes type conversion, filtering, aggregation, and merging of data to create a structure suitable for further analysis. A key aspect of this process is the step-by-step execution of operations, where each new step yields data that increasingly conforms to the expected structure.

What is Data Wrangling?

Data wrangling is the process of collecting, cleaning, and structuring complex and unstructured data for further analysis. Unlike transformation, this process is more business-oriented, as it involves removing invalid data sets or, conversely, creating or supplementing data (filling in gaps). However, the result is still structured data that can be used for further work or analysis.

What is Data Cleanup?

Data cleanup is the process of removing erroneous data or resolving inconsistencies (corrections). The primary goal of this process is to improve data quality, which subsequently affects the accuracy of analysis.

How to Effectively Perform Data Transformation, Wrangling, and Cleanup?

In practice, the line between these three concepts is almost imperceptible, and all three processes are used to bring data to the desired format, often not sequentially.

We recommend using Mipler Data Flow for data transformation, as this tool has all the necessary mechanisms for efficient data work and is integrated with various data sources, from Google Sheets to MySQL.

Conclusion

Although most people consider data work a process for large data volumes, in reality, various approaches are used for any volume or type of data. If this work is done manually each time, it is a significant time sink and prone to errors. Therefore, automating these processes should be a priority for any company.