It is not easy to pick clothes from the wardrobe when they are unorganized and unfolded. Similarly, it is not easy to analyze any data when it is sloppy and unclean. Due to the availability of multiple resources to collect data, it becomes necessary to organize data once it is collected. This demands data wrangling, which is a part of data analytics.
So what exactly is data wrangling? What are its goals and benefits? What is the process of data wrangling? Let us understand it here.
What is Data Wrangling?
Data wrangling is the process of sorting raw data into a more organized form to make it easier to understand. Data cleaning, data remediation, and data munging are alternative terms for data wrangling. There are plenty of methods to perform data remediation but the exact method to be used depends upon the set objective(s).
The main aim of data munging is to convert resourceful data into valuable and understandable data. Nevertheless, depending on the business we are running, the technique may vary.
Steps of the Data Wrangling Process
Data wrangling involves six steps, namely data discovery, data structuring, data cleaning, data enriching, data validating, and publishing. Let’s get to know each one of them.
1. Data Discovery
The first step in data wrangling is data discovery, which means becoming familiar with the data. We will first look at the data we have and think of a way to organize it to make it easier to understand. It is like knowing your data.
To begin with, we will start with the sloppy data collected from multiple sources. At this stage, we aim to combine all the data sources and arrange each one in order to interpret for patterns and trends. So in data discovery, we will know our data, think of ways to organize it, and try to find patterns, if any, in the data.
2. Data Structuring
The first step of data wrangling involves collecting raw data. The foundation of data remediation is laid on data structuring. Because the raw data is completely unorganized and unclean, it is now time to structure it in a way so that it fits the analytical model established by our business.
Unstructured data has plenty of unnecessary information. It may contain numbers such as dates, percentages, or statements not required for our business. First, we will extract the exact data and form a more user-friendly spreadsheet or document. This will give us hold of a structured piece of information.
3. Data Cleaning
The name signifies what we are trying to achieve here. Some people get confused between structuring and cleaning. Structuring forms a structure by extracting the data; however, cleaning involves tracing errors. This may include tacking outliers, making corrections, and deleting incorrect data completely.
After forming a structure, we may encounter some errors in the data. This process deals with the errors that we want to extract or have extracted earlier. Cleaning the data removes outliers, takes care of null values (if any), and identifies duplicate or incorrect values.
4. Data Enriching
At this point, we have immaculate data available with us, but we need to ask ourselves if we need to enrich it or club it with any other data to make it more useful. If required, we may combine our data with the information from other sources. This will help in getting a more accurate analysis. A more detailed and thorough report can be derived with the help of data enriching.
For example, merging the data of customers’ choices and requirements with their addresses and location will give us a better idea of entering a market in a particular area. Data enriching is entirely optional and depends upon the type of information that we have. It gives better results but should be avoided in unnecessary cases.
5. Data Validating
This is the final step before we proceed to publish the data. Every time we give an exam, it is advisable to proofread our answer sheet. Similarly, before we publish our data, we need to proofread it.
This step involves checking quality, consistency, accuracy, security, and authenticity. This is done by checking the sources we collected the data from by checking the report’s framework and how accurate we are in the results. A consistency check refers to the flow of information. The report should be coherent, taking the reader in a specific direction.
Analyzing the sources help to check the data for accuracy, security, and authenticity. Reliable and peer evaluated sources are best to take data from. Blogs are a good source of information too, but their authenticity should be checked before taking data from them. Data validating can be performed multiple times as we are likely to find errors in our data.
6. Data Publishing
Once the aforementioned steps are complete, the data is now ready for analysis. The data will be published and made accessible to other stakeholders. This will allow them to use it further.
The end outcome of our efforts would be high-quality data that stakeholders can utilize to obtain insights, develop business reports, and more if the other procedures were performed successfully.
Benefits of Data Wrangling
There are plenty of benefits that come with data munging. Below are three significant benefits of data munging:
- Wrangling the data helps improve its serviceability, accessibility, reliability, and dependability by preparing it in a more user-friendly format.
- Data wrangling also saves the time of stakeholders. While making decisions, stakeholders need accurate information to make decisions about their business. Data cleaning serves stakeholders with the correct information they need to do so.
- Helps everyone access the data quickly and makes the flow of information in the process better.
To conclude what has been stated so far, without the shadow of any doubt, we can say that data wrangling is an effective way of forming the base of information that will be utilized to make crucial decisions in any business.
Due to the increasing popularity of data wrangling there are a galore of tools available. Alteryx, Datameer, and Talend are some of the best tools for data cleaning. The data wrangling process has six steps that require the publisher’s complete attention and analytical thinking. With sheer determination and careful observation, data cleaning helps to get fruitful results.