Tag Archives: delib vancouver

Garbage 2.0 – thoughts from Vancouver #1

Garbage (or rubbish as we Brits like to refer to it) is not the most obvious area of innovation in the Gov20 space, however following a coffee and chat with David Eaves this morning in downtown Vancouver my view has changed a little.

As David and his team have created a neat little app called *ReCollect* designed to remind you when Garbage day is – by sending you an SMS or email reminder. The great thing about David’s app is that it’s undoubtedly *life improving*, which is the base metric for all government innovation.

Recollect app - screenshot

Chatting more widely to David, he pointed out that from a Gov20 perspective the most interesting bit for him was how Gov20, and in particular open data, effects and works *internally* – within government. Linked to this, he also discussed the need to promote more effective data standards. Our discussion here moved into the work we’ve been doing recently around British Columbia’s budget consultation, as we discussed the effect of creating some kind of common schema around government budget data – similar to how the SCC in the US has mandated XBRL for all corporates to report in. The effect of standardised budget data taxonomies would result in greater usefulness of processes like Budget Simulator, as the data could be extracted and remixed in a number of different ways. Examples include:

  • State / City comparisons: budget data and budget allocation intentions could be compared cross cities / states / countries.
  • Historic comparisons: data could be more accurately compared over time.
  • Detail digging and analysis: it would be more easy to dig down into the detail of specific areas of the data, enabling more detailed insight.

These are all particularly timely points from our perspective, as we’re about to start a full overhaul of our Budget Simulator app, and I think beyond cosmetic changes the whole *standardised data* and open data needs to underpin how Budget Simulator is structured and powered from a data perspective.