I recently had the honour of interviewing the successful Ian Oppermann.

Ian is both the CEO of the NSW Data Analytics Centre and the NSW Chief Data Scientist.

He was kind enough to give up some of his valuable time to share some truly unique, inspirational and valuable insights.

You have the honourable and influential positions of being both the NSW Chief Data Scientist and CEO of the NSW Data Analytics Centre (DAC). Can you please explain why the DAC was set up, what it’s strategic goals are, and some of the achievements you’re most proud of so far?

The DAC was established to address difficult or “wicked” policy challenges using data and new data science techniques. These challenges range from exploring transport optimisation to helping with the reform the out-of-home-care system in NSW. In all cases, we work with Agencies to help them explore their challenge through the lens of data. Our achievements to date are all couched in terms of helping agencies see the art of the possible, and helping them explore or understand their challenge differently.

What are some of the main challenges you’ve faced in the role and how have you overcome them?

Data sharing remains a challenge in many parts of government primarily because of concerns around appropriate use and interpretation of data, concerns about unintended consequences of sharing data, concerns about accidental release of sensitive data and concerns about adherence to privacy legislation. Building “trust preserving” and “privacy preserving” frameworks which go beyond basic data governance has been an ongoing activity for the DAC.

Interpretation of results from data analytics also raises challenges primarily due to how surprising results are (compared to expectations or lived experience), concerns around generalisation of results from specific cohorts to whole populations, and in some cases, the level of data maturity of the clients versus the level of problem space literacy of the data analysts.

What can the Federal Government, industry and academia learn from the successful precedence set by the DAC?

The most import aspects of success to date have been an acknowledgment of the need to rapidly experiment in a safe environment, learn from this experimentation, and build on what we have learned working as closely as possible with the “problem owner”. The DAC has been on a steep learning curve over the last 2 and a half years. We have also been sharing what has worked and what has not over this time.

How important is collaboration amongst Government, academia and industry with such endeavours? What are some of the best ways you’ve found to foster and grow such relationships?

In the world of wicked challenges, no-one has a monopoly on the problem space. Equally, no-one has a monopoly on good ideas. Some of the best outcomes have come from working closely with industry, academia and other government groups. We have actively explored innovative ways to discover and problem solve together including using “directed ideation series” workshops. This includes the Data Sharing Frameworks Taskforce work with the Australian Computer Society (ACS) and Standards Australia, and the innovation in CTP Insurance reform working closely with industry and CSIRO.

How would you rate the Data Science and/or analytics maturity level of most Government departments and agencies, relative to the private sector, both here and abroad, generally speaking?

Maturity is growing across many parts of government. What we see in our daily lives as consumers is starting to influence how we think about what is possible within government. The more we can demonstrate success through appropriate use of data analytics, the more most agencies are willing to explore new approaches.

You’re playing a lead role in helping pioneer some incredibly important work as part of the Data Sharing Taskforce, by developing a framework for automated privacy preserving data sharing. Can you please elaborate on its main aims, challenges faced (such as technical and legislative), and success so far?

The Data Sharing Frameworks Taskforce led by the ACS is an attempt to get to the heart of the challenge of data sharing. It speaks to the technical, regulatory, cultural, trust and social aspects of data sharing. Acknowledging that all of these factors need to be considered has given us a very complex problem to work on, but one which must be addressed to allow future “Smart” services. As of September 2017, the Taskforce released a Technical white paper which frames the problem. We are now working through specific project examples to test and flesh out the conceptual framework.  Link to paper below:


You’ve held a number of senior and influential roles throughout your career, and in notable organisations such as the CSIRO and Nokia Siemens Networks. When selecting a new role, such as becoming CEO of the DAC, what do you look for?

My favourite roles are those which carry the combination of: an organisation about to go through significant growth or significant change; where there is the potential to make a positive difference to people’s lives; and where there is less than 100% chance of success.

What motivates and inspires you most about the field of Data Science?

Data is a way of seeing the world, science is a way of understanding it. The combination of the two gives us new ways of understanding and exploring some of the fundamental challenges we face today: from a growing yet ageing population, to future food production and environmental challenges. With ever richer, more profound data available, the ability to address these challenges is growing rapidly.

How have you found the role of Data Science, and more broadly, data driven decision making, change over time, within both the public and private sectors? What challenges still exist, and what are your views on how to resolve these?

We live in an age of irresistible digitisation. This has changed almost every aspect of our lives and changed it at a rate which is unprecedented in human history. It has allowed creation of new services, new business models and new concepts of “value”.  It has also created new challenges in the form of cyber security, what privacy means in a digital age, as well as new concerns about what we “ought to do” with all of this data.  As we explore the art of the possible, we must also address these new challenges.

People have always used data to help make decisions. Our challenge is to bring more data into decision making processes, ensure we all understand and communicate the limitations of that data, and ensure the questions we address are framed in way which delivers positive benefits.

What are some tips you’d like to share with Data Scientists looking to further their careers, such as aspiring to become Lead and Chief Data Scientists?

We are on the cusp of a very exciting future but a future with a mix of opportunities and challenges. Whilst data analytics provides us a pathway to address that future, there is a fundamental need to ensure we are striving to positively impact our future world. This means remaining people-centric in everything we do. It also means remembering that data is a way of seeing the world rather than the world itself. Science is a way of understanding the world but still comes with limitations. Being able to balance the potential with the limitations is the art of Data Science.

Disclaimer: The opinions expressed by Ian represent his own personal views and not those of his employer.

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