I'm not new working with data, and in a corporate world most of the datasets are highly structured and regulated. With open data... it's bit different, less regulatory norms and sometimes more difficult to work with this data (it's open, whom do you complain to about it).
I've seen cases when a data custodian made datasets available for public with only old data (like very old, 10 years and more), at the same time presenting their own visualization using data which might not have existed moments ago. In other cases, open data was made available in a form of Excel files which you could open and read but to aggregate one geo region for several years it would take almost a hundred of those files, plus their data format was not a very structured and numerous data massage moves were necessary. Good examples of an open data management also exist where data owners responsibly control availability and updateability of their data.
Going back to the soup analogy, this whole idea working with data and especially with open data reminded me the ending scene of the classic movie "Coming to America" with Eddi Murhpy.
In case if this YouTube video gets removed, here is the text of this scene:
"Wait a minute, wait a minute, wait stop right there! Listen: Stop right there, man. A man goes into a restaurant. You listenin'? A man goes into a restaurant, and he sits down, he's having a bowl of soup and he says to the waiter, waiter come taste the soup. Waiter says: Is something wrong with the soup? He says: Taste the soup. He says: Is there something wrong with the soup? Is the soup too hot? He says: Will you taste the soup? What's wrong, is the soup too cold? Will you just taste the soup?! Allright, I'll taste the soup - where's the spoon?? Aha. Aha! ..."
Last week at the Open Data Toronto meetup (https://www.meetup.com/opentoronto/events/234322841/) there was an opportunity to review various open data sets and data visualizations built on them over the course of the last 1.5 years. Different vendor tools along with various visualization techniques were used to present data insights: from simple tables or charts to morphing maps and word clouds.
And then I realized that when an open data inquiry and a right and appropriate data exploration tool intersect with each other, only then you can get this Aha moment; no more explanations are needed and a data visualization speaks for itself. This really motivates me to explore other open datasets, try different tools and visualization techniques and read books about effective data analysis.
Otherwise my data soup may get cold without a right data spoon 🙂
Happy data adventures!
