Data Intelligence Hack (Data journalism, spatial modelling, analytics)

Supported By: 
ATO & ABS

This category is all about using government data to optimize business decisions.
What data intelligence can you derive from official data that would be of value to a business? Can you help industry plan, predict or model future perspectives? Perhaps you can build a tool that helps startups understand the available data and make better business decisions? Maybe you have an idea that will help business solve a problem.

 

Eligibility Criteria

Teams must use at least 2 official datasets that are Open Government data

Anaconda Don't

Team Name: 
Anaconda Don't

​​​​​​​The Water Corporation is the principal supplier of drinking water to the Perth metropolitan area as well as the rest of Western Australia. For some of these towns, the water is supplied through a vast network of above ground pipes. These pipes require regular inspections and maintenance to make sure that the precious cargo it is carrying isn’t leaking out and wasted.

 

Real2Fake

R2F
Team Name: 
The Cooks

Real2Fake is a webapp, that allows government departments to wash their data of all identifiable information.

It does this by combinding the private information with a randomly generated salt, then transforming it into a hash, the hash is then used as a seed to generate a new fake identity for the record. This means that when looking at the data, the user can put a name and face to the data.

This easy process of washing the private information removes the excuse that the data is private, allowing all government departments to securely release all of their big data.

ABS and opengov db

Team Name: 
Will

A video is inappropriate. Essentially I am scraping large volumes of open data from government and public sources, centralising and identifying regular expressions from random samples of data points from across each table and row, alongside selected pattern recognition statastical tools (clustering table data together for initial matching, anf fluctuating glanularity based on a decision framework) to produce decision making trees for the linking of relationships between large quantities of datasets in a logarithmic fashion, without having to process every individual data point.

GO MO GO

Team Name: 
GOMOGO

Meet Charlie, an executive for a multinational company with two kids and a dog. He loves fine dining but enjoys horseback riding with his kids on weekends. He was recently relocated to Melbourne on a new assignment from Brisbane. Becuase he was needed at his new job on short notice, he left his family behind in Brisbane and has been looking for a place to live. For now, he's living in a hotel room provided by his company - but has two weeks to find a new place to live. He misses his family terribly, and is eager to settle down to his new life in Melbourne with his entire family.