Data Wrangling broadly means preparing data for some specific task or system. Originally tied to data science, these days the term is used more broadly to convey the criticality of all data work.
Office workers spend way too much time wrestling with data. In a recent survey, 40% said they spend 1 – 2 days per week on repetitive, data-related tasks. Despite their efforts, data quality and availability are key challenges to digitization for most companies. In this context, data wrangling has become a strategic capability that companies must develop and scale.
Manual data work is the status quo – valuable resources cleaning, blending and transforming by hand. People performing complex, repetitive data work is not only a waste of time but it's usually too little, too late relative to business needs.
Data wrangling can now be done by computers, thanks to machine-learning (ML). ML models can spot all kinds of data problems and fix them. And of course, such computer models can process a million rows of data about as fast as one hundred.
There's more good news! Because machine learning models can do data work, the process can be automated in the cloud. Simple APIs connect your desktop data to powerful models running remotely. And you guessed it - once the capability is cloud-based, it can be delivered "as a service." We call this model-based, automated data cleaning approach "Wrangling as a Service." It saves you time and money, and it's easy to get started with our Data Wrangles Excel add-in.