Enterprises today collect unimaginable amounts of data. But if data is raw, enterprises cannot utilize it to its full potential. Data wrangling helps turn this raw data into valuable data for the enterprise.
Even though data wrangling is the most time-intensive component of data processing, it yields indispensable insights that impact future uses of the data.
What is Data Wrangling?
Data wrangling is the process of cleaning, restructuring, and organizing raw data into a more processed format to make it suitable for analysis and visualization purposes. It helps data teams to improve and expedite decision-making processes, which leads to faster, more accurate, and higher-quality insights for enterprises. It also empowers enterprises to tackle more complex data faster.
Data wrangling tools can be used to generate financial insights, improve data quality, carry out customer behavior analysis, uncover data trends, and detect fraud among other uses.
Data Wrangling Benefits
- Easy Data Handling: Data wrangling transforms raw and unstructured data into usable formats. It also enriches data to make it more useful and helps data offer more impactful insights.
- A Better Understanding of Audiences: When enterprises aggregate different data sources, they can better understand their end users to allow them to drive the right advertising campaigns and content strategies.
- Simpler and More Efficient Data Analysis: The wrangling and transformation of raw data empowers enterprises to swiftly and efficiently perform analysis on complex data.
- Impactful Visualization: Wrangled data is easier to visualize and can be exported to any visual analytics platform for analysis.
- Standardized Data: Enterprises can use automated data integration tools as data wrangling approaches to clean and transform source data into a standardized format. Depending on end requirements, this format is reusable and can allow enterprises to perform cross-dataset analytics.
- Data Usability: By transforming data into a usable format, data wrangling ensures data is compatible with the target system. Accurately wrangled data also ensures quality data is available for downstream analysis.
- Faster Decision-Making: Management teams can make decisions much faster, as data wrangling aims to provide the highest quality output to influence decision-making in the shortest time.
Also read: Tips for Processing Real-Time Data in a Data Center
Data Wrangling Process
As much as data wrangling can be a fluid process, it involves the steps of data extraction, structuring, cleaning, enriching, and validating.
Even though data extraction may not be considered part of the typical data wrangling process, it is crucial. Before transforming data, you have to first collect it. You have to plan on what data you require and determine the sources. Unstructured data can be pulled from sources such as a third-party repository, social media, websites, and more.
Data extraction empowers the consolidation, processing, and refinement of data before transformation. After retrieving heterogeneous data from disparate sources, you would need to compile the data sources to be comprehensible and examinable to reveal trends and patterns in the data.
Exploratory data analysis (EDA)
Exploratory data analysis involves carrying out a set of initial investigations to determine the main characteristics of data, often through graphics and summary statistics. Exploratory data analysis does not have to be carried out as the second step of data wrangling, as it is determined by the condition of the dataset and the work needed.
While dealing with unstructured data, EDA has multiple techniques. The first is quantitative data analysis, which utilizes mathematical and statistical analysis to provide discrete values and results. With this type of analysis, EDA uses visualization techniques like dimensionality reduction to reveal relationships between data variables. This makes it easy to discover patterns and handle anomalies and outliers.
For unstructured text data, qualitative data analysis mainly applies. Qualitative data analysis techniques require social intelligence and contextual reasoning as well as intuition as opposed to mathematical formulae. Artificial intelligence and machine learning techniques also assist with predictive analysis and revealing insights from data.
As data is in an unstructured format while newly collected, it lacks a definite structure. The lack of an existing model causes it to be disorganized and require restructuring to suit the analytical model an enterprise has deployed.
At this stage, the dataset would need to be parsed, as unstructured data contains lots of text in addition to numbers, dates, codes, and more. Parsing allows pertinent information to be extracted from data.
Data structuring may also involve some aspect of feature engineering, where columns or rows may be split to improve accessibility, user-friendliness, and analysis.
Even though data cleaning is a single step of the wrangling process, it is often a complex process. Raw, unstructured data is often full of errors that need to be remedied before the next stage. A great focus of this stage is the detection and correction of rogue data, such as inaccurate, incomplete, redundant, and wrongly formatted data.
The data cleaning stage prevents skewed results from developing during analysis by removing outliers in the dataset. Cleaning the dataset also improves the data quality and consistency by changing null values and standardizing the data format.
Additionally, to make data easier to handle, cleaning corrects typos and structural errors, fixes duplicates, and standardizes systems of measurement. Algorithms can be applied to clean data using Python and R.
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At this stage, you are required to evaluate what is in the data to determine whether to upscale or downsample data or carry out data augmentation. Although, this step is only necessary if the data falls short of specified requirements.
The goal of data enrichment is to improve or increase the accuracy of data analysis, which can be achieved by combining your data with supplementary data from various sources to assemble more data points. For example, lead enrichment can help fill gaps in your data about your customers.
Data validation involves taking iterative programming steps to verify the quality, accuracy, authenticity, security, and consistency of data after processing. Any data quality issues are serviced to allow them to be addressed by the correct transformations.
For instance, you may have to check whether attributes are normally distributed in the dataset or whether fields in the dataset are precise through cross-checking data. Comparison of the attributes of data with defined rules is handled by pre-programmed scripts.
This step signals that data is ready for analytics, as the data is already wrangled. Data should be made accessible to end users like data engineers, data analysts, and data scientists by depositing it into a new database or architecture.
End users can use this published data to generate further insights, power business intelligence, machine learning, and more, opening up endless possibilities for deriving value from data.
Choosing Data Wrangling Tools
Your choice of data wrangling tools will be dependent on your data processing and organizing needs. It will also depend on your cleaning and consolidation requirements and your approach to getting insights from data.
While Excel spreadsheets are the most basic structuring tool for data wrangling, there are a variety of tools, which offer more sophistication and the ability to extract data from PDFs to be able to work with CSV. Moreover, some operational features of the Python and R languages enable data wrangling.
So, before making a decision on which data wrangling tool you will use, be sure to determine what features you need and how different tools compare.
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