Wrangling Data in a Holistic Approach
The Defining Terms
Before discussing the main topic “Wrangling Data in a Holistic Approach”, it is important to understand the term holistic. Holistic can be considered to be an adjective that describes things related to the idea that the complete object is more than the sum of its parts. An example of holistic is the health care sector that examines the health of the entire body and mind rather than just parts of the body.
What is Data wrangling?
Now, what is meant by data wrangling? The process of gathering, selecting, and transforming data to answer an analytical question is what we mean by data wrangling. Also known as data cleaning or “munging”, it is said that this wrangling process costs as much as 80% of the analytics professionals time, leaving only 20% for algorithm exploration and model deployment.
This self-service model with the implementation of data wrangling tools allows data analysts to tackle complex data more quickly, helps to produce more accurate results, and finally make better decisions.
Consequently, data wrangling covers the following steps:
- Gathering data from the various source and storing it one place.
- Separating the data according to the determined setting and putting together the similar ones.
- Finally, cleaning the data from the noise or erroneous, replacing missing elements and, making conversions as per requirements.
Its importance in the business world
Now, the question arises why is it important for organizations to have “holistic” analytics? The foremost reason is all business-oriented sectors want a 360-degree view of their organization at a glance. Because that helps to give them the perspective and insight to optimize their business such as growing customers, acquiring customers, and making customers successful in their choices.
So, once we have understood the importance of holistic data wrangling for the corporate world, let’s check out what are the steps in data wrangling?
What are the steps in data wrangling?
While data wrangling is considered to be the most crucial and first step in data analysis, it is often the most ignored phase because it is the most tedious task to do. To prepare our data completely for analysis, as part of data munging, there are 6 basic steps which we need to follow one after another as stated below:
- Data Discovery: This is an all-encompassing term that describes an understanding of what our data is depicting. In this first step, we get familiar with our data and its structure.
- Data Structuring: When we collect raw data, it is initially present in an unstructured form with no fixed shape and size. Such data needs to be restructured to suit the analytical model that our analysts will deploy models with.
- Data Cleaning: Raw data consists of huge amounts of errors that need to be fixed before data is processed for the next stage. Cleaning involves the tackling of outliers, replacing blank spaces, making corrections, or deleting meaningless data completely.
- Data Enriching: On reaching this stage, we have become familiar with the data and have a draft of it in hand. Now is the time to embellish the raw data and augment it with other data.
- Data Validating: This step 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 our data
- Data Publishing: Once all the above steps are completed, the final output of our data wrangling efforts are pushed downstream for our analytics needs
Data wrangling is a core iterative process that provides us the cleanest and most usable form of data possible before we start with our actual analysis.
The tools and techniques used for data wrangling
The next thing to know is what are the tools and techniques used for data wrangling?
According to surveys, it has been observed that data analysts spend 80% of their time in data wrangling rather than the actual analysis. Data wranglers are usually hired for the job if and only if they are skilled in one of the following domains: cleared knowledge in a statistical language such as R or Python, good knowledge in background programming languages such as SQL, PHP, Scala, etc.
Focusing on Python, they use certain tools and techniques for data wrangling, as described below:
- Python: It is one of the most used programming languages by data scientists and comes up with many operational features as its in-built library provides vectorization of mathematical operations on the NumPy array and speeds up performance and execution of data.
- Pandas: A python library designed for fast and easy interpretation of data analysis.
- Plotly: The library mostly used for interactive graphs like histograms plotting, line and scatter plots, bar charts, heatmaps, etc
- Excel Spreadsheets: This can be considered to be the most basic structuring tool for data munging.
- Open Refine: A more sophisticated computer program preferred over Excel.
- Tabula: this is often referred to as the “all-in-one” data wrangling solution by data analysts.
- CSV Kit: It is used for conversion of data
Focusing on R, the tools include:
- Dplyr: The most useable R framing tool for data wrangling
- Purrr: It is helpful in list function operations and checking for mistakes
- Splitstackshape: This is very useful for shaping complex data sets and simplifying visualization
- JSOnline: A very useful parsing tool
The use of open source languages
A few data experts have also started using open source programming languages R and Python and their libraries for automation and scaling. Using Python, straightforward tasks can be automated without much setup and again, things here are still at a nascent stage.
The summarizing words
Given the huge amount of data being generated almost every minute and at every second, if more ways of automating the data wrangling process are not found soon, there is a very high probability that much of the data the world produces shall continue to just remain idle and wasted, and not provide any value to the business enterprise at all. So, it is quite important to discover various tools and sophisticated techniques for efficient data wrangling.
TDS Editors Andrew Ng The Startup ODSC — Open Data Science Rohit Ghumare Analytics Vidhya Mathias Barra Timothy Key
