RMIT touts data smarts with new munch and crunch centre

A new Centre for Information Discovery and Data Analytics (CIDDA) has opened at RMIT to give partner organisations access to researchers that can help them make sense of the increasing volume and complexity of data.

The centre was officially opened on Wednesday to offer government and industry partners of varying sizes the chance to take advantage of the university’s data science capabilities across a broad range of fields.

It's the latest move in a growing trend across major educational institutions where business and government are increasingly seeking high level R&D expertise 'as a service' to stay ahead of the curve. 

Under the new centre, RMIT staff will also work with partners to come up with individual solutions that can translate research into actionable intelligence and insights for organisations to expolit.

Staff at CIDDA have already been involved in a number of industry projects, including boosting the effectiveness of SEEK’s job search engine, helping the City of Melbourne make better use of sensor data, and work with Microsoft to develop new voice software.

CIDDA director, associate professor J. Shane Culpepper, said that no matter the scale governments, businesses or not-for-profits collect data at the moment, the volume is only set to increase in the future.

“But most organisations don't know what to do with all that data because data analytics is not their primary purpose. Our goal is to partner with these organisations and help them transform that data into knowledge.

“It’s through these partnerships that we solve fundamental research challenges and translate that research into impact,” Culpepper added.

“We’re an externally focussed centre and will be looking to develop new partnerships across all our focus areas.”

Those focus areas include:

• Combining structured data, such as databases, with unstructured data such as digital media, audio files and social media.
• Helping organisations resolve fairness, accountability, transparency and ethical problems in algorithms for machine learning, and helping them understand the grand challenges in responsible and ethical data management.
• Developing the next generation of conversational virtual assistants.
• Improving data visualisation and analysis.
• Improving the scalability of search, storage and retrieval in large data repositories.
• Implementing and benefiting from recent advances in spatial computing and intelligent transportation.
• Applying and benefiting from internet of things (IoT) analytics.