Data wrangling

Data wrangling is the procedure of cleaning and unifying, raw, messy and complex data sets for analysis.  We usually say there are 3 key steps of data wrangling:

   Data Acquisition: Identify and obtain access to the data within your sources

   Joining Data: Combine the edited data for further use and analysis

   Data Cleansing: Redesign the data into a usable/functional format and correct/remove any bad data

The goals of data wrangling:

  • Reveal a “deeper intelligence” within your data, by gathering data from multiple sources
  • Provide accurate, actionable data in the hands of business analysts in a timely matter
  • Reduce the time spent collecting and organizing unruly data before it can be utilized
  • Enable data scientists and analysts to focus on the analysis of data, rather than the wrangling
  • Drive better decision-making skills by senior leaders in an organization