Finding value in asset data
City of Casey
We're applying data science to help us find insights from our asset data. During this short project will run a discovery process to assess the quality of our data.
Council owns assets across the City of Casey that are provided for the benefit of the community. These assets include community facilities, roads, buildings, trees, parks, drains, sports grounds, play grounds and nature reserves. These assets require an ongoing commitment from Council to manage their operations and to maintain them, or to renew, upgrade or expand the assets when they reach the end of their useful life. Council is committed (per the Asset Management Policy) to looking after these assets in a way that meets the expectations of present and future residents, at the lowest economic and environmental cost.
Council maintains several datasets that contain information about these assets. Ideally, these datasets would be used to draw insights to inform long term strategic and budget decisions. We believe there are opportunities to link up our datasets and apply statistical & predictive modelling to save money and improve asset management.
What's the key problem to solve?
Asset data is stored across a range of disparate and inconsistently structured datasets. Each dataset is maintained to different standards. For instance, the parks and reserves dataset is regularly updated by dedicated resources. The bus shelters dataset hasn’t been updated in the last three years.
The inconsistent nature of these datasets means they cannot be used to reliably inform long term budget or strategic planning decisions. We think the most valuable datasets (ie. relating to those assets where Council invests the most) are:
- Civil Assets (for example roads, paths, drains),
- Buildings, and
- Sports grounds.
Because our maturity in analysing data is low, we need some help to identify and capitalise on opportunities.
Who are the users and their needs?
Given the purpose of this project is to run a discovery, we do not yet have a backlog of researched and validated user stories. Instead, we have a hypothesis that we can use our data to make better strategic and tactical decisions that save money and make the best use of our limited Council resources.
We're seeking a data science expert sourced from the Digital Marketplace for 8 weeks with a budget range of $50-75k. During this short term project will run a discovery process to assess the quality of our data and identify opportunities to make better use of our limited resources.