Data is like… clean

Sarah Santos
Sep 8, 2018 · 1 min read

A immediate takeaway from both articles is that the most crucial step when working with data, is maintaining clean organization.

After reading the Data Journalism Handbook, I’ve come to realize how thorough pulling data can be. I feel like I often search for data and immediately trust the resources I am getting such data from, but it’s become apparent that it’s important to consider the “undocumented elements.” This has becoming a learning moment for me specifically because data really can range from a wide span of dates, and within those dates so much can be misrepresented or not represented at all, or lots of numbers can have altered.

I guess I never really thought of data as telling a story. So after reading the Cleaner, Smarter Spreadsheets, I think it’s become more apparent to me that data visualization is less of a presentation but more of a representation. I think the most resourceful takeaway is the difference between data dictionary and data diary. I’m not the most specific person, but when it comes to data visualization I definitely have to remember other people will viewing my work. Due to this, it’s important to set a type of key or legend (data dictionary) so that my work can be read universally. I find myself abbreviating or creating terms for shortcuts that only I may be able to decipher, so I should be able to utilize a data dictionary as a reference for not just myself but others.