Data Futurology - Leadership And Strategy in Artificial Intelligence, Machine Learning, Data Science

This week on the Data Futurology podcast we speak to Sanchit Juneja, the Director of Data Science and Machine Learning at Booking.com. Having worked in roles across SE Asia and Africa before landing in his current role in the Netherlands, Juneja has a truly world view of the role of data in business, especially within the context of product development within large enterprises. One of the challenges that large enterprises face with product and data is the question of whether you should build or buy the tools that the organisation uses. As Juneja states, the ideal approach is a holistic one that does focus on speed to market. “You do still want to build things that are strategic and core to your heart,” he said. “However, having access to things that bring you faster to market is important at the end of the day, as you want to unlock business value.” Juneja then shares insights around the skills that it takes to work in product management. Compared with some other areas of data science, it is perhaps not quite as important to be technical (though being “tech aware” is essential). However, those in product management need to be very good at building consensus across the organisation, from executive right through to those that will implement solutions. They also need to be very comfortable with ambiguity and working with the unknown and have an appetite to learn on their feet. Finally, Juneja shares his insights around how he and his team track the value that they’re adding to the organisation. Critical in building alignment across the business is the ability to show results, so everyone working in product needs to be able to clearly articulate the gains there. For these insights, and many more in the wide-ranging interview with Juneja, tune into the podcast! Enjoy the show! Thank you to our sponsor, Talent Insights Group! Join us in Sydney for OpsWorld: https://www.datafuturology.com/opsworld Join our Slack Community: https://join.slack.com/t/datafuturologycircle/shared_invite/zt-z19cq4eq-ET6O49o2uySgvQWjM6a5ng What we discussed: 00:00 Introduction 03:31 What is the main focus of your role on booking.com? 07:42 How have you found getting the stakeholders and team members on board for the journey? 09:08 How do you define the product work in in our space? 11:57 What are some of the other skills that you see as key for the product manager role? 13:28 How do you make the trade-off decisions around the product as you're implementing or building towards the vision? What are what are some of the trade-offs that need to be done in the in the product decisions? 14:30 What is the mindset shift that that you would recommend for people that may be doing ad hoc pieces of work, or a one off? 16:07 What are you most proud of? 17:14 What are some things that you would have done differently? Quotes: · Even if you're a big tech org, you don't necessarily need to build everything yourself. So the build versus buy call is something that is personally on top of your mind, if you're a product leader. There are so many things that are happening, so many core things that if you go on and build it inside your house, it will take you the next six months. But if you just buy a tool outside in the industry, it will be much quicker for you. I think that is one thing that is always on top of your mind, what to build versus build to buy. --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

On the Data Futurology podcast this week we have AI expert and author, Cortnie Abercrombie. Abercrombie is the CEO of AI Truth, an organisation that empowers business leaders to leverage AI in an ethical and innovative manner. She is also the author of What You Don’t Know: AI’s Unseen Influence On Your Life And How To Take Back Control. We start the conversation on the podcast talking about the challenges that data scientists face with data governance, and the many challenging questions that complicate that. Then we discuss the challenge of maintaining models, and what that means for the safe shepherding of data. As Abercrombie notes, the average tenure of a data scientist at an organisation is only 12 to 18 months. When an organisation is managing dozens, if not hundreds or even thousands of models, it can become difficult to maintain the quality and integrity of the underlying data. As Abercrombie notes, the stakes for this might be very high indeed. “Think about robotic-assisted surgery,” she said. “If there aren’t the proper constraints and management of the data, what’s to say you couldn’t cut a hole bigger than a person can handle, because the AI “sees” cancer material that is significantly larger than it actually is?” Another challenge that we discuss on the podcast is the structure of teams within the organisation, and how, particularly with regards to larger companies, oversight into the applications being developed is too siloed. According to Abercrombie, with too many enterprises there’s a lack of consistency in processes and company-wide oversight and policy across those teams. One of the key steps that is being overlooked in the rush towards AI, Abercrombie notes, is data literacy. Organisations and individuals need to redouble their efforts to truly understand data first. Because without that, the ethical application of AI is always going to be a difficult question. For more deep insights into the thinking that is driving ethical AI and how enterprises are thinking about it, tune into the podcast! Enjoy the show! Find out more about Cortnie’s book at Amazon Thank you to our sponsor, Talent Insights Group! Join us in Sydney for OpsWorld: https://www.datafuturology.com/opsworld Join our Slack Community: https://join.slack.com/t/datafuturologycircle/shared_invite/zt-z19cq4eq-ET6O49o2uySgvQWjM6a5ng What we discussed: 00:00 Introduction 03:56 Cortnie outlines why AI needs regulation and draws on some of her experience as an advisor to Fortune 500 companies on responsible artificial intelligence 07:24 Felipe and Cortnie discuss the importance of having a conversation about data governance in the industry 18:55 Accountability and kill switches in Intelligent Automation 26:06 Corporate AI ethics best practices she has been working on 32:16 Felipe and Cortnie talk about the concept of an external review committee in the AI industry --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

AI is a powerful tool, and as enterprise and government find more sophisticated ways to leverage the technology, there will be untold benefits returned to customers. At the same time, the responsible use of AI is of significant concern to the global population, and people are watching how its use is regulated closely. On this week’s Data Futurology podcast, Felipe Flores presents an update on the status of regulation across Europe, China, and the US, and poses the question about whether AI regulation needs to be a global, rather than regional response. Perhaps surprisingly, China’s taken the lead in regulating how business uses AI, Flores said. “The regulation says that businesses must notify users when an AI algorithm is playing a role in determining which information to display to them and give users the option to opt out of being targeted. The regulation also prohibits algorithms that use personal data to offer different prices to different consumers. It is really interesting that China moved early.” Meanwhile, in the EU, the drafted regulation would categorise AI applications into one of four “risk” profiles, with oversight and accountability being scaled in kind. And in the US, much of the focus around regulation at the federal level is concerned with the potential for discrimination, while states are being left to develop their own broader frameworks. Australia, which doesn’t yet have regulation, does have an ethical framework, which is an indication of where future regulation might go. Flores runs through that framework in this podcast as well. For an in-depth look into the exciting and dynamic discourse around AI regulation across the world, tune into the podcast! Enjoy the show! Thank you to our sponsor, Talent Insights Group! Join us in Sydney for Ops World: https://www.datafuturology.com/opsworld Join our Slack Community: https://join.slack.com/t/datafuturologycircle/shared_invite/zt-z19cq4eq-ET6O49o2uySgvQWjM6a5ng What we discussed: 00:00 Introduction 2:05 Discussion around AI regulation and how should different countries tackle it. 2:50 How have the US, China and the EU approached this. 5:00 EU regulations 8:05 USA regulations 9:40 Thoughts and comparison on the three approaches. 11:10 What’s happening in Australia. Quotes: · In March 2022, China passed a regulation that governs companies and their use of AI. The regulations applies to online recommender systems. They say the AI needs to be used in ways that are moral, ethical, accountable, transparent and that disseminate positive energy. · Companies (in China) are expected to submit their algorithms to the government for review when they are being used at scale. · The EU separates the ways AI can be used into four bands according to the risk involved. They have minimal risk, limited risk, high risk and unacceptable risk. The unacceptable risk covers things like social surveillance, facial recognition, etc. · The US congress enacted a National AI Initiative Act, focused on improving research development, understanding AI and having an AI strategy within the country. --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

Today on the Data Futurology podcast, we have Ann Sebastian, Senior Data Scientist at Wesfarmers OneDigital, as a guest on the podcast. As Sebastian says, she is “in the trenches” building data science products. One of her key projects in recent years has been OnePass, a subscription service that provides free delivery and other services across a range of Australia’s top brands. “It’s an incredible experience to be part of a journey, developing an idea through to proof of concept through to production ideation to a system that is adopted across the organisation,” she said. Through the podcast, Sebastian offers some key insights into that process via some of the projects that she has worked on over the years. Sebastian also spoke about how data science teams can be built, and how a culture of innovation can be structured within them. For just one example of this that she shares on the podcast, in her current role there is a focus on learning and development, which manifests as 10 per cent of each person’s work time being dedicated to research activities. For her part, Sebastian is currently using that research time to work on multimodal product classification, she said. “Given the fast-moving nature of retail catalogue, and need for us as a division to form a unified view across all our divisions products, there is business significance for this research project. “We then have fortnightly quick check ins to discuss the progress on our research projects, and that really helps us to learn from each other. This is one way that data science is embedded into our day to day in a way that makes it more real for us.” For these insights and more on how data science products are built and evaluated, and how data scientists can be motivated and innovative within their careers, tune in to the full podcast. Enjoy the show! Click here to learn more about OnePass Thank you to our sponsor, Talent Insights Group! Join us in Sydney for Ops World: https://www.datafuturology.com/opsworld Join our Slack Community: https://join.slack.com/t/datafuturologycircle/shared_invite/zt-z19cq4eq-ET6O49o2uySgvQWjM6a5ng What we discussed: 0:00 Introduction 3:00 Ann talks about her experience and her remit at Wesfarmers 7:44 What are some of the use cases you’re proudest of? 15:12 Ann shares more information about her favourite use case and how it evolved from an idea to the start of the technical work. 17:58 How did you measure business impact? 20:14 How did you operationalize the models? 24:00 Can you describe your current role? 31:23 What’s your advice for people wanting to get into data science? Quotes: · Business teams can sometimes view data science as a mythical creature, so I love working with them to demystify data science and achieve business benefits through it. · The use case that I'm proudest of is the automation of the complaint classification, where we implemented various natural language processing models to predict the category of the complaints using real time models. --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

This week on the Data Futurology podcast, we have three special guests to share insights on how data works in retail settings. Nick Merry, the Head of Analytics at flybuys (Loyalty Pacific), Kathryn Gulifa, the Head of Data and Analytics at Catch, and Stuart Garland, the Director at Talent Insights Group, join us for a wide ranging and in-depth look into how analytics are changing and the impact this is  having on teams.  “The really good analysts that I see are the ones that are able to crystalise their understanding of what a business is trying to solve, and solve for that problem in particular,” Gulifa said. “I always think that the technical skills can be taught if you’ve got the aptitude. With the technology landscape changing so rapidly, if you try and peg yourself to recruiting people that have experienced only particular tech, you're really limiting your options.” As Garland then notes, those that focus purely on their technical capabilities would limit their career development opportunities, unless they’re willing to learn how to engage with the broader business anyway: “Even if you’re not leading people, you still should be learning the ability to demonstrate the value and impact that a project is going to have on the business at a more senior level,” he said. As Merry also notes, the days where the data team would be separate from the other lines of business are largely over. Now, the digital team is integrated into everything from marketing to security and governance, and people on that team need to be able to have conversations across all of them. “Having digital analytics, not as separate functions, but more integrated with the broader view, is one of the encouraging things that I’m seeing,” he said. For more deep insights from these three thought leaders on the changing dynamics of work in data and analytics, tune in to the podcast! Enjoy the show!  Thank you to our sponsor, Talent Insights Group! Join us in Sydney for Ops World: https://www.datafuturology.com/opsworld Join our Slack Community: https://join.slack.com/t/datafuturologycircle/shared_invite/zt-z19cq4eq- ET6O49o2uySgvQWjM6a5ng Quotes: I’m not a fan of the data translator role because I feel it absolves data analysts from developing the skills of consultation and defining a problem. What differentiates good analysts from really good analysts, is understanding the business context and the ability to drill down into what's actually important to the business. When it comes to recruitment, I always think the technical skills can be taught if you've got the technical aptitude. The technology landscape is changing so rapidly, all the time, that if you really try and peg yourself to recruiting people that have experienced only with particular tech, then you're really limiting your options. I think what you should be trying to find people that have not necessarily the polished and ready to go consulting skills, but the curiosity, the engagement, the wanting to understand why they do something, and what impact their work actually has on the business that they work for. Considering people with longer or shorter tenures depends on what the role is and what you want from that individual. If you're in the process of building a platform and bringing in a data engineer that has gone across three or four different builds over the last four or five years might be useful because from that perspective, you've got three or four different pain sets, lots of experience in regards to what went wrong and, more importantly, what went right. --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

This week on Data Futurology we answer a burning question that is asked in the data space a lot: just what makes a good data strategy? As we discuss, there is no one-size-fits-all approach to data strategy that will work for all organisations. This cannot be approached like a templated “best practice” to business. Instead, there are three factors to consider when devising the data strategy that will work for your business: 1) Defining where the organisation is on its journey today. How a business just starting out with data needs to approach strategy is different to an organisation with a mature data practice. 2) Deciding on where the organisation wants to get to. This refers to the need for organisations to get buy-in across the business to a data strategy. Without that alignment the project is prone to failure. 3) Developing the execution path, to take the organisation from where it is now, to where it is going to be. The better defined this pathway is the more likely it is that the project will stay on-track and on-goal. What distinguishes a good data strategy is one that is aspirational in nature. “Aspirational” doesn’t mean that the goal needs to be futuristic or difficult to achieve. It could be grounded and realistic, and simply an effort to step up from where the organisation is currently. But having that clear goal and a vision for the value it will deliver to the organisation at the end of the journey is critical to motivate the effort behind the data strategy. In this in-depth discussion, we lay out the approaches and use cases that motivate successful data strategies and highlight how organisations can approach each stage of the journey. Enjoy the show! Thank you to our sponsor, Talent Insights Group! Join us in Sydney for Ops World: https://www.datafuturology.com/opsworld Join our Slack Community: https://join.slack.com/t/datafuturolo... WHAT WE DISCUSSED 0:00 Introduction 1:09 How do you devise a data strategy? What sets apart the good from the bad  in a data-driven strategy? 3:30 Data strategies encompass everything, they are broader than analytics, AI and tech strategies. 5:10 Get alignment on where the organisation is today and where it wants to be  in the future. 7:45 The importance of having a prioritised set of use cases for your data strategy. 10:05 The four components needed to help you prioritise data use cases. 12:50 The two sides of organisational readiness. --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

This week on the Data Futurology podcast, we have the special privilege to host Dr. David A. Bader, a Distinguished Professor at the New Jersey Institute of Technology, and the inaugural director of the Institute for Data Science there. Bader joins us on the podcast to discuss massive graph analytics, a topic that he is a recognised expert in and has recently published a book on. He and his team are currently working on a project that will allow anyone, via the Jupyter Notebook and Python, to leverage their data science framework, running on “tens of terabytes” of data. “It is quite exciting to democratise data science – and especially graph analytics – so that anyone with a problem that knows Python can work with some of the largest data sets,” he said. According to Bader, graphs are now a mainstream part of data science and a way to solve the most challenging and complex problems in the enterprise. “A graph abstracts relationships between objects, and any problem that we can abstract where we have relationships between objects, we could use graph analytics to solve,” he said. Much of Bader’s work – including through his book – is focused on helping organisations grapple with the exponential growth in data, and the impact that this has on their ability to dedicate adequate resources to work at scale. As he said, being able to do that is going to be fundamental to humanity’s ability to respond to the many real challenges that it faces ahead. “I want equitable access for everyone to be able to work on these problems, and to find new discoveries that are important, and help solve global grand challenges,” he said. “I think that we have many issues in the world today. And if we give more capabilities to those with data, and let them empower the data will make the world a much better place.” For more deep insights on the importance and value of massive graph analytics, tune in to our conversation with Dr. David A. Bader. Enjoy the show! Thank you to our sponsor, Talent Insights Group! Join us in Sydney for Ops World: https://www.datafuturology.com/opsworld Join our Slack Community: https://join.slack.com/t/datafuturologycircle/shared_invite/zt-z19cq4eq-ET6O49o2uySgvQWjM6a5ng Read the full podcast episode summary here. --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

The motor industry has always been right at the forefront of innovation, and this is also true when it comes to embracing machine learning and AI. This week’s guest on the Data Futurology podcast is Leonard Aukea, the Head of Machine Learning, Engineering & Operations at Volvo, who shares with us insights into what the global vehicle giant is doing to bring value to the operations chain across the company. For Aukea, it has been a story of establishing best processes across the organisation. He said that one of his first priorities was to bring the various data science teams together to minimise the impact of siloing, and encourage the machine learning practitioners to adopt software engineering principles. This might not be immediately comfortable to them, but as Aukea said, ML experts are smart people working on complex problems, and facilitating an open-minded approach across the organisation is key to driving long-term success. “You need to start simple,” he said. “Think about processes, ways of working, and the cultural aspects, and try to fit tooling and infrastructure along that kind of endeavour. You don’t need to choose the most extreme state-of-the-art tools.” At one point, Aukea noted, things being pushed into production were becoming unmanageable, so he and his teams took a step back and reset. “We went back and decided to focus on first principles,” he said. “We evangelised these first principles to develop good ways of working, and then adopted the infrastructure and tooling towards building AI on top of that.” Ultimately, Aukea said, quality comes from the processes, rather than the technology. There are, of course, technical challenges, but for anyone aiming to get true value out of machine learning, the focus needs to be on the processes. Aukea then explains how, with those processes in place, he and his team have been able to start delivering deep and valuable insights. For more on how Aukea was able to structure Volvo for success with machine learning in operations, tune in to the podcast! Enjoy the show! Thank you to our sponsor, Talent Insights Group! Join us in Sydney for Ops World: https://www.datafuturology.com/opsworld Join our Slack Community: https://join.slack.com/t/datafuturologycircle/shared_invite/zt-z19cq4eq-ET6O49o2uySgvQWjM6a5ng Read the full podcast summary here. --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

In today’s episode, we have Romina Sharifpour, Machine Learning Specialist at Amazon Web Services (AWS). Operationalising machine learning models, particularly scaling MLOps capability across teams within an organisation is a difficult feat. Join Romina to find out how you can easily accelerate your MLOps journey using Amazon SageMaker Pipelines. You'll gain insights into how AWS customer Carsales keeps up with increased demand in building and productionising AI models, and their strategy to democratise AI across the whole development teams. This allows any developer to be a citizen data scientist and ML engineer by leveraging Amazon SageMaker. Enjoy the show! If you want to learn more about building modern applications on AWS and attend a virtual conference, just google “AWS Innovate” or click the link below. https://aws.amazon.com/events/aws-innovate/apj/modern-apps/ Thank you to our sponsor, Talent Insights Group! Join us in Melbourne for Scaling AI with MLOPS:  https://www.datafuturology.com/mlops Join our Slack Community: https://join.slack.com/t/datafuturologycircle/shared_invite/zt-z19cq4eq-ET6O49o2uySgvQWjM6a5ng Read the full podcast episode summary here. --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

This week on the Data Futurology podcast, we have the special privilege of talking to the top analytics leader in Australia, according to IAPA (the Institute of Analytics Professionals of Australia). Brad Petry, the Executive Director – Operations, Insights, and Digital Channels at the Department of Jobs, Precincts and Regions in the Victorian Government, was awarded this accolade for his work in leveraging AI and machine learning to overcome biases in the recruitment process. He spends time on the podcast this week talking about what that means for the department, and the implications it has for recruitment more broadly. Over the past 18 months, Petry has been driving a digital transformation program across the department, something that has been made even more challenging because it has happened through the pandemic and because the data that he and his team handle is needed on a daily basis. There was no room for downtime or mistakes while the transformation was executed. At the same time, there was an enormous opportunity within the department to leverage automation and AI with data – in many cases for the first time – to improve the reliability of the data and productivity across the department. As Petry says, the key to success is to remember that it’s the data that’s the important element, rather than the software or context that the data is held and analysed within. “When we started, we said to ourselves that the thing we knew, and what was going to persist, was the data,” he said. “The technology and programs will come and go, but the data is something that will always be there and everything comes back to the data.” For a deep dive into driving a transformation agenda with data, tune in to this week’s podcast! Enjoy the show! Thank you to our sponsor, Talent Insights Group! Connect with Brad  https://www.linkedin.com/in/brad-petry/ See Brad’s presentation at Scaling AI with MLOPS:  https://www.datafuturology.com/mlops Join our Slack Community: https://join.slack.com/t/datafuturologycircle/shared_invite/zt-z19cq4eq-ET6O49o2uySgvQWjM6a5ng Read the full podcast summary here. --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

This week on the Data Futurology podcast, we talk transformation and the importance of having data engineers to guide the strategy and agenda. To provide expert insights into this topic, we have the pleasure of hosting Richard Glew, Chief Technology Officer, and Natalia Dronova, Senior Data Analyst from Aginic. Aginic is a consultancy that assists organisations with their transformation goals, providing expertise in analytics, agile, and the digital experience. Transformation remains a challenging goal, with research showing that most projects fail. Glew and Dronova discuss some of the reasons for this, which are many and varied, but according to Dronova, one of the big ones is that organisations make mistakes in their haste to transform quickly. “One of the challenges with transformation are the people that want everything done within six or eight months,” she said. “They want it now, and they’re finding shortcuts to try and make it happen that are hurting them in the long run. Then, a few years later, when you look at their stack, it’s all over the place.” Dronova and Glew then go in-depth in discussing the structural problems that can affect transformation efforts, as well as the cultural problems across organisations – the impact that a focus on data governance can have on projects, for example, and why organisations need to move to a position of data enablement. Finally, the two also discuss the role of the data engineer. As Glew said, traditionally the role has lagged behind that of the software engineer, but with more focus being placed on their role in transformation, the rapidity with which the role is evolving, and the relative scarcity of engineers resulting in higher salaries, now is a great time to consider a career in data engineering. “With the state of data engineering today, it’s the best time to get into it, because it’s still evolving and innovating really quickly.” Glew said. Tune in to this deep and insightful discussion to learn more about the dynamics behind transformation and the role of the data engineer. Enjoy the show! Thank you to our sponsor, Talent Insights Group! Connect with Richard  https://www.linkedin.com/in/rlglew/ Connect with Natalia  https://www.linkedin.com/in/nataliadronova/ Learn more about Aginic  https://aginic.com/ Join us in Melbourne for Scaling AI with MLOPS:  https://www.datafuturology.com/mlops Join our Slack Community: https://join.slack.com/t/datafuturologycircle/shared_invite/zt-z19cq4eq-ET6O49o2uySgvQWjM6a5ng Read the full podcast episode summary here. --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

It’s safe to say that most people in data science want to do the right thing. However, AI ethics cannot just be an afterthought done in the service of regulatory obligations. It needs to be baked into the way the organisation looks at data, at every level. How organisations can achieve that is the focus of our latest podcast, with Natalie Rouse, General Manager of Eliiza and Brendan Nicholls, the Practice Lead, Machine Learning Engineering, joining us to discuss the topic. Eliiza is a data consultancy, and Rouse and Nicholls are right in the trenches with their customers. There are many questions that organisations should be asking of their data, particularly with regards to how to ensure that it’s free of bias and that it’s being used accurately. As Nicholls and Rouse discuss on the podcast, the questions range from how the data’s being collected, where it came from, whether it accurately reflects demographics, and what the range of uses of the data is, based on the collection policy. These are all relatively straightforward things to think about, but nonetheless, they’re often overlooked, especially within teams that are highly technically orientated. As the Eliiza team acknowledge, one of the challenges in data science is that teams are technical and want to “reduce” everything to numbers that can be measured. However, to fully embrace ethical AI, it becomes important to embrace the ambiguities and the non-measurable side of the discussion as well. Eliiza is deeply engaged in helping its customers achieve this understanding of ethical AI, and regularly hosts monthly MLOps meetups in Melbourne through the MLOps Community, which hosts meetups around the world - 22 different locations - to facilitate knowledge exchange. It will also be holding a hackathon around healthcare shortly to encourage ethical AI in that area. Finally, Nicholls will be presenting at the Scaling AI with MLOps event in Sydney on October 25 on why Ethical AI matters. Tune into this podcast and drop into his presentation to gain a deep understanding on why ethical AI is not just an obligation but, when done right, an opportunity. Enjoy the show! Thank you to our sponsor, Talent Insights Group! Join us in Melbourne for Scaling AI with MLOPS:  https://www.datafuturology.com/mlops Join our Slack Community: https://join.slack.com/t/datafuturologycircle/shared_invite/zt-z19cq4eq-ET6O49o2uySgvQWjM6a5ng Links Eliiza https://eliiza.com.au/ Hackathon: https://www.intellihq.com.au/medical-datathon/ AI Australia Podcast: https://eliiza.com.au/learn/ai-australia-podcast/ MLOPs community: https://www.meetup.com/en-AU/melbourne-mlops-community1/ Connect with Natalie: https://www.linkedin.com/in/natalie-rouse-7115b15a/ Connect with Brendan: https://www.linkedin.com/in/nichollsbrendan/ Read the full podcast episode summary here. --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

Healthcare is an industry that stands to benefit a great deal from data and analytics. At the same time, the sensitivity of the data in the sector is extreme and how organisations manage that data is critical. Yalchin Oytam, the Head Of Clinical Insights And Analytics at South Eastern Sydney Local Health District (SESLHD) is right in the thick of the discussion. He joins us on the Data Futurology podcast to talk through both the challenge and opportunity. One of the big challenges that the Australian health system faces, Oytam said, was that primary healthcare was handled by the federal government and secondary care was handled by the states. How the sharing of data between these two is handled is critical to maintaining the customer experience with their health care. More importantly, if data can be leveraged to improve outcomes in primary care, it can reduce the burden on secondary care. Oytam gives the example of diabetic patients being diagnosed and accurately cared for by their GPs have a much lower risk of an unplanned hospital visit. “When you keep people out of hospital, it also means that they are generally healthier, more productive, and happier. In human terms, the benefit of this goes beyond money,” he said. The other big opportunity in healthcare is the use of data modelling to personalise healthcare services. Modelling can be used to detect warning signs and risk factors, and more proactively communicate with patients. In the longer term this can result in earlier diagnosis and better risk management – and it’s just one area where this approach to data can lead to meaningful change. “The question is how do we best manage our climate, while also maximizing the quality of life for human beings, and other life forms,” Oytam said. “A better world certainly is possible.” Tune in to the podcast for an in-depth discussion on how data can deliver better health and lifestyle outcomes for us all. Enjoy the show! Join us in Melbourne for Scaling AI with MLOPS:  https://www.datafuturology.com/mlops Thank you to our sponsor, Talent Insights Group! Join our Slack Community: https://join.slack.com/t/datafuturologycircle/shared_invite/zt-z19cq4eq-ET6O49o2uySgvQWjM6a5ng Read the full podcast episode summary here. --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

Kevin Ross has had more than 20 years of experience in using data, science and analytics to lead decision-making. Now, as the CEO at Precision Driven Health, and Advisory Board Chair at the NAOI (Natural, Artificial and Organisational Intelligence) Institute, he is placed right at the heart of the data discussion in New Zealand. He joins us on the Data Futurology podcast this week to discuss the evolving role of data in healthcare, and how it has broadened to really start to embrace personalisation. “We have this fantastic opportunity other there where we know that health doesn’t make use of all the data that’s out there,” he said. “Imagine what you could achieve if you added the computational power of AI into diagnosis and healthcare. The potential is amazing for guiding people to understand themselves and their outcomes. “It is being driven by consumers looking for health to provide them the same services that they can get elsewhere.” Ross acknowledges that change is coming slowly, but as it is being driven by customer demand, the change is inevitable. Those healthcare organisations that can adjust will prove to be the disruptive forces in the years ahead. Elsewhere in this wide-ranging podcast, Ross also discusses the advances in data capture technique, and the implication that has for better analytics and AI. He also talks through the privacy and ethical implications of data use in healthcare, and how data outcomes can be understood and measured within healthcare. Healthcare is one of the most fascinating sectors when it comes to data, analytics, and patient outcomes, and we’re only scratching the surface of it. Tune in to this in-depth conversation with Ross to get a sense for what’s coming next. Enjoy the show! To learn more about Precision Driven Health:  https://precisiondrivenhealth.com/ Join us in Melbourne for Scaling AI with MLOPS:  https://www.datafuturology.com/mlops Thank you to our sponsor, Talent Insights Group! Join our Slack Community: https://join.slack.com/t/datafuturologycircle/shared_invite/zt-z19cq4eq-ET6O49o2uySgvQWjM6a5ng Read the full podcast episode summary here. --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

NVIDIA is best known for its production of GPUs and APIs that enable high-performance computing, supercomputing, and power some of the most intense applications across the world. Unsurprisingly, the company is deeply involved in AI, and on this week’s podcast, the company’s VP of Solutions Architecture and Engineering, Marc Hamilton, joins us to share the company’s unique insights into the field. Hamilton explains how NVIDIA’s innovative AI Factory concept allows it to introduce efficiencies into the data gathering process. He uses the example of self-driving cars to just how effective the NVIDA approach is. To manually collect all the data on all the roads in the world, the researchers would need to travel 11 billion miles. However, NVIDIA can leverage simulations of roads to “teach” the AI powering these cars synthetically. Hamilton then describes the fascinating advancements of digital twins – a technology idea that has been around for decades but only just now supported by powerful enough technology to handle the AI and other processing requirements for it. This, Hamilton says, can be used for everything from workplace layout simulations that “test” an environment to make sure it’s safe to work in before real humans do so, through to creating a digital “twin” of the earth as a way of testing the impact of climate change “700 or 7,000” days down the track. “It's going to be many years before we're done, but we're already making some interesting project progress and seeing some interesting early signs of future success,” he said. With AI certain to be critical to how humanity grapples with the increasingly complex challenges facing it into the future, it is companies like NVIDIA that will at the forefront of our response. Tune in to learn more about the very cutting edge of AI. Enjoy the show! About NVIDIA Since its founding in 1993, NVIDIA (NASDAQ: NVDA) has been a pioneer in accelerated computing. The company’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined computer graphics and ignited the era of modern AI. NVIDIA is now a full-stack computing company with data-center-scale offerings that are reshaping industry. More information at https://nvidianews.nvidia.com/ About GTC (GPU Technology Conference) The Technology Conference for the Era of AI and Metaverse Explore the latest technologies and business breakthroughs. Learn from experts how AI and the evolution of the 3D Internet are profoundly impacting industries—and society as a whole. Don’t miss the GTC 2022 keynote. Jensen Huang | Founder and CEO | NVIDIA Take a closer look at the game-changing technologies that are helping us take on the world’s greatest challenges. Free to Register and the event you do not want to miss! Join us in Melbourne for Scaling AI with MLOPS:  https://www.datafuturology.com/mlops Thank you to our sponsor, Talent Insights Group! Join our Slack Community: https://join.slack.com/t/datafuturologycircle/shared_invite/zt-z19cq4eq-ET6O49o2uySgvQWjM6a5ng Read the full podcast episode summary here. --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message