Chief Data and Analytics Officer at RMIT University
Nonna Milmeister
RMIT UNIVERSITY Q & A with Nonna Milmeister
Background:
Tell us a bit about yourself, your career, and your role at RMIT.
I feel I am fortunate. I moved to Australia more than 30 years ago, and it has been the best decision I’ve ever made for my children and me. With a Bachelor’s Degree in Engineering from Russia, I enrolled in RMIT University’s Master’s Degree in Project Management, which spearheaded my career in Australia. I began working for a consulting company specialising in project management but quickly realised I was more interested in data—its quality and the value it delivers to the organisation.
When the opportunity presented itself, I moved on to establish a Data Management Consulting practice in the same company and later held multiple roles in the telecommunications and insurance sectors. These ranged from managing data quality projects, developing data quality capability, and managing large transformational data migration programmes to establishing Data Management Centres of Excellence and developing data strategies for large organisations.
Five years ago, RMIT approached me to consider the role of Chief Data and Analytics Officer. I’ve thoroughly enjoyed working for RMIT and the education sector ever since, feeling grateful to give back to the institution that did so much for me when I needed it most.
In my role at RMIT, I developed and implemented a data and analytics strategy, and we are now expanding our strategy and roadmap into the next three-year period.
RMIT University:
Can you introduce us to the university itself?
I have a soft spot for RMIT University because it gave me the chance to become who I am today. I’m proud of RMIT’s history. It was founded in 1887 as the Working Men’s College, with 320 students on its opening night. Fifty years later, it had nearly 10,000 students. Today, RMIT provides education to more than 90,000 students across campuses in Australia and overseas, with over 11,000 educators and staff globally.
RMIT’s strategy, Knowledge with Action, focuses on using our knowledge, skills, and capabilities to make a difference in the world. RMIT’s vision is to be a leading university of impact in the Asia-Pacific region, leveraging technology, design, and enterprise to create an inclusive and sustainable future. RMIT’s ambition is to lead internationally in four key areas: emerging technologies, smart and sustainable cities, social innovation, and regional collaboration.
RMIT is ranked 123rd globally in the QS World University Ranking and 5th in the THE Impact Ranking, including 1st globally for Reduced Inequalities.
I believe RMIT’s greatest asset is its passionate people, who dedicate their lives to educating the next generation of professionals and researchers, creating an impact in the communities we serve.
Career Transition:
Can you elaborate on the specific challenges you faced when transitioning from project management to data quality management and how you overcame them?
When you truly enjoy what you do, it’s not a challenge but an opportunity. Working as a Project Manager teaches valuable skills such as bringing people together, resolving issues collectively, and leading teams. These skills proved invaluable when I saw the opportunity to shift my career focus to data management. I was fortunate to meet Larry English, the “Father of Data Quality”, and I received my first certification in his information quality management methodology, which provided me with the tools needed for the job. I also developed a simple way to estimate the cost of poor data quality, which is crucial and serves as the first step towards improving data quality.
Most importantly, I was able to assemble a team of like-minded and dedicated individuals with knowledge in key processes, technologies, and people. With great team members who were experts in data quality and its improvement, and through the development of training programmes for different parts of the organisation, we created a recipe for success—one I’ve replicated throughout my career.
Stakeholder Management:
How do you approach convincing stakeholders, particularly in less datadriven sectors, about the importance of data governance and data quality?
That’s an excellent question, one that’s still debated at every industry conference. In my view, the number one critical success factor is having support from the Chief Executive Officer. Before accepting the role at RMIT, I was fortunate to meet with the then Vice-Chancellor and President, Professor Martin Bean CBE. His vision and deep understanding of the value of data in education convinced me to join RMIT, and RMIT executives and the Board have always continued to support me in implementation of the Data & Analytics Strategy.
Even with such support, influencing and collaborating with stakeholders remains a key responsibility for Chief Data Officers. What works for me is establishing governance that spans all levels of the organisation, from data stewards who focus on improving data for their respective areas to executive Data Trustees who are accountable for their data domains and make strategic decisions on data policies, the data lifecycle, data literacy, and other key components of building an active data culture.
When an organisation has solid data quality metrics, clear accountability for data quality, and when stakeholders are proud to resolve key data issues and feel supported by the organisation, you know you’re on the right track.
Additionally, selecting a few issues that can be addressed quickly and without significant investment can help convince stakeholders and gain their support. For us, the work on data definitions was one such success, and it paid off significantly.
Educational Impact:
What are some of the unique challenges of implementing data-driven strategies in the education sector compared to other industries you’ve worked in?
Compared to native digital industries and technology companies, the education sector has largely been a follower rather than a leader in creating data-driven organisations. For many years, educational institutions have focused on technologies to support learning, teaching, and research.
The role of Chief Data Officer, which has grown rapidly in other industries, was slow to be adopted in education. This is changing, however, especially with the rise of Artificial Intelligence (AI), Generative AI, and the realisation that data is a key component of future success. Data literacy is becoming equally important in both student education and staff development.
In fact, students now expect the same level of service from universities that they get from technology companies—quick responses to queries, easy access to information when needed, and real-time assignment grading.
At the same time, universities are home to academics with deep subject knowledge, including analytics and AI, and researchers dedicated to innovation and impact. Collaborating with these experts is extremely rewarding. A recent example of such collaboration at RMIT was the development of an AI Governance Framework, which involved input from across the university, including academics and researchers.
Data Stewardship:
Could you discuss the process of building and maintaining the coalition of 60 data stewards at RMIT? What strategies have proven most effective in getting buy-in from different departments?
First of all, we didn’t build it overnight. What was important to us was identifying people who were interested in ensuring that data supported, rather than hindered, business processes. We started with a single task: defining key RMIT terms and aligning our definitions across the university’s various processes. We formed a working group to focus on definitions, with around 20–25 participants, many of whom became data stewards. We now have more than 400 key terms approved by the Data Trustees, and this work continues.
Since then, we have established more working groups, and we now have four Data Stewards groups: Data Quality, Definitions and Reporting, Information Architecture, and Data and AI Risk Management. Each group works on different but related issues and they come together for showcases to ensure alignment. I view our Data Stewards as key partners in data governance. One of the biggest compliments I’ve received in my career was when someone told me that I had created a “buzz”, and that people across RMIT were talking about data and its value.
“Building a network, finding allies, forming a coalition like-minded within your organisation will lead to success.”
network, allies, and coalition of
people organisation success.”
Ethical Considerations:
How do you ensure that AI and machine learning models developed at RMIT uphold ethical standards, particularly in terms of fairness, transparency, and bias?
RMIT’s AI journey is advancing rapidly, and we’re taking a two-layered approach: experimenting with AI while simultaneously establishing strong AI governance. Our AI Governance Framework represents a carefully measured approach to managing complex risks. It includes nine agreed AI governance principles, defines roles and responsibilities, and outlines specific governance processes that RMIT needs to implement. More than 20 staff members from across RMIT, including our academics and AI experts, contributed
to the development of this framework, which has been approved by the Information Governance Board, a sub-committee of the Vice-Chancellor’s Executive, our highest executive forum. Additionally, a new procedure for Responsible AI is now a resource under the RMIT Information Governance Policy.
To ensure that everyone at RMIT understands their role in the ethical development, implementation, and use of AI, these key documents are supported by the Responsible AI@RMIT Data Literacy Module, which is available to all staff. We still have a long journey ahead to embed responsible AI principles into all core processes at RMIT, but we’ve made a strong start, and we are already integrating these procedures into RMIT’s privacy and other relevant processes.
AI in Education:
Besides the capacity model, what other AI or machine learning initiatives are you excited about at RMIT, and how do you see them impacting the student experience?
When people talk about AI, they often focus on chatbots and Generative AI. These are valuable tools, and we’re already using them at RMIT. However, I always emphasise the concept of “fit for purpose” when discussing AI with stakeholders. For example, we developed a student performance dashboard that is based on more than 20 student cohorts. It monitors attrition and success rates and enables benchmarking. This provides excellent visibility into student performance across the university and helps inform decisions for improvement.
We’re also using a predictive machine learning (ML) model for student retention, which helps us identify students at risk of dropping out. Another use of ML is identifying students near graduation who have a high propensity for further study, allowing us to offer them appropriate postgraduate programmes. Not only has this ML solution enhanced the student experience, but it has also significantly increased admissions to postgraduate courses. Alongside our Information Technology Services colleagues, we developed a private generative AI chatbot called VAL for staff and students, and we’re now expanding it for use as a policy and procedure chatbot.
Another successful generative AI use case is survey analysis. This model saves time and effort by categorising survey responses and presenting them in a user-friendly format. Given that RMIT has surveys in the field almost every day of the year, this has been a major time-saver, allowing us to implement changes that directly benefit the student experience. All credit for these use cases, without any doubt, goes to incredible team of data professionals I am privileged to lead and the relationships Data and Analytics team built with our partners and colleagues across RMIT.
“Governance and ethics play a significant role in these decisions. When done correctly, governance isn’t red tape but rather an enabler and protector for achieving strategic goals.”
Advice for Aspiring Data Leaders:
For those aspiring to be Chief Data Officers, what specific skills or experiences should they focus on to prepare for the role?
Being a Chief Data Officer (CDO) is challenging but ultimately rewarding. It’s widely known that the average tenure for a CDO is around 18 months, often because the role is not well-defined or understood, and there are unrealistic expectations that long-standing data issues can be fixed in a short time. That’s not the case. Data quality, for instance, is not a project but a continuous improvement programme that requires leadership support, accountability, and ongoing effort. Similarly, managing data risks is a continuous task, much like managing cybersecurity risks. However, it’s a rewarding role because you deliver significant value to your organisation, and your stakeholders will increasingly demand more of your services.
My advice to aspiring data leaders is to hone your influencing, presentation, and stakeholder management skills. These are essential in this role. While technical skills are important, curiosity, asking the right questions,
and understanding how organisations work and how you can help are even more valuable.
Building a network, finding allies, and forming a coalition of like-minded people within your organisation will lead to success. Your key allies will include the Chief Information Officer, Chief Risk Officer, Chief Privacy Officer, and Chief Information Security Officer, as well as business leaders who rely on data and analytics to make decisions.
Additionally, partnering with leading data, analytics, and technology companies is crucial, whether large or small. We’ve partnered with Slalom, dbt, Snowflake, and AWS to deliver our data analytics platform, Data Foundry and MIP for critical data skills, and PwC for sensitive data cataloguing.
But most importantly, you must understand your organisation’s strategic priorities and current blockers. Aligning your data and analytics strategy with issues critical to your organisation will ensure your stakeholders promote your data initiatives on your behalf. That’s what success looks like to me.
Future of Data Management: How do you see the role of Chief Data and Analytics Officers evolving in the next decade, particularly with the rise of new technologies like quantum computing and advanced AI?
There are high expectations that AI will solve many problems, boost productivity, and free people to focus on what they do best—thinking. I believe this is true, but only if the data used to train AI models is well understood, meets quality standards, is managed ethically, and is appropriately classified and governed throughout its lifecycle.
The role of the Chief Data Officer is to oversee all of that. Is your organisation monitoring its data quality? Does it have robust data governance? How mature is its data management? How well does it manage data risks, the data lifecycle, and data sharing? Do you have a data and analytics strategy that encompasses culture, data governance, data technologies, analytics, and AI? The latter is a relatively new addition to the already broad responsibilities of CDOs, and they are increasingly
moving towards roles like Chief Data and Analytics Officer (CDAO) or Chief Data and AI Officer (CDAIO). I expect we’ll see further iterations of these roles in the future.
Some companies are creating the new role of Chief AI Officer, while others are adding this responsibility to CDO or CIO roles. Whatever the case, this journey must be supported by the right priorities and funding to stay ahead of the competition. AI involves a substantial amount of modelling, which cannot be separated from data. At the same time, AI technologies are growing exponentially, requiring integration with source systems. All major technology companies are developing their own AI tools, forcing organisations to make well-considered decisions.
Governance and ethics play a significant role in these decisions. When done correctly, governance isn’t red tape but rather an enabler and protector for achieving strategic goals. I’m a strong believer that AI governance doesn’t need to be separate but should be integrated into existing data governance practices, data policies, and data literacy models.
THE DATA FOUNDRY - PARTNERING WITH RMIT UNIVERSITY ON JOURNEY FROM DATA TO INSIGHT
The Data Foundry (TDF) was founded in late 2019 on the principle of always starting with the customer and working backwards from there. We deliberately set out to design and build a company that was everything we looked for as customers in our former lives as technology decision-makers, and yet always struggled to find.
We are deliberately narrow in our focus specialising in all things data only. We stay within our swim lane and go very deep in around that data specialisation.
Over the last 4+ years, we have built a reputation as a “one stop data shop”, with expertise centred around the design, build and run of data-
driven services and solutions on the AWS, Snowflake and Databricks platforms. Our team is comprised of Data Engineers, Data Analysts, Data Scientists, Cloud Architects, Technical Business Analysts, Frontend Developers, a Chief Data & Analytics Officer, and Delivery Assurance specialists. Every one of our customer-facing consultants holds multiple AWS, Snowflake or Databricks certifications.
Since our inception, we have delivered close to 100 data-driven projects for tens of customers, including Universities, High-performance Sports Organisations, Federal Government Agencies, High Tech Manufacturing Organisations, State Government Departments and Agencies.
TDF enjoys a trusted, long-term partnership with RMIT University, to the point where we feel like a natural extension of their Data and Analytics team, allowing them to expand and contract elastically by using TDF to supplement their team when project demands exceed the internal team’s capacity. TDF is a small, agile, highly specialised, Australian-based company that has a proven track record of delivering high-quality, secure, performant, and low-cost data-driven solutions and services for a wide range of customers over the last 4+ years.
THE DATA FOUNDRY - PARTNERING WITH RMIT UNIVERSITY TO COMPRESS TIME TO RESEARCH OUTCOMES
EXECUTIVE SUMMARY
The Data Foundry (TDF), an Australian AWS, Snowflake and Databricks Partner Partner, implemented an AWS-based HPC platform to help RMIT University researchers visualise molecular structures 100 times faster, simulate photonic chips 10 times faster, reduce wait times, and gain better visibility into costs. TDF used Service Workbench on AWS to create the RMIT AWS Cloud Supercomputing Hub.
OVERCOMING THE LIMITATIONS OF AN ON-PREMISES HPC ENVIRONMENT
The Royal Melbourne Institute of Technology (RMIT University), founded in Melbourne, Australia, in 1887, is a leading public research university with 97,000 students. Named one of the world’s top 250 universities, RMIT focuses on art and design, architecture, education, engineering, technology, business, and communications.
For years, RMIT researchers relied on a distributed high performance computing (HPC) environment, which could not scale sufficiently to support increasingly complex research by both researchers and students in areas such as photonics, battery technologies, and geospatial science. “Many of our researchers faced compute, storage, and network constraints that impacted their research,” says Dr. Robert Shen, Director of RMIT AWS Cloud Supercomputing. “Some researchers couldn’t analyse multidimensional datasets or run large computationally intensive data modeling, and a few struggled to even run simulations using small datasets. We needed more scalability and permanent data storage options for researchers.”
RMIT also wanted to provide self-service HPC access to researchers, so they wouldn’t have to rely on external HPC facilities such as Australia’s National Computational Infrastructure (NCI), which allocates public research resources on a quarterly basis. “NCI is very competitive, and not all researchers can get access to resources,” Shen says. “Also, even if you do get resources allocated, you have to submit your job in a queue.”
RMIT sought to move to a cloud-based HPC environment to overcome its challenges. “We knew the cloud would provide scalability and on-demand access,” says Shen.
BUILDING AUSTRALIA’S FIRST DEDICATED CLOUD SUPERCOMPUTING FACILITY
Because RMIT wanted to offer the first dedicated cloud supercomputing facility at an Australian university, it needed to get its new HPC platform up and running as quickly as possible. “We understood that we had limited internal capacity to build our own environment quickly,” says Nick Balkin, Technology Program Manager at RMIT. For this reason, RMIT engaged with Amazon Web Services (AWS), which introduced RMIT to a specialist AWS data partner, The Data Foundry, an Australian technology and services solution provider.
TDF’s team worked directly with RMIT researchers to understand their needs around processing power, speed, and data storage. TDF then implemented Service Workbench on AWS, a solution that offers prebuilt AWS environments with scalable governance and security. With these capabilities, RMIT researchers have self-service access to AWS resources through a web-based catalogue of preconfigured environments. Through the Service Workbench research portal, researchers can upload their study data or software directly into Amazon Simple Storage Service (Amazon S3) for storage.
RMIT and TDF used Service Workbench on AWS to create the RMIT AWS Cloud Supercomputing Hub (RACE), which can scale from 10 Gbps to 400 Gbps, enabling significantly faster data upload times. AARNet provisioned connectivity to AWS from the RACE facility in Melbourne using AWS Direct Connect services. TDF partnered with RMIT to implement the Service Workbench solution in less than two months, working closely with the initial researchers to ensure they had all the required software and configurations to continue their research using RACE. RMIT became the first Australian university to go live with a dedicated cloud supercomputing facility. “We wanted to go live quickly because we knew we had an opportunity to build something here that was somewhat groundbreaking in the sector,” says Balkin. More than 600 RMIT researchers now use RACE, which opened in July 2022, to drive advances in research.
PARTNERING COMPRESS
OUTCOMES
THE
ACCELERATING INNOVATIVE SCIENTIFIC RESEARCH
Relying on the AWS-based RACE platform, RMIT has the scalability and performance to drive faster research outcomes. Researchers can now access greater computing power on demand to address complex challenges in areas such as battery technologies, photonics, and geospatial science. “RMIT researchers using the RACE platform on AWS are able to test ideas and solutions up to 100 times faster compared to our former distributed HPC environment approach,” Shen says. One researcher, Professor Michelle Spencer, is using RACE to analyse data and communicate a new, faster way to screen hundreds of potential molecules that could make electrolytes for lithium-metal batteries. “Professor Spencer can visualise molecular structures 100 times faster than with the on-premises environment, which means she can more quickly analyse how molecules impact each other,” says Shen.
Associate Professor Thach Nguyen at RMIT’s Integrated Photonics and Applications Centre is simulating photonic chips 10 times faster than before by using RACE. The tiny chips can plug into optical fibre networks to make the internet faster or plug into medical diagnostic tools to quickly analyse how cancer cells spread.
RMIT Professor Matt Duckham is using RACE to design new ways to automatically pinpoint a person’s exact location using only a verbal description of the features around them. With RACE, Duckham’s team can now process massive information streams including drone imagery, satellite data, and data from sensor networks.
REDUCING WAIT TIMES AND IMPROVING VISIBILITY INTO COSTS
RMIT researchers no longer need to wait in queues to access HPC resources. Instead of waiting up to 100 hours, researchers only spend a few hours provisioning compute and storage and setting up research parameters. “Rather than waiting and getting approval, our researchers can do their work in a few hours because they no longer need to wait on resources from NCI,” says Shen.
In addition, the RACE portal gives researchers visibility and control over cloud spend. “Our researchers can see their exact cloud resource usage by logging in to the portal,” says Shen. “As a result, they get more accurate cost estimates in a browsable service catalogue, which makes it easier to estimate costs and manage budgets.”
RMIT has since deployed additional AWS services such as AWS ParallelCluster, which helps researchers access more distributed computing. “The partnership with AWS and the RACE team has been a great example of our “one team” project approach. We are delighted that RMIT and AWS chose The Data Foundry to be their technical enablement partner and we are proud of the success stories from the researchers who are on a journey from data to insight at the speed of cloud – on the RACE platform,” says Brad Coughlan, founder and managing director at The Data Foundry. “We look forward to continuing our partnership with RMIT and RACE as we expand the university’s research capabilities on AWS.”
All Information correct at original date of publish, April 2023.
