Episodios
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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.
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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.
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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.
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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.
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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
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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?
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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.
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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.
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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
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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
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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
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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.”
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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-ET6O49o2uySgvQWjM6a5ngWhat 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?”
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In Part 2 of the Leaders of Analytics podcast that was recorded last year with host, Jonas Christensen, Felipe discusses Honeysuckle Health and what he has done at this exciting, innovative company.
Felipe found the perfect home for his ambitions and interest in data at Honeysuckle Health. He was one of the first to join the company a few years ago, and right from the start, data, analytics and AI have been the driving force behind the business.
What’s more, all of that data and analytics are being used in a way that furthers patient outcomes. Felipe had previously had years of experience in the financial services sector, and while the advanced use of data there was an interesting challenge, he wanted to do something that would result in more positive outcomes for people. As coincidence would have it, Honeysuckle Health was looking for a data specialist at the exact time Felipe was looking for his next role. The rest, as they say, is history.
After describing the background and goals of Honeysuckle Health, Felipe then spends the rest of the podcast discussing the way Honeysuckle Health gathers data and gets the support of professionals in the health industry. He also talks about the ethical implications and the challenges of undertaking data methods that are standardised in other sectors. This includes addressing how to engage in experimentation with data in healthcare when the stakes are so high.
Tune in to the full and in-depth podcast, and get some great insights into the role that data will play in healthcare, now and into the future!
Thank you to our sponsor, Talent Insights Group!
Listen to the Leaders of Analytics Podcast: https://www.leadersofanalytics.com/
Join us for our next events Advancing AI and Data Engineering Sydney:
https://www.datafuturology.com/events
Join our Slack Community: https://join.slack.com/t/datafuturologycircle/shared_invite/zt-z19cq4eq-ET6O49o2uySgvQWjM6a5ng
What We Discussed
2:40 Felipe explains his role at Honeysuckle Health and what his day-to-day role looks like.
9:39 Felipe breaks down how Honeysuckle Health leverages data to improve healthcare outcomes and better engage the health industry.
15:07 Jonas asks Felipe where Honeysuckle Health gets its data from, and how the team interacts with the frontline professionals around data.
23:34 Jonas asks Felipe to describe the structures of Honeysuckle Health, and the financial, technological and IP “firepower” that sits behind it.
28:05 Felipe is asked to think ahead and describe where we’re going to be using data to improve health care and society.
35:14 Felipe discusses experimentation in health care – experimentation is essential in determining what works and doesn’t work, but the stakes are entirely different to, say, advertising.
Key Quotes
“Before working in Honeysuckle Health, I'd been in banking and finance for about five years. I found the challenges super interesting, and the applications for AI were almost endless. I think banking and finance are a little ahead of other sectors in embracing this too. But the whole time that I was there, I felt like we were using this amazing technology to sell people money. I was enjoying the technical side, but over time, I wanted to move into something different, something that ideally was more purpose-driven.”
“One of the beautiful things about working in data science is that you can move across industries quite freely.”
“Our mission is to help people live healthier lives, the way that we're doing that is through data science. We’re taking the playbook of the big tech companies in the US and what they did to advertising, and applying it to healthcare, for good outcomes. What I mean by that is that we take key aspects of personalisation, and the ability for data to help us find people at the right time, and offer them a message that will motivate them to actions like developing better habits or preventively seeking treatments.”
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This episode of the Data Futurology podcast is actually the reverse of normal – most of the time Felipe interviews experts in data science, but this time it’s his turn to be interviewed! Last year, he was on Jonas Christensen’s excellent Leaders of Analytics podcast, and we’ve got permission to republish it here.
In the wide-ranging interview, Felipe starts by describing his history. If you haven’t heard the story before, it begins with Felipe growing up in the driest parts of Chile. It then continues with him teaching himself databases in his first job in IT, after originally coming to Australia as a backpacker with very basic English. From there Felipe's career in data has taken off, both with his roles in financial services and healthcare, and the launch of Data Futurology.
Deeper into the interview, Felipe describes the goals behind the podcast and the events that Data Futurology runs. He then ends the conversation with some insights about how data currently works in organisations, and what the future may hold.
One of the most interesting things that Felipe has observed over the years is the potential for data specialists to “graduate” to the most senior roles in organisations. Just as CIOs moved from being a relatively isolated part of the business with few prospects to now being seen as prime candidates for CEO roles, the head of data analytics will increasingly be called on to show broader leadership within their organisations.
What data professionals need to do is step up their “soft” or “power” skills (depending on which term you want to use), Felipe says on the podcast. One of the driving goals of Data Futurology is to help data specialists identify these opportunities within themselves and then work on them.
To get a real sense of just how passionate Felipe is about data and the people that work in this space, his appearance on the Leaders of Analytics is a must-listen.
Thank you to our sponsor, Talent Insights Group!
Listen to the Leaders of Analytics Podcast: https://www.leadersofanalytics.com/
Join us for our next events Advancing AI and Data Engineering Sydney:
https://www.datafuturology.com/events
Join our Slack Community: https://join.slack.com/t/datafuturologycircle/shared_invite/zt-z19cq4eq-ET6O49o2uySgvQWjM6a5ng
What We Discussed
00:00 Intro to Leaders of Analytics
2:30 Jonas Christensen introduces Felipe to his audience.
4:42 Filipe explains his background and history with data science.
14:01 Jonas asks what is unique about Felipe’s career, across all his self-taught knowledge and entrepreneurship?
19:00 Jonas asks what encouraged Felipe to start Data Futurology, and how he got it started.
25:54 Felipe shares his long-term vision for what Data Futurology could turn into.
28:37 Felipe shares his views on what the big trends in data science are.
37:10 Felipe discusses the implications of data science being a relatively new area of specialisation, in the context of the business as a whole.
40:15 Felipe shares some great examples of data analytics being used in a creative, innovative and high-impact manner by companies.
44:15 Felipe shares his vision of what the perfect data-driven organisation would look like and how it would handle data, analytics, and AI
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This special episode was recorded LIVE and in-person with Brian Ferris, Chief Data, Analytics and Technology Officer at Loyalty New Zealand. He shares on how to get value from your AI investment and how to look at the interplay and relationship between data leaders and the senior executive team.
Brian stresses the importance of aligning with execs on the business strategy first, then working backwards to your AI strategy. According to Brian, the first step is for the data leaders themselves to shift their mindset from being an expert in their field, to instead become an enterprise leader. This means developing the capacity to have a conversation with other stakeholders within the organisation on their terms and understand what keeps them up at night. It also means looking at decisions through the lens of what is good for the overall business.
Brian and Felipe also share key steps in nurturing talent to take on leadership roles. It’s imperative to create a culture of psychological safety within the organisation and identify when an individual is ready to start taking on a leadership role and equipping them with enterprise skills. It also means helping them transition beyond looking at the data to their broader role within the organisation.
Finally, Felipe and Brian discuss why data leaders need to leave their egos at the door, and not become emotionally invested in or defensive of projects. The data leader should be one of the leading voices within the organisation, but to get there, a collaborative spirit and a goal to take actions that are beneficial to the organisation are key.
In this interview, Ferris dives deep into all these topics. He offers insights according to his own approach to the subject, and challenges some of the conventions we take for granted. Tune in to learn more!
Thank you to our sponsor Talent Insights Group!Connect with Brian: https://www.linkedin.com/in/brian-ferris-a053532/
Join us at one of our next events!
Data Engineering Summit Sydney:https://www.datafuturology.com/data-engineering-summit-sydney-2023
Advancing AI Sydney: https://www.datafuturology.com/advancing-ai-sydney-2023
Join our Slack Community: https://join.slack.com/t/datafuturologycircle/shared_invite/zt-z19cq4eq-ET6O49o2uySgvQWjM6a5ng
WHAT WE DISCUSSED
00:00: Introduction.
2:05: Felipe introduces Brian Ferris.
2:34: How to get value from your AI investment.
8:29: The value of collaborative approaches within organisations – how can the data team drive this?
13:09: If the data team needs to both support the organisation and lead it, how does it balance those priorities?
18:14: How can a data professional bridge the gap between being a subject matter expert to having a broader understanding of the business?
22:56: Talking about soft influence – what can people do on a peer-to-peer level to build influence within an organisation?
28:47: Why it’s critical to shift thinking away from “being right” and “winning”.
33:15: What are some of the most effective techniques for creating psychological safety between peers?
36:07: What can data leaders do to incentivise adoption across the organisation?
38:58: Why proof-of-concepts are not always the appropriate way to go (and the limited circumstances under which they should be tried).
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This week we welcome to the podcast, Joanna Marsh, the General Manager of Innovation and Advanced Analytics for Investa Property Group. She’s also the CEO and Co-Founder of a “side hustle” at Exomnia, a startup that provides real estate companies with a modular approach to analytics.
Exomnia has only been in operation for four months, but it is already turning heads. It has recently completed a pre-seed funding round for an impressive $1.5 million. On the podcast, Joanna shares some deep insights into the opportunity and challenges of building a data startup.
Data startups need to meet cyber security expectations before they can begin interacting with enterprises around data. The enterprises have strict regulatory requirements in this area. This creates a challenge for the startup, as they need to invest in gaining certifications before they can even build the MVP that most pre-launch startups focus on.
However, the gap in the market is significant, and as Joanna says, Exomnia is already resonating with foundation clients. With advanced analytics available at the click of the button, Exomnia is poised to make some real waves in the property technology space.
Tune in to this podcast for some fascinating insights on building a data company at its earliest stages!
Thank you to our sponsor Talent Insights Group!
Connect with Joanna: https://www.linkedin.com/in/joannamaemarsh/
Join us for our next event Advancing AI Melbourne https://www.datafuturology.com/advancing-ai-melbourne
Join our Slack Community: https://join.slack.com/t/datafuturologycircle/shared_invite/zt-z19cq4eq-ET6O49o2uySgvQWjM6a5ng
What we discussed
9:59: Felipe introduces Joanna, and then asks to overview her career to date.
15:11: How long did Joanna have the idea for Exomnia before pulling the trigger?
24:22: Joanna explains the challenges that she faces in protecting her IP when starting up a data company.
26:34: How was Joanna able to navigate challenging discussions with her first investors?
32:52: How has Joanna avoided conflicts of interest in the first investors and foundational customers being the same?
35:21: One of the biggest challenges for startups when working with corporates is managing all the requirements and processes around insurance, security and privacy that they need to meet. Joanna overviews how her company went about this.
41:49: Joanna explains the value of using open source so other startups can “plug in” to Exomnia’s data and platform.
44:29: Joanna and Felipe compare the challenges of managing different kinds of data, based on how sensitive the sector is towards data.
47:24: What’s next, as Exomnia continues to build up as a startup?
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In the world of data analytics, there are few that have achieved as much as Bora Arslan, who joined us for this week’s podcast. Arslan has driven data transformation exercises across some of the largest organisations in the world. These organisations include Walmart and Ford in the US, and IAG here in Australia.
On the podcast, Arslan shares many insights from his time as a Chief Data Officer. From his strategies for getting organisational buy-in for transformation, to the ways in which he prefers to build and manage teams, Arslan provides us with a blueprint for how the modern data executive should look at the work that they do.
One of the key messages that Arslan shares is that data analytics executives need to get as close to the organisation as possible. If they report to the CIO and their team is nested within IT, they’ll be seen as a support function, rather than a strategic one. The closer the Chief Data Officer can get to other lines of business and the CEO, the better they can understand the needs of the business and develop strategic and transformative solutions in direct collaboration with the other key stakeholders.
The challenge is that to be able to do this, the data team needs to learn how to speak the language of the other executives and lines of business. This has been one of the key reasons for Arslan’s ongoing success in his own roles.
Tune in to hear more great insights from one of the real thought leaders in our space!
Thank you to our sponsor, Talent Insights Group!
Connect with Bora: https://www.linkedin.com/in/bora-arslan/
Hear more from Bora and our awesome speaker faculty at Advancing AI Melbourne: https://www.datafuturology.com/advancing-ai-melbourne
Join our Slack Community: https://join.slack.com/t/datafuturologycircle/shared_invite/zt-z19cq4eq-ET6O49o2uySgvQWjM6a5ng
What we discussed
0:00 - Introduction
3:45 – Bora explains his background and what the last eight years in various executive roles has been like.
8:36 – How to define the role of a chief data officer in a large enterprise?
13:13 – What leaders can do to lead change management across the organisation and bring people on the transformation journey.
15:58 – How data analytics heads benefit from direct interaction with the CEO and executive team, rather than being a support function to the CIO.
18:46 – The most effective ways Chief Data Officers can influence C-level executives around them.
21:21 – On building teams: What are the most effective ways to structure data teams?
23:29 – The most effective ways to optimise project delivery, and the value of having a project management team within the organisation.
26:30 – Should the change management function sit within the data analytics team, or should it be more centralised within the business lines?
28:03 – A summary of the processes and methodologies key to driving successful analytical functions.
32:40 – Looking forward: The technologies to look forward to in the next year or two.
35:57 – Bora shares his career highlights to date.
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