Researcher is able to see the following Key Demographic Data:
Google Trends for cross-border trade in East Africa. Now researchers are able to cross filter a [trade data field] with a [demographic marker - such as gender] and view a chart for that result.
"Sauti’s innovative data collection methods across East Africa’s cross-border spaces allow us to present key attributes of SME border trader’s business and border crossing behaviour. Our unique penetration with cross-border traders gives us superior data-driven insights, enhancing cross-border trade policy advocacy and impact reporting." - Sauti website
Implement using the Users table
When we ask the question, “ What makes good filtering ?” to either researchers that we’ve interviewed or Lance, the common response is Its good if it makes all data points accessible. Eventually this led to them showing us real databases and websites that can serve as an example. They also talked about specific designs these websites use that allow it to be dynamic.
We take these insights into account and continue to gather more info as we go just to make sure our designs meet the standard.
Down below are a few notes from our interviews that pertain to this feature.
Notes: What Lance and Amber are letting us know is that they want to be able to filter the data by demographics and represent it in a way that makes sense.
Video Transcription: https://otter.ai/s/BCaxW9wCR4qkrcLgWqUB9w
"I think one of the problems that you'll have to solve is that we have quite a few dimensions of data, which is to say that you can cross everything by, let's say, let's say you wanted to get the gender of maize, people, but trading and maze. So you've got two dimensions, gender and maze there. Let's say you wanted instead to get what do 20 to 30 year olds trade in, for at this border at this location. So I think you'll need to find a way to present those series of data in a way that makes sense." 3:52