With the world of data rapidly expanding, it is becoming increasingly essential to get the right data to be organized for analysis. Business users rely on data and information to make just about every business decision. Hence, it is important to make raw data used for analytics. Data wrangling is the process of converting and mapping raw data and getting it ready for analysis. 

What is Data Wrangling?

Data wrangling is the process of cleaning, structuring, and enriching raw data into the desired format for better decision-making in less time. Data wrangling is increasingly ubiquitous at today’s top firms. Data has become more diverse and unstructured, demanding increased time spent culling, cleaning, and organizing data ahead of a broader analysis. At the same time, with data informing just about every business decision, business users have less time to wait on technical resources for prepared data.

This necessitates a self-service model, and a move away from IT-led data preparation, to a more democratized model of self-service data preparation or data wrangling. This self-service model with data wrangling tools allows analysts to tackle more complex data more quickly, produce more accurate results, and make better decisions. Because of this ability, more businesses have started using data wrangling tools to prepare before analysis.

data wrangling

Importance of Data Wrangling

Data wrangling is very important because it’s the sole way to make use of raw data. In real-world business settings, the user information comes in different pieces from different backgrounds at different times. Sometimes, we store this information across various computers across different spreadsheets which can lead to data redundancy, incorrect data, or missing data. To create a transparent and efficient system for data management, the best solution is to have all data in a centralized location so it can be used easily.

The following example will explain the importance of data wrangling:

A book-selling website wants to show top-selling books of different domains, according to user preference. For example, a new user searches for motivational books, and the website wants to show those books which sell the most or have a high rating, etc.

But on their website, there may be plenty of raw data. Data wrangling comes to the rescue at this point which is done by the data scientists. The data scientist will wrangle data in such a way that motivational books are sorted to show the ones that sold more or have high ratings on the top of the list. On the basis of that, the new user makes a choice.

Benefits 

  • Data wrangling helps to improve data usability as it converts data into a compatible format for the end system.
  • It helps to quickly build data flows within an intuitive user interface and easily schedule and automate the data-flow process.
  • Integrates various types of information and their sources (like databases, web services, files, etc.)
  • Help users to process very large volumes of data easily and easily share data-flow techniques.

6 steps in Data Wrangling

Similar to most data analytics processes, data wrangling is an iterative one – the data engineer iterates through these steps repeatedly to create the desired predictions. There are 6 broad steps in data wrangling, which are:

  • Discovering: Before you can dive deeply, you must better understand what is in your data, which will inform how you want to analyze it. How you wrangle customer data, for example, may be informed by where they are located, what they bought, or what promotions they received.
  • Structuring: In most cases, the raw data extracted as user information generally doesn’t have structured data. The data should be restructured in a fashion that better suits the analytical method used. Based on the category identified in the first step, the data should be segregated to make use easier. For better analysis we have to select one column that may become two or rows may be split, this is also called feature engineering.
  • Cleaning: The process of cleaning data involves removing anything that would impede the data mining process later on. Errors, null entries, duplicate entries, and datasets that are not in the correct place will all be removed.
  • Enriching: After processing the data, it will have to be enriched – this is performed in the fourth step. This implies that you one has to take stock of what is in the data and strategize whether you have upscale, downsample, or perform data augmentation. There are different methods to resample the data, one downsampling the data, and the other creating synthetic data using upsampling.
  • Validating: Validating is the activity that surfaces data quality and consistency issues, or verifies that they have been properly addressed by applied transformations. Validations should be conducted along multiple dimensions. At a minimum, assess whether the values of an attribute/field adhere to syntactic constraints. For example, boolean fields are encoded as ‘true’/‘false’ as opposed to some other values. Additional validations might involve cross-attribute/field checks like ensuring all negative bank transactions have the appropriate transaction type (e.g., ‘withdrawal’, ‘bill pay’, or ‘cheque’).
  • Publishing: Once your data has been validated, you can publish it. This involves making it available to others within your organization for analysis. The format you use to share the information – such as a written report or electronic file – will depend on your data and the organization’s goals.

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The future of Data Wrangling

Data wrangling used to be handled by developers and IT experts with extensive knowledge of database administration and fluency in SQL, R, and Python. Analytic Process Automation (APA) has changed that, getting rid of cumbersome spreadsheets and making it easy for data scientists, data analysts, and IT experts alike to wrangle and analyze complex data.

Conclusion

Data wrangling in machine learning is a huge necessity in recent times because of the huge amounts of data that get processed every day making user services more efficient. Without a strong infrastructure of data storage and investments in data wrangling techniques, the business would suffer and hence data-wrangling proves its importance in the world of data science.