location_city Sydney schedule May 15th 10:45 - 11:15 AM place Wesley Theatre people 199 Interested

Data scientists dream of crystal clear data lakes and perfectly ordered warehouses with comprehensive dictionaries, consistent formats and never a null value or encoding error to mar their analysis. The reality, however, is that the bulk of time on most data projects is spent sourcing and munging data before the exploration and analysis can begin. Governance is often presented as the solution to all data woes but all too often generates more meetings than results.

The University of Melbourne is home to 8000 staff and 48000 students across seven campuses. Both researchers and professional staff recognise that data is going to be key to understanding this complex community and supporting its members. Sensor data collected from around the campuses promises the opportunity to analyse everything from demands on public transport to the impact of weather on coffee consumption. With researchers spread across ten faculties, there is a danger that multiple projects will collect fragmented data and the real power that comes from joining multiple datasets will never be realised. Conversely, overly prescriptive policies will date quickly and hamper innovation. Is it possible to satisfy both the desire to move rapidly to take advantage of new opportunities and the need to maintain data quality?

This case study will present some of the IoT projects currently being explored at the University and examine the governance efforts that are being trialled to ensure the adoption of standards and future interoperability of devices and data.


Outline/Structure of the Case Study

My session will present a case study of the challenges of implementing a joined-up approach to IoT at the University of Melbourne.

Part One: The Goal. The University of Melbourne has stated its ambition of becoming a 'smart campus' and utilising sensor data to manage its space. Researchers are also eager to use sensor data in their work, examining everything from air quality to traffic flows. The opening section will present the ambitions for conducting advanced analytics with diverse data sources and the conditions necessary to achieve this.

Part Two: Monitor all the Things! The University is large and diverse with academic and professional staff and students all eager to get on with doing great things. This section will outline some of the projects already underway or being planned.

Part Three: Governance - Help or Hindrance? This section will examine the regulatory landscape in which the University operates, including privacy and security requirements. It will also examine common approaches to data governance and key standards in use in the IoT environment.

Part Four: Happily Ever After? The conclusion will present the governance solution being trialled at the University and the proposed data sharing and analytic platform that will enable the joining up of diverse datasets to support data exploration.

Learning Outcome

Attendees will leave with:

  • knowledge of essential components of data governance
  • knowledge of IoT data standards
  • a model approach for collaborative data governance
  • an understanding of the role of data governance in enabling data science

Target Audience

Data scientists; data governance practitioners

Prerequisites for Attendees

Attendees will benefit from a familiarity with IoT and 'smart cities' programs although this is not essential.

schedule Submitted 2 years ago