Before we jump into the need for a data wrangling cheat sheet, first, what is data wrangling? Data wrangling, often referred to as data preparation, is the process of transforming raw data into a refined output. It’s a necessary step for anyone that works with data. Data wrangling remedies missing information, duplicates or errors found in raw datasets and ensures that these datasets are appropriately structured for use in any given machine learning, visualization, or analytics projects.  

The process of preparing data is notoriously laborious. Experts still identify data preparation as the biggest bottleneck in any analytics project, with estimates of time spent preparing data as high as 80%. A traditional data wrangling cheat sheet helps accelerate this process. The majority of data wrangling cheat sheets were created as a handy guide for those using technical languages, such as R or Python, to prepare data. A data wrangling cheat sheet compiles all of the most common scripts used to prepare data for easy reference on one page. Data scientists spend less time second-guessing and simply look at their data wrangling cheat sheet to get the job done. You can see an example of a data wrangling cheat sheet here.

The Trouble with a Data Wrangling Cheat Sheet

For data scientists using technical languages and working in isolation, a data wrangling cheat sheet is a great solution to accelerating the data preparation process. However, for the majority of organizations, a data wrangling cheat sheet is merely a band-aid solution that doesn’t address the root cause of the data preparation bottleneck. To use a data wrangling cheat sheet, understanding technical languages is a must. And that ability simply doesn’t scale across an organization, especially one where business users are hungry to take on an increasing number of analytics projects. By relying on technical languages to prepare data, business users will still have to ship off their data requirements to IT. And even with a data wrangling cheat sheet, those business users will likely be waiting around for longer than necessary.

Instead of quick fixes like a data wrangling cheat sheet, many organizations are beginning to rethink the entire data preparation process. They are adopting new data preparation platforms built for use among both data scientists and data analysts. If the ROI on data is directly proportional to the number of people using it (and how long it takes those people to generate insights), new data preparation platforms allow IT to become the data hero, streamlining the data supply chain and unleashing more data on the organization than ever before. A data wrangling cheat sheet remains a helpful tool, but it doesn’t allow organizations to scale like data

Replacing Your Data Wrangling Cheat Sheet with Designer Cloud

Alteryx Designer Cloud is widely recognized as the leader in data preparation. Its visually-driven platform not only allows users to easily spot data quality issues but also allows anyone in the organization to speak the same language, which improves transparency about how particular data sets have been transformed. We’d love to chat with you about how we can help your organization transition to a user-friendly data preparation platform in place of scaling up IT resources or relying on a data wrangling cheat sheet.

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To learn how Designer Cloud can help you transition to a user-friendly data prep platform. Schedule a demo today.