Name: Anthony Molinia
Title: Chief Information Officer
Company: University of Newcastle
Commenced role: October 2016
Reporting line: Chief Operating Officer
Member of the executive team: No
Technology Function: 127 full-time, 250 overall staff, 7 direct reports
In the dynamic and competitive world of higher education, institutions rely heavily on critical data and analytics to inform strategic decisions that align with their missions and objectives.
University of Newcastle CIO Anthony Molinia has taken deliberate steps to deliver data services that not only support the unique needs of the higher education sector but also provide future-proofed solutions. His approach is rooted in stakeholder collaboration to drive meaningful improvements.
“Access to reliable and secure data is crucial for universities to understand the needs, preferences, behaviors, and academic performance of their students. It also empowers staff to operate more efficiently by giving them the necessary information to make informed decisions,” Molinia explains.
According to Molinia, data is essential not just for ensuring transparency in decision-making, enhancing security, and improving operational efficiency, but also for enabling advanced technologies like Generative AI and Cognitive AI.
“Ensuring that data is both clean and secure is critical to leveraging these cutting-edge technological solutions effectively,” he adds.
Uplifting data literacy
The implementation of an Enterprise Data Ecosystem, utilising cloud-native services, allows the University to acquire, store, and securely publish data to power business intelligence, system integration, and emerging AI use cases. The CAUDIT Data Reference Model provides a blueprint for a domain-based architecture that enhances data discoverability, improving overall data literacy and making information easier to understand.
Cloud-based tools and architecture enable the University’s Digital Technology Services (DTS) to reduce system integration operating costs by up to 50%, improve system performance, and deliver higher-quality data to stakeholders. This foundation of trusted data supports the development of better intelligence, applications, and processes.
Following the principle “Acquire once. Share many. Secure always,” the University has developed a solution that streams data from source systems into a lake, where it undergoes conditioning to make it consumable.
“Focusing on ‘data as a product’ ensures greater data consistency compared to exposing sources through point-to-point ETLs, while an abstraction or virtualisation layer protects source systems,” Molinia notes.
This multi-tiered approach reduces the time required to develop new analytics and explore AI use cases. Machine learning models, which previously took months to build, can now be developed in weeks, while operational reports integrate data more efficiently to provide a comprehensive view of performance.
Standard data services are published via APIs and batch processes, allowing the team to focus on providing foundational data critical to enhancing the student and staff experience.
Cloud services for data efficiency
These data services are designed for maximum reusability, allowing the University to consolidate from six legacy integration tools to just two, significantly reducing the complexity and maintenance overhead of managing duplicate and inconsistent business rules.
Faced with an aging data ecosystem developed before the University’s cloud transformation, and an increasing demand for machine learning and AI, the University saw an opportunity to transform how data is organised and integrated.
When a new cloud service was introduced to the market in November 2023, encapsulating many of the tools and patterns the University had considered building from scratch, the team asked: “Why not use this new service to efficiently acquire data for both analytics and integration?”
This approach would reduce development time, improve data security and visibility, integrate with governance tools, and eventually lower the overall cost of running the data ecosystem.
Although the product was new, the University worked closely with the vendor, as few solution integrators had experience with the implementation.
“While this presented challenges, the vendor was committed to supporting us, recognising the potential value,” Molinia says. Since the initial implementation, the business intelligence teams have quickly begun building machine learning models that can identify students at risk of failing courses with around 80% accuracy.
This forward-looking approach also highlights the potential for more advanced interventions, including the use of generative AI to deliver faster, personalised support to students.
Additionally, new models have been developed to predict potential international student enrollments, enabling the University to prioritise efforts to maximise international revenue.
Effective communication and influence
Although Molinia is not a member of the University’s Executive Leadership Team, which can sometimes be a barrier to driving change, he has effectively communicated the value of digital initiatives and gained the support of other executives to advance the University’s digital strategy.
He has implemented a regular reporting cycle to the University Council on the Digital Excellence Strategy and Cyber Security, ensuring transparency and accountability. His efforts have earned endorsement for digital investment across two horizons.
Molinia’s influence is demonstrated through his ability to deliver quickly and flexibly, showcase successes, and deepen executive engagement. His approach has been pivotal in driving digital transformation across the University.
Title: Chief Information Officer
Company: University of Newcastle
Commenced role: October 2016
Reporting line: Chief Operating Officer
Member of the executive team: No
Technology Function: 127 full-time, 250 overall staff, 7 direct reports
In the dynamic and competitive world of higher education, institutions rely heavily on critical data and analytics to inform strategic decisions that align with their missions and objectives.
University of Newcastle CIO Anthony Molinia has taken deliberate steps to deliver data services that not only support the unique needs of the higher education sector but also provide future-proofed solutions. His approach is rooted in stakeholder collaboration to drive meaningful improvements.
“Access to reliable and secure data is crucial for universities to understand the needs, preferences, behaviors, and academic performance of their students. It also empowers staff to operate more efficiently by giving them the necessary information to make informed decisions,” Molinia explains.
According to Molinia, data is essential not just for ensuring transparency in decision-making, enhancing security, and improving operational efficiency, but also for enabling advanced technologies like Generative AI and Cognitive AI.
“Ensuring that data is both clean and secure is critical to leveraging these cutting-edge technological solutions effectively,” he adds.
Uplifting data literacy
The implementation of an Enterprise Data Ecosystem, utilising cloud-native services, allows the University to acquire, store, and securely publish data to power business intelligence, system integration, and emerging AI use cases. The CAUDIT Data Reference Model provides a blueprint for a domain-based architecture that enhances data discoverability, improving overall data literacy and making information easier to understand.
Cloud-based tools and architecture enable the University’s Digital Technology Services (DTS) to reduce system integration operating costs by up to 50%, improve system performance, and deliver higher-quality data to stakeholders. This foundation of trusted data supports the development of better intelligence, applications, and processes.
Following the principle “Acquire once. Share many. Secure always,” the University has developed a solution that streams data from source systems into a lake, where it undergoes conditioning to make it consumable.
“Focusing on ‘data as a product’ ensures greater data consistency compared to exposing sources through point-to-point ETLs, while an abstraction or virtualisation layer protects source systems,” Molinia notes.
This multi-tiered approach reduces the time required to develop new analytics and explore AI use cases. Machine learning models, which previously took months to build, can now be developed in weeks, while operational reports integrate data more efficiently to provide a comprehensive view of performance.
Standard data services are published via APIs and batch processes, allowing the team to focus on providing foundational data critical to enhancing the student and staff experience.
Cloud services for data efficiency
These data services are designed for maximum reusability, allowing the University to consolidate from six legacy integration tools to just two, significantly reducing the complexity and maintenance overhead of managing duplicate and inconsistent business rules.
Faced with an aging data ecosystem developed before the University’s cloud transformation, and an increasing demand for machine learning and AI, the University saw an opportunity to transform how data is organised and integrated.
When a new cloud service was introduced to the market in November 2023, encapsulating many of the tools and patterns the University had considered building from scratch, the team asked: “Why not use this new service to efficiently acquire data for both analytics and integration?”
This approach would reduce development time, improve data security and visibility, integrate with governance tools, and eventually lower the overall cost of running the data ecosystem.
Although the product was new, the University worked closely with the vendor, as few solution integrators had experience with the implementation.
“While this presented challenges, the vendor was committed to supporting us, recognising the potential value,” Molinia says. Since the initial implementation, the business intelligence teams have quickly begun building machine learning models that can identify students at risk of failing courses with around 80% accuracy.
This forward-looking approach also highlights the potential for more advanced interventions, including the use of generative AI to deliver faster, personalised support to students.
Additionally, new models have been developed to predict potential international student enrollments, enabling the University to prioritise efforts to maximise international revenue.
Effective communication and influence
Although Molinia is not a member of the University’s Executive Leadership Team, which can sometimes be a barrier to driving change, he has effectively communicated the value of digital initiatives and gained the support of other executives to advance the University’s digital strategy.
He has implemented a regular reporting cycle to the University Council on the Digital Excellence Strategy and Cyber Security, ensuring transparency and accountability. His efforts have earned endorsement for digital investment across two horizons.
Molinia’s influence is demonstrated through his ability to deliver quickly and flexibly, showcase successes, and deepen executive engagement. His approach has been pivotal in driving digital transformation across the University.
