Data Leaders Who's Who: Ian Oppermann

---

ian oppermann

Chief Data Scientist, NSW Department of Customer Service

Dr. Ian Oppermann is a renowned expert in the digital economy and has extensive knowledge and experience in big data, broadband services, and technology's impact on society. In this article, he leverages his over 30 years of expertise to outline the key components of a successful data strategy. He believes that data can be difficult to harness because of its value and the lack of standardised methods for sharing and using it. A strong data strategy should embrace the limitless potential of data while establishing clear guidelines for data sharing and usage through a limited set of data handling protocols. Oppermann also shares on his contributions to two exciting government projects; the Out of Home Care reform and the creation of the NSW AI Assurance Framework.

During this interview, Ian shares a wealth of information in answering the following questions:

How do you devise a data strategy? What sets apart the good from the bad?

Data is an elusive commodity. Partly because of the utility of data and partly because we do not really (yet) have good general data sharing and use frameworks. Some really significant complications exist when we think about the general use of data:

• Every data set is unique – it has its own history, its own chain of custody, its own provenance, its own data quality.

• Each data set can potentially be used in an infinite number of ways. Whether a dataset is fit for the purpose it is about to be used for, is currently a very subjective consideration. We lack general frameworks for data quality and for general evaluation of “fitness for purpose”.

• Every product created from data can also potentially be used in an infinite number of ways. We lack general frameworks with recommendations or restrictions on how data products should be appropriately used.

• Context matters: context changes how a dataset or a data product can be safely and appropriately used or governed.

• Every dataset of data product can have a very long and complex life. Datasets or data products can be combined with other data sets / data products. This could change the level of sensitivity of a dataset / data product or the level of personal information in the dataset/ data product. Of course, these recombined outputs can themselves be recombined on ‘ad infinitum’. So, we are challenged by the “next” use of data or the “next next” when thinking about how to appropriately share and use data. This is particularly true if data “escapes” from its intended governance framework and gets into the wild. This leads a lot of data custodians to simply lock it down and not allow future, secondary uses of data and data products.

Ian also shared his thoughts on:

• What are the essential qualities of a data leader?

• How have you found success in raising data literacy in your organisation? How do you get involved in educating peers and execs?

• What work are you most proud of?

• What do you wish senior leadership knew or understood?

• What have been major AI watershed moments in your career – or alternatively in the industry?

• What are some of the lessons learned you’ve encountered when getting AI products into production?

• What new technology and innovations do you see as being the most critical to the industry over the next 18 months?

• How do you ensure you are leveraging new tech for innovation, rather than tech for tech’s sake?

• What legacy do you hope to leave behind you at your organisation?

"A good data strategy also speaks to what happens when things go wrong, when data or products escape from their intended environments of control"

To get the full PDF copy of this and every Data Leaders Who's Who interview coming out, join our Member Space. There you can also access past webinars in the webinar vault. If you are already a member, log in to access the PDF download.