Essentials of Data Wrangling and How Automation Helps

The importance of properly organizing data for analysis is rising as the volume of available data continues to grow. Information and data are the backbones of all decision data users make in a business. As a result, preparing data for analytics purposes is crucial.

To get ahead of this, data wrangling was developed to get data in shape for automation. Here, we’ll look at what is meant by “data wrangling”, the stages involved, and why this process is so important to businesses.

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What is Data Wrangling?

Data wrangling is the procedure taken for preparing raw data for rapid decision-making and analysis by analysts. It is a process that allows businesses to deal with data that are more complex in a shorter amount of time, which ultimately results in more accurate outcomes and improved judgments.

Methods of data wrangling are required to be utilized in a variety of settings and scenarios. Common use cases include the consolidation of several data sets into a single, unified dataset, as well as the identification and removal of gaps or spaces within data sets.

Steps Involved in Data Wrangling


The first thing that has to be done is to become familiar with the raw data. Analyzing the data structures as well as its trends, locating any errors or ambiguities, and determining what aspects of the data may be removed are all necessary steps in getting the data suitable for use.


After you have gained an understanding of what the raw data is and why you are collecting it, you can now begin organizing it. Among these tasks are the organization of data into rows and columns, the translation of images into text, and the creation of an archive-friendly file format.


There are always going to be some extreme occurrences in a dataset that might potentially distort the conclusions. You must clean up the data to get the most out of it. In the third phase, the data is rigorously cleaned to guarantee the greatest possible analytical quality. You will need to convert null values, eliminate duplication and strange characters, and standardize the layout if you want the data to be more uniform.

Data Enrichment

After you have finished Step 3, it is time to “enrich” your data by taking an inventory of what is already there and planning how to enhance it by adding new information. For instance, auto insurance companies might benefit from having access to local crime information to make more accurate risk assessments for their customers.


After collecting enough information, it’s time to validate it. Data consistency is ensured across your whole dataset by applying validation rules in iterative sequences. Data security and integrity can be guaranteed by following a set of validation criteria. This action mimics the reasoning behind the data normalization phase, another method for standardizing data through validation criteria.


Analysts can disclose data when it has been thoroughly vetted and confirmed. The firm might disseminate it as a report or an electronic file. It may be used to make a database or refined into more complex data structures like a data warehouse.

Data analysts will occasionally make updates to their documented reasoning for transforming data. They will be able to make decisions on future projects much more quickly. Like chefs who keep a record of their transformation logic to save time, experienced data analysts and scientists do the same thing.

How Automation Helps Data Wrangling

We’ve covered some of the theories behind data wrangling. The importance of this procedure may be appreciated by considering the role that automation technologies play in facilitating the attainment of data-wrangling tasks.

Use of DataOps

The term “DataOps” refers to a collection of processes for managing data that, when implemented throughout an organization, make for more efficient data flows and consistent data consumption. It enhances the quality of the data and also the data structures, which speeds up processes and provides faster insights from the data, matching of the data, and security from beginning to finish.

Time Reduction

Data scientists can spend more of their time on modeling and analysis when they use tools that are automated throughout the data preparation process. These have the potential to significantly reduce the amount of time required to clean and validate data, hence allowing for more fruitful studies.

More In-depth Intelligence

The collection, organization, and analysis of data are fundamental to the successful operation of every facet of a business, from sales and marketing to accounting and finance. By utilizing data and doing manipulations on that data, you may be able to get insight into the present state of health of your business. With this knowledge, you will be able to direct your efforts to the areas of the organization that require them the most.

Prevents Data Leakage

When an algorithm for machine learning is given access to a data set that contains information that is not important to the task at hand, this can lead to data leakage. It is strongly encouraged to use data-wrangling automated tools to review and prepare data promptly. This will help to reduce the likelihood of this leakage occurring.

Faster Better Decision-making

The fast delivery of information is crucial to the making of sound business decisions. You can make the best decision in less time using automated tools for data wrangling and analytics. 


Cleaning, understanding, and analyzing raw data is impossible without data wrangling. As a result, useful information is gathered, new insights are developed, and company procedures may be altered or improved. There are several methods for performing data wrangling. If you want to save time and get the most out of the procedure, follow these best practices.

  • Learn to read the signs in the data and use that knowledge to guide businesses to success.
  • Gather relevant information.
  • Determine what level of precision and accuracy is required for your data.
  • To guarantee accuracy and cut down on waste, reevaluate the wrangled data

Indium Case Study:

For a leading Consulting Firm in the US, Indium helped the client to build the Data Analytics Platform (DAP) using Tableau to store, process and provide the data access layers to support Net Promoter Score (NPS) Next gen initiatives.

Indium designed a robust data model, implemented ingestion workflows, leveraged data tables and data pipelines and created dashboards using tools such as Amazon EC2, AWS S3, Postgres, Alteryx, Tableau.

The solution provided automated survey data extraction and storage in data lake, Data analysis and Insights by Product Categories and 10x efficiency in Dashboard Performance.

To know more about how Indium can help you with your data preparation needs, visit or write to us at

Author: Indium
Indium Software is a leading digital engineering company that provides Application Engineering, Cloud Engineering, Data and Analytics, DevOps, Digital Assurance, and Gaming services. We assist companies in their digital transformation journey at every stage of digital adoption, allowing them to become market leaders.