(Continued) Stop saying “I think…” with Statwing
In my last post I ranted about the importance of using data, or more specifically, the need to stop making decisions on uneducated guesses or ill-founded feelings. I suggested a three-step process to address the issue. Today I want to follow up with a few thoughts on what to do once you’ve gone through those initial three steps. In other words, how do you extracting meaning from the data.
There are plenty of ways for this and there’s generally no need need to complicate things. Avid Excel users will make wonders there. Some may calculate the numbers on the back of a napkin, and then there are some online tools to help.
Enter Statwing. A YC-backed product to “Turn data into insight. In seconds.” You upload the oh-so-familiar spreadsheets and Statwing enables you to describe and relate the data contained in its columns to each other.
For example, an automatically generated description of age-data:
I extracted some of the registration data from Lägr1 to explore how well we are reaching our target groups:
- Participants in the ages 15-25 years old.
- Staff, preferably over 25, but at least 18 years old.
- and, are the registrations balanced across the five Scout associations in Sweden? (Yes, I know nowadays there’s only one, but that’s only true on paper so far)
Creating the relations is straightforward and happens almost instantly on smaller datasets. Mine contains about 300 data points. The figure below indicates an answer to the first two questions by relating Type (staff or participant) with Age.
Surely these are just two simple histograms. I could probably have made those with Excel, gnuplot, matplotlib, or by hand for that matter. The big-win, however, is that this only took me about five minutes from logging in the first time to when I was having the result. And I had never used Statwing before that!
Further I wanted to see the distribution across the five associations. Relating the association name with type I quickly yielded the following figure.
Skewed! We know Equmenia is organising their own national Jamboree thus we expect lower participation from their side. However, that KM and NSF are as low is somewhat surprising. We certainly have to do some work there.
Data quality will impact the usefulness of the description and relations. Some data extracted from the registrations is simply too diverse to be of any good use. Or, put it another way, require other methods not (yet?) available in Statwing. For example, mapping participants to a geographical area, i.e, making some form of heatmap, would be nice. If I could overlay that with census data from the Swedish Scouts I could see in which areas our marketing efforts have had the most impact.
In conclusion, Statwing is still in its infancy but it is refreshing to see tools like this emerge on the market. There is nothing revolutionary about the graphs above, part from one simple fact: they were dead simple to generate. Tools like Statwing are surely going to influence the way we analyse and use data. And (hopefully!) it will push more people to base decisions on data rather than vague beliefs.