SoloNav

Team Name

Insight Hackers

Team Members

Project Description

SoloNav

Extracting information from open Government datasets can be a frustrating and overwhelming process. Data analysists and researchers spend more than 80% of their time trying to find accessible, reliable and up-to-date resources (Quora, 2018).
Issues with the current open sources data bases
Connectivity – there are issues with collaboration and connectivity - as Government datasets are set up to be highly siloed in specialised systems which use proprietary data schemas. Vendors have little incentive to collaborate or integrate as they have a vested interest in creating a monopoly. Therefore it’s difficult to connect multiple API’s across multiple systems into one simple solution.
Complexity – there are huge complexity issues regarding disparate data sources, formats, schemas and scalability, which lead to poor data quality and low levels of data maturity across the industry.

Personalisation – current solutions do not enable multiple datasets to be compared, correlated, nor customised in a way that can be easily tailored to suit the needs of the individual user or benefit the community in a meaningful and collaborative way.

Benefits of SoloNav
Accessible - SoloNav provides a one-stop shop where users can access multiple open datasets and derive searchable metadata in an efficient manner.
User Friendly - It enables users to find insights in a quick, easy and simple way, when navigating through multiple complex data sets
Story telling – users can extract and tell powerful stories through correlations of different features and datasets, using data journalism and visualisation.
Insights – users will feel more empowered to make better decisions, by identifying the latest performance trends and insights.
Community – SoloNav helps to bridge the gap between academia and industry, and helps to increase collaboration, innovation, awareness and deliver better services for the overall community.
Sustainable– SoloNav enables councils to provide short and long-term recommendations for local initiatives, to help reduce environmental impacts and stimulate local communities’ economies.

Data Story

Which data sets were used?
Here are some examples of the used data
Australian Bureau of Statistics
Description of Use: Used to download detailed statistics from 2016 Australian Census. Data was used to compare different municipalities (Kingston, Casey & Wyndham). Individual Council statistical data sets were analysed and compared to the raw ABS data and combined to provide a demographic profile of the three municipalities.
VIF 2016 LGA Kingston -
Description of Use: The one page summary was analysed to find key insights of the Kingston demographics, this was then used to download additional raw data from the ABS Table Builder.
Australian Institute Health and Welfare (aihw.gov.au)
Description of Use: This data was analysed to determine whether there is an increased cost to government for health care due to our ageing population. This was used to reinforce the Ageing population statistic as the key statistic to focus on as a performance metric.