Data Wrangling is a vital process of cleaning, restructuring and enriching available raw data into a more usable format to ensure robust, accurate data analytics. Raw, or unstructured, data goes through a transformation during the wrangling process and is prepared in a new composition. The prepared data is now more valuable, as it can be used for other tools or applications involving, for example, visualizations and predictive analytics.
Regardless of the industry or organization you work for, here are three reasons you need to wrangle your data.
Why do we need Data Wrangling?
- To Improve Data Quality
Through data preparation and cleaning, there are many opportunities to address data quality issues. Data wranglers use many different techniques and technologies for improving data quality. To clean the data a recipe is built, i.e. a step by step process. The recipe can be modified depending on how structured or unstructured the data is, among other factors. Once conformed to a standard format, data can be reused, and your company will be able to see value in cross-data set analytics.
- To Accelerate Business Purposes
A company or organization always has a business purpose in mind when making a transaction or decision. The business purpose must advance the goals and objectives of the organization. Often, too much time is consumed by in-house teams on data preparation. This could lead to data barriers such as not providing expected results on time, failing to generate useful insights from data or other technological issues. In sum, manual processes used to address data quality greatly delay organizations in making use of the meaning of their data, meanwhile, making use of Data Wrangling skills and technologies saves valuable time and accelerates business decisions.
- To Enable Machine Learning
With the abundance of data available today, a continually increasing number of companies are using machine learning to make more accurate decisions for the future. Data is a fundamental aspect of machine learning. A company’s raw data contains cluttered information. If your data is corrupt, your machine learning tools will not provide proper results. Therefore, data wrangling is essential for performing quality inferential statistical processes like machine learning and predictive analytics.
Working with data is chaotic. Let’s simplify things.
Data-Core Systems’ experience crosses many industries, allowing us to prepare data for companies, public sectors and private institutions.
Click here to learn more about Data-Core’s Data Wrangling Services.