Now we get to the serious part, as well as the fun part – what happens here sorta blows people’s minds a bit. The AI provided an initial 15 indicators, based on recognised standards, that represent factors that would have a direct impact on the success of the outcomes. One was the K10, another one directly related to mental health, but the others were around youth homelessness, family violence, education, food and exposures to drugs, health, medical and criminal issues.
We selected 7 out of the first 15 indicators to set up straight away – only two of these had a data-gap, in that we’d need to chat to one of the social-services partners for the data-feed for a couple of fields. There were another 20 recommendations made (from the thousands in the system). We selected two of these, so ended up with nine active indicators to connect our data sources to. Selecting these from the recommendation list was just a tick in a checkbox, that we wanted to use each one.
Having set up the project workspace, the GUW team could then connect directly to the relevant fields in their CRM using a straightforward user authentication process; in order to bring survey results, as well as demographic data and other output data into the system.
At the same time we set-up the graph or visualisation type and hey presto – realtime visualisations were all set up on the project’s data dashboard! We also helped set up a couple of other “personal” dashboards, one to share with their funding stakeholders and another one for one of their locations, so they can have automated local reporting and insights whenever they need.