It’s not a secret that many companies use public data because it helps make various strategic decisions. However, getting valuable insights from this information can be a challenge. Usually, public data that companies collect is generally raw. This is where the data wrangling process comes in.
This article discusses what data wrangling is, the key steps of data wrangling, and why it’s crucial for data-driven decision making.
What is data wrangling?
Data wrangling (also known as data preparation or munging) is the process of restructuring and cleaning the raw data into a more processed format. After data wrangling, data analysts can fasten the decision-making process. The exact methods differ depending on the size and format of the data, and the goal data specialists are trying to achieve.
Data cleaning is often a manual but essential process.
According to Forbes, data specialists spend most of their time – around 80% on preparing and managing data for analysis.
Steps of data wrangling
Even if data wrangling usually requires a unique approach to ensure that the final dataset is readable and helpful, this process has the most common steps. To prepare the unclean public data for analysis, there are six basic steps to follow.
This step involves simply understanding what the unclean data is all about. It’s a process of familiarizing with raw information – data analysts can conceptualize how they might use it. It’s a crucial part of the data wrangling process because data analysts can discover patterns in the data and issues that need to be solved before further steps, such as missing or incomplete values, etc.
Most of the time, when raw public data is extracted from multiple data sources, it’s typically unusable because this information hasn’t a definite structure or schema, and it’s hard to work with it. Data analysts need to transform this data into a more readable format. Of course, to avoid this step, there are advanced data collection tools that gather structured data in the first place. For example, Oxylabs’ web scraping solutions provide structured data in JSON format.
The main goal of this step is to ensure there are no issues left, or, at least, data analysts deal with all the errors they find at the time. Unexpected problems can distort the final analysis results; this is why this step requires thoroughness and caution. Data cleaning includes simple actions such as deleting empty cells or rows, removing outliers, standardizing inputs, etc.
The next step is to determine whether this data is enough for the goals set in the beginning. Simply put, it’s essential to understand if this information provides valuable insights. If data specialists decide not, they need to augment this data by incorporating values from other datasets. Of course, repeating the steps above for any new information is a must.
Validation requires programming knowledge because it’s usually achieved through various automated processes. The primary purpose of this step is to verify the consistency and the quality of data after processing.
It’s the final step of the data wrangling process. Once the data is ready, data analysts can make it accessible to others for actual analysis. Usually, they prepare a written report for easier further usage.
The importance of data wrangling
It’s crucial to understand that if the required data is incomplete or incorrect, the further data analysis process can become unclear. It simply means that all the insights might be wrong, which can cost businesses time and money. Data wrangling helps to reduce that risk by ensuring information is in a reliable state.
When done manually, data wrangling can be time-consuming. Companies usually come up with the best practices that help data analysts simplify the whole process. This is why clearly understanding the steps of the data wrangling process is crucial because it helps determine which parts can be improved.
Data wrangling tools and techniques
The most basic structuring tool that data analysts use for data wrangling is Excel Spreadsheets. Of course, there are more sophisticated tools such as OpenRefine or Tabula. Data analysts also use open-source programming languages R and Python for data wrangling. Specifically, these languages have helpful open-source libraries for the data munging process.
Here are commonly used libraries and packages of each programming language.
Data wrangling in Python
- Pandas. Data alignment sometimes prevents common errors that can be extracted from misaligned data during the scraping process. Pandas library is helpful for dealing with data structures with labeled axes.
- Matplotlib. This library can help create various professional graphs and charts. When the data is ready to be published, data analysts usually make written reports. Visualization of information is needed for others to understand it easier.
- NumPy. It offers various mathematical functions, random number generators, linear algebra routines, and more. NumPy syntax is simple for programmers from any background or experience level.
- Plotly. It’s similar to Matplotlib because Plotly is used for creating interactive graphs and charts.
Data wrangling in R
- Purrr. This toolkit is mostly used for error-checking and creating list function operations.
- Dplyr. This data munging R package is especially useful for operating on categorical data. Dplyr provides a consistent set of verbs that help data analysts solve the most common challenges of data manipulation.
- Splitstackshape. It’s a useful tool for restructuring complicated datasets: splitting concatenated data, stacking columns of the datasets, etc.
- Magrittr. This tool is used for munging scattered datasets and putting them into a more consistent form.
Even if R and Python can help speed up the data wrangling process, data analysts still need to do many operations with caution and thoroughness. As mentioned above, it’s a time-consuming but essential process.
Data wrangling is the process of making raw data ready for analysis. Usually, data wrangling is done in 6 steps: discovering, structuring, cleaning, enriching, validating, and publishing. It’s a crucial process because, without it, companies can make wrong data-driven decisions – they would simply rely on incomplete or incorrect information. Data wrangling reduces this risk by ensuring that data for analysis is revised and correct.