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#45 Mastering the Domain of Your Work Before Becoming a Data Scientist with Warwick Graco - Senior Director Data Science
Warwick Graco is the Senior Director of Data Science at the Australian Taxation Office (ATO). He has worked in defence, health, and taxation and has been involved in analytics for 25 years. He is a practicing analytics professional and is currently convenor of the Whole of Government Data Analytics Centre of Excellence and is a senior data scientist in Data Science and Special Acquisition Group of the Smarter Data Program of the ATO. He has a BSc from the University of New South Wales and a Ph.D. from the University of New England Australia. His professional interests include organisational innovation and learning, organisational decision making and analytics.
In this episode, Warwick tells us how he got started in data research the skills gained that led him to his successes today. Warwick explains why transparency is a business requirement for software and tools in the data science field. People with more analytical backgrounds will be more willing to accept an opaque solution over a transparent solution. When analytics was in the early stages, some organisations pushed back from data science; feeling they were on top of their portfolio and did not need any outside resources. No matter what results Warwick would come up with for these organisations, they would continue to have the same attitudes. Since 2010, there has been a shift in attitudes because data science has shifted from the background to the foreground.
Then, Warwick tells us the difference between good support and lousy support in the workplace. While Warwick was working with organisations, instead of providing results, he did the reverse. Ask the organisation what they want rather than telling them the findings. Providing the outputs clients wish to see led to incremental improvements built into their business intelligence reports. Warwick also explains why you can no longer be a data scientist; you will need to learn and master the domain of your work. For instance, Warwick learned everything about ophthalmology while working on data science with an ophthalmologist. Later, Warwick explains his process of publishing research, improving privacy concerns, and automated supports.
Enjoy the show!
Show Notes:
• [02:20] How Warwick started in data science
• [05:55] Aptitude for research
• [08:40] Purpose-built software + decision trees
• [12:20] Accepting opaque solutions vs. transparent solutions
• [16:45] Pushback of data analytics
• [21:15] Difference between good support and bad support on the job
• [25:25] Necessity to learn the domain first
• [29:00] How to learn on the job
• [32:20] Process of publishing research
• [41:50] Improving legal and privacy concerns
• [44:25] Automated support + decision-making operations
• [52:40] Developing an analytical + practical mindset
• [58:10] Hyperspecialized
• [64:30] Moving toward data + analytics as a service
• [66:25] Advice from Warwick
Now you can support Data Futurology on Patreon!
https://www.patreon.com/datafuturology
Thank you to our sponsors:
UNSW Master of Data Science Online: studyonline.unsw.edu.au
Datasource Services: datasourceservices.com.au or email Will Howard on [email protected]
Fyrebox - Make Your Own Quiz!
And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!
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By Felipe Flores
4.4
1414 ratings
Warwick Graco is the Senior Director of Data Science at the Australian Taxation Office (ATO). He has worked in defence, health, and taxation and has been involved in analytics for 25 years. He is a practicing analytics professional and is currently convenor of the Whole of Government Data Analytics Centre of Excellence and is a senior data scientist in Data Science and Special Acquisition Group of the Smarter Data Program of the ATO. He has a BSc from the University of New South Wales and a Ph.D. from the University of New England Australia. His professional interests include organisational innovation and learning, organisational decision making and analytics.
In this episode, Warwick tells us how he got started in data research the skills gained that led him to his successes today. Warwick explains why transparency is a business requirement for software and tools in the data science field. People with more analytical backgrounds will be more willing to accept an opaque solution over a transparent solution. When analytics was in the early stages, some organisations pushed back from data science; feeling they were on top of their portfolio and did not need any outside resources. No matter what results Warwick would come up with for these organisations, they would continue to have the same attitudes. Since 2010, there has been a shift in attitudes because data science has shifted from the background to the foreground.
Then, Warwick tells us the difference between good support and lousy support in the workplace. While Warwick was working with organisations, instead of providing results, he did the reverse. Ask the organisation what they want rather than telling them the findings. Providing the outputs clients wish to see led to incremental improvements built into their business intelligence reports. Warwick also explains why you can no longer be a data scientist; you will need to learn and master the domain of your work. For instance, Warwick learned everything about ophthalmology while working on data science with an ophthalmologist. Later, Warwick explains his process of publishing research, improving privacy concerns, and automated supports.
Enjoy the show!
Show Notes:
• [02:20] How Warwick started in data science
• [05:55] Aptitude for research
• [08:40] Purpose-built software + decision trees
• [12:20] Accepting opaque solutions vs. transparent solutions
• [16:45] Pushback of data analytics
• [21:15] Difference between good support and bad support on the job
• [25:25] Necessity to learn the domain first
• [29:00] How to learn on the job
• [32:20] Process of publishing research
• [41:50] Improving legal and privacy concerns
• [44:25] Automated support + decision-making operations
• [52:40] Developing an analytical + practical mindset
• [58:10] Hyperspecialized
• [64:30] Moving toward data + analytics as a service
• [66:25] Advice from Warwick
Now you can support Data Futurology on Patreon!
https://www.patreon.com/datafuturology
Thank you to our sponsors:
UNSW Master of Data Science Online: studyonline.unsw.edu.au
Datasource Services: datasourceservices.com.au or email Will Howard on [email protected]
Fyrebox - Make Your Own Quiz!
And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!