Big data has completely exploded over the past few years and businesses today are awash with massive amounts of data. This data is a valuable asset for organizations as business data analytics can be used to identify trends, patterns and behaviors. This, in turn, enables organizations to increase productivity, adapt to changing customer demands and acquire more customers.
In the era IoT and sophisticated data collection models, there are no limits on how much data can be collected. However, the problem here is that most organizations have more data on their hands than they can deal with. Here’s where data wrangling comes into the picture.
What is Data Wrangling?
Most business data that is collected is unclean or raw and is available in large sets. Before organizations can implement data analytics, this raw data must be cleaned first. This process of converting raw data into clean and unified sets of data is known as data wrangling.
Data wrangling is a complex process that requires a lot of time and effort to be put in. The process involves converting raw data into smaller, digestible formats using aggregation, data visualization and other tools.
Data Wrangling Solutions
Conventionally, data scientists spend up to 80% of their time in manual data wrangling. This is because business data is available in a very diverse form, and manually cleaning the data is cumbersome. However, data wrangling solutions can make the process much faster, and they can also help improve data analytics.
While data wrangling solutions are designed to simplify the entire data analytics process, these solutions are not meant for executives. Rather, these solutions are meant to help reduce the time that data professionals and data analysts spend on mundane tasks.
As businesses continue to collect more and more data, the need for data wrangling solutions is also going up. Effective data wrangling solutions can empower businesses to perform accurate data analytics. Data wrangling is an essential tool that can help organizations gain a competitive edge. For this very reason, data wrangling solutions continue to develop and evolve.