The Pain and Glory of Data Preparation
Discover & Integrate
Data science, predictive analysis … the glamourous stuff makes headlines. But one realm of analytics is only for the bold. It takes a special kind of person to merge, purge, cleanse, and shape data in preparation for the fancy jobs. Without this dirty work, analysis can go wrong. The results can range from embarrassment to imprisonment.
This dirty part of analytics isn’t formally taught. There are no standard tricks for cleaning data. Strategy and experience are the data warrior's weapons.
People who can clean and shape data are true heroes: They can remove duplicates, get data into the right fields, merge data, and so on. Sometimes it’s elegant. Other times, it’s ugly. The result is that any, analysis, and suggestions are rooted in trustworthy data quality.
In this session, you’ll learn strategies for assessing data quality, tips for cleaning and shaping data and determining how clean is “clean enough”, ideas for creating policies and processes that can guide you when working with unclean data, and which Excel features are most useful during prep work.