What is Data Wrangling?

Data wrangling refers to collecting, choosing, and formatting data to answer an analytical question. It is also called “data cleaning” or “munging.” And data experts spend more time on it than actual exploration and modeling.

Data wrangling may include further munging, visualization, aggregation, and statistical model training. It follows general steps that start with extracting raw data from a source. The data is then sorted or parsed into a predefined structure. After that, the resulting content is stored in a data sink for storage and future use.

Other interesting terms…

• What is Deep Data?
• What is Data Vaulting?

Read More about “Data Wrangling”

Even if data wrangling is very time-consuming and can sometimes cause project delays, it is necessary. Sometimes, the process can lead to important discoveries that can redirect a project.

Data Sets That Require Data Wrangling

Data wrangling is needed to create the data sets described in more detail below.

Analytic Base Tables

An analytic base table (ABT) is used in machine learning (ML). In such a table, every row represents a unique entity (e.g., a person, a product, etc.). Each column shows a characteristic (e.g., history, relationship with others, etc.) of that entity at a given time. Supervised ML analyzes the table to look for consistent patterns indicative of desired outcomes.

Transactions

Businesses need transactional information to show prior customer contact. In this case, the data includes notes and actions taken during the calls to address concerns raised during a current call.

If the information on a specific product order is needed, data wrangling can provide details. It can also help with medical and dental records to recall what has been done before.

In analytics, transactions are summarized so managers can obtain business intelligence. In a sense, transactional data obtained from wrangling is ABT’s predecessor.

Time Series

Data wrangling separates information by attribute over time. In standard time-series analyses, observations are divided into consistent periods (e.g., seconds, days, months, etc.).

Document Libraries

Data wrangling provides consistently formatted documents, predominantly text, for text mining analyses.

Steps in the Data Wrangling Process

The steps in the data wrangling process below aren’t done only once sometimes.

Discovering

It is critical to understand what your data contains before you start wrangling. This step will tell you how to analyze it. If you want to wrangle customer data, for instance, you need to know if it contains their locations, purchases, and the like.

Structuring

After looking closely at your data, you need to organize it. This step is essential since raw data comes in varied shapes and sizes. You may need to turn a column into rows, for example. Organizing the data makes computation and analysis easier to do.

Cleaning

Cleaning your data means getting rid of errors and outliers that may skew your analysis. For example, if the country names are not consistent (i.e., some are spelled whole while others use abbreviations), your research may become inaccurate.

Enriching

In this step, you can think of ways or additional data that can enrich your analysis. You can answer questions like “What new information can I derive from what I have?” or “What other data can enhance my decision making?”

Validating

Validation needs to be done repeatedly to ensure data consistency, quality, and security. You need to ensure, for instance, that characteristics are distributed normally (i.e., they are consistent with what you see in the real world).

Publishing

Other users or applications use the data that results from wrangling. As such, it’s essential to take note of the steps taken or logic used in the data wrangling process. That way, confusion does not occur. Only when the data is published it is ready for analytics.

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So when asked “What is data wrangling?” you can liken it to building a house’s foundation to ensure it survives an earthquake, for example, or just the ravages of time. Also, skipping the process results in modeling errors that can negatively affect the accuracy of analytics.