The term “data wrangling” refers to the process of cleansing and consolidating complex, disparate data sets to make them ready for downstream applications.
The process can vary from company to company and use case to use case, but it can be broken down into six key steps:
- Discover – Focus on understanding what data you already have. Look at the data and consider how you want to organize it. This will make it easier for you to use, analyze and answer specific questions or deploy specific use cases
- Structure – Determine what data you can use, and how easily you can use that data for AI and ML models that need highly structured data to work
- Clean – Identify inconsistent or incomplete data sets before sending them to an end user. This ensures your data quality is high and allows you to perform more accurate and efficient analysis
- Enrich – Add third-party data to your first party data. This stage adds further contextual information to your dataset, making it more useful for analysis or downstream applications
- Validate – Add programming sequences to verify the consistency, quality, and security of the data. For example, you may need to verify that attributes (such as birth dates) are consistently distributed.
- Publish – Prepare your wrangled data for further use, either by a specific user or software. Document any steps you took or logic used to prepare the data for analysis.
Why is data wrangling important?
If you’re a data scientist, data wrangling can feel like an arduous task. But the practice is far too important to neglect or ignore.
Here’s why. We create around 2.5 quintillion bytes of raw data every day. The data is abstract. It’s meaningless. And it’s pretty much useless on its own.
To make full use of this data, you need to prepare and convert it into a composable format for your end system, e.g., an AI and ML model.
Sending raw data through the pipeline without wrangling it, can result in extensive inaccuracies, and with that, bad decision making. And we all know the impact of bad decision making in business.
So it’s very important that you take data wrangling seriously and follow the steps described above.
Data wrangling – an example
To illustrate data wrangling in action, here’s a hypothetical example:
You have a data set which contains information on dental patients. Your aim is to find correlation for patients requiring root canal surgery.
Firstly, you need to look at the data and think about how you’d like it to be organized and what questions you need to answer. Are you looking for patients who have had root canal surgery? Are there particular dental problems that result in patients requiring the surgery?
From there, you can start to determine the structure of the outcome and what is important to understand about patients requiring root canal surgery.
With your final structure in place, you then clean the data by identifying inconsistent or incomplete data points and remove them. This may include patients who have not had root canal surgery.
Following the cleansing stage, you start to consider whether you can enrich the data using third party sources. For instance, you may want to look at regional statistics for root canal surgery.
Then you enter the validation phase. Here, you add validation rules to verify the data’s consistency, quality, and security. This could include dates of birth or checking for specific dental problems.
Finally, you prepare your wrangled data for further use. By following this process, you’ll have reduced your initial data set down and will be left with something that can be easily analyzed for an accurate result.
An alternative to data wrangling: Data Creation
What is data creation?
Data Creation is the process of generating, enhancing, and modeling data for your organization’s analytics and AI needs, as opposed to using the data that happens to be available as a byproduct of another system, such as an analytics platform. This data is optimal for sophisticated data applications powered by AI and advanced analytics.
