Understanding Data Wrangling vs. Cleaning: Key Distinctions

Data analytics is not more crucial than ever before. With the rapid increase in digitization, the amount of data being created on a daily basis is enormous. In 2023, 120 zettabytes of data were generated, 70% of which is user-generated data.

With IoT and other technologies in the mix, the amount of data being generated will only increase further. This means organizations must prioritize not just analysis but every data process, from when data is sourced, cleansed, analyzed, and used for reporting.

To manage this information and data, data scientists and engineers work on refining the data for further usage. This is done using data wrangling and data cleaning, both part of the data preparation process. However, even the experts make the mistake of using these terms interchangeably, which does not provide a clear picture.

To understand the subtle differences and why both processes matter, here is a comparison of data wrangling Vs data cleaning.

Understanding Data Wrangling

Let’s start by understanding the complex term - data wrangling. This process includes the restructuring, cleaning, and transformation of raw data into usable formats. Since data flow into the system occurs via multiple sources and in disparate formats, data wrangling is crucial to prepare this data for further analysis.

Data wrangling is characterized by its flexibility, allowing data engineers and scientists to adapt to varying requirements and formats. It involves six steps, including:

1. Data Discovery and Acquisition
2. Data Structuring
3. Data Cleaning
4. Data Enrichment or Transformation
5. Data Mapping or Verification
6. Data Publishing

Understanding Data Cleaning

Data cleaning is the subset of data wrangling and focuses on refining and transforming datasets. This process involves rectifying any errors, inaccuracies, or inconsistencies in the dataset, ensuring that only well-formatted and accurate data is used for analysis.

The primary objective of data cleaning is to ensure data accuracy and reliability, ensuring that any anomalies or inconsistencies that may have crept into the data are corrected. Some of these activities can include:

• Matching of transformed data with the master database
• Removal of invalid information
• Addition of data for blank or inconsistent cells
• Using steps like three-factor authentication, client checks, and checks by experts to ensure that data mismatch and inconsistencies are minimized

Data Wrangling vs. Data Cleaning: Key Differences

Now that you have seen the basic tasks that take place under data wrangling vs data cleaning, let us look at the more subtle differences between the two:

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Harmonizing Data Wrangling and Data Cleaning

Different tools are often employed for data wrangling and data cleaning. These tools focus on distinct tasks, such as data preparation and rectifying errors or anomalies. However, combining them into a unified workflow ensures a more seamless data refinement process, which can also help in streamlining business operations.

To do this, organizations can focus on particular aspects such as:

1. Unified Workflow

To combine data wrangling and data cleaning aspects into a unified workflow, businesses need to train ML models to effectively rectify anomalies in data and get this data in the correct format.

Consider a healthcare patient record database, where anomalies in patient records need to be identified during the cleaning phase. 

Using a unified workflow, ML algorithms can ensure that the entire database is seamlessly adjusted to maintain accuracy and reliability. This ultimately improves patient care and medical decision-making.

This holistic approach streamlines operations and reduces the risk of overlooking critical data quality.

2. Continuous Iteration

Once the model is trained into a unified workflow, the next step is to have multiple iterations of the process to help the ML model provide accurate output. The ML model can use iterative processes to understand how raw data files are cleansed and formatted, creating a continuous feedback loop for further refinement.

For example, suppose you are analyzing the performance of a particular fund over the years. In that case, the ML model can analyze historical data on the fund’s track record over the years, including impacts or fluctuations. 

Using continuous iterative training, the ML model can recognize and rectify anomalies in financial records during the data-cleaning process. Subsequent iterations allow the model to adapt to new patterns and variations in data, creating a continuous feedback loop.

3. Synergies in Task Execution

Bringing data wrangling and data cleaning together creates synergies in task execution. For example, identifying outliers during data cleaning may prompt a revisit to the wrangling phase to better handle such anomalies.

4. Impact on Data Quality

The collaborative effort of data wrangling and data cleaning significantly impacts data quality. A well-structured and clean dataset not only facilitates accurate analysis but also instills confidence in the reliability of the results.

For instance, if you are working on machinery sensor data, the ML model can be trained to identify irregularities during the cleaning phase and adjust the data in the wrangling phase.

This not only streamlines operations but also maintains data integrity, which is crucial for precise analysis and informed decision-making in the manufacturing process.

5. Impact on Data Analysis and Decision-Making

Despite the automation available today, data professionals still spend a huge amount of time on activities like data preparation (22%) and data cleaning (16%). In comparison, only 9% of the time is spent on model selection and model training, respectively.

This gap is one of the main reasons why data wrangling and cleaning processes must be done more efficiently, using reliable solutions for more data-driven insights.

A clean and well-prepared dataset can be the foundation for more accurate and reliable analysis and also help to train the data model with relevant insights. This, in turn, enables the ML model to be more efficient and make accurate decisions, fostering a culture of data-driven decision-making.

Real-world Examples

1. Future Trends

As technology keeps evolving, data transformation techniques are constantly being updated. The speed and volume of data have made it crucial for businesses to adopt a Big Data mindset, with the mix of AI and ML algorithms to help streamline data processes further.

Future trends in data wrangling and data cleaning will focus prominently on the following:

2. Advanced Automation 

While industries have started using workflows to automate repetitive tasks, advanced automation enables a range of activities to be done without any human interventions.

For example, using event-based or trigger-based workflows, data cleaning steps can be done based on an event being triggered. So, if the date format is DD/MM/YY and the input format is MM/DD/YY, the data cleaning process can first format each column in the required date format and then perform the next set of activities for improved accuracy.

3. Machine-based Insights

ML algorithms will also become adept at identifying patterns and outliners in data by analyzing existing data cleaning frameworks, helping make improvements to the overall data management process.

So if a data column for the prices of a particular commodity has an outlier like a text field or negative value, the algorithm can flag this or get it corrected, enabling accurate data insights.

Thus, data scientists and engineers can focus their attention on core activities like data analytics, visualization, and business intelligence rather than the refinement of data for these processes.

Conclusion

To conclude, data wrangling and data cleaning are not terms that you can use interchangeably. There are a lot of differences between the two, and both processes play a crucial role in ensuring that the data you use for analysis is clean and refined.

As technology advances, the future promises more automation and the use of ML algorithms to help automate this process. One such futuristic platform is MarkovML, a no-code, easy-to-use AI platform that helps you with data transformation features.

Using Auto Data Analysers, it can identify data gaps, deviations, and errors in your raw data, helping you streamine the data cleaning process. Plus, with Data Catalog features, you can easily monitor data, define workflows, and create an efficient data management ecosystem.

Explore MarkovML’s robust AI-enabled data management and intelligence features to get started.