How Does Data Wrangling Work?

EM Admin
Apr 15, 2024
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Did you know that a study by Gartner confirmed that bad quality data costs an average of $12.9 million to organizations?

To get rid of it, data wrangling is evolved. Let’s understand what it is.

Data wrangling is also known as data cleansing, which facilitates restructuring, enriching, and standardizing datasets so that they can be transformed. This data transformation practice helps in preparing data for quick analysis.

Considering the specifics, it targets typos, inconsistencies, missing records, dupes, and unformatted data entries to clean and process. Once done, these records become ready to be integrated with analytics tools.

This is how the raw data becomes comprehensive, accessible, and meaningful. The stakeholders follow this practice to get insights effortlessly through analysis.

Why Do Businesses Require Wrangling?

Certainly, there are multiple reasons behind its use and demand in the corporate world. Let’s figure out some obvious reasons.

1. Pool of Data in Diversity

Electronic devices, applications, social media, the Internet of Things, and many other sources are frequently generating data through customer interactions. Because these records come from customers or end-users, they appear different and in massive volume. This is where cleansing is required to properly analyze the collected datasets.

2. The Evolution of Artificial Intelligence (AI)

With the advent of transformative artificial intelligence (AI), the demand for data is rising. It requires clean records in abundance to evolve and refine algorithms for machine learning. The debugged and accurate models make AI work. So, this process follows data collection, which invites imperfect entries, typos, and inconsistencies.

3. Faster decision-making

The fast-paced corporate world requires quick decisions. Data-driven decisions prove realistically effective because they are insightful. These insights enable strategists to see beyond facts and figures and make informed decisions. Having such data guides helps you stay competitive.

4. Compliance and data governance

Compliance is related to regulations and governance. The collected records may have sensitive details like email IDs, account details, etc. These details must be secured and kept confidential under some regulatory acts, such as GDPR and HIPAA. This cleansing process helps in determining sensitive details, which are later strictly managed.

5. Enhanced Data Quality and Accuracy

It’s challenging to make accurate and practical decisions unless accurate information is available. Overall, this process ensures quality and accuracy, which enhances the reliability of data-driven insights.

How Does Data Wrangling Work?

Data wrangling or cleansing is a significant process that is incomplete without key steps. It’s true that every step makes the collected datasets better, more accurate, and more insightful. This process leads to the transformation of valuable insights, which are going to play an influential role in decision-making and strategic planning.

Let’s discover this step-by-step guide to how wrangling works.

1. Data Collection

Before doing anything, the datasets are extracted and collected from various online or offline sources. These sources can be databases, files, external reports, APIs, web scraping, and many others. This collection may have structured (like SQL databases), semi-structured (which can be XML or JSON files), or unstructured datasets.

2. Cleaning Datasets

Finally, the cleansing begins after collection. It is further divided into more subsets, which kick out typos, inconsistencies, and duplicate entries. Overall, these subsets can be the following:

Eliminating irrelevant or redundant entries that can misfeed eventually

Fixing typos, like spelling mistakes and inaccurate values

Enriching records by integrating missing values and completely removing incomplete datasets

Discovering and removing inconsistencies, which may appear in diverse formats of currency, dates, or anything

3. Structuring Records

Once the cleansing is done, the data wranglers focus on data structuring. When extracting and collecting data, it can be structured, semi-structured, or unstructured. To introduce integrity and uniformity, the entire format is optimized. Unstructured or semi-structured data entries are transformed into structured form, which can be an Excel or CSV file. Particularly, these steps enable structuring:

· Verifying and interpreting data into structured records

· Normalizing abbreviations or short forms to ensure consistent formats or units

· Transforming the format of the whole data, which can be in lowercase or uppercase

4. Enriching Database

The next step is enrichment, which is all about integrating contextual records with incomplete entries so that the data can be easier to understand and analyze. Typically, these steps take place:

· Merging records from various online or offline sources to introduce understandability

· Adding new variables or features so that the analysis can be more insightful

5. Validating Entries

The next step is validation, which refers to determining the accuracy of datasets. It defines the quality of records. This step can be further split into the following subsets:

· Examining the integrity of data

· Testing datasets to determine if the data meet defined protocols or rules

6. Storing Data

The aforementioned steps are mostly dedicated to introducing premium quality to datasets. Now comes the storage. Typically, data repositories like databases or servers are integrated. This is how the data becomes virtually accessible for analysis and reporting at any time from remote locations. It would be incorrect to consider that the storage is for securing data only. It is also helpful in organizing and managing them efficiently for quick analysis.

7. Documenting Data

Documentation is critical throughout the data-wrangling process. It records what was done to the data, including the transformations and decisions. This documentation is valuable for data cleansing consultants who audit, and understand the data for analysis process.

The last step is reserved for documentation, which consists of the management of the data cycle, transformation, and its wrangling. Also, informed decisions are reported in the document. It helps in auditing, finding gaps, filling gaps, and understanding the voice of data.

Conclusion

Data wrangling and cleansing are similar. This process is helpful in making guided decisions that prove actionable. The expected results can be expected by wrangling datasets because it cleans records, which will be analyzed. It involves multiple steps, which can be carried out the way we have mentioned in this blog.

Data

Data Cleansing