Office workers are spending more time than ever wrestling with data — 40% say they spend 1 – 2 days per week doing repetitive, data-related tasks. Despite their efforts, data quality and quantity remain as key challenges to digitization for most companies. In this context, data wrangling has become a strategic capability that companies must master and scale.
There are two approaches to data work: Manual and Automated.
Manual Data Work
The Manual approach is probably happening all around you – non-technical people wrestling with rows and rows of ad hoc data in excel. Sometimes IT helps, but this often results in a time-consuming “back-and-forth” to get it right. We count spreadsheet macros and ETL rules as Manual approaches because they are hand-crafted scripts that require continuous ongoing support.
Inevitably, manual data works feels like a waste of time to the people doing it. And their productivity almost always falls short of business needs.
Automated Data Wrangling
By contrast, Automated Data Wrangling does data work in real time and can operate “hands off.” It is far faster and more resilient because it is built using machine-learning (ML) models. ML models can process one million rows of data about as fast as one hundred and can resolve unforeseen issues. Yes, there is work up front to build and deploy models. But ML models learn as they work so they perform better and require less maintenance over time.
Automated data wrangling not only provides rich, and reliable data, it also frees your people to focus on more valuable activities that tap into their skills and passions.
Machine Learning is the Key
Machine learning algorithms generally convert data into predictions. But they can also convert raw input data into higher quality, refined output data. In fact, cleaning and enriching textual data is one of the main applications for a type of machine learning called Natural Language Processing (NLP).
NLP is far better than manual or rules-based approached for data work because it infers the meaning of words and phrases. In other words, it develops domain expertise. This makes NLP is the only scalable approach to automated data wrangling.