Raw data received from many different sources is often unusable in its original form. Data wrangling is the process of cleaning raw data so that it can be put into an analytical algorithm. The process of cleaning and unifying complex data allows individuals using that data to reach better decisions in less time.
The need for data wrangling has become increasingly necessary as information that is gathered online is involved in almost every decision in contemporary business. Manual conversion and mapping of data from one raw form into another format is part of the process.
The Goals of Data Wrangling
Data Watch notes that data wrangling as five specific goals:
- Reveal a deeper intelligence by gathering data from multiple sources
- Place actionable information in the hands of analysts in a timely way
- Reduce the amount of time collecting and organizing data
- Enable everyone involved to focus on data analysis instead of wrangling
- Assist decision-making skills in an organization
What to Expect From the Process
Six key steps are part of the data wrangling process as noted by Trifacta.
1. Discovery. This is also known as acquiring data and is the process of identifying and obtaining access to data with sources. Understanding what is in the data gathered helps inform analysts how they want to analyze it. How data is wrangled may be determined by where it the information comes from and other particulars of the information received.
2. Structuring. Organizing data is a necessary part of the process because raw data is obtained in man different forms. Removing data with null values, for example, will make the gathered data easier to understand and can result in the elimination of needless rows or columns in a spreadsheet.
3. Cleaning. Another word for this step is standardization. Let’s say information about a state is entered as Illinois, IL or Ill. When data is cleaned, the state field will be uniform, using one of those three options across the whole data set. Missing fields may also be added. Standardization results in superior data quality.
4. Enriching. Data enrichment can work in different ways. A common part of this step is correcting typographical errors through the use of algorithms. Extrapolation is another process often used. In other words, the analyst looks at the information obtained through fuzzy logic to come to a conclusion about how the data can be used to better inform an audience.
5. Validating. This step ensures that validation rules are followed for data consistency, quality, and security. These include uniform distribution of attributes such as dates and times and confirming the accuracy of data through crosschecks.
6. Publishing. Once you have completed the previous five steps, it’s time to export it and publish it for your audience. Thinking carefully about the data formats used by the intended audience is the key to making it usable. The implementation of the insights that wrangled data provides depends on the ease with which it can be accessed and used.
Making sure that data is in premier shape and ready for consumption is what data wrangling is all about. Proper data preparation is the key to valuable data analysis.