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

In this episode of the Data Futurology podcast, where we delve into the world of Generative AI in recruitment. Our guests today are industry experts: Grant Wright, the General Manager of Marketplace and AI Products at Seek, and James Eichhorn, Principal Consultant for Data Engineering, Machine Learning, and Data Science at Talent Insights Group. Grant and James provide a wealth of insights into how Generative AI is transforming the recruitment landscape, both from a technology perspective and the human element. --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

In this episode of Data Futurology, Felipe Flores and Grant Case, Regional Vice President, Head of Sales Engineering - APJ at Dataiku delve into the realm of Generative AI and its applications in the business world. They kick off by underlining the vital role Generative AI plays in organisations, and then they explore the challenges that come along with adopting this technology. --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

In this informative podcast episode, Felipe Flores speaks with Jade Haar, the Head of Privacy and Data Ethics at National Australia Bank (NAB). Jade shares her inspiring journey into the field of data ethics, driven by her passion for doing right by people and contributing to the public good. --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

In this episode, Kendra Vant and Tracy Moore delve into the world of generative AI and its potential for unlocking commercial value. They kick off by addressing the excitement and hype surrounding generative AI technologies and emphasise the importance of grasping the fundamentals to extract real value from these advancements.  --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

In this episode, host Felipe Flores interviews Alan Lowthorpe, co-founder of Adaptive Data (who advise organisations on how to accelerate the value delivered from data and AI) and James Lecoutre, Director at Talent Insights Group as they delve into the world of data analytics and AI leadership, sharing insights on building successful teams, embracing diversity, fostering a growth mindset and navigating challenges in data analytics and AI. --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

This week on the Data Futurology podcast, we host Chad Sanderson, the Chief Operator of Data Quality Camp. Over the ten years Sanderson has been involved in data, he has held key roles in companies including Convoy, a late-stage freight technology company, and Microsoft, where he worked on the AI platform team. Sanderson’s experience with these companies made him realise that there was a need for a platform where data specialists could come together and discuss strategies for maintaining high-quality data in their organisations. His group, Data Quality Camp, has since attracted nearly 8,000 members, and has become a real meeting place to discuss everything from the technical implementation of a data strategy, through to helping members find work in an increasingly dynamic and disrupted workplace environment. On the podcast, Sanderson highlights the strategies he has seen to deliver high-quality data environments, some of the traps and pitfalls to avoid, and how data specialists can better engage with and gain buy-in from the other lines of business within the organisation. For insights direct from someone at the heart of the data quality conversation, don’t miss this in-depth conversation with Chad Sanderson. Join the Data Quality Camp on Slack (https://dataquality.camp/slack) Connect with Chad: https://www.linkedin.com/in/chad-sanderson/ Thank you to our sponsor, Talent Insights Group! Join us for our next events: Advancing AI and Data Engineering Sydney (5-7 September) and OpsWorld: Deploying Data & ML Products (Melbourne, 24-25 October): https://www.datafuturology.com/events  Join our Slack Community: https://join.slack.com/t/datafuturologycircle/shared_invite/zt-z19cq4eq-ET6O49o2uySgvQWjM6a5ng --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

At Data Futurology’s OpsWorld conference in March, a panel of experts came together to discuss the importance of getting measurements, processes and methodologies right to drive DataOps and MLOps across the organisation. The panel consisted of Katherine Fowler, Head of Business Transformation at L’Occitane Australia, Amar Poddatooru, Head of Data and Technology at Australian Ethical, and Emyr James, Head of Data at Resolution Life and moderating the discussion was Andrew Aho, Regional Director, Data Platforms at InterSystems. It became a far-reaching discussion that started with methods to define and measure the ROI of data and analytics initiatives and how to get those projects off the ground. The discussion moved on to overhyped technologies in the data space, and then looked forward to what is on the horizon for the years ahead. As the panel discussed, there is a lot of interest among consumers in some innovative technologies, including ChatGPT. This is in turn driving a lot of interest at the executive level at rolling out solutions that use these tools. However, without the right foundations in place, and without proper concern for the privacy and regulatory risks associated with these tools, they will cause the data team more headaches than they’re worth. This panel discussion is essential for understanding how to structure a foundation for data success, be disciplined in deploying the available resources across the data team, gain executive buy-in, and then steadily build the practice up. Enjoy the show!  Thank you to our sponsor, Talent Insights Group! Join us for our next events: Advancing AI and Data Engineering Sydney (5-7 September) and OpsWorld: Deploying Data & ML Products (Melbourne, 24-25 October): https://www.datafuturology.com/events  Join our Slack Community: https://join.slack.com/t/datafuturologycircle/shared_invite/zt-z19cq4eq-ET6O49o2uySgvQWjM6a5ng What we discussed 2:07: Felipe introduces the Measurements Thought Leaders panel and moderator, Andrew Aho. 3:48: How do you define and measure data and analytics ROI? 7:21: A discussion on metrics that help get data initiatives off the ground. 9:41: How a data leader needs to focus on the data platform, and articulate both the “big picture” view and the details. 12:35: As more organisations adopt ops, processes and methodologies, what challenges might people anticipate arising, and how can those be addressed? 17:24: What can data professionals do to help solve the change management challenge? 18:34: What are the challenges and impact of upcoming “silver bullet” technologies like ChatGPT? 20:16: What is currently overhyped in the data space (and why)? 24:03: What can we as data scientists do to ensure that we’re looking at the right risks and drawing accurate conclusions on what is right for the business? 26:13: If the goal is to focus on data science, how can we also keep experimentation and creativity going? 29:49: How do you estimate the value of change to get executive buy-in? 31:18: What upcoming developments and trends will emerge over the next five to ten years?   --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

This week on the Data Futurology podcast, we welcome Orla Glynn, Executive – AI, Reporting, Insights and Automation Configuration at Telstra. Glynn leads one of the biggest groups of data specialists to drive innovative AI and analytics across the company. --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

At the recent Advancing AI event in Melbourne, we were privileged to have a presentation by Vinay Joseph, the Pre-Sales Lead for IDOL at OpenText in APAC. Vinay gives an overview of the features of IDOL and how they can help data science teams bring automation and AI to the use of unstructured data. He presents a wide range of case studies and use cases. These include how law enforcement and the military, right through to news organisations and political campaigns might be able to use the data to draw real-time and in-depth insights that would otherwise be inaccessible. --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

This week on the Data Futurology podcast we host Paul Milinkovic, the APAC Regional Director for the leading data integration platform, StreamSets. Milinkovic joins us to share his insights into data engineers' challenges and the pipelines they manage and maintain. One statistic really highlights just how challenging work environments have become for data engineers: 76 per cent of organisations have a pipeline break at least monthly and for 36 per cent, it's weekly. Rather than contributing strategically to their organisations, engineers split their time between diagnosis and repair, and building new pipelines. This costs the organisation, as half the time the engineer isn’t being used strategically. It also leads to cultures of over-working, burnout, and high levels of churn within the data engineering team. Another challenge data teams struggle with is competing priorities. When multiple lines of business need pipelines developed, teams often need to triage to accommodate priority tasks, and this affects overall company outcomes. Being able to help organisations deliver a low or no-code environment that is highly visual and accessible to non-data specialists has been a critical benefit for organisations that have adopted StreamSets. Milinkovic then shares two case studies where StreamSets has helped with overcoming these challenges. In one, a bank achieved a seemingly impossible task – becoming compliant with looming Consumer Data Act requirements within four months. Then, a second bank was able to leverage StreamSets to its data to detect and thwart $9 million in fraudulent activity in a single month. For more deep insights into overcoming the challenges facing modern data engineering teams, tune into the podcast! Links Website: https://streamsets.com Follow on LinkedIn: https://www.linkedin.com/company/streamsets/ Whitepapers:  https://go.streamsets.com/Whitepaper-Dollars_and_Sense_UGLP.html?utm_medium=website&utm_source=DataFuturology&utm_campaign=eg_dollars_and_sense_of_dataops https://go.streamsets.com/Whitepaper-Dollars_and_Sense_UGLP.html?utm_medium=website&utm_source=DataFuturology&utm_campaign=eg_dollars_and_sense_of_dataops  https://go.streamsets.com/230214-lifting-the-lid-on-data-integration-UGLP.html?utm_me[…]turology&utm_campaign=eg_lifting_the_lid_on_data_integration What we discussed: 00:00 Introduction  02:22: Felipe introduces Paul Milinkovic.  03:38: Milinkovic shares his background and his history with data at various levels and applications.  06:04: Milinkovic overviews StreamSets – when and why the company was founded, and what its core capabilities are.  09:04: What are the main issues that StreamSets helps data engineering teams solve?  12:57: How does StreamSets address traditional data pipeline design and build challenges?  12:33: What are the benefits of having a solution that is visual and accessible to non-technical users?  22:51: One of the common questions with the self-service approach to data is governance. How can that be handled while still allowing full flexibility?  26:46: Data engineers care a great deal about the quality and accuracy of data and the platforms that it sits on. Milinkovic explains why it is so important that they have the tools to be able to deliver that to the organisation.  31:24: What is the financial impact of data engineering teams spending as much time fixing pipelines as they are?  33:49: Milinkovic shares some case studies and use cases to highlight the value of StreamSets’ approach to data engineering. --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

At our recent Advancing AI Melbourne event, Jonas Christensen, formerly Head of Data Science at Maurice Blackburn Lawyers, hosted a lively and insightful panel discussion featuring three prominent leaders in data and AI: ·             Christine Smyth, Chief Strategy Officer, Defence Health ·             Dr Michelle-Joy Low, Head of Data & AI, Reece Group ·             Nonna Milmeister, Chief Data and Analytics Officer, RMIT University The panellists emphasise the importance of building a culture that embraces AI and data-driven insights. Dr. Christine Smyth highlights the need for cooperation within the organisation, involving data students and building cross-functional teams with their technology counterparts. Christine also emphasises the significance of building trust in AI by being transparent about biases and addressing legitimate concerns. In order to combat fear and misunderstanding, increasing data literacy across the entire organisation is crucial. In a data context, a significant amount of effort goes into developing communication structures and accountability frameworks. These structures enable all teams involved to effectively communicate their contributions towards delivering tangible business value. However, this process is an ongoing journey, especially as organisations evolve and grow. Dr. Michelle-Joy Low highlights the importance of establishing a common language and effective communication channels within data teams. By doing so, organisations can foster collaboration, enhance accountability, and ultimately deliver value through their data initiatives. Whilst this endeavour may require continuous effort and adaptation, it is a vital discipline that directly contributes to the success of data-driven organisations. This episode also reveals insights from Nonna Milmeister who believes that to achieve success as data leaders, cooperation is key. Building strong collaboration with every part of the organisation is absolutely essential. Only by being transparent about biases and addressing them head-on, trust can be established. Trust leading to firm foundations that will foster successful data impact and outcomes. People often have concerns about AI replacing their jobs entirely, but here's an interesting stat: according to the World Economic Forum, while 85 million jobs may be replaced by 2025, a staggering 97 million new jobs will be created. So, instead of fearing job displacement, our role as data leaders should focus on increasing data literacy within our organisations. As the role of the data leader evolves our mindsets and approaches need to also.  This is an insightful and important podcast for anyone interested in learning how organisations can build effective, productive, and innovative teams around data. Thank you to our sponsor, Talent Insights Group! Join us for our next events, Data Engineering and Advancing AI Sydney (5-7 September): https://www.datafuturology.com/events  Join our Slack Community: https://join.slack.com/t/datafuturologycircle/shared_invite/zt-z19cq4eq-ET6O49o2uySgvQWjM6a5ng --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

In this episode, Alex Jenkins, Director at WA Data Science Innovation Hub, discusses the potential of AI advancements in revolutionising the education system. Jenkins envisions a future where education moves away from the one-size-fits-all approach and embraces a mastery model, allowing students to progress at their own pace and ensuring complete understanding before moving on to the next topic. The use of AI as virtual educational assistants can provide personalised tutoring, benefiting students by improving their educational outcomes. Studies have shown that one-on-one tutoring can significantly elevate students' performance. Large language models, such as AI assistants, can be tailored to individual students' learning styles and strengths. This personalisation can enhance critical thinking skills, broaden students' worldview, and help them make informed decisions about their academic journey. By leveraging AI, teachers can manage classrooms with the assistance of virtual teaching aides, enabling each student to master the material before progressing to the next level. Looking ahead to the next twelve months, Jenkins anticipates the transition to a mastery model of education, especially in STEM subjects like mathematics. This approach will ensure students achieve true mastery of concepts before moving forward. Furthermore, AI technology can enhance teacher productivity by providing resources, such as lesson plans and tailored exercises, that cater to individual students' skill levels. Khan Academy's Carmego AI serves as a leading example in this field, offering personalised tutoring and empowering teachers with effective teaching tools. Jenkins acknowledges the importance of considering the practical implementation of AI in education. While the technology holds immense potential, it should not replace socialisation, interaction, and hands-on learning in the classroom.  While concerns about hallucinations and AI-generated errors exist, Jenkins believes these risks are manageable and can be minimised through guided use cases and ongoing improvements in technology. He compares the trajectory of large language models to the development of space travel, where initial imperfections and limitations pave the way for future advancements and increased reliability. Reflecting on his personal journey in technology and data science, Jenkins emphasises the importance of promoting AI and data science education. He focuses on stimulating demand for AI services, fostering collaboration between academia, public services, and private industry, and encouraging students to pursue data science as a career path. Through initiatives like hackathons, the potential of AI in areas like emergency services becomes evident, showcasing how technology can save lives. Lastly, Jenkins discusses the upcoming Data & AI for Business Conference & Exhibition, scheduled to take place in August in Western Australia. The conference aims to explore the potential of data analytics and artificial intelligence in transforming businesses. It welcomes participants regardless of their AI or data backgrounds, as the focus is on understanding how these technologies can drive business growth and change. Enjoy the show! Thank you to our sponsor, Talent Insights Group! Visit the WA Data Science Innovation Hub https://wadsih.org.au/ Learn more about the Data & AI for Business Conference & Exhibition 2nd & 3rd August: https://wadsih.org.au/conference/ Join us for our next events Advancing AI and Data Engineering Sydney (5-7 September): https://www.datafuturology.com/events  Join our Slack Community: https://join.slack.com/t/datafuturologycircle/shared_invite/zt-z19cq4eq-ET6O49o2uySgvQWjM6a5ng --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

In this episode, we explore an engaging talk given by Niall Keating, General Manager for Technology Data Platforms at Sportsbet, during his recent appearance at the Data Engineering Summit in Melbourne. Niall generously imparts invaluable insights on the journey of cultivating a data culture that yields long-lasting business impact. Throughout the conversation, Niall showcases tangible examples of how Sportsbet has effectively utilised data and technology to drive innovation and elevate customer experiences. Sportsbet, Australia's largest online bookmaker, faces unique challenges due to the dynamic nature of their product, where prices constantly change.  To overcome these challenges, Sportsbet has invested significantly in technology and data infrastructure. One use case Niall highlights is their adoption of machine learning, with over 20 models currently in production. These models are employed to extract actionable insights, enabling Sportsbet to make data-driven decisions and enhance their offerings. Niall emphasises the importance of establishing a solid foundation in data culture and leveraging data for decision-making and financial reporting. He provides a specific use case of how Sportsbet utilises quantitative analytics to calculate probabilities and set prices for their core product. By harnessing data and analytics, Sportsbet optimises generosity, personalised experiences, and aims to provide the best value to their customers. Another use case Niall discusses is the application of data in safer gambling. Sportsbet is committed to making gambling safer, and they leverage data to identify potentially risky behaviours and intervene when necessary. Niall highlights the journey Sportsbet has undertaken over the past five years in building effective data products to promote safer gambling practices. When it comes to sustainability in data, Niall shares three educational stories that provide valuable insights. In one use case, he emphasises the importance of avoiding quick wins and taking an iterative approach aligned with strategic goals. He discusses the challenges involved in transitioning from human to AI automated decisions and the need to bridge the gap effectively. Lastly, Niall shares a use case centred around Sportsbet's product journey in safer gambling. He highlights the time and collaboration required to build effective data products that prioritise customer safety. This use case demonstrates the impact that data-driven approaches can have in creating a safer gambling environment. By adopting a long-term perspective and focusing on values such as safer gambling and customer-centricity, Sportsbet sets an example of how data culture can drive innovation and create positive outcomes. Enjoy the show! Thank you to our sponsor, Talent Insights Group! Join us for our next events Advancing AI and Data Engineering Sydney (5-7 September): https://www.datafuturology.com/events  Join our Slack Community: https://join.slack.com/t/datafuturologycircle/shared_invite/zt-z19cq4eq-ET6O49o2uySgvQWjM6a5ng Topics Discussed: 02:39. Introduction to Niall Keating and his background in software engineering. 04:08  Overview of Sportsbet as Australia's largest online bookmaker, serving one million active customers. 05:04 Investments in technology and data infrastructure, with a focus on machine learning and the impact of over 20 models in production. 07:06 The importance of getting the basics right in data-driven decision-making, financial reporting, and core product development. 09:14 The journey towards sustainability, including the focus on personalization, safer gambling, and aligning products with the company's vision and mission. 15:37 The challenges and lessons learned in evolving the data platform, including the adoption of lake house architecture and partnerships with AWS and Databricks. 22:04 The importance of building data products over time, collaboration between data science and analytics teams --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

This week on the Data Futurology podcast, we have an in-depth conversation with Conor O’Neill, the Head of Data Science at Compare The Market exploring his career journey and current role leveraging data and innovating with machine learning. When O’Neill landed at Compare The Market, he quickly found himself in a senior data role within an organisation that needed to both transform and mature its approach to data. On the podcast, O’Neill walks through the various stages of transformation, and getting the rest of the organisation aligned with that vision. He also shares some use cases that Compare The Market is effectively leveraging data for, as well as how they have been building ML products. He explains how he involves data scientists in this process and offers advice on building ML as a product when it comes to planning, delivery and infrastructure. Finally, O’Neill shares some thoughts on the difference between a data scientist’s role and that of a senior manager, and how this shifts the perspective and how a data professional will look at projects. He then rounds out the conversation with some thoughts about where data science is heading as a profession. For anyone interested in data science, O’Neill’s unconventional journey into and through the profession is both interesting and inspiring. Enjoy the show! Connect with Conor: https://www.linkedin.com/in/conoroneill1/ Thank you to our sponsor, Talent Insights Group! Join us for our next events Advancing AI and Data Engineering Sydney (5-7 September): https://www.datafuturology.com/events  Join our Slack Community: https://join.slack.com/t/datafuturologycircle/shared_invite/zt-z19cq4eq-ET6O49o2uySgvQWjM6a5ng What we discussed 2:26: Felipe introduces Conor O’Neill. 3:23: O’Neill shares his journey from astrophysics to data science. 6:49: In astrophysics, the data sets that scientists work on are massive. O’Neill shares some insights about how he managed data in that role. 8:40: O’Neill shares his journey at Compare The Market so far. 12:04: O’Neill shares some information about a current data project that he and his team are working on. 18:08: Compare The Market had to do significant foundational work in transformation. O’Neill shares insights into that process. 21:18: O’Neill shares his experience in getting the Compare The Market organisation aligned behind their data vision. 25:12: O’Neill explains the value of having data scientists involved at the earliest stages of transformation design.  28:44: O’Neill describes his experience in moving from a data scientist role to heading a team, and the differences between these roles.  32:56: O’Neill explains some of the thinking that goes into reusing data projects, as well as how they decide the projects to not follow through. 34:04: Getting a model in front of the end users and driving adoption is a critical step – O’Neill explains how he has approached it for Compare The Market. 37:54: O’Neill overviews the various consumers of the work done by the data team, and how the data team needs to think about each of them. 40:51: Tips and guidance for creating ML as a product to be consumed internally 45:48: O’Neill shares some thoughts on how the data science industry is evolving. Key Quotes “We’ve been on a transformational journey now for a little over a year, and that’s been really good. We’ve been migrating off our legacy on-prem stack to Databricks. We’ve also been focused on getting the right people, and then also establishing a process, because if you just change the tool, you haven't fixed the issues, typically.” “You don't want your control group to be too large and you then miss opportunities. But you also don't want it to be so small that you don't get sufficient data. That's where the algorithm behind our recommendation system controls that, to optimise according to our confidence that we are or are not exceeding the required threshold, and adjust the weighting of the control group accordingly.” --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message

This week we bring you a special episode of the Data Futurology podcast, featuring the keynote panel from our OpsWorld conference earlier this year featuring guests at different levels of data maturity. They shared their stories of the journey to enabling and unlocking the true value of data self-service..  The panel featured Kate Carruthers, Chief Data & Insights Officer, UNSW Sydney. She shared the university's experience, which has had a mature data environment for several years. At the other end of the table was Conor O'Neill, Head of Data Science, Compare The Market. He represented an organisation that is rapidly addressing a lack of data maturity across the organisation. The third person on the panel was Arvee Manaog, Head of Enterprise Systems, Data & Information Management, and Integration, EG Australia. She shared insights on how to effectively get organisation-wide buy-in, and then effectively educate all stakeholders on how to effectively use self-service. The panel was wide-ranging, starting off with a discussion around best practices in data self-service, before moving on to an in-depth summary of how to effectively approach self-service from each level of data maturity. There was also a robust Q & A session at the end of the panel. Through the robust audience questions, the panellists discussed strategies for ensuring data trustworthiness in self-service. They also discussed how ROI is best measured with self-service data practices. Businesses of all sizes that want to maximise data value should look at effective self-service approaches. This panel provides invaluable insights into both getting started and continuing to innovate once the data environment has been fully modernised and transformed. Enjoy the show!  Thank you to our sponsor, Talent Insights Group! Join us for our next events Advancing AI and Data Engineering Sydney (5-7 September): https://www.datafuturology.com/events  Join our Slack Community: https://join.slack.com/t/datafuturologycircle/shared_invite/zt-z19cq4eq-ET6O49o2uySgvQWjM6a5ng What we discussed: 2:07: Felipe introduces the three panelists. 3:19: Carruthers explains UNSW’s perspective around best practices in data self-service. 6:23:  Manaog explains the challenges of secure self-service in EG Australia. 10:38: Manaog explains the initial steps EG Australia took to get started on the data self-service journey. 14:40: O’Neill describes some self-service approaches he's seen work well. 19:50: Carruthers describes how UNSW has kept engagement with DevOps-created dashboards and models high across the organisation. 22:50: The panel takes audience questions, with the first being “How do we influence and motivate data silo owners to share for indirect enterprise outcomes?” 27:07: How can a mature data organisation bring together data literacy and digital literacy across users? 28:11: For a less mature data organisation, how can data leads ensure data trustworthiness in self-service? 30:14:  There are trade-offs involved in self-service models. How can those be managed in the pursuit of a self-service culture? 35:38: What are the most effective techniques for measuring ROI with self-service data practices? Key quotes: Manaog: “We’re using DataIQ. And it actually helps because it's easier for users. I got a good adoption rate for that because it’s possible to do drag and drop, there are recipes and users don't need to code. They can easily do their analysis, create their workflows and then come to the hub and say, can you productionise this?” O’Neill: “In one model, we're doing a hub and spoke approach, where we have champions placed within the business units. We are working with those champions to ensure that we understand how they're using the report. It’s not just what they want to see. But in practice, what are they doing with it?” --- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message