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How Data Wrangling is Helping Businesses Make Better Decisions
In 2022, most major organizations are led by data while making business decisions. As a result, data professionals are essential for businesses to function. At the same time, Gartner research recently found that organizations believe that poor-quality data is costing them an average of $15 million in losses annually. This combination of high dependency on data as well as uncertainty about data quality is making practices like data wrangling vital for businesses to function efficiently. Which brings us to the question: what is data wrangling and how does it help? Let’s explore.
What is Data Wrangling?
It is the practice of removing errors from data sets or restructuring complex datasets to make them more suitable for analysis. Wrangling consists of cleaning, organizing, and transforming raw data into the desired format for analysts to use. It helps businesses use more complex data, faster, and more accurately.
What are its Benefits?
It transforms raw data and makes it usable for businesses. Here are the key benefits of data wrangling:
It helps turn raw data into consistent data sets which businesses can use. For example, data collected from consumers is usually error-ridden. Data wrangling can help eliminate these human errors and make the data more uniform.
The consistency brought through wrangling often provides better insights about metadata.
Cleaning up and organizing data through wrangling reduces errors in the data, saves time for the person who will be using the data, and thus reduces costs for the company.
Importance of Data Wrangling
McKinsey has estimated that big data projects could account for a reduction of $300-450 billion in US healthcare spending. It is clear that data analysis has a significant impact on business practices. However, any analyses that businesses perform will only be as effective as the data informing them. To ensure accurate results, consistent, reliable data is necessary. Data wrangling proves to be essential to achieve this accuracy.
Best Practices for Data Wrangling
To ensure effective results, there are certain practices one should be aware of:
Remember Your Objective
Think about the objective of the person who needs the data you are working with. By doing this, you will be focused on the data that they need.
Choosing the Right Data
Selecting the right data is necessary. To ensure quality:
- Avoid duplicate data
- Use the original source
- Use recent data
Humans are always capable of errors, even data wranglers. It is necessary to re-check the data once wrangling is complete.
Steps to Perform Data Wrangling
Step 1: Discovery
This process involves thinking about the desired results, understanding what kind of data is necessary to achieve the objectives, and collecting the desired data.
Step 2: Organization
After the raw data is gathered, it needs to be structured into a less overwhelming and more organized form.
Step 3: Cleaning
After the data is structured, you can start cleaning it. This involves removing outliers, null, and duplicate data.
Step 4: Enrichment
In this step, you review if you have gathered enough data. If a data set is too small, it may compromise the results of the analysis.
Step 5: Validation
Once enrichment is complete, you can apply validation rules to your data. Validation rules applied in iterations can confirm if your data is consistent.
Step 6: Publishing
The last step is data publishing. Here you prepare the data for future use. This includes making notes and documenting the entire process.
Data Wrangling Examples
Data wrangling can be used to discover insights hidden in data, predict trends, and forecast markets. It helps in making informed investment decisions.
Creating reports with unstructured data can be a challenge. Data wrangling improves data quality and helps in reporting.
Understanding Customer Base
Customers exhibit different behaviors which can be reflected in the data they generate. Data wrangling can help identify common behavioral patterns.
Who Uses Data Wrangling?
Data analysts spend most of their time conducting data wrangling rather than data analysis. This is to ensure that the best results are delivered for businesses using the most accurate data. It is essential for businesses in nearly every industry.
Frequently Asked Questions.
1. What are Popular Data Wrangling Tools?
- Spreadsheets / Excel Power Query
- Google DataPrep
- Data wrangler
2. What’s the Difference Between Data Wrangling and Data Cleaning
The objective of data cleaning is to remove inaccurate data from the data set, whereas the objective of wrangling is to transform the data into a more usable format.
3. How can Data Wrangling Improve Data Quality?
Data wrangling helps remove errors from the data set and also structures it in a more usable format. When the data is well structured and error-free, the subsequent data analysis is able to yield more accurate results which in turn end up in better business outcomes.
As big data finds even greater acceptance in business, the need for data professionals is only going to be on the rise. Having learned what is data wrangling, if you are interested in going deeper into this field, explore the courses on data science and analytics on Emeritus. These are offered in collaboration with top universities and will help you in your career as a data professional.
By Tanish Pradhan
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