Data is the most useful resource for businesses. Its analysis aids data science professionals to make informed decisions and leverage them to further their business goals. But data doesn’t come pre-packaged in neat little bundles. It is mostly messy, disorganized, and often full of noise that hinders analysis. Any insight that business professionals glean will only be as good as the data they have. So it becomes crucial to clean and structure this data and prep it before data analysts can take over. This is where data-wrangling comes in.
What is data wrangling and why is it important?
Data wrangling also known as data munging or data cleaning – is a process used by data scientists for transforming raw data into an organized format that can later be used to extract valuable information depending on the goal of data analysis. The exact processes of data wrangling will vary based on what a given business is trying to achieve.
Here are a few broad principles of the data wrangling process:
- Cleaning up data and ensuring format consistency.
- Merging data from multiple sources for a singular purpose.
- Identifying and eliminating anomalies and outliers for easier analysis.
- Validating data to ensure quality and adherence to structure.
Once the data wrangling process is finished and valuable information can be extracted out of the data, it is said to have been prepped or cleaned for further analysis or use downstream. It is important to document the steps taken for the data wrangling process to help data analysts understand the wrangling logic.
Data wrangling is indispensable
Data wrangling software has evolved into a critical component of data processing. The fundamental benefit of employing data wrangling technologies can be summarized as follows:
- Getting all of the data from multiple sources into one central spot so that it may be utilized.
- Making unprocessed data usable. Data that has been correctly wrangled ensures that quality data is processed into the downstream analysis.
- Allows data scientists to effortlessly look over vast amounts of data and exchange data-flow methodologies.
- Putting raw data together in the proper format and apprehending the data’s business context
- Data wrangling techniques such as automated data integration tools are used by data science professionals to clean and convert source data into a common format that may be reused according to end needs. This standardized data is used by businesses to undertake critical cross-data set analytics.
- Cleaning the data to remove any noise or missing or faulty elements.
- Before data is ready for analytics, data wrangling software typically conducts six iterative steps: discovering, scouring, enriching, structuring, validating, and publishing.
Benefits of data wrangling
Data wrangling has fast emerged as a vital prerequisite for data analysis. Given the rapidly growing amount of data that is generated daily, due in no small part to new technologies and services used to mine data, businesses need to separate the wheat from the data chaff, so to speak, and exploit it for their use. Not only does data-wrangling speed things up for analysis, but also streamlines the data for data analysis and the goals of the business.
The following are a few ways in which businesses benefit from data wrangling:
- Enriching data by isolating it from data noise.
- Ensure speedy analysis of data to acquire insights.
- Allows for concrete and clear-sighted decision-making.
- Creates a simplified and transparent scheme for data organization and management.
- Helps data analysts and data scientists focus on analysis instead of spending time on data wrangling themselves.
- It enables data science professionals to create data flows quickly and easily using an easy user interface, as well as plan and automate the data-flow process.
Final takeaways…
Data wrangling is not a new phenomenon. Every time a business professional is provided a set of data, the same has to be organized and structured to ensure that discrepancies don’t come in the way of analysis and jeopardize the results. But, given the massive influx of data in the digital age, data wrangling has taken on a prominent and well-defined role for businesses looking to base their decisions on the insights that a set of data has to offer. This is why, instead of manual data wrangling, which can be quite taxing and time-consuming, businesses now have a set of guidelines and best practices that simplify the task of data wrangling for their employees, and also ensures quality, consistency, and efficient organization in a database.
