Important Data Wrangling steps and how automation helps

Data Science is a popular technology that leverages the power of data to drive insightful decision-making in an organization. The Data Science industry consists of several processes used to convert raw data into insights for predicting patterns and trends to enhance business operations and customer experience.

And one of the most important parts of data science is data wrangling. Data wrangling refers to the process of cleaning, maintaining, and storing in a suitable format that can be further used for analyses and building data science models. It ensures correct, and most accurate data is fed to the data science models, and thus it can heavily impact the decision-making process directly.

In this article, let us explore the different steps involved in data wrangling and how automation can help enhance its efficiency.

Steps in the Data Wrangling Process

Data wrangling is an important part of the global data science process and it consists of the following steps:

• Discovery

  The first step in the data wrangling process is having a thorough look at the dataset and identifying patterns and structures. Discovery refers to noticing if there are any kind of ambiguities and errors in the data and listing out what changes are needed, like adding something to data or removing anything from it, and ensuring it is ready for analysis.

• Structuring

  Once the data engineers have decided the purpose of the data set i.e. what kind of data is needed, they use their data science skills to structure the raw data in an organized form. It might include converting images to text, rows to columns, changing storage format, etc.

• Cleaning

  There are various data science tools available to perform this important data-wrangling step. In this process, data science professionals need to plan which are essential data points that must remain in the data set and which ones to remove. After this process, the final dataset has minimum to no errors or repetitions.

  Some methods to clean data include removing rows or columns, checking outliers, conversion of string characters into numerical, removing null values, etc.

• Enhancing

  This step involves enhancing the quality of the current data set. This includes identifying whether this dataset needs more data and adding from other datasets if required. Doing so, it helps to enhance the quality of analysis and data science models.

• Validating

  This step ensures the data are consistent throughout the dataset. Automated processes are involved to check this consistency. A final validation is needed to ensure there are no errors in the data and variables follow correct distributions.

• Publishing

  Finally, the data is published after validation and is made available to the organization for analysis.

Automating these tasks with automation tools

According to a survey, published in Forbes, stated that data scientists spend around 60% of their time cleaning and structuring data, and around 19% of the time is spent on collecting data sets. Raw data are not suitable for analysis and they won’t provide any fruitful insight. Therefore, preparing these data for analysis consumes the majority of the time for professionals in the data science industry. However, with the use of automation and data science tools, the data wrangling process can be fully automated in several ways.

• Use of DataOps: When it comes to global data science operations, DataOps refers to a collection of data management practices. They aim to optimize the data flow processes and continuous use of data within the organization.

  It can also help to gain better insights, match data, and provide end-to-end security that can help improve data quality and architecture.

• Reduced Time: With automation, a data scientist can free up their time from doing repetitive tasks and focus on more productive works like modeling and analysis. With the use of automated data science tools, they can speed up the data-wrangling process along with saving time and achieving better accuracy.
• Improves Data Security: Automation also helps to prevent data leakage which refers to data exceeding the amount needed for machine learning algorithms and results in over estimated outcomes. The data wrangling automation tools can efficiently reduce data leakage by properly scrutinizing data and preparing it accordingly.

Conclusion

Data wrangling is a process that cannot be skipped when building an efficient data science model aimed at solving real-world problems. With data-wrangling, organizations can fuel their data science projects with the right set of data that is accurate and relevant to the business problem. By employing the most advanced data science tools, and automation techniques, the data wrangling processes can also be automated at various steps improving overall efficiency and accuracy and reducing time.

If you want to get into a data science career as a data engineer or data scientist, then it may be worth noting that, as we move ahead in the future, it will be exciting to see what more technical advancements bring to this industry.