Data wrangling is the process of cleaning and unifying messy and complex data sets for easy access and analysis. The readers of this article can expect to have a decent level of understanding of all the underlying phenomena and processes of data wrangling. The article will cover broader topics such as what data wrangling is all about, its associated processes, its importance in the field of data science, and essential tools involved in data wrangling, among other things.
- Grounds for the birth of Big Data
We may not have realized it, but a revolution has taken place in the last few years. This time the revolution has not taken place in the political or industrial sphere, but it has taken place in the realm of information technology. The internet became fast and ubiquitous. The computer hardware capability increased multifold so much that now we have large enough memory, GPU, and fast enough processors to perform highly complicated tasks. The reliance on computers, smartphones, and other similar machines to perform routine tasks through the internet has led to the Internet of Things (IoT). All these circumstances created a perfect ground that showed the world and, specifically the tech industry, the concept of Big Data.
- The 4 Vs
The first thing to understand is that big data doesn’t simply refer to a dataset lying somewhere that takes up a lot of space. Big Data is instead a phenomenon. The phenomenon of Big Data is fuelled by, as the IBM researchers put it: the 4Vs. These 4 Vs are Volume, Variety, Velocity, and Veracity, and are also referred to as characteristics of Big Data. Volume is generally referred to as Big Data, i.e., data occupying a massive space on a drive. Variety refers to the sheer ways in which data manifests itself, i.e., the table in your spreadsheet is as worthy of being referred to as data as CCTV footage of a traffic stop. Every individual, organization, system, etc., produces data at every second of its existence, which increases the pace at which the data is being generated, i.e., Velocity. Lastly, for a data scientist, the question is always regarding how useful, valuable, and trustworthy the information is, leading to the concept of data Veracity.
- Impact of Big Data
Big Data’s significant change is that now researchers, analysts, and data scientists can see patterns, predict values, and analyze the world around them, which was previously impossible. This is due to the sheer amount of data that we have. For example, we can take a person’s demographic information and their transaction information on various platforms, information gathered through their publicly available reviews, comments, etc. This can give us a complete understanding of that person. And this can be done for millions of records which help us create products utterly unheard of. Let’s pick any of the domains that make Big Data, such as Social Media, Hospitality, Banking, Finance, etc. For example, if we consider Stock Prediction using Big Data Machine Learning, the predictive model used in the Medallion hedge fund of Renaissance Technologies uses historical data and inputs from so many places that creating a predictive model would not have been possible in the ’90s.
Thus, while the scope of Big Data is immense, so are the challenges faced by a user or an organization before they can leverage big data for their benefit.
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Table of Contents:
- What Is Data Wrangling?
- Steps in Data Wrangling Process
- Why is Data Wrangling necessary?
- Data Wrangling Tools and Techniques
- How Machine Learning can help in Data Wrangling
- FAQs- Frequently Asked Questions
- Concluding Thoughts
1. What Is Data Wrangling?
The answer to the question “what is data wrangling” connects to big data, and there is a reason why Big Data and Data wrangling go hand in hand. While the use of big data allows us to find unique insights, the problem is that now as the data is being generated from various sources in all shapes and forms, we simply can’t use it as it is. This is where the concept of Data Wrangling comes into play. It is the process of taking data that is often unstructured, raw, disorganized, messy, complicated, incomplete, etc., and making it, for lack of a better word, “proper.” This “proper” or wrangled data can be used for further consumption in the typical analytical and modeling processes. Thus, once data wrangling is done then, other processes can begin, such as data mining (which includes exploratory data analysis, visualization, descriptive statistics), bivariate statistical analysis, statistical or machine learning modeling, etc.
2. Steps in Data Wrangling Process
While Data Wrangling remains a largely manual task that users have to perform before the data can be put to any use, there is a broad consensus regarding the number of steps to be taken in a chronology to complete a data wrangling process. Following are the common Data Wrangling steps:
- Data Identification
The first step in the process of Data wrangling is the identification of the sources from which the relevant data can be obtained. Data acquisition in machine learning is a foremost and fundamental step. The data can be present in remote servers or may be available on the internet that can be used through web scrapping. The data may be accessible through some other platform for which access rights might be required, which the client/administrator can provide. In this step, several Big Data concepts such as HDFS, Spark, etc., come in as they are used to access a large volume of data from an organization’s database.
- Data Understanding
This step is critical to properly performing the remaining steps of Data Wrangling. The user needs to have some essential to intermediate level of understanding regarding the data or datasets they will be using ahead. This can include
– identification of the crucial variables and what part of the business they represent
– understanding statistic properties such as mean, median, mode, the variance of a numerical, and count of the distinct categories of the categorical variable
– Identification of the ID variables that can eventually help in combining datasets
– Identification of the plausible dependent variable
– Identification of the independent variables and also identifying if there are derived, protected, etc. types among them
- Structuring Data
As mentioned earlier, often, big data is in an unstructured format, and we require the data to be in a structured format so that we can exploit it for our use cases. A quintessential example of this would be when performing Text Mining. Text can be a valuable data source; however, it is unstructured, and we need to create structures such as Document Term Matrix to make it in a structured format. Similar things are to be done with other unstructured datasets such as audio, images, video, HTML, XML, JSON, etc.
Another subprocess could be joining structured datasets to get a combined dataset that can then be used. To join using a one-to-one relationship, the user has to aggregate the data so that merging results are coherent to perform predictive modeling.
- Data Cleaning
Under Data Cleaning, several content-based manipulations are to be done to the dataset. This is done in order to reduce the noise and unnecessary signals that might be there in the data. Following processes are done as part of data cleaning.
– Missing Value Treatment: Getting rid of missing values by either removing rows or columns with excessive missing values. Imputation methods such as mean, median, or mode value imputation can also be performed based on the data type and distribution of the variable.
– Outlier Treatment: Outliers can simply be understood as abnormally large and small values, and they are particularly harmful when performing bivariate statistical tests or statistical modeling. The outliers can be capped to a value that, while preserving the distribution of the data, gets rid of the adverse impact of the outliers. The main problem here is the identification of an appropriate upper-cap(UC) and lower-cap(LC) value. While it is difficult to ascertain the best value for capping a particular variable, methods such as percentile analysis, IQR, percentile 1(LC), and percentile 99(UC) can provide a decent value.
– Feature Reduction
Certain variables that are redundant, have no statistical and aggregation properties, and make little sense from an analytical point of view can be removed.
- Data Enriching
Once the data is clean, the user can then look for ways for value addition to the data. This can be achieved by deriving more variables using the existing feature set. For example, a variable having phone numbers might be useless data as it has extreme cardinality and no statistical properties but can be used to identify the residence location of the customers. Similarly, the user in this step looks for other information that may not be present in the data and may not be readily available but can be added to the dataset to enrich it further. This can be, for example, adding a variable having the crime rate in an area, so if a model is being created that approves insurance claims, then the information can be useful.
- Data Validation
The second last step of the data wrangling process is to ensure that the quality of the data is not compromised by following all the steps till this point. By validation, we mean having consistent and accurate data. Data validation is typically done by laying down a bunch of rules and checking if the dataset is fulfilling those criteria or not. The rules for a dataset can be based on the variables’ data type, expected value, and cardinality. For example, in the dataset, a variable Age will be expected to have a numerical data type with a range between 1 and 110, a high level of cardinality, no missing value, no value in 0 or negative, etc. Thus, similar rules can be created, and the dataset can be cross-checked against them. Often programs can be written to validate if the dataset is following the prescribed rules.
- Data Publishing
Eventually, once all the above steps are taken care of and the data is structured, cleaned, enriched, and validated, it is safe to be pushed downstream and used for the analytical and modeling processes.
3. Why is Data Wrangling necessary?
Given the current state of data, it is often impossible to use the data as it is for any analytical purposes. Thus, the data wrangling steps mentioned above are almost mandatory. Data wrangling fundamentally changes the data-
– Firstly, it makes the data structured to be easily manipulated, mined, analyzed, etc.
– Secondly, it reduces the noise found in the data as it can badly affect the analysis
– Thirdly, it brings up the underlying or subdued information, which enhances the knowledge gained from the dataset
Lastly, by going through all the data wrangling steps, the user gets a better sense of the nature of the data they are dealing with, which can aid them when in the analytical and predictive stages.
4. Data Wrangling Tools and Techniques
There are several tools that can be used to perform data wrangling. The user must be proficient in whichever tool they use, as most of the time (~70%-80%) goes into data wrangling, and a decent skill-set is required. Among the most common tools for data wrangling are as follows-
Data wrangling in python can be performed with a lot of ease if one knows some basic libraries of python. Thus, one of the most popular languages, Python, can be used as a data wrangling tool. There are several techniques through which data wrangling in python can be performed. For example:
– Using pandas or Pyspark to access the data
– Using pandas, tabula, nltk to convert it into a structured format
– Using csvkit, plotly to understand the data
– Using NumPy or pandas or clean and enrich the data
You may also like to read: 10 Steps to Mastering Python for Data Science | For Beginners
R is a statistical tool that can be used to perform data wrangling. It has many libraries that can help achieve the various steps of data wrangling steps, making it a good candidate for a data wrangling tool. A technique can be of
– Using dplyr to access the data
– Using dplyr, reshape2 to convert it into a structured format
– Using hmisc, dplyr, ggplot2, plotly to understand the data
– Using purr, dplyr, splitsstackshape to clean the data
This popular spreadsheet software by Microsoft can also be used for data wrangling. There are hundreds of formulas in excel that can be used to perform data manipulation. However, unlike R and Python, this is a much more manual process as all the data wrangling steps are to be performed even for similar datasets. In contrast, in R or Python, a script can be written that can be made to rerun; of course, a way around is by writing macros in excel.
Apart from these significant tools, commercial suites such as Traficta, Altair, etc., perform automated data wrangling.
You may also like to read: 16 Best Big Data Tools And Their Key Features
5. How Machine Learning can help in Data Wrangling
The idea of machine learning always has boiled down to “rather than the user writing the code, the machine will write the code for you.” This has led to a plethora of changes in which we work, such as increased speed, accuracy, efficiency, and reduction in the resource required to perform a task. Machine Learning is currently being exploited for the latter half of the data science process, i.e., data analytics and predictive modeling; however, the most time-consuming step lies before that – data wrangling. This is where Machine Learning can be used. As mentioned above, there are some automated data wrangling tools, but there is a need for more.
In this regard, Machine Learning processes can be exploited, such as creating machine learning models that work in a supervised learning setup to combine multiple data sources and making them in a structured format. Models working in an unsupervised learning setup can be used to understand the data. Classification models can also be created to look for certain predefined patterns present in the data. A combination of all these models then can be used to automate the data wrangling processes.
You may also like to read: Machine Learning Vs. Data Mining Vs. Pattern Recognition
6. FAQs – Frequently Asked Questions
Q1. What are the challenges in data wrangling?
Data wrangling is a dynamic process and requires some subjective decisions to be taken at the user end, making it difficult to automate. Thus, the challenges in data wrangling include difficulty identifying the appropriate steps, a significant amount of time consumed in the processes, etc.
Q2. What are the benefits of data wrangling?
A good data wrangling process can provide data analysts and scientists with quality data. This, in turn, can lead to better insights and predictions.
Q3. What are the skills required for data wrangling?
A good command over data manipulation using Python or R is required to be used as data wrangling tools. For small datasets, Excel or similar spreadsheet software can also be used. Having a good understanding of the business is also required as that helps in making a more informed decision when performing data wrangling.
7. Concluding Thoughts
Data Wrangling is a crucial step in a data science project cycle that is often overlooked and less talked about. While there are many well-built and well-defined tools for all the other processes that can automate the task, data wrangling processes remain manual mainly, and users have to use their understanding of data to make them usable. For anyone dealing with data, be it any vertical in an organization, it is essential to have a sense of data wrangling and its processes. How well data wrangling is done dictates how good the analysis or predictions will be.
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