Applications of Data Wrangling
Explore the reasons for applying data wrangling techniques and understand their practical uses across sectors like finance, healthcare, agriculture, and social media. Learn when and why to perform data wrangling to improve data quality, combine sources, and support better decision-making.
Reasons for applying data wrangling
The following are reasons why data wrangling is an essential part of any data project.
- The application of data wrangling techniques saves time for stakeholders. For example, data engineers apply data wrangling techniques to data so that other stakeholders won’t need to apply them once they start working with data.
- The data collected for analysis can be unreliable and faulty, and because of this, we perform data wrangling techniques to make data usable. For example, we find and handle outliers resulting from data entry errors using data wrangling techniques.
- To aggregate data originating from multiple sources, we apply data wrangling techniques. For example, we combine data originating from multiple CSV files, databases, and web services by using joins.
- As data within an organization increases, extracting meaningful insights becomes difficult. Organizations can make this easy by using automated data wrangling techniques that guarantee data quality and reliability, leading to better decision-making in a shorter time.
When do we perform data wrangling?
Data wrangling techniques are applied once we've defined the business problem.
For example, if we were data scientists working on a project and using the following data methodology, we would apply data wrangling techniques during the data understanding and preparation phases.
When shouldn't we use data wrangling?
Typically, we must apply data wrangling techniques when working on data projects. Such techniques would include exploration techniques that give us an idea of what the dataset entails.
Aside from that, we may not find it necessary to perform other data wrangling techniques if our data is in the format required for further analysis. Stakeholders such as data engineers or other professionals might have already prepared that data for further analysis.
Applications of data wrangling
To understand the application of data wrangling techniques across various sectors, let's see how they're practical in the finance, agricultural, healthcare, and social media sectors.
- Finance: Data professionals within financial institutions and organizations apply data wrangling techniques to understand their users and products and predict future trends. They use such techniques to prepare data for loan applications or customer experience.
- Agriculture: Organizations can use data wrangling techniques to clean data retrieved from sensor data. Then, they can use this data to make informed decisions that affect farmers, factories, grocery outlets, and customers.
- Healthcare: Organizations can clean data retrieved from patient data to improve data quality. They can transform imaging data from patient scans into a readable format for interpretation using data wrangling techniques.
- Social media: Organizations can collect data from various web services to understand customer behavior patterns, track product issues, and transform data using data wrangling techniques.
The future of data wrangling
According to a report published in 2020 by Mordor Intelligence, the data wrangling market was valued at USD 1.31 billion in 2020 and is expected to reach USD 2.28 billion by 2026. The cause of this is the rapid growth of data generated by companies across different markets. While this data growth continues, many organizations increasingly invest in developing further data wrangling tools for handling such data.
For example, Trifacta, an organization that develops data wrangling tools, announced that it had raised USD 100 million in 2019 to “Support Explosive Growth of Data Wrangling for AI and the Cloud.” Mindtech Global, an organization offering data science solutions, has recently announced the availability of its new tools that simplify data wrangling when working with datasets for machine learning. With many more such developments already underway, the future of data wrangling is expected to be promising.
Myths about data wrangling
There are many misconceptions about data wrangling. Some of these include:
- Data wrangling is about working with SQL databases: Data wrangling isn’t about working with SQL databases only. It also involves working with other databases, such as graph and document databases.
- We need knowledge of machine learning to perform data wrangling: We only require knowledge of working with data wrangling tools to transform data. Machine learning is about creating and maintaining machine learning models and is not a data wrangling requirement. However, if we want to automate some data wrangling tasks and have knowledge of machine learning, we could incorporate machine learning aspects in our solutions.
- We need deep programming knowledge to perform data wrangling: A basic knowledge of working with programming languages such as Python and R can help transform data from a raw format to a useful one. There are tools like pandas and tidyverse that allow us to transform data with ease. We only need to know how to work with functions provided by such tools.
