What is Data Wrangling? What are the Steps in Data Wrangling?

Data wrangling has been the complete domain of trained and professional data scientists; this process may require up to 80% of the analysis cycle.   

Data wrangling tools rapidly format siloed information from social media, transactional, and various origins to convert customer reactions into insights that are used to boost marketing efforts. 

Boost customer loyalty

Customer information can be used by marketers to cultivate stronger connections with fresh and present customers. 

By examining information from each interaction a client has had with a business, marketers can offer tailored client experiences according to real-time intelligence.   

Data Wrangling Challenges

Inspecting use cases: Analysts should recognize use cases thoroughly by researching what subset of entities are suitable, whether they are attempting to forecast the likelihood of an event or evaluating a future amount. 

Inspecting identical entities: After downloading impure data, it’s difficult to judge what’s unrelated and what’s related.

For instance, we consider “consumer” as an entity. The data sheet may have a consumer named “Simon Joseph.” Another column might contain a different consumer, “Simon J.”

In these situations, you have to deeply examine numerous factors while concluding the columns. 

Exploring data: Eliminate redundancies in the data ahead of exploring the relationships between the results.

Preventing selection bias: Make sure that the training sample data describes the implementation sample.

One of the biggest challenges in machine learning today continues to be in automating data wrangling. One of the main hurdles here is data leakage.

The latter refers to the fact that during the training of the predictive model using ML, it uses data outside of the training data set, which is unverified and unlabeled.

Data Wrangling vs. Data Cleaning vs. Data Mining

The activity of transforming and mapping data from one raw form to another is called data wrangling.

This involves feature engineering, aggregation and summarization of data, and data reformatting. 

Data cleaning is the activity of taking impure data and storing it in precisely the same format, erasing, adjusting, or improving issues associated with data validity.

The data cleaning process can start only after reviewing and characterizing the data source.  

Data mining is a narrower field that identifies invisible patterns in massive datasets for forecasting results in the future.

Important Characteristics of Data Wrangling

Useable data

Data wrangling formats the information for the end user, which enhances data usability. 

Data preparation

Data preparation is challenging to achieve better results from deep learning and ML initiatives, so data munging is important. 

Automation

Data wrangling techniques like automated data integration tools clean and convert raw or impure data into a standard form that can be used frequently according to end needs.

Businesses use this standardized data to adopt challenging cross-dataset analytics. 

Saves time

As mentioned earlier, data analysts spend enough time sourcing data from numerous origins and updating data sets instead of conducting fundamental analysis.

Data wrangling offers errorless data to analysts quickly.

Why the Need for Automated Solutions?

What are the 6 Steps in Data Wrangling?

It is often said that while data wrangling is the most important first step in data analysis, it is the most ignored because it is also the most tedious.

To prepare your data for analysis, as part of data munging, there are 6 basic steps one needs to follow.

They are:

Data Discovery: This is an all-encompassing term that describes understanding what your data is all about. In this first step, you get familiar with your data.

Data Structuring: When you collect raw data, it initially is in all shapes and sizes, and has no definite structure.

Such data needs to be restructured to suit the analytical model that your enterprise plans to deploy

Data Cleaning: Raw data comes with some errors that need to be fixed before data is passed on to the next stage.

Cleaning involves the tackling of outliers, making corrections, or deleting bad data completely

Data Enriching: By this stage, you have kind of become familiar with the data in hand.

Now is the time to ask yourself this question – do you need to embellish the raw data? Do you want to augment it with other data?

Data Validating: This activity surfaces data quality issues, and they have to be addressed with the necessary transformations.

The rules of validation rules require repetitive programming steps to check the authenticity and the quality of your data

Data Publishing: Once all the above steps are completed, the final output of your data wrangling efforts is pushed downstream for your analytics needs.

Data wrangling is a core iterative process that throws up the cleanest, most useful data possible before you start your actual analysis.

When Should You Use Data Wrangling?

You need to use data wrangling when you obtain data from multiple origins and require modification before adding it to a database and executing queries.

Listed below are a few examples of when data wrangling would be useful:

Gathering information from various countries:

Data from various origins like this has to be standardized to be queried together in a single big database. 

Scraping data from websites: Data on websites is kept and displayed in a way that is readable and usable by humans.

When data is scraped from websites, it has to be organized into a format that is fit for querying and databases.   

Additionally, data wrangling is also used to:

1. Save steps related to the preparation and implementation of comparable datasets
2. Find duplicates, anomalies, and outliers
3. Preview and offer feedback
4. Reshape and pivot data
5. Aggregate data
6. Merge information across different origins via joins
7. Schedule a procedure to execute a trigger-oriented or time-oriented event

How Machine Learning can help in Data Wrangling