What Is Data Wrangling?
Data is everywhere. In today’s world, businesses need to be proficient in not only collecting data, but understanding how to actually use the data they collect to make a positive impact on their bottom line. If organizations can’t effectively analyze and put their data into action, they’re unlikely to be seen as key competitors in the market.
Due to the proliferation of data-rich smart devices and other technological advancements, it’s more necessary than ever before to develop systems capable of harnessing large volumes of data.
Luckily, there’s also a growing supply of data science professionals with the skills necessary to understand how to collect, prep, and analyze data. One essential (and often overlooked) skill is data wrangling. Also called data munging or data remediation, data wrangling involves cleaning and repurposing data to make it more usable for conducting analysis or creating a machine learning model.
Data wrangling can oftentimes feel like herding cats. It involves deleting unnecessary data, aggregating data from various sources, and identifying gaps and outliers in data. The goal is to prep the data so that it’s ready to use, whether that be for creating more accurate reports or improving overall data quality.
Why Is Data Aggregation Important?
Let’s back up for a second. Data wrangling may not be the sexiest aspect of data science, but it is super essential. If you don’t start your data analysis with clean data, it’s going to be nearly impossible to achieve your overall goal, especially if you’re working with a large data set.
Data wrangling is a complex and time-consuming process—data scientists spend nearly 40% of their time on data preparation. But, it’s worth it. If you start with quality data and conduct a thorough data wrangling process, you can ascertain clear, actionable business insights.
Here are a few real-world ways data wrangling can make an impact:
- Allowing quick, data-based decision-making
- Accelerating actionable insights from data
- Cleaning data and eliminating missing values
- Improving data quality
- Transforming data into more usable formats
- Creating efficient, centralized data management systems
- Improving data compliance
Disclaimer: Data wrangling doesn’t guarantee highly informative results. The data wrangling process is only as good as the data you collect. If your data quality decreases, so will the eventual results, even after data cleaning.
The 6 Steps of the Data Wrangling Process
Want easily digestible data to inform the future success of your business? By following this six-step data wrangling process, you can cut a path through the piles of unwieldy data at your organization.
It’s essential to start the data wrangling process by familiarizing yourself with the data. What trends immediately jump out at you? Is there anything missing that might cause issues down the line? Discovery sets the stage for the rest of the process and gives data scientists an opportunity to start thinking about how the data will be best used.
Data structuring is the process of transforming raw data into a more usable state. It might not be the last form the data takes, but it’s the form you need to plug into the analytical model you choose to interpret the data. Your data form will depend on which analytical model you’re using.
Data cleaning involves removing errors in the data that could distort your analysis. Cleaning involves deleting empty cells or groups of cells, removing outliers within the compiled data, and standardizing the inputs you decide to keep.
The goal of this step is to ensure that there are as few errors as possible. Any leftover errors can negatively impact your results in the publishing step.
After cleaning the data, you can see gaps and undeveloped data. Enriching datasets involves finding more external data to further back up your analysis. This could involve filling gaps by bringing in more informative datasets or creating synthetic data.
Keep in mind that if you want to augment your current data with exterior datasets, you will need to repeat the first three steps.
Validation guarantees the data’s quality and consistency. Often, automated systems conduct validation to make this step of the process faster and more thorough. You can also do a final look through the dataset to proofread it, make sure it’s cleaned meticulously, and has a uniform format. If the data doesn’t fit these requirements, you might run into reading errors.
Once the data has been validated, you’re ready to publish it and share it formally with others! This typically looks like sharing the data more widely with your organization in the form of a written report, so that your findings can be used to improve business processes and make operations more efficient.
Overcoming the Common Challenges of Data Wrangling
As we mentioned before, data wrangling is essential for performing business analysis. If the data you use to make important business decisions is incomplete or unreliable, you could face major consequences. With stakes this high, it’s important to be aware of common challenges with data wrangling so you can avoid making these mistakes.
Incorporating too much data
If you use too great a volume of data, especially if that data is comprised of different formats, the data wrangling process won’t be as efficient. Make sure to only use data that’s within the scope of the specific business problem you’re trying to solve.
Can data really be too clean? In short, yes. If you filter data too tightly and delete essential values, you risk creating an inaccurate model that isn’t ready to be used on real-world use cases.
Accessing relevant data
Negotiating access policies and fees for utilizing necessary data can be complicated and time-consuming. Sometimes, the hardest part is getting started.
Having a well-defined process
Avoid having too many cooks in the kitchen. If you have more than one person working on the data wrangling process, be sure you effectively communicate and have clear roles to play within the process itself. Untangling a mess of data further down the line will be much more difficult than ensuring everyone understands in the beginning.
Reducing manual input
Manual data wrangling is time-consuming and tedious. When raw data comes from old sources, like hard copies of documents, you’ll need to manually integrate that data. Be sure to plan extra time to do so.
Work Smarter with Data Wrangling
Don’t underestimate the importance of data wrangling. With the rise of IoT, there are more interconnected smart devices than ever before sending and receiving data constantly. If your organization wants to harness and use that data, you need to start by collecting and cleaning it thoroughly and properly.
Data wrangling can be intimidating—by using data science platforms like RapidMiner, you can automate steps in the data prep process and free up your time for high-level, impactful initiatives. Ready to start influencing how your business uses data? Request a demo to get started today!
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