Sydney WATER and Data61 Collaboration

Sydney Water and Data61 are collaboratively researching advanced analytics approaches to solving water industry challenges, including water pipe failure prediction, customer segmentation, demand analysis, sewer corrosion prediction, optimising water quality, predicting sewer chokes and prioritising active leakage detection areas. The aim is to achieve better services for customers and to deliver world class network performance. Data61 is Australia’s leading data innovation group which was officially formed in 2016 from the integration of CSIRO’s Digital Productivity flagship and the National ICT Australia Ltd (NICTA). The collaboration is a partnership arrangement, with Data61 providing data analytics research and development expertise and Sydney Water providing data and industry knowledge. Both organisations have partnered to understand complex data sets that can be translated into knowledge, where the knowledge adds business insights. They help to see further, understand deeper and see it sooner about the data driven benefits from the collaboration, such as reducing cost, optimizing resources, minimizing uncertainty and mitigating risks, and improving services to customers. Within this partnership, both organisations have developed skills about data driven analytic on how to think about it, how to use it, and how to value it. This paper outlines how Sydney Water has progressed on predictive analytics to develop capabilities of using machine learning and valuable tools for operators, shareholders and customers, with the collaborative effort from Data61. Knowledge transfer has been applied as a key part of the collaborative partnership to facilitate the implementation of project outcomes. Introduction Sydney Water is taking an enterprise wide approach to building an analytics capability and culture within the organisation. The motivation behind this is two-fold: Firstly, it enables predicting the likelihood of certain scenarios or events, such as a pipe failure. This supports a pro-active response to these situations as quickly and efficiently as possible; Secondly, implementing analytics can help to identify and interpret contributing factors in these scenarios or events. Once these factors are investigated and comprehended, it is possible to mitigate them if they lead to detrimental or disruptive events or enhance them if they result in positive situations. Machine learning, as a subfield of computer science, constructs systems that can learn from data, rather than follow explicitly programmed instructions. Machine Learning is being used for six collaborative research initiatives between Sydney Water and Data61: 1. Improving the prediction of the likelihood of failure for critical water pipes and small reticulation pipes; 2. Customer segmentation and demand analysis; 3. Predicting critical factors related to preventing corrosion in concrete sewers; 4. Optimising water quality in delivery systems a case study; 5. Predicting sewer chokes; 6. Prioritising active leakage detection areas. In these projects, the use of data analytics techniques has demonstrated a higher level of confidence for the asset performance prediction. For example, the improved prediction tools for demand analysis and optimisation have a potential to improve customer services and regulator confidence Data Analytics ISSN 2206-1991 Volume 3 No 1 2018 https://doi.org/10.21139/wej.2018. 002