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6 Top Data Wrangling Tips

Reading time: 5 minutes
data wrangling

By Rapid Insight

Running an analysis with messy, unstructured data leads to poor results. That’s why data wrangling is an integral step in the analysis process.

Data wrangling means rounding up data from disparate sources and storage locations, then cleaning and shaping the data into a digestible package of information, either for direct delivery to stakeholders, increased efficiency and accuracy of analysis, or for use in advanced applications like predictive modeling.

While the end goal of your efforts is typically clear, the process itself can be unnecessarily complicated and frustrating. Establishing best practices can significantly lighten your workload and streamline your process.

With that in mind, Rapid Insight’s data analysts shared their top 6 tips to improve your data wrangling process!

Tip 1: Filter your data to lighten the load

Our first tip comes from Data Analyst Lily Brennan:

I often suggest that the very first step in data wrangling is to filter the data. By “filtering” I mean being selective about which rows and which columns you keep. 

Selecting only the columns you will need to work with from the beginning will make the whole process of data wrangling easier. From sorting through column titles to find the one you need or viewing the final output, you won’t regret shedding those extra columns. As for rows, conditionally selecting only the specific records that are a part of the cohort you are interested in is a great way to pare down and get rid of unnecessary information and bulk. 

All of this will make it easier to work with the data later and allow you to identify trends, outliers, and errors. 

Filtering the dataset will also make it faster and easier to clean up dirty records as part of your data wrangling process. You wouldn’t spend time cleaning your trash before bringing it to the dump; why clean data records that you won’t even use? 

Filtering first ensures that processes that need to look through every single record don’t waste time and processing power on unnecessary tasks. This increase in efficiency is true beyond just cleaning the records, but also for calculations, merging data and any other function that runs through all records. 

The bottom line is, when you’re wrangling data – filter first.



Tip 2: Keep your desired output in mind throughout your wrangling process

Customer Success Manager Alex Ziko says:

When prepping your data, it’s important to have a clear idea of what you want your output to be. 

This could be creating several elements within a report or dashboard, or it could be a dataset containing a specific cohort of observations. 

It always helps to work backward when curating a dataset. Before you touch the data, know what you want the result to look like.

Tip 3: Ensure redundancy in your wrangling process

Alex also says:

Always maintain the ability to roll back to a previous version of the data.

When working in Excel for example, if you start deleting columns and reformatting cells, you may find yourself wishing that you could go back to a previous version and make different data prep choices. 

In Rapid Insights’s Construct, the read-only format and visual query design inherent in the tool makes it easy to undo any data prep steps that you took. This means you can always go back to the original data format, or remove the most recent steps to return your dataset to a different version.

Data wrangling is alternatively referred to as data preparation, data blending, data munging, or data franchising. It is similar to the ETL process, and incorporates data ingestion.

Tip 4: Understand where and how your data is stored

Product Manager Jon MacMillan said:

Perhaps the most important step in data wrangling is understanding where and how your data is stored. Do you have access to live data or a daily snapshot? Have you been given direct access to the raw data, or are you working with prepared data? 

These questions help set reasonable expectations of the data and underlying assumptions. 

Once these points have been addressed, make sure to pass this information along to others who will use or view the data. Detail how the data was accessed, provide intuitive names for the data points, and explain what they represent. 

With a clear understanding of the underlying data and good communication, you and the end users can have greater trust in the data.

Data Dictionary

Tip 5: Make a Data Dictionary

Another tip from Customer Success Manager Alex Ziko:

When working with unfamiliar data, you sometimes end up with lots of questions about data type, character counts, or categorical examples. 

A data dictionary can be used as a reference guide (for yourself and for end users of the data) to explain what each variable is, what an example value may look like, and details on the data type. 

A map gives you details on terrain, elevation, and geographic orientation. Similarly, a data dictionary orients you to important details of your dataset.

Tip 6: Include subject matter experts whenever possible

 Analyst Manager James Cousins said:

Data practitioners often find themselves in the position of needing to clean data they’re unfamiliar with. Reaching out to a subject matter expert who knows what the data signifies, what changes in policy impacted the data, and whether the data is even related to the outcome can mean all the difference in your data wrangling efforts. 

Recruiting subject matter experts isn’t just an opportunity to ensure that you are cleaning the data in accordance with its meaning. It is also an opportunity to enhance your results. Hearing the expert insight of people who know the data best gives you an opportunity to infuse your technical expertise with context, leading to more creative measurements of your key outcomes, innovative ways to predict your outcomes, and an improved ability to communicate results.

Make Data Wrangling Easy

Rapid Insight’s Construct allows you to easily perform all of the technical steps above while facilitating the practical steps with its intuitive visual interface.

Construct Job

Filter, append, transpose, and otherwise manipulate your data by dragging your sources into the workspace. Then connect the sources with the node named for the operation you want to perform. Finally, connect an output node to the final step in your process to generate a report or dataset.

Non-technical stakeholders and users can easily follow your process, making collaboration with subject matter experts easy. Saved, repeatable, read-only workflows ensure the integrity of your original data sources, and automated scheduling makes delivering regular reports instantaneous.

To see how Construct can make data wrangling easy for you, click the button below to request a customized demo!


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