What is data wrangling and how can you leverage it for your business?
The term “wrangling” evokes images of cowboys lassoing runaway cattle, getting them under control, and taking them back to the herd. Data wrangling is also like lassoing data and putting it to work in your business.
According to IBM, our output of data was roughly 2.5 quintillion bytes a day in 2017, and by the year 2020, it’s estimated that about 1.7 megabytes of new information will be created every secondfor every human being on the planet.
As we deal with such an ever-increasing amount of data, we need to find better and more efficient ways of rounding up, cleaning and organizing that data, and using it to our advantage.
Data wrangling is the process of taking messy, incomplete or complex data, and cleaning and simplifying it so it’s easier to access, organize and analyze.
In practical terms, that might mean targeting a field, row or column in a data set, and applying an action such as joining, parsing, cleansing, consolidating, or filtering to create the desired output, which will then be used down the road.
This wrangled data could then be used by businesses, data architects, or data scientists, who may process the data further or reuse it in other formats.
Data wrangling has emerged in recent years as a fast-growing segment of the analytics industry. Messy and complex data sets were once a roadblock to data analysis, but the ability to wrangle data has transformed the process, replacing tedious, time-consuming efforts to master diverse data sources.
In data science, it’s common to deal with disorganized or incomplete data, and the raw data obtained initially is often unusable in its native form, so wrangling the data can make it more appropriate and valuable for other purposes, such as analytics.
According to a 2018 IDC Business Analytic Solutions survey, data scientists spent a 73% of their time just wrangling the data – so it’s an essential part of the data science role.
But as important as this function is, it’s not exactly seen as fun. As Lukas Biewald of Computerworld says, “Data wrangling is cleaning data, connecting tools and getting data into a usable format; the sexy stuff is predictive analysis and modeling. Considering that the first is sometimes referred to as ‘janitor work,’ you can guess which one is a bit more enjoyable.”
Regardless of how unexciting the process of data wrangling might be, it’s still critical because it makes your data useful. Properly wrangled data can provide value through analysis or be fed into a collaboration and workflow tool to drive downstream action once it’s been conformed to the target form.
Conformance or transforming disparate data elements into the same format also addresses the problem of siloed data. Siloed data assets cannot “talk” to each other without translating data elements between the different formats, which is often time or cost prohibitive.
Another benefit of data wrangling is that it can be organized into a standardized and repeatable process that moves and transforms data sources into a common format, which can be reused multiple times. Once your data has been conformed to a standard format, you’re in a position to do some very valuable, cross-data set analytics.
Conformance is even more valuable when multiple data sources are wrangled into the same format. For example, if you’re a healthcare organization, you may need to compare physician performance across a large set of patients using health insurance claims data. If there are multiple health insurers involved, their membership and claims data will be in different formats. Wrangling data into a common, conformed format will enable you to compare physician performance metrics, regardless of payer.
